librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-12 12:31:00	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 555 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-12 12:28:53	card stringlengths 11 1.01M
tushar772/mini_ads.py	tushar772	2025-09-12T06:51:35Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-12T06:51:35Z	--- license: apache-2.0 ---
LandCruiser/sn21_omg3_1209_3	LandCruiser	2025-09-12T06:49:28Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-12T06:38:31Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
LandCruiser/sn21_omg3_1209_1	LandCruiser	2025-09-12T06:49:12Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-12T06:38:26Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
Adanato/Llama-3.2-1B-Instruct-high_openthoughts_1k	Adanato	2025-09-12T06:48:34Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "fyksft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T06:46:55Z	--- library_name: transformers tags: - fyksft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
LarryAIDraw/SchwarzPDXL_Lora__v1_0	LarryAIDraw	2025-09-12T06:48:00Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-09-12T06:47:36Z	--- license: creativeml-openrail-m ---
mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF	mradermacher	2025-09-12T06:47:26Z	0	0	transformers	[ "transformers", "gguf", "programming", "code generation", "code", "coding", "coder", "chat", "brainstorm", "qwen", "qwen3", "qwencoder", "brainstorm 20x", "creative", "all uses cases", "Jan-V1", "Stargate", "SG1", "horror", "science fiction", "fantasy", "finetune", "thinking", "reasoning", "unsloth", "en", "base_model:DavidAU/Qwen3-SG1-256k-ctx-6B", "base_model:quantized:DavidAU/Qwen3-SG1-256k-ctx-6B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-09-12T05:48:42Z	--- base_model: DavidAU/Qwen3-SG1-256k-ctx-6B language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - programming - code generation - code - coding - coder - chat - code - chat - brainstorm - qwen - qwen3 - qwencoder - brainstorm 20x - creative - all uses cases - Jan-V1 - Stargate - SG1 - horror - science fiction - fantasy - finetune - thinking - reasoning - unsloth --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/DavidAU/Qwen3-SG1-256k-ctx-6B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-SG1-256k-ctx-6B-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.8 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.4 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q2_K.gguf) \| i1-Q2_K \| 2.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 3.5 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q4_0.gguf) \| i1-Q4_0 \| 3.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 3.8 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 3.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q4_1.gguf) \| i1-Q4_1 \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q6_K.gguf) \| i1-Q6_K \| 5.3 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
LarryAIDraw/IL_Ade_Agent_Bunny	LarryAIDraw	2025-09-12T06:44:58Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-09-11T08:31:23Z	--- license: creativeml-openrail-m --- https://civitai.com/models/1932786/ade-agent-bunny-nikke-sdxl-lora-illustrious
mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF	mradermacher	2025-09-12T06:44:04Z	0	0	transformers	[ "transformers", "gguf", "programming", "code generation", "code", "coding", "coder", "chat", "brainstorm", "qwen", "qwen3", "qwencoder", "brainstorm 20x", "creative", "all uses cases", "Jan-V1", "Stargate", "SG1", "horror", "science fiction", "fantasy", "finetune", "thinking", "reasoning", "unsloth", "en", "base_model:DavidAU/Qwen3-SG1-256k-ctx-6B", "base_model:quantized:DavidAU/Qwen3-SG1-256k-ctx-6B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-12T05:17:03Z	--- base_model: DavidAU/Qwen3-SG1-256k-ctx-6B language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - programming - code generation - code - coding - coder - chat - code - chat - brainstorm - qwen - qwen3 - qwencoder - brainstorm 20x - creative - all uses cases - Jan-V1 - Stargate - SG1 - horror - science fiction - fantasy - finetune - thinking - reasoning - unsloth --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/DavidAU/Qwen3-SG1-256k-ctx-6B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-SG1-256k-ctx-6B-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q3_K_M.gguf) \| Q3_K_M \| 3.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q3_K_L.gguf) \| Q3_K_L \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.IQ4_XS.gguf) \| IQ4_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q4_K_S.gguf) \| Q4_K_S \| 3.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q4_K_M.gguf) \| Q4_K_M \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q5_K_S.gguf) \| Q5_K_S \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q5_K_M.gguf) \| Q5_K_M \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q6_K.gguf) \| Q6_K \| 5.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q8_0.gguf) \| Q8_0 \| 6.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.f16.gguf) \| f16 \| 12.8 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
100Pudoff/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_large_clam	100Pudoff	2025-09-12T06:42:33Z	166	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am pensive_large_clam", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-04T09:12:04Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am pensive_large_clam --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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AdvRahul/Axion-Flash-Reasoning-2B	AdvRahul	2025-09-12T06:40:25Z	329	0	transformers	[ "transformers", "gguf", "llama-cpp", "text-generation", "en", "base_model:nvidia/Nemotron-Research-Reasoning-Qwen-1.5B", "base_model:quantized:nvidia/Nemotron-Research-Reasoning-Qwen-1.5B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-08-24T17:23:34Z	--- base_model: nvidia/Nemotron-Research-Reasoning-Qwen-1.5B language: - en license: cc-by-nc-4.0 pipeline_tag: text-generation library_name: transformers tags: - llama-cpp --- # AdvRahul/Axion-Flash-Reasoning-2B-Q8_0-GGUF This model was built on top of Nemotron-Research-Reasoning-Qwen-1.5B with advanced safety protocols. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo AdvRahul/Axion-Flash-Reasoning-2B-Q8_0-GGUF --hf-file axion-flash-reasoning-2B-Q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo AdvRahul/Axion-Flash-Reasoning-2B-Q8_0-GGUF --hf-file axion-flash-reasoning-2B-Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo AdvRahul/Axion-Flash-Reasoning-2B-Q8_0-GGUF --hf-file axion-flash-reasoning-2B-Q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo AdvRahul/Axion-Flash-Reasoning-2B-Q8_0-GGUF --hf-file axion-flash-reasoning-2B-Q8_0.gguf -c 2048 ```
stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757658967	stonermay	2025-09-12T06:37:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T06:37:09Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
raniero/ax-real-001-repo	raniero	2025-09-12T06:34:48Z	0	0	peft	[ "peft", "safetensors", "lora", "bittensor", "subnet-56", "gradients", "it", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2025-09-12T06:34:46Z	--- language: - it license: apache-2.0 library_name: peft tags: [lora, bittensor, subnet-56, gradients] base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 --- # ARES56 — LoRA adapter Upload ID: ax-real-001_1757658886 upload_id: unknown_1757404904 File inclusi: - `adapter_model.safetensors` — SHA256: `e5a00aa9991ac8a5ee3109844d84a55583bd20572ad3ffcd42792f3c36b183ad` - `adapter_config.json` — SHA256: `4f39b39f151e0d31a8135b89599746fd2e06285a8594595589d7974f553af441` - `tokenizer_config.json` — SHA256: `missing` - `special_tokens_map.json` — SHA256: `missing` Output generato via Axolotl (CPU / smoke). Nessun checkpoint completo incluso.
omerbektasss/blockassist-bc-keen_fast_giraffe_1757658832	omerbektasss	2025-09-12T06:34:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T06:34:08Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
skatzR/USER-BGE-M3-ONNX-INT8	skatzR	2025-09-12T06:33:05Z	182	0	null	[ "onnx", "xlm-roberta", "quantization", "sentence-embeddings", "semantic-search", "ru", "en", "base_model:deepvk/USER-bge-m3", "base_model:quantized:deepvk/USER-bge-m3", "license:apache-2.0", "region:us" ]	null	2025-09-08T06:26:35Z	--- license: apache-2.0 base_model: - deepvk/USER-bge-m3 language: - ru - en tags: - onnx - quantization - sentence-embeddings - semantic-search --- # 🧩 DeepVK-USER-BGE-M3 — Quantized ONNX (INT8) ✨ This repository contains a quantized INT8 ONNX version of [`deepvk/USER-bge-m3`](https://huggingface.co/deepvk/USER-bge-m3). It is designed for fast CPU inference with [ONNX Runtime](https://onnxruntime.ai/), making it a great choice for semantic search, embeddings generation, and text similarity tasks in Russian 🇷🇺 and English 🇬🇧. --- ## 🔍 Model Card \| Property \| Value \| \|---------------------\|-----------------------------------------------------------------------\| \| Base model \| [`deepvk/USER-bge-m3`](https://huggingface.co/deepvk/USER-bge-m3), [`BAAI/bge-m3`](https://huggingface.co/BAAI/bge-m3) \| \| Quantization \| INT8 (Dynamic) \| \| Format \| ONNX \| \| Libraries \| `transformers`, `onnxruntime`, `optimum`, `sentence-transformers` \| \| Embedding dim \| 1024 \| \| Supported HW \| CPU (optimized for Intel AVX512-VNNI, fallback to AVX2) \| \| License \| Apache-2.0 \| --- ## 🚀 Features - ⚡ Fast CPU inference — ONNX + INT8 gives a strong speed-up. - 📦 Lightweight — reduced model size, lower memory footprint. - 🔄 Drop-in replacement — embeddings compatible with the FP32 version. - 🌍 Multilingual — supports Russian 🇷🇺 and English 🇬🇧. --- ## 🧠 Intended Use ✅ Recommended for: - Semantic search & retrieval systems - Recommendation pipelines - Text similarity & clustering - Low-latency CPU deployments ❌ Not ideal for: - Absolute maximum accuracy scenarios (INT8 introduces minor loss) - GPU-optimized pipelines (prefer FP16/FP32 models instead) --- ## ⚖️ Pros & Cons of Quantized ONNX Pros ✅ - Easy to use (no calibration dataset required). - Smaller & faster than FP32. - Works out of the box with ONNX Runtime. Cons ❌ - Slight accuracy drop compared to static quantization. - AVX512 optimizations only on modern Intel CPUs. - No GPU acceleration in this export. --- ## 📊 Benchmark \| Metric \| Value \| \|------------------------------\|-------------- \| \| Avg cosine similarity (vs FP32) \| ~0.988 \| \| Median cosine similarity \| ~0.988 \| \| Orig model time (s) \| 0.7504 \| \| Quant model time (s) \| 0.3539 \| \| Inference speed \| ~2× faster \| \| Model size (MB) \| 347.5 \| --- ## 📂 Files model_quantized.onnx — quantized model tokenizer.json, vocab.txt, special_tokens_map.json — tokenizer config.json — model config --- ## 🧩 Examples You can try the model directly in Google Colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/#fileId=https%3A//huggingface.co/skatzR/USER-BGE-M3-ONNX-INT8/blob/main/notebooks/TEST_USER-BGE-M3-ONNX-INT8.ipynb) This notebook demonstrates: - Loading the original FP32 model [`deepvk/USER-bge-m3`](https://huggingface.co/deepvk/USER-bge-m3) - Loading the quantized INT8 ONNX model [`skatzR/USER-BGE-M3-ONNX-INT8`](https://huggingface.co/skatzR/USER-BGE-M3-ONNX-INT8) - Comparing quality (cosine similarity) and inference speed side by side You can try this model with ready-to-use scripts in the `examples` folder: - [`quantmodel.py`](./examples/quantmodel.py) — universal Python module for loading and encoding texts with the quantized ONNX model. - [`app-console.py`](./examples/app-console.py) — console script to compare FP32 vs INT8 embeddings (cosine similarity + inference time). - [`app-streamlit.py`](./examples/app-streamlit.py) — interactive demo with Streamlit.
Sunik93/gemma-3-1b-pt-MED	Sunik93	2025-09-12T06:30:51Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T06:30:10Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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aruboi/llama-32-11b-vlm_peft_output33	aruboi	2025-09-12T06:30:26Z	3	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-11B-Vision", "base_model:adapter:meta-llama/Llama-3.2-11B-Vision", "license:llama3.2", "region:us" ]	null	2025-09-09T10:59:54Z	--- library_name: peft license: llama3.2 base_model: meta-llama/Llama-3.2-11B-Vision tags: - generated_from_trainer model-index: - name: llama-32-11b-vlm_peft_output33 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama-32-11b-vlm_peft_output33 This model is a fine-tuned version of [meta-llama/Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3393 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_HF with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.873 \| 0.1 \| 5 \| 1.6084 \| \| 1.6055 \| 0.2 \| 10 \| 1.5360 \| \| 2.0931 \| 0.3 \| 15 \| 1.4717 \| \| 1.3995 \| 0.4 \| 20 \| 1.4390 \| \| 1.4799 \| 0.5 \| 25 \| 1.4170 \| \| 1.2999 \| 0.6 \| 30 \| 1.3999 \| \| 1.6356 \| 0.7 \| 35 \| 1.3855 \| \| 1.3253 \| 0.8 \| 40 \| 1.3713 \| \| 1.4132 \| 0.9 \| 45 \| 1.3630 \| \| 1.4824 \| 1.0 \| 50 \| 1.3557 \| \| 1.0958 \| 1.1 \| 55 \| 1.3478 \| \| 1.3437 \| 1.2 \| 60 \| 1.3490 \| \| 1.3517 \| 1.3 \| 65 \| 1.3459 \| \| 1.0089 \| 1.4 \| 70 \| 1.3415 \| \| 1.1556 \| 1.5 \| 75 \| 1.3369 \| \| 1.3741 \| 1.6 \| 80 \| 1.3393 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.47.0 - Pytorch 2.7.0a0+7c8ec84dab.nv25.03 - Datasets 3.5.0 - Tokenizers 0.21.1
mgparkzone/gemma-3-1b-pt-MED	mgparkzone	2025-09-12T06:29:26Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T06:28:55Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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GANGSSSSSSSS/gemma-3-1b-pt-MED	GANGSSSSSSSS	2025-09-12T06:29:06Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T06:28:26Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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deadman44/Wan2.2_T2i_T2v_LoRA	deadman44	2025-09-12T06:28:41Z	0	16	null	[ "text-to-image", "t2i", "wan video", "safetensors", "text-to-video", "en", "license:apache-2.0", "region:us" ]	text-to-video	2025-07-27T00:45:26Z	--- license: apache-2.0 pipeline_tag: text-to-video language: - en tags: - text-to-image - t2i - wan video - safetensors --- <style> .title{ font-size: 2.5em; letter-spacing: 0.01em; padding: 0.5em 0; } .thumbwidth{ max-width: 180px; } .font_red{ color:red; } .font_blue{ color:blue; } .font_grey{ color: #aaaaaa; } </style> # models - [Wan2.2_myjc_v02](#myjc) (<span class="font_red">Wan2.2 LoRA</span>):2025-09-04<br /> - [Wan2.2_myob_v01](#myob) (<span class="font_red">Wan2.2 LoRA</span>):2025-08-31<br /> - [Wan2.2_myjd_v01](#myjd) (<span class="font_red">Wan2.2 LoRA</span>):2025-08-26<br /> - [Wan2.2_myjy_v01](#myjy) (<span class="font_red">Wan2.2 LoRA</span>):2025-08-21<br /> - [Wan2.2_myjk_v01](#myjk) (<span class="font_red">Wan2.2 LoRA</span>):2025-08-18<br /> - [Wan2.2_myjs_v01](#myjs) (<span class="font_red">Wan2.2 LoRA</span>):2025-08-11<br /> - Add [Workflow page](https://huggingface.co/deadman44/Wan2.2_Workflow_for_myxx_series_LoRA): 2025-08-04<br /> --- <br> - Workflow ### - [Sample Workflow for myxx series LoRA](https://huggingface.co/deadman44/Wan2.2_Workflow_for_myxx_series_LoRA)<br> <br> --- <a id="myob"></a> <h1 class="title"> <span>[Wan2.2] lora_wan_myob_v01</span> </h1> -<span class="font_blue">Wan video 2.2 for t2i, t2v for ComfyUI</span><br/> -<span class="font_blue">The optimal resolution is 768 x 1280, 1024 x 1536.(T2i), 512x768 (T2v)</span><br/> -<span class="font_blue">natural Japanese OB face</span><br/> <br/> <br/> # Download [Download: myob_High_v01](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myob_High_v01.safetensors?download=true) <br /> [Download: myob_Low_v01](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myob_Low_v01.safetensors?download=true) <br /> <br /> # Trigger ```bash myob, japanese/european, photorealistic and 23-30yo ``` <br /> # Sample prompt (v01) <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <strong>wan2.2 T2v</strong> <video controls loop style="width: 360px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250831073513_T2V_00002.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash myob, japanese, A Japanese woman, 30 years old, standing at kichen and holding pan. She wears a white sweater and pink apron. She has a brown bob hair. She tilts her head slightly and smiles with closed lips. A mole is visible on her neck. She looks at the viewer calmly. Motion: subtle breathing, head tilt Style: photorealistic Camera: medium close-up Mood: serene ``` <br/> --- <a id="myjd"></a> <h1 class="title"> <span>[Wan2.2] lora_wan_myjd_v01</span> </h1> -<span class="font_blue">Wan video 2.2 for t2i, t2v for ComfyUI</span><br/> -<span class="font_blue">The optimal resolution is 768 x 1280, 1024 x 1536.(T2i), 512x768 (T2v)</span><br/> -<span class="font_blue">natural Japanese JD face</span><br/> <br/> <br/> # Download [Download: myjd_High_v01](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myjd_High_v01.safetensors?download=true) <br /> [Download: myjd_Low_v01](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myjd_Low_v01.safetensors?download=true) <br /> <br /> # Trigger ```bash myjd, japanese/european, photorealistic and 19-22yo ``` <br /> # Sample prompt (v01) <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <strong>wan2.2 T2v</strong> <video controls loop style="width: 360px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250826065050_T2V_00002.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash 22yo, myjd, japanese, A woman dressed in a maid costume carries coffee on a tray in a café. She has black hair tied in a ponytail and wears a maid headdress. ``` <br/> --- <a id="myjk"></a> <h1 class="title"> <span>[Wan2.2] lora_wan_myjk_v01</span> </h1> -<span class="font_blue">Wan video 2.2 for t2i, t2v for ComfyUI</span><br/> -<span class="font_blue">The optimal resolution is 768 x 1280, 1024 x 1536.(T2i), 512x768 (T2v)</span><br/> -<span class="font_blue">natural Japanese JK face</span><br/> <br/> <br/> # Download [Download: myjk_High_v01](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myjk_High_v01.safetensors?download=true) <br /> [Download: myjk_Low_v01](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myjk_Low_v01.safetensors?download=true) <br /> <br /> # Trigger ```bash myjk, japanese/european, photorealistic and 16-18yo ``` <br /> # Sample prompt (v01) <strong>wan2.2 T2i generated</strong> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px; margin-bottom: 32px;"> <strong>T2i</strong> <a href="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250818092119_T2I_00001_.jpg" target="_blank"> <img src="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250818092119_T2I_00001_.jpg" alt="T2I" style="width: 360px; height: auto; object-fit: contain; border: 1px solid #ccc;"> </a> </div> ```bash 18yo, myjk, japanese, A photorealistic upper-body portrait of a beautiful young woman with long black hair and black eyes, dressed in a school uniform. She is sitting on a stool, smiling with one eye closed in a playful grin, showing her teeth. Her hand is raised gently near her face, and a hair ornament with a black bow. The background is softly blurred, enhancing the cinematic atmosphere. She looks directly at the viewer, evoking a sense of charm and realism. ``` <br/> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <strong>wan2.2 T2v</strong> <video controls loop style="width: 360px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250818093735_T2V_00002.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash 18yo, myjk, japanese, A Japanese idol is performing live on a brightly lit concert stage. She is wearing idol costume with Lace-up flared skirt. She sings and dances energetically, moving across the stage with graceful steps and expressive gestures. The camera follows her with dynamic motion: starting from a low-angle close-up of her smiling face, then pulling back to reveal the full stage with flashing lights and cheering fans. Her long hair flows with her movements, and her outfit sparkles under the spotlights. The scene includes cinematic lighting, fog effects, and smooth camera transitions that emphasize her presence and charm. ``` <br/> --- <a id="myjc"></a> <h1 class="title"> <span>[Wan2.2] lora_wan_myjc_v02</span> </h1> -<span class="font_blue">Wan video 2.2 for t2i, t2v for ComfyUI</span><br/> -<span class="font_blue">The optimal resolution is 768 x 1280, 1024 x 1536.(T2i), 512x768 (T2v)</span><br/> -<span class="font_blue">natural Japanese JC face</span><br/> <br/> <br/> # Download [Download: myjc_High_v02](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myjc_High_v02.safetensors?download=true) <br /> [Download: myjc_Low_v02](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myjc_Low_v02.safetensors?download=true) <br /> <br /> # Trigger ```bash myjc, japanese/european, photorealistic and 13-15yo ``` <br /> # Sample prompt <strong>wan2.2 T2i generated</strong> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px; margin-bottom: 32px;"> <strong>T2i (v01)</strong> <a href="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250814111852_T2I_00001_.png" target="_blank"> <img src="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250814111852_T2I_00001_.png" alt="T2I" style="width: 360px; height: auto; object-fit: contain; border: 1px solid #ccc;"> </a> </div> ```bash 15yo, myjc, japanese, photorealistic, A girl in school unifrom sitting seat at train. She has black hair with sidelocks. She is holding a smartphone and looking at it. ``` <br/> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <strong>wan2.2 T2v (v02)</strong> <video controls loop style="width: 360px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250904192015_T2V_00001.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash 15yo, myjc, japanese, A realistic video of a girl sitting alone on a swing in a sunny park. She wears a neat school uniform: white collared short-sleeve shirt tucked into a pleated blue suspender skirt, white socks, and black loafers. Her long black hair is styled in twin braids with a blue ribbon tied at the neck. She gently swings back and forth, hands resting between her legs, occasionally looking at the viewer with a calm, closed-mouth expression. The background is a slightly blurry photo-style park with trees, bugs flying past, and a playground visible. Her backpack lies beside the swing. As the swing moves, her skirt sways naturally, and sunlight filters through the leaves above. The scene captures a quiet moment of reflection, with subtle wind moving her hair over her shoulder. ``` <br/> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <strong>wan2.2 T2v (v01)</strong> <video controls loop style="width: 480px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250814112118_T2V_00002.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash myjc, japanese, photorealistic, Close-up portrait of a girl walking at street. She has a black twintails. She is wearing white blouse. ``` <br/> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <video controls loop style="width: 480px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250814112156_T2V_00002.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash 15yo, myjc, japanese, photorealistic, A girl in school unifrom with short sleeves sitting chair at night time classroom. She has black hair with sidelocks. She is talking camera with smily. ``` <br/> --- <a id="myjs"></a> <h1 class="title"> <span>[Wan2.2] lora_wan_myjs_v01</span> </h1> -<span class="font_blue">Wan video 2.2 for t2i, t2v for ComfyUI</span><br/> -<span class="font_blue">The optimal resolution is 768 x 1280, 1024 x 1536.(T2i), 512x768 (T2v)</span><br/> -<span class="font_blue">natural Japanese JS face</span><br/> <br/> <br/> # Download [Download: myjs_High_v01](https://huggingface.co/deadman44/WAN_T2i_LoRA/resolve/main/lora_wan2.2_myjs_High_v01.safetensors?download=true)<br> [Download: myjs_Low_v01](https://huggingface.co/deadman44/WAN_T2i_LoRA/resolve/main/lora_wan2.2_myjs_Low_v01.safetensors?download=true)<br> <br /> # Trigger ```bash (myjsh / myjsm / myjsl), japanese/european, photorealistic and 6-12yo ``` <br /> # Sample prompt (v01) <strong>wan2.2 T2i generated</strong> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px; margin-bottom: 32px;"> <strong>T2i</strong> <a href="https://huggingface.co/deadman44/WAN_T2i_LoRA/resolve/main/samples/20250811083806_T2I_LastImage_00001_.png?download=true" target="_blank"> <img src="https://huggingface.co/deadman44/WAN_T2i_LoRA/resolve/main/samples/20250811083806_T2I_LastImage_00001_.png" alt="T2I" style="width: 360px; height: auto; object-fit: contain; border: 1px solid #ccc;"> </a> </div> ```bash myjsh, japanese, photorealistic, A Japanese girl with shoulder-length black hair, wearing a white textured blouse, standing outdoors in soft sunlight. She gently lifts her hand to brush her hair aside, as a breeze flows through the trees behind her. Her blouse flutters slightly, and her gaze shifts subtly toward the camera. ``` <br/> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <strong>wan2.2 T2v</strong> <video controls loop style="width: 360px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/WAN_T2i_LoRA/resolve/main/samples/20250811084132_T2V_00001.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash 12yo, myjsh, japanese, photorealistic, A stylish girl posing for a fashion photoshoot in a minimalist studio. She wears a high-fashion outfit with layered textures: a translucent blouse over a structured corset, paired with wide-leg trousers. She shifts her pose gracefully, turning slightly to the side, adjusting her posture with subtle hand movements. Studio lights flash intermittently, casting soft shadows and highlights on her face and outfit. Her expression changes subtly from confident to playful. The camera slowly pans around her, capturing her elegance and motion. Cinematic lighting, fashion editorial style, photorealistic, expressive gesture, shallow depth of field, dynamic motion. ``` <br/> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <video controls loop style="width: 360px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/WAN_T2i_LoRA/resolve/main/samples/20250811104546_T2V_00002.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash 9yo, myjsm, japanese, photorealistic, A girl wearing a white blouse and pleated skirt with suspenders walks the crowded school hallway. She has a black ponytail. Finally she turns around and smiles. ``` <br/> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <video controls loop style="width: 480px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/WAN_T2i_LoRA/resolve/main/samples/20250811112548_T2V_00002.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash 6yo, myjsl, japanese, photorealistic, Girls are crossing the street with their one hand raised as their car waits. ``` <br/> --- <a id="myjy"></a> <h1 class="title"> <span>[Wan2.2] lora_wan_myjy_v01</span> </h1> -<span class="font_blue">Wan video 2.2 for t2i, t2v for ComfyUI</span><br/> -<span class="font_blue">The optimal resolution is 768 x 1280, 1024 x 1536.(T2i), 512x768 (T2v)</span><br/> -<span class="font_blue">natural Japanese JY face</span><br/> <br/> <br/> # Download [Download: myjy_High_v01](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myjy_High_v01.safetensors?download=true)<br> [Download: myjy_Low_v01](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myjy_Low_v01.safetensors?download=true)<br> <br /> # Trigger ```bash myjy, japanese/european, photorealistic and 3-5yo ``` <br /> # Sample prompt (v01) <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <strong>wan2.2 T2v</strong> <video controls loop style="width: 480px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250821095521_T2V_00002.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash myjy, japanese, A heartwarming indoor scene of three cheerful kindergarten girls clasping their own hands together in playful prayer. They wear colorful long-sleeved uniforms with blunt bangs and varied hairstyles: black hair in twintails, brown short hair, and long hair with a cute hair ornament. One girl holds a picture book with animal illustrations, another giggles softly, and the third looks up with wide, curious eyes. Their fingers are gently interlocked, lips slightly parted in a whisper of joy, and their expressions glow with innocence and wonder. The softly blurred background shows a cozy classroom with pastel decorations, adding warmth and charm to the moment. ``` <br/> ---
GodShin/gemma-3-1b-pt-MED	GodShin	2025-09-12T06:28:38Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T06:28:03Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Adanato/Llama-3.2-1B-Instruct-high_nemotron_1k	Adanato	2025-09-12T06:27:40Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "fyksft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T06:25:40Z	--- library_name: transformers tags: - fyksft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sidhantoon/Goldentouch_V3_G17	sidhantoon	2025-09-12T06:27:26Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-12T06:23:35Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
NotoriousH2/gemma-3-1b-pt-MED	NotoriousH2	2025-09-12T06:26:56Z	12	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T13:38:10Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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trkbt10/finetuned_model	trkbt10	2025-09-12T06:25:35Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gpt_oss", "trl", "en", "base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-12T06:25:30Z	--- base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gpt_oss - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: trkbt10 - License: apache-2.0 - Finetuned from model : unsloth/gpt-oss-20b-unsloth-bnb-4bit This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
shinebear/qwen100_va_agent-Q8_0-GGUF	shinebear	2025-09-12T06:25:28Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen3", "llama-cpp", "gguf-my-repo", "en", "base_model:shinebear/qwen100_va_agent", "base_model:quantized:shinebear/qwen100_va_agent", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-12T06:24:52Z	--- base_model: shinebear/qwen100_va_agent tags: - text-generation-inference - transformers - unsloth - qwen3 - llama-cpp - gguf-my-repo license: apache-2.0 language: - en --- # shinebear/qwen100_va_agent-Q8_0-GGUF This model was converted to GGUF format from [`shinebear/qwen100_va_agent`](https://huggingface.co/shinebear/qwen100_va_agent) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/shinebear/qwen100_va_agent) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo shinebear/qwen100_va_agent-Q8_0-GGUF --hf-file qwen100_va_agent-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo shinebear/qwen100_va_agent-Q8_0-GGUF --hf-file qwen100_va_agent-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo shinebear/qwen100_va_agent-Q8_0-GGUF --hf-file qwen100_va_agent-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo shinebear/qwen100_va_agent-Q8_0-GGUF --hf-file qwen100_va_agent-q8_0.gguf -c 2048 ```
timduck8/nsfwnew	timduck8	2025-09-12T06:23:26Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-09-12T06:23:26Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/1751260672898828907_PgNTXYeu.jpg text: '-' base_model: black-forest-labs/FLUX.1-dev instance_prompt: null license: other license_name: faipl-1.0-sd license_link: LICENSE --- # nsfwnew <Gallery /> ## Download model [Download](/timduck8/nsfwnew/tree/main) them in the Files & versions tab.
Continual-Mega/ADCT	Continual-Mega	2025-09-12T06:22:32Z	0	1	null	[ "dataset:Continual-Mega/Continual-MEGA-Benchmark", "arxiv:2506.00956", "license:cc-by-nc-4.0", "region:us" ]	null	2025-06-16T04:12:17Z	--- license: cc-by-nc-4.0 datasets: - Continual-Mega/Continual-MEGA-Benchmark --- # 🧠 Continual-MEGA: A Large-scale Benchmark for Generalizable Continual Anomaly Detection This repository provides model checkpoints for Continual-MEGA, a benchmark introduced in the paper: [![Paper](https://img.shields.io/badge/Paper-arXiv%3A2506.00956-b31b1b?logo=arxiv&style=flat-square)](https://arxiv.org/abs/2506.00956) 🔗 Codebase: [Continual-Mega/Continual-Mega](https://github.com/Continual-Mega/Continual-Mega) --- ## 🚀 Overview Continual-MEGA introduces a realistic and large-scale benchmark for continual anomaly detection that emphasizes generalizability across domains and tasks. The benchmark features: - ✅ Diverse anomaly types across domains - 🔁 Class-incremental continual learning setup - 📈 A large-scale evaluation protocol surpassing previous benchmarks This repository hosts pretrained model checkpoints used in various scenarios defined in the benchmark. --- ## 📦 Available Checkpoints \| Model Name \| Scenario \| Task \| Description \| \|---------------------------------------\|------------\|-------------------------\|----------------------------------------------------------\| \| `scenario2/prompt_maker` \| Scenario 2 \| Base \| Prompt maker trained on Scenario 2 base classes \| \| `scenario2/adapters_base` \| Scenario 2 \| Base \| Adapter trained on Scenario 2 base classes \| \| `scenario2/30classes/adapters_task1` \| Scenario 2 \| Task 1 (30 classes) \| Adapter trained on Task 1 (30 classes) in Scenario 2 \| \| `scenario2/30classes/adapters_task2` \| Scenario 2 \| Task 2 (30 classes) \| Adapter trained on Task 2 (30 classes) in Scenario 2 \| \| (More to come) \| – \| – \| – \| --- ## 🛠 Usage Example ### Continual Setting Evaluation ``` sh eval_continual.sh ``` ### Zero-Shot Evaluation ``` sh eval_zero.s
omerbektasss/blockassist-bc-keen_fast_giraffe_1757658028	omerbektasss	2025-09-12T06:21:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T06:20:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
de-Rodrigo/idefics2-merit	de-Rodrigo	2025-09-12T06:17:20Z	0	1	null	[ "safetensors", "vision", "document-understanding", "donut", "image-text-to-text", "conversational", "en", "es", "dataset:de-Rodrigo/merit", "base_model:HuggingFaceM4/idefics2-8b", "base_model:finetune:HuggingFaceM4/idefics2-8b", "license:mit", "region:us" ]	image-text-to-text	2024-07-21T17:45:31Z	--- license: mit datasets: - de-Rodrigo/merit language: - en - es base_model: - HuggingFaceM4/idefics2-8b pipeline_tag: image-text-to-text --- # IDEFICS2 Merit <a href="https://x.com/nearcyan/status/1706914605262684394"> <div style="text-align: center;"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/de-Rodrigo/donut-merit/resolve/main/assets/dragon_huggingface.png"> <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/de-Rodrigo/donut-merit/resolve/main/assets/dragon_huggingface.png"> <img alt="DragonHuggingFace" src="https://huggingface.co/de-Rodrigo/donut-merit/resolve/main/assets/dragon_huggingface.png" style="width: 200px;"> </picture> </div> </a> ## Model Architecture This model is based on the Donut architecture and fine-tuned on the Merit dataset for form understanding tasks. - Backbone: [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b) - Training Data: [Merit](https://huggingface.co/datasets/de-Rodrigo/merit) ## Example Usage ```python from transformers import AutoModel model = AutoModel.from_pretrained("de-Rodrigo/idefics2-merit") ``` WIP 🛠️
stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757657739	stonermay	2025-09-12T06:17:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T06:16:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
TAUR-dev/M-sft_exp_zayneV2-sft	TAUR-dev	2025-09-12T06:15:58Z	0	0	null	[ "safetensors", "qwen2", "region:us" ]	null	2025-09-12T06:15:28Z	# M-sft_exp_zayneV2-sft This model was created as part of the sft_exp_zayneV2 experiment using the SkillFactory experiment management system. ## Model Details - Training Method: LLaMAFactory SFT (Supervised Fine-Tuning) - Stage Name: sft - Experiment: sft_exp_zayneV2 ## Training Configuration {"model_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct", "trust_remote_code": true, "stage": "sft", "do_train": true, "finetuning_type": "full", "deepspeed": "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/examples/deepspeed/ds_z2_config.json", "dataset": "TAUR_dev__D_SFT_C_sft_exp_zayneV2_sft_data__sft_train", "template": "qwen", "cutoff_len": 16384, "max_samples": 1000000, "overwrite_cache": true, "preprocessing_num_workers": 1, "dataloader_num_workers": 0, "disable_tqdm": false, "output_dir": "/scratch/10416/zaynesprague/skill_inject_outputs/sf_experiments/new_sft_v3_9_11__zaynev2/llamafactory/checkpoints", "logging_steps": 10, "save_steps": 100000, "plot_loss": true, "overwrite_output_dir": true, "per_device_train_batch_size": 1, "gradient_accumulation_steps": 1, "learning_rate": 1e-06, "num_train_epochs": 3, "lr_scheduler_type": "cosine", "warmup_ratio": 0.05, "weight_decay": 0.0001, "adam_beta1": 0.9, "adam_beta2": 0.95, "bf16": true, "ddp_timeout": 180000000, "gradient_checkpointing": true, "save_only_model": true, "enable_masked_ranges": false, "save_strategy": "steps", "save_total_limit": 5, "sf_tracker_dataset_id": "TAUR-dev/D-ExpTracker__sft_exp_zayneV2__v1", "sf_eval_before_training": false, "sf_wandb_project": "sft_exp_zayneV2_sft", "sf_eval_steps": null, "run_name": "sft_exp_zayneV2_sft"} ## Experiment Tracking 🔗 View complete experiment details: [Experiment Tracker Dataset](https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__sft_exp_zayneV2__v1) ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-sft_exp_zayneV2-sft") model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-sft_exp_zayneV2-sft") ```
Anhlq/gemma-3-finetune-16bit-copy-v2	Anhlq	2025-09-12T06:14:55Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/gemma-3-270m-it", "base_model:finetune:unsloth/gemma-3-270m-it", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T06:13:07Z	--- base_model: unsloth/gemma-3-270m-it tags: - text-generation-inference - transformers - unsloth - gemma3_text license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: Anhlq - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-270m-it This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
omerbektasss/blockassist-bc-insectivorous_bold_lion_1757657625	omerbektasss	2025-09-12T06:14:38Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous bold lion", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T06:14:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous bold lion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF	mradermacher	2025-09-12T06:14:05Z	0	0	transformers	[ "transformers", "gguf", "chat", "Fusion", "en", "base_model:huihui-ai/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030", "base_model:quantized:huihui-ai/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-12T05:40:42Z	--- base_model: huihui-ai/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030 language: - en library_name: transformers license: mit mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - chat - Fusion --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/huihui-ai/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030 <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF).* weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q2_K.gguf) \| Q2_K \| 11.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q3_K_S.gguf) \| Q3_K_S \| 13.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q3_K_M.gguf) \| Q3_K_M \| 14.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q3_K_L.gguf) \| Q3_K_L \| 16.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q4_K_S.gguf) \| Q4_K_S \| 17.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q4_K_M.gguf) \| Q4_K_M \| 18.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q5_K_S.gguf) \| Q5_K_S \| 21.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q5_K_M.gguf) \| Q5_K_M \| 21.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q6_K.gguf) \| Q6_K \| 25.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q8_0.gguf) \| Q8_0 \| 32.6 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
nobu222/gemma2-9b-it-qlora-adapter-for-banner-gen	nobu222	2025-09-12T06:13:46Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-12T06:13:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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midwestern-simulation/essence-3b-v1.1	midwestern-simulation	2025-09-12T06:13:26Z	0	0	null	[ "safetensors", "dataset:mlfoundations/dclm-baseline-1.0", "base_model:HuggingFaceTB/SmolLM3-3B-Base", "base_model:finetune:HuggingFaceTB/SmolLM3-3B-Base", "region:us" ]	null	2025-09-11T11:35:20Z	--- datasets: - mlfoundations/dclm-baseline-1.0 base_model: - HuggingFaceTB/SmolLM3-3B-Base --- # Essence 3B V1.1 This is a system using two versions of SmolLM3-3B-Base, the 'encoder', is finetuned to turn a text into a set of embedding tokens which can be reconstituted back into the original text by the decoder. In addition to “vanilla” reconstruction, this model was trained for span-corruption and masked language modelling. We use LoRA at rank 64 on QKVO along with trainable LayerNorms and, for the encoder, LoRA on all MLP layers as well as trainable token embeddings. The model was trained to encode text into any of 1-128 embedding tokens. ## Simple Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel from torch import nn import torch from huggingface_hub import hf_hub_download device = torch.device("cuda:0") dtype = torch.bfloat16 base_model_id = "HuggingFaceTB/SmolLM3-3B-Base" compressor_id = "midwestern-simulation/essence-3b-v1.1" # === MODEL LOADING === tokenizer = AutoTokenizer.from_pretrained(base_model_id, padding_side='left') encoder = AutoModelForCausalLM.from_pretrained(base_model_id, device_map={"":device}, torch_dtype=dtype) decoder = AutoModelForCausalLM.from_pretrained(base_model_id, device_map={"":device}, torch_dtype=dtype) encoder = PeftModel.from_pretrained(encoder, compressor_id, subfolder="encoder") decoder = PeftModel.from_pretrained(decoder, compressor_id, subfolder="decoder") projector = nn.Linear(2048, 2048).to(device).to(dtype) projector.load_state_dict(torch.load(hf_hub_download(repo_id=compressor_id, filename="projector.pt"))) # === MODEL INFERENCE === text = "mary had a little lamb, little lamb, little lamb, mary had a little lamb whose fleece was white as snow" n_embed_tokens = 4 # for best performance, can be any within the range of 1-128 encoder_input = text.strip() + f"\n[[/END DOCUMENT]]\n[[START SUMMARY ntoks={n_embed_tokens}]]" + "<\|im_end\|>" * n_embed_tokens tokenized = tokenizer(encoder_input, return_tensors='pt', add_special_tokens=False) tokenized = {k: v.to(device) for k, v in tokenized.items()} encoding = encoder.model.model(**tokenized).last_hidden_state[:, -n_embed_tokens:, :] encoding = projector(encoding) tokenized_prefix = tokenizer("\n[[/END SUMMARY]]\n[[START DOCUMENT]]\n", return_tensors="pt", add_special_tokens=False) prefix_embeds = decoder.model.model.embed_tokens(tokenized_prefix['input_ids'].to(device)) inputs_embeds = torch.cat([encoding, prefix_embeds], 1) output = decoder.generate( inputs_embeds=inputs_embeds, temperature=0.7, max_new_tokens=1024, do_sample=True, top_k=128, min_new_tokens=8, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id ) print(tokenizer.decode(output[0])) # mary had a little lamb, little lamb, little lamb, mary had a little lamb whose fleece was white as snow # [[/END DOCUMENT]]<\|end_of_text\|> ```
ttkairamkonda/whisper-large-v3-faa-atc-500k-LoRA32	ttkairamkonda	2025-09-12T06:12:41Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-12T06:12:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mradermacher/InfoLLM-32B-i1-GGUF	mradermacher	2025-09-12T06:11:56Z	0	0	transformers	[ "transformers", "gguf", "zh", "en", "base_model:HFSUN123/InfoLLM-32B", "base_model:quantized:HFSUN123/InfoLLM-32B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-09-11T23:56:12Z	--- base_model: HFSUN123/InfoLLM-32B language: - zh - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/HFSUN123/InfoLLM-32B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#InfoLLM-32B-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/InfoLLM-32B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 11.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q2_K.gguf) \| i1-Q2_K \| 12.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 12.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 13.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 14.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 14.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 15.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 16.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 17.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q4_0.gguf) \| i1-Q4_0 \| 18.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 18.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 19.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q4_1.gguf) \| i1-Q4_1 \| 20.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 23.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q6_K.gguf) \| i1-Q6_K \| 27.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
TAUR-dev/M-0911__qrepeat1_ref5_0C.-C.-C-IC.-CC_3args_grpo-rl	TAUR-dev	2025-09-12T06:09:23Z	0	0	null	[ "safetensors", "qwen2", "en", "license:mit", "region:us" ]	null	2025-09-11T21:16:11Z	--- language: en license: mit --- # M-0911__qrepeat1_ref5_0C.-C.-C-IC.-CC_3args_grpo-rl ## Model Details - Training Method: VeRL Reinforcement Learning (RL) - Stage Name: rl - Experiment: 0911__qrepeat1_ref5_0C.-C.-C-IC.-CC_3args_grpo - RL Framework: VeRL (Versatile Reinforcement Learning) ## Training Configuration ## Experiment Tracking 🔗 View complete experiment details: https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__0911__qrepeat1_ref5_0C.-C.-C-IC.-CC_3args_grpo__v1 ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-0911__qrepeat1_ref5_0C.-C.-C-IC.-CC_3args_grpo-rl") model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-0911__qrepeat1_ref5_0C.-C.-C-IC.-CC_3args_grpo-rl") ```
Adanato/Llama-3.2-1B-Instruct-high_acereason_1k	Adanato	2025-09-12T06:08:08Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "fyksft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T05:39:43Z	--- library_name: transformers tags: - fyksft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Adanato/Llama-3.2-1B-Instruct-baseline_1k	Adanato	2025-09-12T06:07:55Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "fyksft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T06:06:07Z	--- library_name: transformers tags: - fyksft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ZombitX64/Wilai-1.5	ZombitX64	2025-09-12T06:07:51Z	0	0	transformers	[ "transformers", "pytorch", "OpenThaiWilai", "text-generation", "generated_from_trainer", "custom_code", "autotrain_compatible", "region:us" ]	text-generation	2025-09-12T04:20:07Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: OpenThaiWilai results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # OpenThaiWilai This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.56.1 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0
Aagmon/gemma-3-12b-ft-trans-imp-3	Aagmon	2025-09-12T06:06:49Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "unsloth", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-09-11T21:04:42Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Karthikappi0011/qwen3-1.7b-translation-en-tu-test4-small-data	Karthikappi0011	2025-09-12T06:06:42Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-1.7B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-1.7B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-12T06:06:37Z	--- base_model: unsloth/Qwen3-1.7B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Karthikappi0011 - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-1.7B-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757657117	stonermay	2025-09-12T06:06:40Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T06:06:19Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vnegi1011/kyrgyz-asr	vnegi1011	2025-09-12T06:03:30Z	0	0	null	[ "onnx", "wav2vec2", "region:us" ]	null	2025-09-11T01:42:07Z	# Kyrgyz ASR Model Fine-tuned Wav2Vec2 model for Kyrgyz speech recognition, exported to ONNX format. - Model: facebook/wav2vec2-base-960h (fine-tuned) - Task: Automatic Speech Recognition (ASR) - Language: Kyrgyz - Files: `model.onnx`, `vocab.json`, processor configs
NCSOFT/GME-VARCO-VISION-Embedding	NCSOFT	2025-09-12T06:01:44Z	1,261	11	transformers	[ "transformers", "safetensors", "qwen2_vl", "image-to-text", "multimodal", "video embedding", "ncsoft", "ncai", "varco", "feature-extraction", "en", "base_model:Qwen/Qwen2-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2-VL-7B-Instruct", "license:cc-by-nc-4.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-06-10T05:26:52Z	--- license: cc-by-nc-4.0 base_model: - Qwen/Qwen2-VL-7B-Instruct library_name: transformers tags: - multimodal - video embedding - ncsoft - ncai - varco pipeline_tag: feature-extraction language: - en --- ## About GME-VARCO-VISION-Embedding <div align="center"> <img src="./varco-vision-Embedding.png" width="100%" style="background-color:white; padding:10px;"/> </div> `GME-VARCO-VISION-Embedding` is a multimodal embedding model that computes semantic similarity between text, images, and videos in a high-dimensional embedding space. In particular, the model focuses on video retrieval, which demands greater complexity and contextual understanding compared to image retrieval. It achieves high retrieval accuracy and strong generalization performance across various scenarios, such as scene-based search, description-based search, and question-answering-based search. ## Demo Video Check out our demo videos showcasing our multimodal embedding model in action: - [English Demo Video](https://www.youtube.com/watch?v=kCvz82Y1BQg) - [Korean Demo Video](https://youtube.com/shorts/jC2J7rbAfxs) The demo demonstrates how our embedding model works together with an AI agent to search for relevant videos based on user queries and generate responses using the retrieved video content. ### Model Architecture and Training Method `GME-VARCO-VISION-Embedding` is based on [`Qwen/Qwen2-VL-7B-Instruct`](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct), and uses the parameters of [`Alibaba-NLP/gme-Qwen2-VL-7B-Instruct`](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-7B-Instruct) to improve the model's general retrieval ability. #### 1. Fine-tuning (Contrastive Learning) on video preference dataset To efficiently fine-tune the model, we utilize [ShareGPTVideo’s 17𝑘 video preference dataset](https://huggingface.co/datasets/ShareGPTVideo/train_video_and_instruction), which includes prompts, videos, gold answers, and chosen-rejected text pairs. We treat the prompts and videos as queries, and the rejected responses as hard-negatives for the gold answers. . Each query is trained with in-batch negatives as well as one hard negative using the InfoNCE loss. The model is fully fine-tuned for two epochs on 8 A100 GPUs with a batch size of 8, requiring only a few hours for training. #### 2. Adding Retrieval Vector To compensate for the insufficiency of training instances and enhance the generalization ability of the fine-tuned model, we compute a retrieval vector 𝜏 by subtracting the weights of the original [`Qwen/Qwen2-VL-7B-Instruct`](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) model from those of [`Alibaba-NLP/gme-Qwen2-VL-7B-Instruct`](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-7B-Instruct), a Qwen2-VL based image-text retrieval model. This approach is inspired by Chat Vector, which is a method to equip pre-trained language models with chat capabilities in new languages by adding a vector obtained from the weight difference between a base model and its chat-optimized counterpart. ### Performance Our model achieves state-of-the-art (SOTA) zero-shot performance on the MultiVENT2.0 dataset as of July 2025. See the [official leaderboard](https://eval.ai/web/challenges/challenge-page/2507/leaderboard/6262) for detailed results. <br> ## Code Examples `GME-VARCO-VISION-Embedding` adopts the inference pipeline of [`Qwen/Qwen2-VL-7B-Instruct`](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct). ### Image-Text Retrieval ```python import torch import requests from PIL import Image from transformers import Qwen2VLForConditionalGeneration, AutoProcessor from qwen_vl_utils import process_vision_info model_name = "NCSOFT/GME-VARCO-VISION-Embedding" model = Qwen2VLForConditionalGeneration.from_pretrained( model_name, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2", device_map="auto", ) processor = AutoProcessor.from_pretrained(model_name) tokenizer = processor.tokenizer device = model.device qry_msg = [ { "role": "user", "content": [ {"type": "text", "text": "Find a photo of a cat."}, ], }, ] qry_txt = processor.apply_chat_template( qry_msg, tokenize=False, add_generation_prompt=True ) + tokenizer.eos_token qry_input = processor( text=[qry_txt], padding=True, return_tensors="pt", ).to(device) img_msg = [ { "role": "user", "content": [{ "type": "image", "image": "image" }] } ] img_txt = processor.apply_chat_template( img_msg, tokenize=False, add_generation_prompt=True ) + tokenizer.eos_token candidate_imgs= [ # Photo of two cats { "role": "user", "content": [{ "type": "image", "image": "http://images.cocodataset.org/val2017/000000039769.jpg"}] }, # Photo of two dogs { "role": "user", "content": [{ "type": "image", "image": "https://farm1.staticflickr.com/116/290755713_a5de6c1079_z.jpg"}] }, # photo of two people playing baseball { "role": "user", "content": [{ "type": "image", "image": "http://farm3.staticflickr.com/2418/2193688811_d9f5e23bbd_z.jpg"}] }, # Photo of a large crowd in a busy city street { "role": "user", "content": [{ "type": "image", "image":"http://farm7.staticflickr.com/6049/6329686751_997c68fff9_z.jpg"}] }, ] candidate_images, _ = process_vision_info(candidate_imgs) image_inputs = processor( text=[img_txt] * len(candidate_images), images=candidate_images, # videos=, padding=True, return_tensors="pt", ).to(device) with torch.inference_mode(): qry_emb = model( qry_input, output_hidden_states=True, return_dict=True ).hidden_states[-1][:, -1, :] img_emb = model( image_inputs, output_hidden_states=True, return_dict=True ).hidden_states[-1][:, -1, :] qry_emb = F.normalize(qry_emb, dim=-1) img_emb = F.normalize(img_emb, dim=-1) score = qry_emb @ img_emb.t() # tensor([[0.3066, 0.1108, 0.1226, 0.1245]], device='cuda:0', dtype=torch.bfloat16) # corresponding to the score of photos (cat, dog, baseball, crowd) ``` <br> ### Video Embedding ```Python vid_message = [ { "role": "user", "content": [{ "type": "video", "video": video_path, "max_pixels": 262144, "fps": 2.0,}] } ] video_text = processor.apply_chat_template( vid_message, tokenize=False, add_generation_prompt=True ) + tokenizer.eos_token image_input, video_input = process_vision_info(vid_message) video_input = processor( text=[video_text], images=image_input, videos=video_input, padding=True, return_tensors="pt", ).to(device) with torch.inference_mode(): video_emb = model( **video_input, output_hidden_states=True, return_dict=True ).hidden_states[-1][:, -1, :] video_emb = F.normalize(video_emb, dim=-1) ``` <br> --- license: cc-by-nc-4.0 ---
RijalMuluk/bbca.jk_time_forecast_lightgbm	RijalMuluk	2025-09-12T06:01:32Z	0	0	null	[ "joblib", "license:apache-2.0", "region:us" ]	null	2025-09-12T06:01:03Z	--- license: apache-2.0 ---
NCSOFT/VARCO-VISION-14B	NCSOFT	2025-09-12T06:01:26Z	5,231	36	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "multimodal", "conversational", "ncsoft", "varco", "image-text-to-text", "en", "ko", "arxiv:2411.19103", "arxiv:2408.03326", "base_model:Qwen/Qwen2.5-14B-Instruct", "base_model:finetune:Qwen/Qwen2.5-14B-Instruct", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-11-25T05:08:04Z	--- language: - en - ko license: cc-by-nc-4.0 tags: - multimodal - conversational - ncsoft - varco base_model: - Qwen/Qwen2.5-14B-Instruct - google/siglip-so400m-patch14-384 library_name: transformers pipeline_tag: image-text-to-text --- # VARCO-VISION-14B ## 🚨News🎙️ - The 2.0 model has been released. Please use the new version. - 📰 2025-07-16: We released VARCO-VISION-2.0-14B at [link](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-14B) - 📰 2025-07-16: We released GME-VARCO-VISION-Embedding at [link](https://huggingface.co/NCSOFT/GME-VARCO-VISION-Embedding) ## About the VARCO-VISION-1.0-14B Model VARCO-VISION-14B is a powerful English-Korean Vision-Language Model (VLM). The training pipeline of VARCO-VISION consists of four stages: Feature Alignment Pre-training, Basic Supervised Fine-tuning, Advanced Supervised Fine-tuning, and Preference Optimization. In both multimodal and text-only benchmarks, VARCO-VISION-14B not only surpasses other models of similar size in performance but also achieves scores comparable to those of proprietary models. The Model currently accepts a single image and a text as inputs, generating an output text. It supports grounding, referring as well as OCR (Optical Character Recognition). - Developed by: NC Research, Multimodal Generation Team - Technical Report: [VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models](https://arxiv.org/pdf/2411.19103) - Blog(Korean): [VARCO-VISION Technical Report Summary](https://ncsoft.github.io/ncresearch/95ad8712e60063e9ac97538504ac3eea0ac530af) - Demo Page: The demo page is no longer available. - Languages: Korean, English - License: CC BY-NC 4.0 - Architecture: VARCO-VISION-14B follows the architecture of [LLaVA-OneVision](https://arxiv.org/abs/2408.03326). - Base Model: - Language Model: [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) - Vision Encoder: [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) - Huggingface Version Model: [NCSOFT/VARCO-VISION-14B-HF](https://huggingface.co/NCSOFT/VARCO-VISION-14B-HF) - Korean VLM Benchmarks: - You can use the following benchmark datasets in the [LLMs-Eval toolkit](https://github.com/EvolvingLMMs-Lab/lmms-eval). - [NCSOFT/K-MMBench](https://huggingface.co/datasets/NCSOFT/K-MMBench) - [NCSOFT/K-SEED](https://huggingface.co/datasets/NCSOFT/K-SEED) - [NCSOFT/K-MMStar](https://huggingface.co/datasets/NCSOFT/K-MMStar) - [NCSOFT/K-DTCBench](https://huggingface.co/datasets/NCSOFT/K-DTCBench) - [NCSOFT/K-LLaVA-W](https://huggingface.co/datasets/NCSOFT/K-LLaVA-W) - you can also evaluate VARCO-VISION-14B in the [VLMEval kit](https://github.com/open-compass/VLMEvalKit). - This model is for research purposes only. Commercial use is prohibited. ## Uses ### Direct Use To load VARCO-VISION-14B, start by cloning and installing LLaVA-NeXT: ```bash git clone https://github.com/LLaVA-VL/LLaVA-NeXT cd LLaVA-NeXT pip install -e ".[train]" ``` After installing LLaVA-NeXT, you can load VARCO-VISION-14B using the following code: ```python import torch from transformers import AutoTokenizer from llava.model.language_model.llava_qwen import LlavaQwenForCausalLM from llava.mm_utils import tokenizer_image_token, process_images model_name = "NCSOFT/VARCO-VISION-14B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = LlavaQwenForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, attn_implementation="flash_attention_2", low_cpu_mem_usage=True, device_map="auto" ) vision_tower = model.get_vision_tower() image_processor = vision_tower.image_processor ``` Prepare an image and a text input. You need to preprocess the image and tokenize the text. Pass the processed inputs to the model to generate predictions. ```python import requests from PIL import Image # Define a chat history and use `apply_chat_template` to get correctly formatted prompt # Each value in "content" has to be a list of dicts with types ("text", "image") conversation = [ { "role": "user", "content": [ {"type": "text", "text": "Describe this image."}, {"type": "image"}, ], }, ] prompt = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False) IMAGE_TOKEN_INDEX = -200 EOS_TOKEN = "<\|im_end\|>" input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") input_ids = input_ids.unsqueeze(0).to(model.device) image_url = "http://images.cocodataset.org/val2017/000000039769.jpg" raw_image = Image.open(requests.get(image_url, stream=True).raw) image_tensors = process_images([raw_image], image_processor, model.config) image_tensors = [image_tensor.half().to(model.device) for image_tensor in image_tensors] image_sizes = [raw_image.size] with torch.inference_mode(): output_ids = model.generate( input_ids, images=image_tensors, image_sizes=image_sizes, do_sample=False, max_new_tokens=1024, use_cache=True, ) outputs = tokenizer.batch_decode(output_ids)[0] if outputs.endswith(EOS_TOKEN): outputs = outputs[: -len(EOS_TOKEN)] outputs = outputs.strip() print(outputs) ``` ### Specialized Features If a question is based on bounding boxes or require bounding boxes as an output, please include the special tokens in the input text. The following special tokens are used to define specific tasks, inputs, and outputs for the model: - `<gro>`: Indicates that the model's response should include bounding box information. - `<ocr>`: Specifies OCR tasks for recognizing text within an image. - `<char>` and `</char>`: Used to mark a text phrase. - `<obj>` and `</obj>`: Used to indicate an object. - `<bbox>` and `</bbox>`: Used to represent a bounding box. - `<delim>`: Represents multiple location points for a single object or text. #### Grounding Grounding refers to a task where the model needs to identify specific locations within an image to provide an appropriate answer. To perform grounding, prepend the special token `<gro>` to the question. ```python conversation = [ { "role": "user", "content": [ {"type": "text", "text": "<gro>\nDescribe the image in detail."}, {"type": "image"}, ], }, ] ``` Expected Output Example: ```html The image shows <obj>two cats</obj><bbox>0.521, 0.049, 0.997, 0.783<delim>0.016, 0.108, 0.512, 0.99</bbox> lying on <obj>a pink blanket</obj><bbox>0.002, 0.231, 0.999, 0.999</bbox>. The cat on the left is lying on its side with its head resting on the blanket and its body stretched out. The cat on the right is lying on its back with its paws stretched out and its head turned to the side. Both cats appear relaxed and comfortable. There are also <obj>two remote controls</obj><bbox>0.039, 0.138, 0.283, 0.257<delim>0.508, 0.166, 0.581, 0.295</bbox> placed near the cats, one on each side of them. ``` <img src="assets/grounding.png" alt="Grounding Example" width="400"/> #### Referring VARCO-VISION-14B can handle location-specific questions using bounding boxes. To perform referring tasks, make a conversation including the object of interest within `<obj>` and `</obj>` tags. You have to specify its location with `<bbox>` and `</bbox>` tags. This allows the model to understand the context and focus on the object at the specified location. A bbox is represented in a form of (x1, y1, x2, y2). The first two values indicate the top-left position of a bbox, and the latter two values are the bottom-right position. ```python conversation = [ { "role": "user", "content": [ { "type": "text", "text": "<obj>이 물건</obj><bbox>0.039, 0.138, 0.283, 0.257</bbox>은 어떻게 쓰는거야?", }, {"type": "image"}, ], }, ] ``` Expected Output Example: ``` 이 물건은 리모컨으로, 주로 텔레비전이나 다른 전자 기기를 원격으로 조작하는 데 사용됩니다. 버튼을 누르면 채널 변경, 볼륨 조절, 전원 켜기/끄기 등의 기능을 수행할 수 있습니다. 리모컨의 버튼에는 일반적으로 숫자, 메뉴, 설정, 재생/일시정지 등의 기능이 포함되어 있으며, 사용자는 이를 통해 손쉽게 기기를 제어할 수 있습니다. ``` #### OCR To perform Optical Character Recognition (OCR), use the `<ocr>` token. ```python image_file = "./assets/ocr_1.png" raw_image = Image.open(image_file) conversation = [ { "role": "user", "content": [ {"type": "text", "text": "<ocr>"}, {"type": "image"}, ], }, ] ``` Expected Output Example: ``` <char>백범로</char><bbox>0.172, 0.265, 0.328, 0.34</bbox> <char>124번길</char><bbox>0.349, 0.265, 0.512, 0.34</bbox> <char>Baekbeom-ro</char><bbox>0.171, 0.335, 0.432, 0.391</bbox> <char>124</char><bbox>0.444, 0.34, 0.508, 0.391</bbox> <char>만수주공아파트</char><bbox>0.109, 0.528, 0.335, 0.594</bbox> <char>시흥</char><bbox>0.443, 0.516, 0.522, 0.578</bbox> <char>시청</char><bbox>0.711, 0.521, 0.811, 0.594</bbox> <char>Mansu</char><bbox>0.103, 0.601, 0.181, 0.647</bbox> <char>Jugong</char><bbox>0.186, 0.601, 0.273, 0.658</bbox> <char>Apt</char><bbox>0.281, 0.601, 0.327, 0.651</bbox> <char>42</char><bbox>0.377, 0.601, 0.416, 0.647</bbox> <char>Shieung</char><bbox>0.445, 0.578, 0.53, 0.623</bbox> <char>인천대공원</char><bbox>0.431, 0.623, 0.609, 0.684</bbox> <char>모래내시장역</char><bbox>0.651, 0.591, 0.873, 0.664</bbox> <char>IncheonGrand</char><bbox>0.433, 0.684, 0.561, 0.723</bbox> <char>Park</char><bbox>0.564, 0.684, 0.611, 0.723</bbox> ``` <img src="assets/ocr_2.jpg" alt="OCR Example" width="350"/> ## Citing the Model If you use VARCO-VISION-14B in your research, please cite the following: ```bibtex @misc{ju2024varcovisionexpandingfrontierskorean, title={VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models}, author={Jeongho Ju and Daeyoung Kim and SunYoung Park and Youngjune Kim}, year={2024}, eprint={2411.19103}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2411.19103}, } ```
omerbektasss/blockassist-bc-insectivorous_bold_lion_1757656852	omerbektasss	2025-09-12T06:01:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous bold lion", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T06:01:10Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous bold lion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
nuphoto-ian/Qwen3-8B-QAT-NVFP4	nuphoto-ian	2025-09-12T06:01:05Z	0	0	null	[ "safetensors", "qwen3", "license:apache-2.0", "8-bit", "modelopt", "region:us" ]	null	2025-09-12T05:33:47Z	--- license: apache-2.0 ---
nightmedia/ERNIE-4.5-21B-A3B-Thinking-qx86-hi-mlx	nightmedia	2025-09-12T06:00:49Z	0	0	mlx	[ "mlx", "safetensors", "ernie4_5_moe", "ERNIE4.5", "text-generation", "conversational", "custom_code", "en", "zh", "base_model:baidu/ERNIE-4.5-21B-A3B-Thinking", "base_model:quantized:baidu/ERNIE-4.5-21B-A3B-Thinking", "license:apache-2.0", "8-bit", "region:us" ]	text-generation	2025-09-12T04:49:36Z	--- license: apache-2.0 language: - en - zh pipeline_tag: text-generation tags: - ERNIE4.5 - mlx library_name: mlx base_model: baidu/ERNIE-4.5-21B-A3B-Thinking --- # ERNIE-4.5-21B-A3B-Thinking-qx86-hi-mlx This model [ERNIE-4.5-21B-A3B-Thinking-qx86-hi-mlx](https://huggingface.co/ERNIE-4.5-21B-A3B-Thinking-qx86-hi-mlx) was converted to MLX format from [baidu/ERNIE-4.5-21B-A3B-Thinking](https://huggingface.co/baidu/ERNIE-4.5-21B-A3B-Thinking) using mlx-lm version 0.27.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("ERNIE-4.5-21B-A3B-Thinking-qx86-hi-mlx") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
iori-ltn/nanj-qwen2.5-3b-merged	iori-ltn	2025-09-12T05:58:10Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit", "lora", "sft", "transformers", "trl", "unsloth", "text-generation", "conversational", "dataset:p1atdev/open2ch", "license:apache-2.0", "region:us" ]	text-generation	2025-09-12T04:33:22Z	--- base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit library_name: peft model_name: nanj-qwen tags: - base_model:adapter:unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit - lora - sft - transformers - trl - unsloth pipeline_tag: text-generation license: apache-2.0 datasets: - p1atdev/open2ch --- # Nanj Qwen2.5-3B LoRA (DAPT + SFT) このモデルは `Qwen/Qwen2.5-3B-Instruct` をベースに、 open2ch データで DAPT、 livejupiter データで SFT を行った LoRA です。なんJ風の会話生成に最適化されています。 --- ## Usage ```python from unsloth import FastLanguageModel # BaseModek base = "Qwen/Qwen2.5-3B-Instruct" # Load LoRa Adapter model, tok = FastLanguageModel.from_pretrained(base, load_in_4bit=True, max_seq_length=1024) model.load_adapter("your-username/nanj-qwen2.5-3b-lora") # example msgs = [{"role":"user", "content":"藤浪復活したら阪神どうなる？"}] prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True) out = model.generate( tok(prompt, return_tensors="pt").to(model.device), max_new_tokens=128, do_sample=True, top_p=0.9, temperature=0.8 ) print(tok.decode(out[0], skip_special_tokens=True).split(prompt)[-1].strip()) ``` ## Training procedure 本モデルの DAPT / SFT はどちらも LoRA を用いて実施しました。 - Phase 1: DAPT (LoRA) open2ch (all-corpus-cleaned) を用いた継続事前学習（次トークン予測） → open2ch の語彙・言い回しにモデルを適応 - Phase 2: SFT (LoRA)** livejupiter-cleaned から作成した会話ペアで教師あり微調整 → なんJ的な対話スタイルを獲得 ### Framework versions - PEFT 0.17.1 - TRL: 0.23.0 - Transformers: 4.56.1 - Pytorch: 2.8.0+cu126 - Datasets: 3.6.0 - Tokenizers: 0.22.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757656502	stonermay	2025-09-12T05:56:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T05:56:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
N-A-Me/Alaska	N-A-Me	2025-09-12T05:55:19Z	0	0	null	[ "base_model:Liberata/illustrious-xl-v1.0", "base_model:finetune:Liberata/illustrious-xl-v1.0", "license:cc", "region:us" ]	null	2025-05-07T06:04:03Z	--- widget: - text: Alaska base_model: Liberata/illustrious-xl-v1.0 instance_prompt: Alaska license: cc --- # Alaska <Gallery /> ## Trigger words You should use `Alaska` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/N-A-Me/Alaska/tree/main) them in the Files & versions tab.
omerbektasss/blockassist-bc-keen_fast_giraffe_1757656465	omerbektasss	2025-09-12T05:55:19Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T05:54:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
nyu-dice-lab/VeriThoughts-Reasoning-32B-Qwen3	nyu-dice-lab	2025-09-12T05:53:05Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-32B", "base_model:finetune:Qwen/Qwen3-32B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-11T01:14:35Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-32B tags: - llama-factory - full - generated_from_trainer model-index: - name: VeriThoughts-Reasoning-32B-Qwen3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # VeriThoughts-Reasoning-32B-Qwen3 This model is a fine-tuned version of [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) on the reasoning_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 64 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.8.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
VizitonMexico/VizitonMexico	VizitonMexico	2025-09-12T05:51:54Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-12T05:51:03Z	--- license: apache-2.0 --- ¿Qué es Viziton? Viziton cápsula es una cápsula para la vista especialmente formulada, diseñada para apoyar la salud visual y mantener la visión sana. En el mundo actual, donde el tiempo frente a las pantallas se ha vuelto inevitable, cuidar la vista nunca ha sido tan importante. Viziton Pastillas está creado para nutrir los ojos, protegerlos del estrés diario y promover la claridad a largo plazo. Ya sea que experimente fatiga ocular leve o simplemente desee mantener una visión saludable a medida que envejece, Viziton tabletas está diseñado para convertirse en su sistema de apoyo natural Viziton Cómo utilizar. Sitio web oficial:<a href="https://www.nutritionsee.com/vizitoexico">www.Viziton.com</a> <p><a href="https://www.nutritionsee.com/vizitoexico"> <img src="https://www.nutritionsee.com/wp-content/uploads/2025/09/viziton-Mexico.png" alt="enter image description here"> </a></p> <a href="https://www.nutritionsee.com/vizitoexico">¡Compra ya! Haz clic en el enlace de abajo para más información y obtén un 50% de descuento. ¡Date prisa!</a> Sitio web oficial:<a href="https://www.nutritionsee.com/vizitoexico">www.Viziton.com</a>
omerbektasss/blockassist-bc-insectivorous_bold_lion_1757656089	omerbektasss	2025-09-12T05:48:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous bold lion", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T05:48:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous bold lion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
JobixAi/tts-mapalo-pipeliner	JobixAi	2025-09-12T05:47:38Z	0	0	null	[ "safetensors", "llama", "region:us" ]	null	2025-09-12T04:43:09Z	This model finetunes the pretrained model `canopylabs/orpheus-3b-0.1-pretrained` using the finetuning pipeline. Full finetuning with Unsloth for 5 epochs. ### Datasets JobixAi/mapalo-higgs-20250912_021709 ### Inference ```bash temperature = 0.7 top_p = 0.9 repetition_penalty = 1.1 ```
stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757655885	stonermay	2025-09-12T05:46:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T05:45:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
CriteriaPO/qwen2.5-3b-dpo-finegrained-20-vanilla	CriteriaPO	2025-09-12T05:46:10Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:CriteriaPO/qwen2.5-3b-sft-10", "base_model:finetune:CriteriaPO/qwen2.5-3b-sft-10", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-10T01:31:35Z	--- base_model: CriteriaPO/qwen2.5-3b-sft-10 library_name: transformers model_name: qwen2.5-3b-dpo-finegrained-20-vanilla tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for qwen2.5-3b-dpo-finegrained-20-vanilla This model is a fine-tuned version of [CriteriaPO/qwen2.5-3b-sft-10](https://huggingface.co/CriteriaPO/qwen2.5-3b-sft-10). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="CriteriaPO/qwen2.5-3b-dpo-finegrained-20-vanilla", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/bborges/CriteriaPreferences/runs/7bj9ejy1) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.2 - Transformers: 4.46.3 - Pytorch: 2.1.2+cu121 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
diabolic6045/Sanskrit-Qwen2.5-VL-7B-Instruct-OCR	diabolic6045	2025-09-12T05:46:08Z	7	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "axolotl", "text-generation", "conversational", "sa", "dataset:diabolic6045/sanskrit-ocr-parallel-corpus-chat-template", "dataset:snskrt/Sanskrit_OCR_Parallel_Corpus", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "license:apache-2.0", "model-index", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-08T06:10:48Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-VL-7B-Instruct tags: - axolotl - transformers datasets: - diabolic6045/sanskrit-ocr-parallel-corpus-chat-template - snskrt/Sanskrit_OCR_Parallel_Corpus pipeline_tag: text-generation model-index: - name: qwen2-5-vl-sanskrit-ocr results: - task: type: image-to-text dataset: name: Sanskrit OCR Test Set type: sanskrit-ocr metrics: - name: Exact Match Accuracy type: exact_match value: 1.59 - name: Character-level Accuracy type: character_accuracy value: 86.38 - name: Token-level Jaccard Similarity type: jaccard_similarity value: 50.44 - name: Success Rate type: success_rate value: 100 source: name: Sanskrit OCR Evaluation url: https://huggingface.co/datasets/diabolic6045/sanskrit-ocr-parallel-corpus-chat-template/viewer/default/test language: - sa --- # Sanskrit-Qwen2.5-VL-7B-Instruct-OCR This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) on the [diabolic6045/sanskrit-ocr-parallel-corpus-chat-template](https://huggingface.co/datasets/diabolic6045/sanskrit-ocr-parallel-corpus-chat-template) dataset. This data is converted from [snskrt/Sanskrit_OCR_Parallel_Corpus](https://huggingface.co/datasets/snskrt/Sanskrit_OCR_Parallel_Corpus) by [Sanskrit Datasets](https://huggingface.co/snskrt). It achieves the following results on the evaluation set: - Loss: 0.2660 - Memory/max Mem Active(gib): 20.79 - Memory/max Mem Allocated(gib): 20.79 - Memory/device Mem Reserved(gib): 21.46 ## Model description This is a fine-tuned version of Qwen2.5-VL-7B-Instruct, specifically adapted for Sanskrit OCR (Optical Character Recognition) tasks. The model has been trained using LoRA (Low-Rank Adaptation) on a dataset of Sanskrit text images and their corresponding transcriptions. Key Features: - Base Model: Qwen/Qwen2.5-VL-7B-Instruct (7 billion parameters) - Task: Sanskrit OCR - converting Sanskrit text images to machine-readable text - Training Method: LoRA fine-tuning with vision-language capabilities - Dataset: Sanskrit OCR Parallel Corpus with chat template formatting - Architecture: Vision-Language Model with multimodal understanding Capabilities: - Read and transcribe Sanskrit text from images - Handle various Sanskrit scripts and fonts - Process both text and visual inputs simultaneously - Generate accurate Sanskrit text transcriptions The model maintains the original Qwen2.5-VL's vision-language capabilities while being specialized for Sanskrit text recognition tasks. ## Training and evaluation data ### Training Dataset The model was trained on the [diabolic6045/sanskrit-ocr-parallel-corpus-chat-template](https://huggingface.co/datasets/diabolic6045/sanskrit-ocr-parallel-corpus-chat-template) dataset, which contains Sanskrit text images paired with their corresponding transcriptions. The dataset was converted from the original [snskrt/Sanskrit_OCR_Parallel_Corpus](https://huggingface.co/datasets/snskrt/Sanskrit_OCR_Parallel_Corpus) and formatted with chat templates for vision-language training. ### Evaluation Results The model was evaluated on a test set of 314 Sanskrit text samples: \| Metric \| Value \| \|:------:\|:-----:\| \| Total Samples \| 314 \| \| Successful Samples \| 314 \| \| Failed Samples \| 0 \| \| Success Rate \| 100.00% \| \| Exact Match Accuracy \| 1.59% \| \| Character-level Accuracy \| 86.38% \| \| Token-level Jaccard Similarity \| 50.44% \| Key Insights: - The model successfully processes all test samples without failures - High character-level accuracy (86.38%) indicates good recognition of individual Sanskrit characters - Lower exact match accuracy (1.59%) suggests room for improvement in complete text transcription - Moderate token-level similarity (50.44%) shows reasonable semantic understanding ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - total_eval_batch_size: 4 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 110 - training_steps: 1105 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mem Active(gib) \| Mem Allocated(gib) \| Mem Reserved(gib) \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------------:\|:------------------:\|:-----------------:\| \| No log \| 0 \| 0 \| 3.3372 \| 17.59 \| 17.59 \| 17.66 \| \| 0.2428 \| 1.0 \| 369 \| 0.3075 \| 20.69 \| 20.69 \| 21.27 \| \| 0.2057 \| 2.0 \| 738 \| 0.2660 \| 20.79 \| 20.79 \| 21.46 \| <br> This model was trained using: <br> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.12.2` ```yaml base_model: Qwen/Qwen2.5-VL-7B-Instruct processor_type: AutoProcessor # these 3 lines are needed for now to handle vision chat templates w images skip_prepare_dataset: true remove_unused_columns: false sample_packing: false chat_template: qwen2_vl datasets: - path: sanskrit_multimodal_train.json type: chat_template field_messages: messages dataset_prepared_path: last_run_prepared val_set_size: 0.01 output_dir: ./outputs/out-qwen2-5-vl adapter: lora lora_model_dir: sequence_len: 2048 pad_to_sequence_len: false lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_modules: 'model.language_model.layers.[\d]+.(mlp\|cross_attn\|self_attn).(up\|down\|gate\|q\|k\|v\|o)_proj' wandb_project: Sanskrit-OCR wandb_entity: wandb_watch: wandb_name: qwen2-5-vl-sanskrit-ocr wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 3 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0002 bf16: true fp16: tf32: true gradient_checkpointing: true logging_steps: 1 flash_attention: true eager_attention: warmup_ratio: 0.1 evals_per_epoch: 1 saves_per_epoch: 1 weight_decay: 0.0 # Automatically upload checkpoint and final model to HF hub_model_id: diabolic6045/qwen2-5-vl-sanskrit-ocr-lora # save_first_step: true # uncomment this to validate checkpoint saving works with your config ``` </details><br> ### Framework versions - PEFT 0.17.0 - Transformers 4.55.2 - Pytorch 2.7.1+cu128 - Datasets 4.0.0 - Tokenizers 0.21.2
Asap7772/qwen3-4b-arc-second-stage-star-sftinit-lr1e-6-0908	Asap7772	2025-09-12T05:42:54Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T04:47:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
omerbektasss/blockassist-bc-keen_fast_giraffe_1757655736	omerbektasss	2025-09-12T05:42:36Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T05:42:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
dan2-ux/mistral-500	dan2-ux	2025-09-12T05:42:16Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "generated_from_trainer", "trl", "sft", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-11T08:44:56Z	--- base_model: mistralai/Mistral-7B-v0.1 library_name: transformers model_name: mistral-500 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for mistral-500 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="dan2-ux/mistral-500", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/danh25911-bosch-global/Fine%20tuning%20of%20Mistral%207B/runs/v73tk7fm?apiKey=ed94c7e2762b3e7af52e2b746f14631975e151c4) This model was trained with SFT. ### Framework versions - TRL: 0.23.0 - Transformers: 4.56.1 - Pytorch: 2.6.0 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
davidilag/wav2vec2-xls-r-300m-pt-500h-FO-500h-SE-cp-best-faroese-100h-30-epochs_run9_2025-09-11	davidilag	2025-09-12T05:42:07Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-09-11T20:19:26Z	--- library_name: transformers tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-xls-r-300m-pt-500h-FO-500h-SE-cp-best-faroese-100h-30-epochs_run9_2025-09-11 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xls-r-300m-pt-500h-FO-500h-SE-cp-best-faroese-100h-30-epochs_run9_2025-09-11 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1013 - Wer: 18.8175 - Cer: 4.0350 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 3.2822 \| 0.4877 \| 1000 \| 3.2104 \| 100.0 \| 99.3017 \| \| 0.6811 \| 0.9754 \| 2000 \| 0.3949 \| 38.8377 \| 10.4119 \| \| 0.3765 \| 1.4628 \| 3000 \| 0.2219 \| 29.8894 \| 7.5540 \| \| 0.3338 \| 1.9505 \| 4000 \| 0.1891 \| 28.0169 \| 6.9157 \| \| 0.2546 \| 2.4379 \| 5000 \| 0.1699 \| 26.6423 \| 6.4739 \| \| 0.2495 \| 2.9256 \| 6000 \| 0.1524 \| 25.7171 \| 6.2017 \| \| 0.1873 \| 3.4131 \| 7000 \| 0.1380 \| 24.4570 \| 5.7646 \| \| 0.1933 \| 3.9008 \| 8000 \| 0.1384 \| 24.2455 \| 5.7732 \| \| 0.1467 \| 4.3882 \| 9000 \| 0.1331 \| 23.6155 \| 5.5405 \| \| 0.1585 \| 4.8759 \| 10000 \| 0.1340 \| 23.1793 \| 5.5302 \| \| 0.141 \| 5.3633 \| 11000 \| 0.1229 \| 22.7123 \| 5.2264 \| \| 0.1365 \| 5.8510 \| 12000 \| 0.1174 \| 22.7078 \| 5.2730 \| \| 0.1255 \| 6.3385 \| 13000 \| 0.1212 \| 22.5228 \| 5.2004 \| \| 0.1255 \| 6.8261 \| 14000 \| 0.1217 \| 22.3862 \| 5.0868 \| \| 0.1137 \| 7.3136 \| 15000 \| 0.1140 \| 21.8355 \| 5.0237 \| \| 0.1174 \| 7.8013 \| 16000 \| 0.1083 \| 21.5711 \| 4.9069 \| \| 0.1056 \| 8.2887 \| 17000 \| 0.1148 \| 21.5227 \| 4.8761 \| \| 0.1011 \| 8.7764 \| 18000 \| 0.1157 \| 21.5932 \| 4.8998 \| \| 0.0895 \| 9.2638 \| 19000 \| 0.1078 \| 21.2054 \| 4.7988 \| \| 0.0989 \| 9.7515 \| 20000 \| 0.1075 \| 21.1085 \| 4.7593 \| \| 0.0864 \| 10.2390 \| 21000 \| 0.1040 \| 20.7781 \| 4.6339 \| \| 0.083 \| 10.7267 \| 22000 \| 0.1050 \| 20.9455 \| 4.6986 \| \| 0.0751 \| 11.2141 \| 23000 \| 0.1112 \| 20.7296 \| 4.6489 \| \| 0.0725 \| 11.7018 \| 24000 \| 0.1066 \| 20.6327 \| 4.5463 \| \| 0.0774 \| 12.1892 \| 25000 \| 0.1054 \| 20.5622 \| 4.5952 \| \| 0.069 \| 12.6769 \| 26000 \| 0.1076 \| 20.5578 \| 4.5503 \| \| 0.0706 \| 13.1644 \| 27000 \| 0.1087 \| 20.3287 \| 4.5100 \| \| 0.0635 \| 13.6520 \| 28000 \| 0.1156 \| 20.4080 \| 4.5471 \| \| 0.0652 \| 14.1395 \| 29000 \| 0.1022 \| 20.2053 \| 4.4887 \| \| 0.067 \| 14.6272 \| 30000 \| 0.1015 \| 20.0864 \| 4.4382 \| \| 0.0585 \| 15.1146 \| 31000 \| 0.1035 \| 19.9850 \| 4.3893 \| \| 0.0499 \| 15.6023 \| 32000 \| 0.1044 \| 20.1040 \| 4.4193 \| \| 0.0634 \| 16.0897 \| 33000 \| 0.1069 \| 19.9938 \| 4.3980 \| \| 0.0577 \| 16.5774 \| 34000 \| 0.1034 \| 19.7559 \| 4.3475 \| \| 0.0506 \| 17.0649 \| 35000 \| 0.1008 \| 19.9366 \| 4.3349 \| \| 0.0444 \| 17.5525 \| 36000 \| 0.1034 \| 19.6854 \| 4.2828 \| \| 0.0511 \| 18.0400 \| 37000 \| 0.0985 \| 19.6854 \| 4.2615 \| \| 0.0449 \| 18.5277 \| 38000 \| 0.1018 \| 19.2669 \| 4.2149 \| \| 0.0445 \| 19.0151 \| 39000 \| 0.1028 \| 19.4651 \| 4.2007 \| \| 0.0407 \| 19.5028 \| 40000 \| 0.1071 \| 19.4387 \| 4.2268 \| \| 0.0365 \| 19.9905 \| 41000 \| 0.1060 \| 19.3418 \| 4.2134 \| \| 0.0397 \| 20.4779 \| 42000 \| 0.1088 \| 19.2889 \| 4.1597 \| \| 0.0305 \| 20.9656 \| 43000 \| 0.1031 \| 19.3197 \| 4.1723 \| \| 0.0321 \| 21.4531 \| 44000 \| 0.1048 \| 19.3153 \| 4.1652 \| \| 0.0462 \| 21.9407 \| 45000 \| 0.1029 \| 19.2448 \| 4.1297 \| \| 0.0404 \| 22.4282 \| 46000 \| 0.1021 \| 19.1303 \| 4.1258 \| \| 0.0402 \| 22.9159 \| 47000 \| 0.1030 \| 19.0862 \| 4.1124 \| \| 0.0417 \| 23.4033 \| 48000 \| 0.1036 \| 19.0378 \| 4.1005 \| \| 0.0315 \| 23.8910 \| 49000 \| 0.1039 \| 18.9717 \| 4.0737 \| \| 0.0381 \| 24.3784 \| 50000 \| 0.1031 \| 18.9673 \| 4.0840 \| \| 0.0368 \| 24.8661 \| 51000 \| 0.1045 \| 18.9012 \| 4.0571 \| \| 0.0328 \| 25.3536 \| 52000 \| 0.1032 \| 18.9144 \| 4.0666 \| \| 0.0328 \| 25.8413 \| 53000 \| 0.1029 \| 18.8615 \| 4.0776 \| \| 0.0344 \| 26.3287 \| 54000 \| 0.1016 \| 18.9100 \| 4.0792 \| \| 0.0328 \| 26.8164 \| 55000 \| 0.1018 \| 18.8968 \| 4.0682 \| \| 0.0365 \| 27.3038 \| 56000 \| 0.1009 \| 18.8439 \| 4.0540 \| \| 0.0399 \| 27.7915 \| 57000 \| 0.1016 \| 18.8131 \| 4.0390 \| \| 0.0398 \| 28.2790 \| 58000 \| 0.1016 \| 18.8042 \| 4.0335 \| \| 0.0298 \| 28.7666 \| 59000 \| 0.1014 \| 18.8042 \| 4.0279 \| \| 0.0364 \| 29.2541 \| 60000 \| 0.1013 \| 18.8263 \| 4.0366 \| \| 0.0355 \| 29.7418 \| 61000 \| 0.1013 \| 18.8175 \| 4.0350 \| ### Framework versions - Transformers 4.56.1 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0
Anhlq/gemma-3-finetune-4bit-copy	Anhlq	2025-09-12T05:42:05Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "en", "base_model:unsloth/gemma-3-270m-it", "base_model:finetune:unsloth/gemma-3-270m-it", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-12T05:41:46Z	--- base_model: unsloth/gemma-3-270m-it tags: - text-generation-inference - transformers - unsloth - gemma3_text license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: Anhlq - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-270m-it This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Anhlq/gemma-3-finetune-16bit-copy	Anhlq	2025-09-12T05:41:43Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/gemma-3-270m-it", "base_model:finetune:unsloth/gemma-3-270m-it", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T05:41:15Z	--- base_model: unsloth/gemma-3-270m-it tags: - text-generation-inference - transformers - unsloth - gemma3_text license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: Anhlq - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-270m-it This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
RMCian/Qwen3-0.6B-Gensyn-Swarm-fast_rabid_ram	RMCian	2025-09-12T05:39:41Z	38	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am fast_rabid_ram", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-30T14:32:41Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am fast_rabid_ram --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
muscularstingingpenguin/blockassist	muscularstingingpenguin	2025-09-12T05:36:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gregarious tropical kiwi", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T05:36:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gregarious tropical kiwi --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757655275	stonermay	2025-09-12T05:35:42Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T05:35:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
NCSOFT/VARCO-VISION-2.0-1.7B-OCR	NCSOFT	2025-09-12T05:34:48Z	6,273	19	transformers	[ "transformers", "safetensors", "llava_onevision", "image-to-text", "multimodal", "OCR", "ncsoft", "ncai", "varco", "image-text-to-text", "conversational", "en", "ko", "arxiv:2408.03326", "base_model:Qwen/Qwen3-1.7B", "base_model:finetune:Qwen/Qwen3-1.7B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-07-08T06:27:27Z	--- license: cc-by-nc-4.0 base_model: - Qwen/Qwen3-1.7B - google/siglip2-so400m-patch16-384 library_name: transformers tags: - multimodal - OCR - ncsoft - ncai - varco pipeline_tag: image-text-to-text language: - en - ko --- # VARCO-VISION-2.0-1.7B-OCR <div align="center"> <img src="./varco-vision.png" width="100%" style="background-color:white; padding:10px;" /> </div> ## Introduction VARCO-VISION-2.0-1.7B-OCR is a lightweight yet powerful OCR-specialized model derived from VARCO-VISION-2.0-1.7B, designed to deliver efficient and accurate text recognition in real-world scenarios. Unlike conventional vision-language models (VLMs) that primarily focus on transcribing visible text, this model performs both recognition and spatial localization by detecting bounding boxes around each character, enabling structured, layout-aware OCR outputs. The model supports both Korean and English, making it well-suited for multilingual environments where mixed-script documents are common. Each recognized character is paired with its precise position in the image, formatted as `<char>{characters}</char><bbox>{x1}, {y1}, {x2}, {y2}</bbox>`, where the coordinates correspond to the top-left (`x1`, `y1`) and bottom-right (`x2`, `y2`) corners of the character's bounding box. While VARCO-VISION-2.0-14B demonstrates strong OCR capabilities as part of its broader multimodal reasoning skills, deploying such a large model for single-task use cases can be computationally inefficient. VARCO-VISION-2.0-1.7B-OCR addresses this with a task-optimized design that retains high accuracy while significantly reducing resource requirements, making it ideal for real-time or resource-constrained applications. ![image/gif](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-1.7B-OCR/resolve/main/examples.gif) ## 🚨News🎙️ - 🛠️ 2025-08-22: We updated the checkpoint of VARCO-VISION-2.0-1.7B for improved performance. - 📰 2025-07-28: We released VARCO-VISION-2.0-1.7B-OCR at [link](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-1.7B-OCR) - 📰 2025-07-28: We released VARCO-VISION-2.0-1.7B at [link](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-1.7B) - 🛠️ 2025-07-18: We updated the checkpoint of VARCO-VISION-2.0-14B for improved performance. - 📰 2025-07-16: We released VARCO-VISION-2.0-14B at [link](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-14B) - 📰 2025-07-16: We released GME-VARCO-VISION-Embedding at [link](https://huggingface.co/NCSOFT/GME-VARCO-VISION-Embedding) ## VARCO-VISION-2.0 Family \| Model Name \| Base Models (Vision / Language) \| HF Link \| \| :------------------------: \| :-------------------------------------------------------------------------------------------------------------------------------------------: \| :--------------------------------------------------------------: \| \| VARCO-VISION-2.0-14B \| [siglip2-so400m-patch16-384](https://huggingface.co/google/siglip2-so400m-patch16-384) / [Qwen3-14B ](https://huggingface.co/Qwen/Qwen3-14B) \| [link](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-14B) \| \| VARCO-VISION-2.0-1.7B \| [siglip2-so400m-patch16-384](https://huggingface.co/google/siglip2-so400m-patch16-384) / [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) \| [link](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-1.7B) \| \| VARCO-VISION-2.0-1.7B-OCR \| [siglip2-so400m-patch16-384](https://huggingface.co/google/siglip2-so400m-patch16-384) / [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) \| [link](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-1.7B-OCR) \| \| GME-VARCO-VISION-Embedding \| [Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) \| [link](https://huggingface.co/NCSOFT/GME-VARCO-VISION-Embedding) \| ## Model Architecture VARCO-VISION-2.0 follows the architecture of [LLaVA-OneVision](https://arxiv.org/abs/2408.03326). ## Evaluation ### OCR Benchmark \| Benchmark \| CLOVA OCR \| PaddleOCR \| EasyOCR \| VARCO-VISION-2.0-1.7B-OCR \| \| :-------: \| :--------:\| :-------: \| :-----: \| :-----------------------: \| \| CORD \| 93.9 \| 91.4 \| 77.8 \| 95.6 \| \| ICDAR2013 \| 94.4 \| 92.0 \| 85.0 \| 95.5 \| \| ICDAR2015 \| 84.1 \| 73.7 \| 57.9 \| 75.4 \| ## Usage To use this model, we recommend installing `transformers` version 4.53.1 or higher. Additionally, for best results, we *recommend upscaling input images to a minimum resolution of 2,304*** on the longer side if they are smaller. ```python import torch from PIL import Image from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration model_name = "NCSOFT/VARCO-VISION-2.0-1.7B-OCR" model = LlavaOnevisionForConditionalGeneration.from_pretrained( model_name, torch_dtype=torch.float16, attn_implementation="sdpa", device_map="auto", ) processor = AutoProcessor.from_pretrained(model_name) image = Image.open("file:///path/to/image.jpg") # Image upscaling for OCR performance boost w, h = image.size target_size = 2304 if max(w, h) < target_size: scaling_factor = target_size / max(w, h) new_w = int(w * scaling_factor) new_h = int(h * scaling_factor) image = image.resize((new_w, new_h)) conversation = [ { "role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": "<ocr>"}, ], }, ] inputs = processor.apply_chat_template( conversation, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" ).to(model.device, torch.float16) generate_ids = model.generate(**inputs, max_new_tokens=1024) generate_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generate_ids) ] output = processor.decode(generate_ids_trimmed[0], skip_special_tokens=False) print(output) ```
NCSOFT/VARCO-VISION-14B-HF	NCSOFT	2025-09-12T05:34:13Z	1,103	29	transformers	[ "transformers", "safetensors", "llava_onevision", "image-to-text", "multimodal", "conversational", "ncsoft", "varco", "image-text-to-text", "en", "ko", "arxiv:2411.19103", "arxiv:2408.03326", "base_model:Qwen/Qwen2.5-14B-Instruct", "base_model:finetune:Qwen/Qwen2.5-14B-Instruct", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-11-27T01:11:00Z	--- language: - en - ko license: cc-by-nc-4.0 tags: - multimodal - conversational - ncsoft - varco base_model: - Qwen/Qwen2.5-14B-Instruct - google/siglip-so400m-patch14-384 library_name: transformers pipeline_tag: image-text-to-text --- # VARCO-VISION-14B-HF ## 🚨News🎙️ - The 2.0 model has been released. Please use the new version. - 📰 2025-07-16: We released VARCO-VISION-2.0-14B at [link](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-14B) - 📰 2025-07-16: We released GME-VARCO-VISION-Embedding at [link](https://huggingface.co/NCSOFT/GME-VARCO-VISION-Embedding) ## About the VARCO-VISION-1.0-14B Model VARCO-VISION-14B is a powerful English-Korean Vision-Language Model (VLM). The training pipeline of VARCO-VISION consists of four stages: Feature Alignment Pre-training, Basic Supervised Fine-tuning, Advanced Supervised Fine-tuning, and Preference Optimization. In both multimodal and text-only benchmarks, VARCO-VISION-14B not only surpasses other models of similar size in performance but also achieves scores comparable to those of proprietary models. The Model currently accepts a single image and a text as inputs, generating an output text. It supports grounding, referring as well as OCR (Optical Character Recognition). - Developed by: NC Research, Multimodal Generation Team - Technical Report: [VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models](https://arxiv.org/pdf/2411.19103) - Blog(Korean): [VARCO-VISION Technical Report Summary](https://ncsoft.github.io/ncresearch/95ad8712e60063e9ac97538504ac3eea0ac530af) - Demo Page: The demo page is no longer available. - Languages: Korean, English - License: CC BY-NC 4.0 - Architecture: VARCO-VISION-14B follows the architecture of [LLaVA-OneVision](https://arxiv.org/abs/2408.03326). - Base Model: - Language Model: [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) - Vision Encoder: [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) - LLaVA-NeXT Codebase Model: [NCSOFT/VARCO-VISION-14B](https://huggingface.co/NCSOFT/VARCO-VISION-14B) - Korean VLM Benchmarks: - You can use the following benchmark datasets in the [LLMs-Eval toolkit](https://github.com/EvolvingLMMs-Lab/lmms-eval). - [NCSOFT/K-MMBench](https://huggingface.co/datasets/NCSOFT/K-MMBench) - [NCSOFT/K-SEED](https://huggingface.co/datasets/NCSOFT/K-SEED) - [NCSOFT/K-MMStar](https://huggingface.co/datasets/NCSOFT/K-MMStar) - [NCSOFT/K-DTCBench](https://huggingface.co/datasets/NCSOFT/K-DTCBench) - [NCSOFT/K-LLaVA-W](https://huggingface.co/datasets/NCSOFT/K-LLaVA-W) - you can also evaluate VARCO-VISION-14B in the [VLMEval kit](https://github.com/open-compass/VLMEvalKit). - This model is for research purposes only. Commercial use is prohibited. ## Uses ### Direct Use To use this model, ensure you have `transformers >= 4.45.0` installed. ```python import torch import requests from PIL import Image from transformers import LlavaOnevisionForConditionalGeneration, AutoProcessor model_name = "NCSOFT/VARCO-VISION-14B-HF" model = LlavaOnevisionForConditionalGeneration.from_pretrained( model_name, torch_dtype="float16", device_map="auto", attn_implementation="flash_attention_2" ) processor = AutoProcessor.from_pretrained(model_name) device = model.device # Define a chat history and use `apply_chat_template` to get correctly formatted prompt # Each value in "content" has to be a list of dicts with types ("text", "image") conversation = [ { "role": "user", "content": [ {"type": "text", "text": "Describe this image."}, {"type": "image"}, ], }, ] prompt = processor.apply_chat_template(conversation, add_generation_prompt=True) EOS_TOKEN = "<\|im_end\|>" image_file = "http://images.cocodataset.org/val2017/000000039769.jpg" raw_image = Image.open(requests.get(image_file, stream=True).raw) inputs = processor(images=raw_image, text=prompt, return_tensors='pt').to(device, torch.float16) output = model.generate(inputs, max_new_tokens=1024, do_sample=False) output = processor.decode(output[0][inputs.input_ids.shape[1]:]) if output.endswith(EOS_TOKEN): output = output[: -len(EOS_TOKEN)] output = output.strip() print(output) ``` ### Specialized Features If a question is based on bounding boxes or require bounding boxes as an output, please include the special tokens in the input text. The following special tokens are used to define specific tasks, inputs, and outputs for the model: - `<gro>`: Indicates that the model's response should include bounding box information. - `<ocr>`: Specifies OCR tasks for recognizing text within an image. - `<char>` and `</char>`: Used to mark a text phrase. - `<obj>` and `</obj>`: Used to indicate an object. - `<bbox>` and `</bbox>`: Used to represent a bounding box. - `<delim>`: Represents multiple location points for a single object or text. #### Grounding Grounding refers to a task where the model needs to identify specific locations within an image to provide an appropriate answer. To perform grounding, prepend the special token `<gro>` to the question. ```python conversation = [ { "role": "user", "content": [ {"type": "text", "text": "<gro>\nDescribe the image in detail."}, {"type": "image"}, ], }, ] ``` Expected Output Example: ```html The image shows <obj>two cats</obj><bbox>0.014, 0.106, 0.51, 0.996<delim>0.51, 0.054, 0.996, 0.787</bbox> lying on <obj>a pink blanket</obj><bbox>0.003, 0.231, 0.999, 0.999</bbox>. The cat on the left is lying on its side with its head resting on the blanket, while the cat on the right is lying on its stomach with its head also resting on the blanket. Both cats appear to be relaxed and comfortable. There are <obj>two remote controls</obj><bbox>0.037, 0.141, 0.283, 0.253<delim>0.506, 0.171, 0.581, 0.295</bbox> placed near the cats, one on the left side and one on the right side of the image. ``` <img src="assets/grounding.png" alt="Grounding Example" width="400"/> #### Referring VARCO-VISION-14B can handle location-specific questions using bounding boxes. To perform referring tasks, make a conversation including the object of interest within `<obj>` and `</obj>` tags. You have to specify its location with `<bbox>` and `</bbox>` tags. This allows the model to understand the context and focus on the object at the specified location. A bbox is represented in a form of (x1, y1, x2, y2). The first two values indicate the top-left position of a bbox, and the latter two values are the bottom-right position. ```python conversation = [ { "role": "user", "content": [ { "type": "text", "text": "<obj>이 물건</obj><bbox>0.039, 0.138, 0.283, 0.257</bbox>은 어떻게 쓰는거야?", }, {"type": "image"}, ], }, ] ``` Expected Output Example: ``` 이 물건은 리모컨으로, 주로 텔레비전이나 다른 전자 기기를 원격으로 조작하는 데 사용됩니다. 리모컨에는 다양한 버튼이 있으며, 각 버튼은 채널 변경, 볼륨 조절, 전원 켜기/끄기 등의 기능을 수행합니다. 사용자는 리모컨을 손에 들고 버튼을 누르면, 해당 기기에 신호를 보내 원하는 조작을 할 수 있습니다. 리모컨은 일반적으로 가정이나 사무실에서 편리하게 전자 기기를 조작할 수 있도록 사용됩니다. ``` #### OCR To perform Optical Character Recognition (OCR), use the `<ocr>` token. ```python image_file = "./assets/ocr_1.png" raw_image = Image.open(image_file) conversation = [ { "role": "user", "content": [ {"type": "text", "text": "<ocr>"}, {"type": "image"}, ], }, ] ``` Expected Output Example:** ``` <char>백범로</char><bbox>0.172, 0.266, 0.328, 0.341</bbox> <char>124번길</char><bbox>0.347, 0.266, 0.512, 0.341</bbox> <char>Baekbeom-ro</char><bbox>0.171, 0.337, 0.433, 0.392</bbox> <char>124</char><bbox>0.444, 0.341, 0.508, 0.392</bbox> <char>만수주공아파트</char><bbox>0.109, 0.531, 0.335, 0.601</bbox> <char>시흥</char><bbox>0.443, 0.518, 0.522, 0.581</bbox> <char>시청</char><bbox>0.711, 0.521, 0.811, 0.594</bbox> <char>Mansu</char><bbox>0.102, 0.601, 0.181, 0.648</bbox> <char>Jugong</char><bbox>0.186, 0.601, 0.273, 0.658</bbox> <char>Apt</char><bbox>0.28, 0.601, 0.327, 0.651</bbox> <char>42</char><bbox>0.377, 0.601, 0.416, 0.648</bbox> <char>Shieung</char><bbox>0.445, 0.578, 0.53, 0.625</bbox> <char>인천대공원</char><bbox>0.43, 0.621, 0.609, 0.684</bbox> <char>모래내시장역</char><bbox>0.651, 0.59, 0.873, 0.665</bbox> <char>IncheonGrand</char><bbox>0.432, 0.681, 0.561, 0.723</bbox> <char>Park</char><bbox>0.564, 0.681, 0.611, 0.723</bbox> ``` <img src="assets/ocr_2.jpg" alt="OCR Example" width="350"/> ## Citing the Model If you use VARCO-VISION-14B in your research, please cite the following: ```bibtex @misc{ju2024varcovisionexpandingfrontierskorean, title={VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models}, author={Jeongho Ju and Daeyoung Kim and SunYoung Park and Youngjune Kim}, year={2024}, eprint={2411.19103}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2411.19103}, ```
jo-mengr/mmcontext-pubmedbert-scvi_fm-cxg_100k	jo-mengr	2025-09-12T05:30:18Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "dense", "generated_from_trainer", "dataset_size:81143", "loss:MultipleNegativesRankingLoss", "code", "dataset:jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:NeuML/pubmedbert-base-embeddings", "base_model:finetune:NeuML/pubmedbert-base-embeddings", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-09-12T05:29:58Z	--- language: - code tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:81143 - loss:MultipleNegativesRankingLoss base_model: NeuML/pubmedbert-base-embeddings widget: - source_sentence: sample_idx:census_421e5f54-5de7-425f-b399-34ead0651ce1_41 sentences: - This measurement was conducted with 10x 3' v3. Neuron cell type from the cerebral cortex (Frontal agranular insular cortex, or FI) of a 50-year-old male European donor, analyzed at the nucleus level. - sample_idx:census_421e5f54-5de7-425f-b399-34ead0651ce1_670 - This measurement was conducted with 10x 3' v3. Neuron cell type from a 42-year old male cerebral cortex tissue, specifically the Frontal agranular insular cortex (FI) region, identified as a CGE interneuron. - source_sentence: sample_idx:census_d7d7e89c-c93a-422d-8958-9b4a90b69558_1563 sentences: - This measurement was conducted with 10x 5' v1. Activated CD4-positive, alpha-beta T cell from a 26-year-old male with Common variable immunodeficiency (CVID), undergoing naïve-to-memory B cell differentiation. - sample_idx:census_d7d7e89c-c93a-422d-8958-9b4a90b69558_4002 - This measurement was conducted with 10x 5' v1. Naive B cell from blood of a 26-year old male, activated with CD3. - source_sentence: sample_idx:census_e1f595f6-ba2c-495e-9bee-7056f116b1e4_1239 sentences: - sample_idx:census_e1f595f6-ba2c-495e-9bee-7056f116b1e4_822 - This measurement was conducted with 10x 3' v3. Neuron cell type from a 29-year-old male cerebral cortex, specifically from the Middle Temporal Gyrus (MTG), with European ethnicity, using nucleus suspension type. - This measurement was conducted with 10x 3' v3. Neuron cell type from a 50-year-old male cerebral cortex, specifically the Middle Temporal Gyrus (MTG), with European self-reported ethnicity, analyzed at the nucleus level. - source_sentence: sample_idx:census_1cf24082-59de-4029-ac81-6e398768af3a_304 sentences: - This measurement was conducted with 10x 3' v3. Nucleus sample from a 50-year-old male neuron, specifically an MGE interneuron, located in the Inferior temporal gyrus (ITG) region of the cerebral cortex, with European ethnicity. - This measurement was conducted with 10x 3' v3. Nucleus suspension of neurons from the inferior temporal gyrus region of the cerebral cortex, taken from a 29-year-old male of European descent. - sample_idx:census_1cf24082-59de-4029-ac81-6e398768af3a_426 - source_sentence: sample_idx:census_18500fcd-9960-49cb-8a8e-7d868dc14efe_602 sentences: - sample_idx:census_18500fcd-9960-49cb-8a8e-7d868dc14efe_247 - This measurement was conducted with 10x 3' v3. Neuron cell type from the cerebral nuclei, specifically from the external segment of globus pallidus (GPe) in a 42-year-old male. - This measurement was conducted with 10x 3' v3. Oligodendrocyte cells from the external segment of globus pallidus (GPe) in a 29-year-old male cerebral nuclei sample. datasets: - jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy model-index: - name: SentenceTransformer based on NeuML/pubmedbert-base-embeddings results: - task: type: triplet name: Triplet dataset: name: cellxgene pseudo bulk 100k multiplets natural language annotation cell sentence 1 caption type: cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption metrics: - type: cosine_accuracy value: 0.7883697748184204 name: Cosine Accuracy --- # SentenceTransformer based on NeuML/pubmedbert-base-embeddings This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings) on the [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings) <!-- at revision d6eaca8254bc229f3ca42749a5510ae287eb3486 --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 1024 dimensions - Similarity Function: Cosine Similarity - Training Dataset: - [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) - Language: code <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): MMContextEncoder( (text_encoder): BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(30522, 768, padding_idx=0) (position_embeddings): Embedding(512, 768) (token_type_embeddings): Embedding(2, 768) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (encoder): BertEncoder( (layer): ModuleList( (0-11): 12 x BertLayer( (attention): BertAttention( (self): BertSdpaSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) ) ) (pooler): BertPooler( (dense): Linear(in_features=768, out_features=768, bias=True) (activation): Tanh() ) ) (text_adapter): AdapterModule( (net): Sequential( (0): Linear(in_features=768, out_features=1024, bias=True) (1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (pooling): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (omics_adapter): AdapterModule( (net): Sequential( (0): Linear(in_features=50, out_features=1024, bias=True) (1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (omics_encoder): MiniOmicsModel( (embeddings): Embedding(90155, 50, padding_idx=0) ) ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("jo-mengr/mmcontext-pubmedbert-scvi_fm-cxg_100k") # Run inference sentences = [ 'sample_idx:census_18500fcd-9960-49cb-8a8e-7d868dc14efe_602', "This measurement was conducted with 10x 3' v3. Oligodendrocyte cells from the external segment of globus pallidus (GPe) in a 29-year-old male cerebral nuclei sample.", "This measurement was conducted with 10x 3' v3. Neuron cell type from the cerebral nuclei, specifically from the external segment of globus pallidus (GPe) in a 42-year-old male.", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[1.0000, 0.4753, 0.1343], # [0.4753, 1.0000, 0.7385], # [0.1343, 0.7385, 1.0000]]) ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Triplet * Dataset: `cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption` * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy \| 0.7884 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption * Dataset: [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) at [8b940b4](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation/tree/8b940b48a15534edc2689cf70afe98d82375bb59) * Size: 81,143 training samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, and <code>negative_2</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative_1 \| negative_2 \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| string \| \| details \| <ul><li>min: 56 characters</li><li>mean: 58.72 characters</li><li>max: 60 characters</li></ul> \| <ul><li>min: 22 tokens</li><li>mean: 48.4 tokens</li><li>max: 159 tokens</li></ul> \| <ul><li>min: 24 tokens</li><li>mean: 45.96 tokens</li><li>max: 155 tokens</li></ul> \| <ul><li>min: 56 characters</li><li>mean: 58.74 characters</li><li>max: 60 characters</li></ul> \| * Samples: \| anchor \| positive \| negative_1 \| negative_2 \| \|:--------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------\| \| <code>sample_idx:census_218acb0f-9f2f-4f76-b90b-15a4b7c7f629_26009</code> \| <code>This measurement was conducted with 10x 3' v2. A proliferating lymphocyte cell sample, obtained from a 34-year-old female Asian individual, derived from peripheral blood mononuclear cells.</code> \| <code>This measurement was conducted with 10x 3' v2. CD8-positive, alpha-beta T cell derived from a 51-year old European female with managed systemic lupus erythematosus (SLE), obtained from blood tissue and enriched as a peripheral blood mononuclear cell.</code> \| <code>sample_idx:census_218acb0f-9f2f-4f76-b90b-15a4b7c7f629_38905</code> \| \| <code>sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_6333</code> \| <code>This measurement was conducted with 10x 5' v1. Sample is a cell from the omentum tissue, specifically an effector memory CD4-positive, alpha-beta T cell, from a female in her sixth decade.</code> \| <code>This measurement was conducted with 10x 3' v3. A cell sample from the spleen, belonging to the naive thymus-derived CD4-positive, alpha-beta T cell category, specifically Tnaive/CM_CD4, and identified as Tcm/Naive helper T cells within the T cells group.</code> \| <code>sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_4412</code> \| \| <code>sample_idx:census_adda0684-f8ea-4403-b393-2a25607077c4_271</code> \| <code>This measurement was conducted with 10x 3' v3. Neuron cell type from a 29-year-old male, specifically from the thalamic complex, specifically the thalamus (THM) - posterior nuclear complex of thalamus (PoN) - medial geniculate nuclei (MG).</code> \| <code>This measurement was conducted with 10x 3' v3. Fibroblast cells from the thalamic complex, specifically from the thalamus (THM) - posterior nuclear complex of thalamus (PoN) - medial geniculate nuclei (MG) region, of a 42-year-old male.</code> \| <code>sample_idx:census_adda0684-f8ea-4403-b393-2a25607077c4_585</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption * Dataset: [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) at [8b940b4](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation/tree/8b940b48a15534edc2689cf70afe98d82375bb59) * Size: 9,011 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, and <code>negative_2</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative_1 \| negative_2 \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| string \| \| details \| <ul><li>min: 56 characters</li><li>mean: 58.73 characters</li><li>max: 60 characters</li></ul> \| <ul><li>min: 23 tokens</li><li>mean: 47.49 tokens</li><li>max: 157 tokens</li></ul> \| <ul><li>min: 21 tokens</li><li>mean: 48.98 tokens</li><li>max: 157 tokens</li></ul> \| <ul><li>min: 56 characters</li><li>mean: 58.74 characters</li><li>max: 60 characters</li></ul> \| * Samples: \| anchor \| positive \| negative_1 \| negative_2 \| \|:--------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------\| \| <code>sample_idx:census_0b4a15a7-4e9e-4555-9733-2423e5c66469_490</code> \| <code>This measurement was conducted with 10x 3' v3. Cell sample from the cortex of kidney, taken from a 43-year-old male of European ethnicity with a reported history of kidney cancer. The cell type is identified as a kidney collecting duct intercalated cell.</code> \| <code>This measurement was conducted with 10x 3' v3. Epithelial cells derived from the cortex of a kidney of a 50-year old female European individual, preserved by cryopreservation.</code> \| <code>sample_idx:census_0b4a15a7-4e9e-4555-9733-2423e5c66469_280</code> \| \| <code>sample_idx:census_4976b234-9028-4b4b-8a2f-8ac59d636219_269</code> \| <code>This measurement was conducted with 10x 3' v3. Neuron cell type from a 29-year-old male cerebellum, specifically from the Cerebellar Vermis - CBV region, with European self-reported ethnicity, analyzed at the nucleus level.</code> \| <code>This measurement was conducted with 10x 3' v3. Fibroblast cells derived from the cerebellum tissue of a 50-year-old male, specifically from the Cerebellum (CB) - Cerebellar Vermis - CBV dissection.</code> \| <code>sample_idx:census_4976b234-9028-4b4b-8a2f-8ac59d636219_826</code> \| \| <code>sample_idx:census_44882825-0da1-4547-b721-2c6105d4a9d1_10258</code> \| <code>This measurement was conducted with 10x 5' v1. Cell sample from the tonsil of a 9-year-old female with recurrent tonsillitis, characterized as a centroblast B cell with IGLC2, IGLV7-43, IGLJ3 immunoglobulin genes expressed.</code> \| <code>This measurement was conducted with 10x 5' v1. This sample represents a tonsil germinal center B cell from a three-year-old human male with obstructive sleep apnea and recurrent tonsillitis. The study provides a comprehensive roadmap of human B cell maturation, including gene expression, antibody repertoires, and clonal sharing of B cell states at single-cell resolution, as well as memory B cell heterogeneity reflecting diverse functional and signaling states.</code> \| <code>sample_idx:census_44882825-0da1-4547-b721-2c6105d4a9d1_243</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `warmup_ratio`: 0.1 - `bf16`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `hub_revision`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {} </details> ### Training Logs \| Epoch \| Step \| Training Loss \| cellxgene pseudo bulk 100k multiplets natural language annotation cell sentence 1 caption loss \| cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption_cosine_accuracy \| \|:------:\|:----:\|:-------------:\|:----------------------------------------------------------------------------------------------:\|:---------------------------------------------------------------------------------------------------------:\| \| 0.1577 \| 100 \| 14.8711 \| 14.6553 \| 0.5955 \| \| 0.3155 \| 200 \| 13.5634 \| 12.5052 \| 0.6968 \| \| 0.4732 \| 300 \| 11.0631 \| 9.8851 \| 0.7264 \| \| 0.6309 \| 400 \| 9.0001 \| 8.3624 \| 0.7431 \| \| 0.7886 \| 500 \| 7.8481 \| 7.5177 \| 0.7510 \| \| 0.9464 \| 600 \| 7.0916 \| 6.9894 \| 0.7555 \| \| 1.1041 \| 700 \| 6.7088 \| 6.6373 \| 0.7622 \| \| 1.2618 \| 800 \| 6.2834 \| 6.3739 \| 0.7664 \| \| 1.4196 \| 900 \| 6.0694 \| 6.1536 \| 0.7701 \| \| 1.5773 \| 1000 \| 5.8974 \| 5.9650 \| 0.7736 \| \| 1.7350 \| 1100 \| 5.6823 \| 5.8622 \| 0.7774 \| \| 1.8927 \| 1200 \| 5.6118 \| 5.7346 \| 0.7778 \| \| 2.0505 \| 1300 \| 5.4439 \| 5.6417 \| 0.7805 \| \| 2.2082 \| 1400 \| 5.3634 \| 5.5389 \| 0.7833 \| \| 2.3659 \| 1500 \| 5.298 \| 5.4966 \| 0.7838 \| \| 2.5237 \| 1600 \| 5.2391 \| 5.4338 \| 0.7840 \| \| 2.6814 \| 1700 \| 5.1817 \| 5.3612 \| 0.7852 \| \| 2.8391 \| 1800 \| 5.1506 \| 5.3411 \| 0.7844 \| \| 2.9968 \| 1900 \| 5.116 \| 5.2958 \| 0.7870 \| \| 3.1546 \| 2000 \| 5.0382 \| 5.2824 \| 0.7879 \| \| 3.3123 \| 2100 \| 5.0976 \| 5.2416 \| 0.7871 \| \| 3.4700 \| 2200 \| 5.012 \| 5.2307 \| 0.7871 \| \| 3.6278 \| 2300 \| 5.0273 \| 5.2196 \| 0.7900 \| \| 3.7855 \| 2400 \| 5.0156 \| 5.2232 \| 0.7901 \| \| 3.9432 \| 2500 \| 4.9684 \| 5.2027 \| 0.7884 \| ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 5.0.0 - Transformers: 4.55.0.dev0 - PyTorch: 2.5.1+cu121 - Accelerate: 1.9.0 - Datasets: 2.19.1 - Tokenizers: 0.21.4 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
rsadaphule/llava-1.5-7b-package-damage-lora-ADAPTER	rsadaphule	2025-09-12T05:30:09Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-12T05:30:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
omerbektasss/blockassist-bc-keen_fast_giraffe_1757654928	omerbektasss	2025-09-12T05:29:42Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T05:29:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/jina-reranker-v1-tiny-en-GGUF	mradermacher	2025-09-12T05:27:50Z	7,599	0	transformers	[ "transformers", "gguf", "reranker", "cross-encoder", "transformers.js", "sentence-transformers", "en", "base_model:jinaai/jina-reranker-v1-tiny-en", "base_model:quantized:jinaai/jina-reranker-v1-tiny-en", "license:apache-2.0", "endpoints_compatible", "region:us", "feature-extraction" ]	null	2025-01-07T05:57:29Z	--- base_model: jinaai/jina-reranker-v1-tiny-en language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - reranker - cross-encoder - transformers.js - sentence-transformers --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/jinaai/jina-reranker-v1-tiny-en <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#jina-reranker-v1-tiny-en-GGUF).* weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q2_K.gguf) \| Q2_K \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q3_K_S.gguf) \| Q3_K_S \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.IQ4_XS.gguf) \| IQ4_XS \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q3_K_M.gguf) \| Q3_K_M \| 0.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q3_K_L.gguf) \| Q3_K_L \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q4_K_S.gguf) \| Q4_K_S \| 0.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q4_K_M.gguf) \| Q4_K_M \| 0.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q5_K_S.gguf) \| Q5_K_S \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q5_K_M.gguf) \| Q5_K_M \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q6_K.gguf) \| Q6_K \| 0.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q8_0.gguf) \| Q8_0 \| 0.1 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.f16.gguf) \| f16 \| 0.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757654658	stonermay	2025-09-12T05:25:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T05:25:30Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Asap7772/qwen3-4b-arc-second-stage-awr-sftinit-lr1e-5-0908	Asap7772	2025-09-12T05:24:31Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T04:46:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
birder-project/cswin_transformer_s_eu-common	birder-project	2025-09-12T05:19:05Z	10	0	birder	[ "birder", "image-classification", "pytorch", "arxiv:2107.00652", "license:apache-2.0", "region:us" ]	image-classification	2025-09-07T16:48:18Z	--- tags: - image-classification - birder - pytorch library_name: birder license: apache-2.0 --- # Model Card for cswin_transformer_s_eu-common A CSWin Transformer small image classification model. This model was trained on the `eu-common` dataset containing common European bird species. The species list is derived from the Collins bird guide [^1]. [^1]: Svensson, L., Mullarney, K., & Zetterström, D. (2022). Collins bird guide (3rd ed.). London, England: William Collins. Note: A 256 x 256 variant of this model is available as `cswin_transformer_s_eu-common256px`. ## Model Details - Model Type: Image classification and detection backbone - Model Stats: - Params (M): 34.5 - Input image size: 384 x 384 - Dataset: eu-common (707 classes) - Papers: - CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows: <https://arxiv.org/abs/2107.00652> ## Model Usage ### Image Classification ```python import birder from birder.inference.classification import infer_image (net, model_info) = birder.load_pretrained_model("cswin_transformer_s_eu-common", inference=True) # Note: A 256x256 variant is available as "cswin_transformer_s_eu-common256px" # Get the image size the model was trained on size = birder.get_size_from_signature(model_info.signature) # Create an inference transform transform = birder.classification_transform(size, model_info.rgb_stats) image = "path/to/image.jpeg" # or a PIL image, must be loaded in RGB format (out, _) = infer_image(net, image, transform) # out is a NumPy array with shape of (1, 707), representing class probabilities. ``` ### Image Embeddings ```python import birder from birder.inference.classification import infer_image (net, model_info) = birder.load_pretrained_model("cswin_transformer_s_eu-common", inference=True) # Get the image size the model was trained on size = birder.get_size_from_signature(model_info.signature) # Create an inference transform transform = birder.classification_transform(size, model_info.rgb_stats) image = "path/to/image.jpeg" # or a PIL image (out, embedding) = infer_image(net, image, transform, return_embedding=True) # embedding is a NumPy array with shape of (1, 512) ``` ### Detection Feature Map ```python from PIL import Image import birder (net, model_info) = birder.load_pretrained_model("cswin_transformer_s_eu-common", inference=True) # Get the image size the model was trained on size = birder.get_size_from_signature(model_info.signature) # Create an inference transform transform = birder.classification_transform(size, model_info.rgb_stats) image = Image.open("path/to/image.jpeg") features = net.detection_features(transform(image).unsqueeze(0)) # features is a dict (stage name -> torch.Tensor) print([(k, v.size()) for k, v in features.items()]) # Output example: # [('stage1', torch.Size([1, 64, 96, 96])), # ('stage2', torch.Size([1, 128, 48, 48])), # ('stage3', torch.Size([1, 256, 24, 24])), # ('stage4', torch.Size([1, 512, 12, 12]))] ``` ## Citation ```bibtex @misc{dong2022cswintransformergeneralvision, title={CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows}, author={Xiaoyi Dong and Jianmin Bao and Dongdong Chen and Weiming Zhang and Nenghai Yu and Lu Yuan and Dong Chen and Baining Guo}, year={2022}, eprint={2107.00652}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2107.00652}, } ```
alberto-lorente/Meta-Llama-3_1-8B-Instruct-bnb-4bit-GRANULAR-TASK-minorities	alberto-lorente	2025-09-12T05:17:42Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "unsloth", "endpoints_compatible", "region:us" ]	null	2025-08-26T04:04:46Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit library_name: transformers model_name: Meta-Llama-3_1-8B-Instruct-bnb-4bit-GRANULAR-TASK-minorities tags: - generated_from_trainer - trl - sft - unsloth licence: license --- # Model Card for Meta-Llama-3_1-8B-Instruct-bnb-4bit-GRANULAR-TASK-minorities This model is a fine-tuned version of [unsloth/meta-llama-3.1-8b-instruct-bnb-4bit](https://huggingface.co/unsloth/meta-llama-3.1-8b-instruct-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="alberto-lorente/Meta-Llama-3_1-8B-Instruct-bnb-4bit-GRANULAR-TASK-minorities", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.23.0 - Transformers: 4.56.1 - Pytorch: 2.8.0+cu126 - Datasets: 3.6.0 - Tokenizers: 0.22.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/Medllama-3-8b-GGUF	mradermacher	2025-09-12T05:16:39Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Axcel1/Medllama-3-8b", "base_model:quantized:Axcel1/Medllama-3-8b", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-12T03:32:28Z	--- base_model: Axcel1/Medllama-3-8b language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/Axcel1/Medllama-3-8b <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Medllama-3-8b-GGUF).* weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Adanato/Llama-3.2-1B-Instruct-low_acereason_1k-high_acereason_1k-high_acereason_1k	Adanato	2025-09-12T05:15:54Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "fyksft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T05:14:09Z	--- library_name: transformers tags: - fyksft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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faodl/20250909_model_g20_multilabel_MiniLM-L12-all-labels-artificial-governance-multi-output	faodl	2025-09-12T05:15:09Z	0	0	setfit	[ "setfit", "safetensors", "bert", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", "base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", "region:us" ]	text-classification	2025-09-12T05:14:53Z	--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: Emergency insurance payouts complement humanitarian assistance by providing timely financial resources that facilitate quicker recovery from climate disasters. - text: "c) Establish strategic and operational partnerships and alliances with private,\ \ public and civil society \norganizations in food and nutrition." - text: 'COVID-19: The Development Program for Drinking Water Supply and Sanitation Systems of the Kyrgyz Republic until 2026 was approved. The Program is aimed at increasing the provision of drinking water of standard quality, improving the health and quality of life of the population of the republic, reducing the harmful effects on the environment through the construction, reconstruction, and modernization of drinking water supply and sanitation systems.' - text: "The program mainly aims at \nthe construction of rural roads, capacity building\ \ of local bodies, and \nawareness raising activities." - text: "Mr. Speaker, the PF Government \n\nremains committed to ensuring that all\ \ \n\nZambians have access to clean water supply \n\nand sanitation services." metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: false base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 --- # SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) as the Sentence Transformer embedding model. A MultiOutputClassifier instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) - Classification head: a MultiOutputClassifier instance - Maximum Sequence Length: 128 tokens <!-- - Number of Classes: Unknown --> <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("faodl/20250909_model_g20_multilabel_MiniLM-L12-all-labels-artificial-governance-multi-output") # Run inference preds = model("The program mainly aims at the construction of rural roads, capacity building of local bodies, and awareness raising activities.") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:--------\|:-----\| \| Word count \| 1 \| 41.6795 \| 1753 \| ### Training Hyperparameters - batch_size: (32, 32) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 50 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:------:\|:-----:\|:-------------:\|:---------------:\| \| 0.0001 \| 1 \| 0.184 \| - \| \| 0.0039 \| 50 \| 0.1927 \| - \| \| 0.0078 \| 100 \| 0.1729 \| - \| \| 0.0117 \| 150 \| 0.1484 \| - \| \| 0.0156 \| 200 \| 0.1301 \| - \| \| 0.0196 \| 250 \| 0.1134 \| - \| \| 0.0235 \| 300 \| 0.1079 \| - \| \| 0.0274 \| 350 \| 0.1021 \| - \| \| 0.0313 \| 400 \| 0.0876 \| - \| \| 0.0352 \| 450 \| 0.0834 \| - \| \| 0.0391 \| 500 \| 0.0886 \| - \| \| 0.0430 \| 550 \| 0.0728 \| - \| \| 0.0469 \| 600 \| 0.0775 \| - \| \| 0.0508 \| 650 \| 0.0811 \| - \| \| 0.0548 \| 700 \| 0.0745 \| - \| \| 0.0587 \| 750 \| 0.0753 \| - \| \| 0.0626 \| 800 \| 0.0745 \| - \| \| 0.0665 \| 850 \| 0.07 \| - \| \| 0.0704 \| 900 \| 0.0702 \| - \| \| 0.0743 \| 950 \| 0.0707 \| - \| \| 0.0782 \| 1000 \| 0.0702 \| - \| \| 0.0821 \| 1050 \| 0.0607 \| - \| \| 0.0860 \| 1100 \| 0.067 \| - \| \| 0.0899 \| 1150 \| 0.065 \| - \| \| 0.0939 \| 1200 \| 0.0659 \| - \| \| 0.0978 \| 1250 \| 0.066 \| - \| \| 0.1017 \| 1300 \| 0.066 \| - \| \| 0.1056 \| 1350 \| 0.06 \| - \| \| 0.1095 \| 1400 \| 0.0609 \| - \| \| 0.1134 \| 1450 \| 0.0587 \| - \| \| 0.1173 \| 1500 \| 0.0542 \| - \| \| 0.1212 \| 1550 \| 0.0523 \| - \| \| 0.1251 \| 1600 \| 0.0559 \| - \| \| 0.1291 \| 1650 \| 0.052 \| - \| \| 0.1330 \| 1700 \| 0.0487 \| - \| \| 0.1369 \| 1750 \| 0.053 \| - \| \| 0.1408 \| 1800 \| 0.0477 \| - \| \| 0.1447 \| 1850 \| 0.0492 \| - \| \| 0.1486 \| 1900 \| 0.0474 \| - \| \| 0.1525 \| 1950 \| 0.0488 \| - \| \| 0.1564 \| 2000 \| 0.0461 \| - \| \| 0.1603 \| 2050 \| 0.0481 \| - \| \| 0.1643 \| 2100 \| 0.0463 \| - \| \| 0.1682 \| 2150 \| 0.0432 \| - \| \| 0.1721 \| 2200 \| 0.0482 \| - \| \| 0.1760 \| 2250 \| 0.0444 \| - \| \| 0.1799 \| 2300 \| 0.0466 \| - \| \| 0.1838 \| 2350 \| 0.0423 \| - \| \| 0.1877 \| 2400 \| 0.041 \| - \| \| 0.1916 \| 2450 \| 0.0422 \| - \| \| 0.1955 \| 2500 \| 0.0401 \| - \| \| 0.1995 \| 2550 \| 0.0405 \| - \| \| 0.2034 \| 2600 \| 0.0448 \| - \| \| 0.2073 \| 2650 \| 0.0387 \| - \| \| 0.2112 \| 2700 \| 0.0371 \| - \| \| 0.2151 \| 2750 \| 0.0429 \| - \| \| 0.2190 \| 2800 \| 0.0379 \| - \| \| 0.2229 \| 2850 \| 0.0384 \| - \| \| 0.2268 \| 2900 \| 0.0378 \| - \| \| 0.2307 \| 2950 \| 0.0392 \| - \| \| 0.2346 \| 3000 \| 0.038 \| - \| \| 0.2386 \| 3050 \| 0.0325 \| - \| \| 0.2425 \| 3100 \| 0.0345 \| - \| \| 0.2464 \| 3150 \| 0.0341 \| - \| \| 0.2503 \| 3200 \| 0.0415 \| - \| \| 0.2542 \| 3250 \| 0.0313 \| - \| \| 0.2581 \| 3300 \| 0.0355 \| - \| \| 0.2620 \| 3350 \| 0.033 \| - \| \| 0.2659 \| 3400 \| 0.0308 \| - \| \| 0.2698 \| 3450 \| 0.0343 \| - \| \| 0.2738 \| 3500 \| 0.0379 \| - \| \| 0.2777 \| 3550 \| 0.032 \| - \| \| 0.2816 \| 3600 \| 0.0358 \| - \| \| 0.2855 \| 3650 \| 0.0334 \| - \| \| 0.2894 \| 3700 \| 0.0312 \| - \| \| 0.2933 \| 3750 \| 0.0336 \| - \| \| 0.2972 \| 3800 \| 0.0291 \| - \| \| 0.3011 \| 3850 \| 0.0268 \| - \| \| 0.3050 \| 3900 \| 0.034 \| - \| \| 0.3090 \| 3950 \| 0.0337 \| - \| \| 0.3129 \| 4000 \| 0.0266 \| - \| \| 0.3168 \| 4050 \| 0.0269 \| - \| \| 0.3207 \| 4100 \| 0.0326 \| - \| \| 0.3246 \| 4150 \| 0.0317 \| - \| \| 0.3285 \| 4200 \| 0.0271 \| - \| \| 0.3324 \| 4250 \| 0.0313 \| - \| \| 0.3363 \| 4300 \| 0.0263 \| - \| \| 0.3402 \| 4350 \| 0.0267 \| - \| \| 0.3442 \| 4400 \| 0.0273 \| - \| \| 0.3481 \| 4450 \| 0.026 \| - \| \| 0.3520 \| 4500 \| 0.0252 \| - \| \| 0.3559 \| 4550 \| 0.0261 \| - \| \| 0.3598 \| 4600 \| 0.0243 \| - \| \| 0.3637 \| 4650 \| 0.0252 \| - \| \| 0.3676 \| 4700 \| 0.0291 \| - \| \| 0.3715 \| 4750 \| 0.0286 \| - \| \| 0.3754 \| 4800 \| 0.0245 \| - \| \| 0.3794 \| 4850 \| 0.0263 \| - \| \| 0.3833 \| 4900 \| 0.0249 \| - \| \| 0.3872 \| 4950 \| 0.0209 \| - \| \| 0.3911 \| 5000 \| 0.0245 \| - \| \| 0.3950 \| 5050 \| 0.0278 \| - \| \| 0.3989 \| 5100 \| 0.0277 \| - \| \| 0.4028 \| 5150 \| 0.0266 \| - \| \| 0.4067 \| 5200 \| 0.0249 \| - \| \| 0.4106 \| 5250 \| 0.0279 \| - \| \| 0.4145 \| 5300 \| 0.027 \| - \| \| 0.4185 \| 5350 \| 0.0283 \| - \| \| 0.4224 \| 5400 \| 0.022 \| - \| \| 0.4263 \| 5450 \| 0.0232 \| - \| \| 0.4302 \| 5500 \| 0.0198 \| - \| \| 0.4341 \| 5550 \| 0.0254 \| - \| \| 0.4380 \| 5600 \| 0.0186 \| - \| \| 0.4419 \| 5650 \| 0.0237 \| - \| \| 0.4458 \| 5700 \| 0.0249 \| - \| \| 0.4497 \| 5750 \| 0.0241 \| - \| \| 0.4537 \| 5800 \| 0.0239 \| - \| \| 0.4576 \| 5850 \| 0.0258 \| - \| \| 0.4615 \| 5900 \| 0.0212 \| - \| \| 0.4654 \| 5950 \| 0.0208 \| - \| \| 0.4693 \| 6000 \| 0.0227 \| - \| \| 0.4732 \| 6050 \| 0.0262 \| - \| \| 0.4771 \| 6100 \| 0.0257 \| - \| \| 0.4810 \| 6150 \| 0.0227 \| - \| \| 0.4849 \| 6200 \| 0.0226 \| - \| \| 0.4889 \| 6250 \| 0.0231 \| - \| \| 0.4928 \| 6300 \| 0.0255 \| - \| \| 0.4967 \| 6350 \| 0.0199 \| - \| \| 0.5006 \| 6400 \| 0.022 \| - \| \| 0.5045 \| 6450 \| 0.0253 \| - \| \| 0.5084 \| 6500 \| 0.0209 \| - \| \| 0.5123 \| 6550 \| 0.0207 \| - \| \| 0.5162 \| 6600 \| 0.0215 \| - \| \| 0.5201 \| 6650 \| 0.0225 \| - \| \| 0.5241 \| 6700 \| 0.0185 \| - \| \| 0.5280 \| 6750 \| 0.019 \| - \| \| 0.5319 \| 6800 \| 0.0214 \| - \| \| 0.5358 \| 6850 \| 0.0252 \| - \| \| 0.5397 \| 6900 \| 0.0216 \| - \| \| 0.5436 \| 6950 \| 0.0205 \| - \| \| 0.5475 \| 7000 \| 0.0205 \| - \| \| 0.5514 \| 7050 \| 0.0244 \| - \| \| 0.5553 \| 7100 \| 0.0223 \| - \| \| 0.5592 \| 7150 \| 0.0181 \| - \| \| 0.5632 \| 7200 \| 0.0199 \| - \| \| 0.5671 \| 7250 \| 0.0217 \| - \| \| 0.5710 \| 7300 \| 0.0198 \| - \| \| 0.5749 \| 7350 \| 0.0224 \| - \| \| 0.5788 \| 7400 \| 0.0234 \| - \| \| 0.5827 \| 7450 \| 0.0193 \| - \| \| 0.5866 \| 7500 \| 0.0168 \| - \| \| 0.5905 \| 7550 \| 0.0193 \| - \| \| 0.5944 \| 7600 \| 0.0232 \| - \| \| 0.5984 \| 7650 \| 0.0183 \| - \| \| 0.6023 \| 7700 \| 0.0255 \| - \| \| 0.6062 \| 7750 \| 0.0209 \| - \| \| 0.6101 \| 7800 \| 0.0262 \| - \| \| 0.6140 \| 7850 \| 0.0228 \| - \| \| 0.6179 \| 7900 \| 0.0208 \| - \| \| 0.6218 \| 7950 \| 0.0167 \| - \| \| 0.6257 \| 8000 \| 0.0217 \| - \| \| 0.6296 \| 8050 \| 0.0175 \| - \| \| 0.6336 \| 8100 \| 0.0196 \| - \| \| 0.6375 \| 8150 \| 0.0215 \| - \| \| 0.6414 \| 8200 \| 0.0186 \| - \| \| 0.6453 \| 8250 \| 0.0181 \| - \| \| 0.6492 \| 8300 \| 0.0171 \| - \| \| 0.6531 \| 8350 \| 0.0224 \| - \| \| 0.6570 \| 8400 \| 0.0214 \| - \| \| 0.6609 \| 8450 \| 0.0214 \| - \| \| 0.6648 \| 8500 \| 0.0192 \| - \| \| 0.6688 \| 8550 \| 0.0213 \| - \| \| 0.6727 \| 8600 \| 0.0185 \| - \| \| 0.6766 \| 8650 \| 0.02 \| - \| \| 0.6805 \| 8700 \| 0.0218 \| - \| \| 0.6844 \| 8750 \| 0.0163 \| - \| \| 0.6883 \| 8800 \| 0.0183 \| - \| \| 0.6922 \| 8850 \| 0.0177 \| - \| \| 0.6961 \| 8900 \| 0.0178 \| - \| \| 0.7000 \| 8950 \| 0.0157 \| - \| \| 0.7039 \| 9000 \| 0.0201 \| - \| \| 0.7079 \| 9050 \| 0.017 \| - \| \| 0.7118 \| 9100 \| 0.0198 \| - \| \| 0.7157 \| 9150 \| 0.0196 \| - \| \| 0.7196 \| 9200 \| 0.0189 \| - \| \| 0.7235 \| 9250 \| 0.018 \| - \| \| 0.7274 \| 9300 \| 0.0193 \| - \| \| 0.7313 \| 9350 \| 0.0179 \| - \| \| 0.7352 \| 9400 \| 0.0218 \| - \| \| 0.7391 \| 9450 \| 0.0186 \| - \| \| 0.7431 \| 9500 \| 0.0175 \| - \| \| 0.7470 \| 9550 \| 0.0168 \| - \| \| 0.7509 \| 9600 \| 0.0193 \| - \| \| 0.7548 \| 9650 \| 0.0183 \| - \| \| 0.7587 \| 9700 \| 0.0168 \| - \| \| 0.7626 \| 9750 \| 0.0194 \| - \| \| 0.7665 \| 9800 \| 0.021 \| - \| \| 0.7704 \| 9850 \| 0.0178 \| - \| \| 0.7743 \| 9900 \| 0.018 \| - \| \| 0.7783 \| 9950 \| 0.0171 \| - \| \| 0.7822 \| 10000 \| 0.0191 \| - \| \| 0.7861 \| 10050 \| 0.0147 \| - \| \| 0.7900 \| 10100 \| 0.0193 \| - \| \| 0.7939 \| 10150 \| 0.0174 \| - \| \| 0.7978 \| 10200 \| 0.0171 \| - \| \| 0.8017 \| 10250 \| 0.0156 \| - \| \| 0.8056 \| 10300 \| 0.0176 \| - \| \| 0.8095 \| 10350 \| 0.0195 \| - \| \| 0.8135 \| 10400 \| 0.0151 \| - \| \| 0.8174 \| 10450 \| 0.0192 \| - \| \| 0.8213 \| 10500 \| 0.0201 \| - \| \| 0.8252 \| 10550 \| 0.0192 \| - \| \| 0.8291 \| 10600 \| 0.015 \| - \| \| 0.8330 \| 10650 \| 0.0181 \| - \| \| 0.8369 \| 10700 \| 0.0143 \| - \| \| 0.8408 \| 10750 \| 0.0177 \| - \| \| 0.8447 \| 10800 \| 0.015 \| - \| \| 0.8487 \| 10850 \| 0.0193 \| - \| \| 0.8526 \| 10900 \| 0.0168 \| - \| \| 0.8565 \| 10950 \| 0.0169 \| - \| \| 0.8604 \| 11000 \| 0.0166 \| - \| \| 0.8643 \| 11050 \| 0.0148 \| - \| \| 0.8682 \| 11100 \| 0.0163 \| - \| \| 0.8721 \| 11150 \| 0.0189 \| - \| \| 0.8760 \| 11200 \| 0.0197 \| - \| \| 0.8799 \| 11250 \| 0.0138 \| - \| \| 0.8838 \| 11300 \| 0.0168 \| - \| \| 0.8878 \| 11350 \| 0.0153 \| - \| \| 0.8917 \| 11400 \| 0.0147 \| - \| \| 0.8956 \| 11450 \| 0.0178 \| - \| \| 0.8995 \| 11500 \| 0.0184 \| - \| \| 0.9034 \| 11550 \| 0.0158 \| - \| \| 0.9073 \| 11600 \| 0.0183 \| - \| \| 0.9112 \| 11650 \| 0.0127 \| - \| \| 0.9151 \| 11700 \| 0.0169 \| - \| \| 0.9190 \| 11750 \| 0.018 \| - \| \| 0.9230 \| 11800 \| 0.0156 \| - \| \| 0.9269 \| 11850 \| 0.0156 \| - \| \| 0.9308 \| 11900 \| 0.0162 \| - \| \| 0.9347 \| 11950 \| 0.0124 \| - \| \| 0.9386 \| 12000 \| 0.0175 \| - \| \| 0.9425 \| 12050 \| 0.0179 \| - \| \| 0.9464 \| 12100 \| 0.0182 \| - \| \| 0.9503 \| 12150 \| 0.0176 \| - \| \| 0.9542 \| 12200 \| 0.0182 \| - \| \| 0.9582 \| 12250 \| 0.0189 \| - \| \| 0.9621 \| 12300 \| 0.0125 \| - \| \| 0.9660 \| 12350 \| 0.0176 \| - \| \| 0.9699 \| 12400 \| 0.0143 \| - \| \| 0.9738 \| 12450 \| 0.0162 \| - \| \| 0.9777 \| 12500 \| 0.017 \| - \| \| 0.9816 \| 12550 \| 0.0196 \| - \| \| 0.9855 \| 12600 \| 0.0192 \| - \| \| 0.9894 \| 12650 \| 0.0184 \| - \| \| 0.9934 \| 12700 \| 0.0149 \| - \| \| 0.9973 \| 12750 \| 0.0172 \| - \| ### Framework Versions - Python: 3.12.11 - SetFit: 1.1.3 - Sentence Transformers: 5.1.0 - Transformers: 4.56.1 - PyTorch: 2.8.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757654035	stonermay	2025-09-12T05:15:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T05:14:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
omerbektasss/blockassist-bc-insectivorous_bold_lion_1757653840	omerbektasss	2025-09-12T05:11:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous bold lion", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T05:10:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous bold lion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
CIRCL/vulnerability-severity-classification-roberta-base	CIRCL	2025-09-12T05:10:06Z	165	4	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "dataset:CIRCL/vulnerability-scores", "arxiv:2507.03607", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "doi:10.57967/hf/5926", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-02-27T07:25:26Z	--- library_name: transformers license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: vulnerability-severity-classification-roberta-base results: [] datasets: - CIRCL/vulnerability-scores --- # VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification # Severity classification This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the dataset [CIRCL/vulnerability-scores](https://huggingface.co/datasets/CIRCL/vulnerability-scores). The model was presented in the paper [VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification](https://huggingface.co/papers/2507.03607) [[arXiv](https://arxiv.org/abs/2507.03607)]. Abstract: VLAI is a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated into the Vulnerability-Lookup service. You can read [this page](https://www.vulnerability-lookup.org/user-manual/ai/) for more information. ## Model description It is a classification model and is aimed to assist in classifying vulnerabilities by severity based on their descriptions. ## How to get started with the model ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer import torch labels = ["low", "medium", "high", "critical"] model_name = "CIRCL/vulnerability-severity-classification-roberta-base" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) model.eval() test_description = "SAP NetWeaver Visual Composer Metadata Uploader is not protected with a proper authorization, allowing unauthenticated agent to upload potentially malicious executable binaries \ that could severely harm the host system. This could significantly affect the confidentiality, integrity, and availability of the targeted system." inputs = tokenizer(test_description, return_tensors="pt", truncation=True, padding=True) # Run inference with torch.no_grad(): outputs = model(**inputs) predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) # Print results print("Predictions:", predictions) predicted_class = torch.argmax(predictions, dim=-1).item() print("Predicted severity:", labels[predicted_class]) ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 It achieves the following results on the evaluation set: - Loss: 0.5072 - Accuracy: 0.8282 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\| \| 0.5519 \| 1.0 \| 28760 \| 0.6553 \| 0.7357 \| \| 0.5365 \| 2.0 \| 57520 \| 0.5647 \| 0.7746 \| \| 0.3656 \| 3.0 \| 86280 \| 0.5397 \| 0.7997 \| \| 0.4367 \| 4.0 \| 115040 \| 0.4903 \| 0.8191 \| \| 0.3609 \| 5.0 \| 143800 \| 0.5072 \| 0.8282 \| ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu128 - Datasets 4.0.0 - Tokenizers 0.21.4
cike-dev/GemmaToxic	cike-dev	2025-09-12T05:08:49Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T05:06:56Z	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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constehub/DeepSeek-R1-0528-Qwen3-8B-rag-evaluation	constehub	2025-09-12T05:07:37Z	0	0	transformers	[ "transformers", "gguf", "qwen3", "text-generation-inference", "unsloth", "en", "base_model:unsloth/DeepSeek-R1-0528-Qwen3-8B-unsloth-bnb-4bit", "base_model:quantized:unsloth/DeepSeek-R1-0528-Qwen3-8B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-12T05:06:53Z	--- base_model: unsloth/DeepSeek-R1-0528-Qwen3-8B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: constehub - License: apache-2.0 - Finetuned from model : unsloth/DeepSeek-R1-0528-Qwen3-8B-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
jxue/whisper-small-jiangyin	jxue	2025-09-12T05:05:05Z	20	1	null	[ "safetensors", "whisper", "fine-tuned", "dialect", "mandarin", "chinese", "jiangyin", "zh", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:mit", "region:us" ]	null	2025-06-15T00:06:16Z	--- license: mit language: - zh base_model: - openai/whisper-small tags: - whisper - fine-tuned - dialect - mandarin - chinese - jiangyin --- This is a finetuned version of [Whisper-small](https://huggingface.co/openai/whisper-small) on [WuSutra.com](https://wusutra.com/). Wusutra.com is a dialect crowdsourcing website which implements the entire ML workflow — including audio upload, model training, validation, and inference. You can upload your own audios and even trigger the training yourself on wusutra.com. If you have further questions, feel free to message me. ### 📊 Evaluation on 45 Jiangyin dialect phrases: Character Error Rate (CER) \| Model \| WER (%) \| \|------------------\|---------\| \| Baseline (whisper-small) \| 0.46 \| \| Fine-tuned (Jiangyin Dialect) \| 0.00 \| Significant improvement observed after fine-tuning on 119 dialect audio samples. --- ✅ Correct recognition example \| REF (参考) \| Transliteration (音译) \| HYP (预测) \| CER \| \| ----------------- \| --------------------------- \| ----------------- \| ----- \| \| 吃什么 \| 切刀样 \| 吃什么 \| 0.000 \| \| 不知道 \| 佛晓得 \| 不知道 \| 0.000 \| \| 素菜 \| 搜菜 \| 素菜 \| 0.000 \| \| 红烧肉 \| 红搜牛 \| 红烧肉 \| 0.000 \| \| 谁啊?小偷 \| 啥人啦?贼骨头 \| 谁啊?小偷 \| 0.000 \| \| 谁啊?老公 \| 啥人啦?老官 \| 谁啊?老公 \| 0.000 \| \| 节约 \| 做人家 \| 节约 \| 0.000 \| \| 闪电 \| 忽显 \| 闪电 \| 0.000 \| \| 下雨 \| 落雨 \| 下雨 \| 0.000 \| \| 丢人 \| 坍台 \| 丢人 \| 0.000 \| \| 泥土 \| 难泥 \| 泥土 \| 0.000 \| \| 好 \| 灵个 \| 好 \| 0.000 \| \| 到处都是 \| 一天世界 \| 到处都是 \| 0.000 \| \| 最后 \| 压末落落 \| 最后 \| 0.000 \| \| 睡觉 \| 困觉 \| 睡觉 \| 0.000 \| \| 小偷 \| 贼骨头 \| 小偷 \| 0.000 \| \| 拿不定主意 \| 疑三惑四 \| 拿不定主意 \| 0.000 \| \| 轻浮 \| 轻骨头 \| 轻浮 \| 0.000 \| \| 明天 \| 明朝 \| 明天 \| 0.000 \| \| 后天 \| 后朝 \| 后天 \| 0.000 \| \| 前天 \| 先夜子 \| 前天 \| 0.000 \| \| 妻子 \| 阿嬷 \| 妻子 \| 0.000 \| \| 这样 \| 实梗 \| 这样 \| 0.000 \| \| 出去 \| 出去 \| 出去 \| 0.000 \| \| 明天见 \| 明朝会 \| 明天见 \| 0.000 \| \| 什么东西 \| 啥个物事 \| 什么东西 \| 0.000 \| \| 什么时候 \| 啥辰光 \| 什么时候 \| 0.000 \| \| 回来 \| 嘎来 \| 回来 \| 0.000 \| \| 老公 \| 老官 \| 老公 \| 0.000 \| \| 十分寒冷 \| 毕结骨 \| 十分寒冷 \| 0.000 \| \| 谁啊 \| 啥人啦 \| 谁啊 \| 0.000 \| \| 男孩 \| 细七煞 \| 男孩 \| 0.000 \| \| 傍晚 \| 夜快头 \| 傍晚 \| 0.000 \| \| 肩膀 \| 肩胛 \| 肩膀 \| 0.000 \| \| 男子 \| 老小家 \| 男子 \| 0.000 \| \| 女子 \| 丫头家 \| 女子 \| 0.000 \| \| 今天吃点什么? \| 今朝吃点刀样啦? \| 今天吃点什么? \| 0.000 \| \| 你这小子,是不是欠捧! \| 你个细棺材,阿要吃生活! \| 你这小子,是不是欠捧! \| 0.000 \| \| 今天吃什么?不知道 \| 今朝切刀样?佛晓得 \| 今天吃什么?不知道 \| 0.000 \| \| 今天吃什么?红烧肉 \| 今朝切刀样?红搜牛 \| 今天吃什么?红烧肉 \| 0.000 \| \| 什么时候出去?明天 \| 啥辰光出去?明朝 \| 什么时候出去?明天 \| 0.000 \| \| 什么时候出去?后天 \| 啥辰光出去?后朝 \| 什么时候出去?后天 \| 0.000 \|
camiellia/qwen2_5_vl_fiubench_checkpoint_4	camiellia	2025-09-12T05:03:15Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-11T20:22:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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nitingensyn/blockassist	nitingensyn	2025-09-12T05:02:57Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "slithering bold koala", "arxiv:2504.07091", "region:us" ]	null	2025-09-11T13:50:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - slithering bold koala --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DoppelReflEx/MiniusLight-24B-v3	DoppelReflEx	2025-09-12T05:00:14Z	9	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:Delta-Vector/Rei-24B-KTO", "base_model:merge:Delta-Vector/Rei-24B-KTO", "base_model:TheDrummer/Cydonia-24B-v4.1", "base_model:merge:TheDrummer/Cydonia-24B-v4.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-26T10:39:13Z	--- base_model: - Delta-Vector/Rei-24B-KTO - TheDrummer/Cydonia-24B-v4.1 library_name: transformers tags: - mergekit - merge --- <style> @import url('https://fonts.googleapis.com/css2?family=Playwrite+CA+Guides&display=swap'); .playwrite-ca-guides-regular { font-family: "Playwrite CA Guides", cursive !important; font-weight: 400; font-style: normal; } body { margin:0; padding:0; font-size: 16px; } .main-container { background-color: #ebf3ff; border: 1px solid #466db9; border-radius: 8px; color: #050315; margin:16px; padding:16px; font-size: 16px; width: 95%; } h1, h2, h3 { color: #050315; margin-top: 16px; } .soft-blue-custom { color: #466db9 !important; } .alink { font-weight:400; text-decoration:none; } .main-banner-image { max-width:100%; max-height:600px; border-radius:8px; align-self:center; justify-self: center; border: 1px solid #466db9; margin: 8px 16px } pre.code-block, pre { font-size: clamp(10px, 1.3vw, 14px); white-space: pre; margin: 1em 0; background-color: #1a1a1a; padding: 1em; border-radius: 4px; color: #a9a6de; overflow-x: auto; } p { font-weight:500; } .pb { padding-bottom: 8px; } .mb { margin-bottom: 8px; } .bold { font-weight: 600; } .secondary { color: #a9a6de; } .accent { color: #403bb7; } .tac { text-align:center; } .border-custom-dot { border: 1px dashed #466db9; border-radius: 16px; padding:0 8px; } .border-custom { border: 1px solid #466db9; border-radius: 8px; padding:0 8px; } .as { padding-left: 16px; } .as2 { padding-left: 24px; } </style> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <link href="https://fonts.googleapis.com/css2?family=Playwrite+CA+Guides&display=swap" rel="stylesheet"> </head> <body> <div class="main-container"> <div class="playwrite-ca-guides-regular pb tac"> <h1 class="soft-blue-custom">MiniusLight-24B-v3</h1> <h2 class="soft-blue-custom"><a href="https://huggingface.co/DoppelReflEx/MN-12B-Mimicore-Nocturne" class="accent bold">12B</a> - <a href="https://huggingface.co/DoppelReflEx/MiniusLight-24B" class="accent bold">24B-v1</a> - <a href="https://huggingface.co/DoppelReflEx/MiniusLight-24B-v1.01" class="accent bold">24B-v1.01</a> - <a href="https://huggingface.co/DoppelReflEx/MiniusLight-24B-v2" class="accent bold">24B-v2</a> - <a href="https://huggingface.co/DoppelReflEx/MiniusLight-24B-v2.1" class="accent bold">24B-v2.1</a> - 24B-v3</h2> <img src="https://cdn.donmai.us/original/19/66/__shorekeeper_wuthering_waves_drawn_by_narase_ffrv5573__196631e35c2167d31cfb9dd5ff224ed4.png" alt="cover image" class="main-banner-image"/> <a href="https://www.pixiv.net/en/artworks/122951208" class="alink soft-blue-custom">Origin Content (Click Here)</a> </div> <div class="info"> <div class="border-custom-dot mb"> <h2 class="soft-blue-custom">What is this?</h2> <div class="as"> <p> Maybe this is last 24B Mistral model of this series. I'm tired (laugh). </p> <p>Thanks for two base models, this model archive very good styles and consistency in long context. 30th test btw, that mean there are 29 models fail to find and create this model.</p> <p>Best model of the series (for me). :)</p> <p></p> </div> </div> <div class="border-custom-dot mb"> <h2 class="soft-blue-custom">GGUF</h2> <h3 class="accent"><a href="https://huggingface.co/mradermacher/MiniusLight-24B-v3-GGUF" class="accent bold">Static</a> - <a class="accent bold" href="https://huggingface.co/mradermacher/MiniusLight-24B-v3-i1-GGUF">iMatrix</a></h3> </div> <div class="border-custom-dot"> <h2 class="soft-blue-custom">Other information</h2> <div class="as"> <h3><span class="soft-blue-custom">Chat Template? </span>Mistral V7 - Tekken<span class="soft-blue-custom">. ChatML are also good to use, but Mistral V7 - Tekken is recommend</span></h3> <h3 class="soft-blue-custom">Merge Method<h3/> <details class="border-custom"> <summary class="soft-blue-custom">Detail YAML Config</summary> <pre> { models: - model: TheDrummer/Cydonia-24B-v4.1 - model: Delta-Vector/Rei-24B-KTO merge_method: slerp base_model: TheDrummer/Cydonia-24B-v4.1 parameters: t: [0.1, 0.2, 0.3, 0.5, 0.8, 0.5, 0.3, 0.2, 0.1] dtype: bfloat16 tokenizer_source: base } </pre> </detail> </div> </div> </div> </div> </body>
RaghavM12/lora_model	RaghavM12	2025-09-12T04:59:31Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-12T04:59:18Z	--- base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: RaghavM12 - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
omerbektasss/blockassist-bc-insectivorous_bold_lion_1757653128	omerbektasss	2025-09-12T04:59:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous bold lion", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T04:59:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous bold lion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Godfung/qwen-3-4B-content-moderation-adaptor	Godfung	2025-09-12T04:56:35Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-12T04:56:28Z	--- base_model: unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Godfung - License: apache-2.0 - Finetuned from model : unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757652815	stonermay	2025-09-12T04:54:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T04:54:40Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Godfung/qwen-3-4B-content-moderation-merged-vllm	Godfung	2025-09-12T04:53:53Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T04:50:38Z	--- base_model: unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: Godfung - License: apache-2.0 - Finetuned from model : unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
cuongdk253/unsloth-gpt-oss-20b-ft-bnb4bit-12092025	cuongdk253	2025-09-12T04:52:53Z	0	0	transformers	[ "transformers", "safetensors", "gpt_oss", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "base_model:quantized:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-09-12T04:50:49Z	--- base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gpt_oss - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: cuongdk253 - License: apache-2.0 - Finetuned from model : unsloth/gpt-oss-20b-unsloth-bnb-4bit This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

tushar772/mini_ads.py

tushar772

2025-09-12T06:51:35Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-09-12T06:51:35Z

--- license: apache-2.0 ---

LandCruiser/sn21_omg3_1209_3

LandCruiser

2025-09-12T06:49:28Z

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

2025-09-12T06:38:31Z

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

LandCruiser/sn21_omg3_1209_1

LandCruiser

2025-09-12T06:49:12Z

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

2025-09-12T06:38:26Z

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

Adanato/Llama-3.2-1B-Instruct-high_openthoughts_1k

Adanato

2025-09-12T06:48:34Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "fyksft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-12T06:46:55Z

--- library_name: transformers tags: - fyksft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

LarryAIDraw/SchwarzPDXL_Lora__v1_0

LarryAIDraw

2025-09-12T06:48:00Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2025-09-12T06:47:36Z

--- license: creativeml-openrail-m ---

mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF

mradermacher

2025-09-12T06:47:26Z

0

transformers

[ "transformers", "gguf", "programming", "code generation", "code", "coding", "coder", "chat", "brainstorm", "qwen", "qwen3", "qwencoder", "brainstorm 20x", "creative", "all uses cases", "Jan-V1", "Stargate", "SG1", "horror", "science fiction", "fantasy", "finetune", "thinking", "reasoning", "unsloth", "en", "base_model:DavidAU/Qwen3-SG1-256k-ctx-6B", "base_model:quantized:DavidAU/Qwen3-SG1-256k-ctx-6B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-09-12T05:48:42Z

--- base_model: DavidAU/Qwen3-SG1-256k-ctx-6B language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - programming - code generation - code - coding - coder - chat - code - chat - brainstorm - qwen - qwen3 - qwencoder - brainstorm 20x - creative - all uses cases - Jan-V1 - Stargate - SG1 - horror - science fiction - fantasy - finetune - thinking - reasoning - unsloth --- ## About         weighted/imatrix quants of https://huggingface.co/DavidAU/Qwen3-SG1-256k-ctx-6B  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-SG1-256k-ctx-6B-i1-GGUF).*** static quants are available at https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ1_M.gguf) | i1-IQ1_M | 1.8 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.0 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.2 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.4 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q2_K.gguf) | i1-Q2_K | 2.6 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.7 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.0 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.3 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.5 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q4_0.gguf) | i1-Q4_0 | 3.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 3.8 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 3.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q4_1.gguf) | i1-Q4_1 | 4.1 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.i1-Q6_K.gguf) | i1-Q6_K | 5.3 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

LarryAIDraw/IL_Ade_Agent_Bunny

LarryAIDraw

2025-09-12T06:44:58Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2025-09-11T08:31:23Z

--- license: creativeml-openrail-m --- https://civitai.com/models/1932786/ade-agent-bunny-nikke-sdxl-lora-illustrious

mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF

mradermacher

2025-09-12T06:44:04Z

0

transformers

[ "transformers", "gguf", "programming", "code generation", "code", "coding", "coder", "chat", "brainstorm", "qwen", "qwen3", "qwencoder", "brainstorm 20x", "creative", "all uses cases", "Jan-V1", "Stargate", "SG1", "horror", "science fiction", "fantasy", "finetune", "thinking", "reasoning", "unsloth", "en", "base_model:DavidAU/Qwen3-SG1-256k-ctx-6B", "base_model:quantized:DavidAU/Qwen3-SG1-256k-ctx-6B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2025-09-12T05:17:03Z

--- base_model: DavidAU/Qwen3-SG1-256k-ctx-6B language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - programming - code generation - code - coding - coder - chat - code - chat - brainstorm - qwen - qwen3 - qwencoder - brainstorm 20x - creative - all uses cases - Jan-V1 - Stargate - SG1 - horror - science fiction - fantasy - finetune - thinking - reasoning - unsloth --- ## About         static quants of https://huggingface.co/DavidAU/Qwen3-SG1-256k-ctx-6B  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-SG1-256k-ctx-6B-GGUF).*** weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q2_K.gguf) | Q2_K | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q3_K_S.gguf) | Q3_K_S | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q3_K_M.gguf) | Q3_K_M | 3.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q3_K_L.gguf) | Q3_K_L | 3.5 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.IQ4_XS.gguf) | IQ4_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q4_K_S.gguf) | Q4_K_S | 3.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q4_K_M.gguf) | Q4_K_M | 4.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q5_K_S.gguf) | Q5_K_S | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q5_K_M.gguf) | Q5_K_M | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q6_K.gguf) | Q6_K | 5.3 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.Q8_0.gguf) | Q8_0 | 6.8 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Qwen3-SG1-256k-ctx-6B-GGUF/resolve/main/Qwen3-SG1-256k-ctx-6B.f16.gguf) | f16 | 12.8 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

100Pudoff/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_large_clam

100Pudoff

2025-09-12T06:42:33Z

166

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am pensive_large_clam", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-04T09:12:04Z

--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am pensive_large_clam --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

AdvRahul/Axion-Flash-Reasoning-2B

AdvRahul

2025-09-12T06:40:25Z

329

0

transformers

[ "transformers", "gguf", "llama-cpp", "text-generation", "en", "base_model:nvidia/Nemotron-Research-Reasoning-Qwen-1.5B", "base_model:quantized:nvidia/Nemotron-Research-Reasoning-Qwen-1.5B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2025-08-24T17:23:34Z

--- base_model: nvidia/Nemotron-Research-Reasoning-Qwen-1.5B language: - en license: cc-by-nc-4.0 pipeline_tag: text-generation library_name: transformers tags: - llama-cpp --- # AdvRahul/Axion-Flash-Reasoning-2B-Q8_0-GGUF This model was built on top of Nemotron-Research-Reasoning-Qwen-1.5B with advanced safety protocols. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo AdvRahul/Axion-Flash-Reasoning-2B-Q8_0-GGUF --hf-file axion-flash-reasoning-2B-Q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo AdvRahul/Axion-Flash-Reasoning-2B-Q8_0-GGUF --hf-file axion-flash-reasoning-2B-Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo AdvRahul/Axion-Flash-Reasoning-2B-Q8_0-GGUF --hf-file axion-flash-reasoning-2B-Q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo AdvRahul/Axion-Flash-Reasoning-2B-Q8_0-GGUF --hf-file axion-flash-reasoning-2B-Q8_0.gguf -c 2048 ```

stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757658967

stonermay

2025-09-12T06:37:17Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T06:37:09Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

raniero/ax-real-001-repo

raniero

2025-09-12T06:34:48Z

0

peft

[ "peft", "safetensors", "lora", "bittensor", "subnet-56", "gradients", "it", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]

null

2025-09-12T06:34:46Z

--- language: - it license: apache-2.0 library_name: peft tags: [lora, bittensor, subnet-56, gradients] base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 --- # ARES56 — LoRA adapter Upload ID: ax-real-001_1757658886 upload_id: unknown_1757404904 File inclusi: - `adapter_model.safetensors` — SHA256: `e5a00aa9991ac8a5ee3109844d84a55583bd20572ad3ffcd42792f3c36b183ad` - `adapter_config.json` — SHA256: `4f39b39f151e0d31a8135b89599746fd2e06285a8594595589d7974f553af441` - `tokenizer_config.json` — SHA256: `missing` - `special_tokens_map.json` — SHA256: `missing` Output generato via Axolotl (CPU / smoke). Nessun checkpoint completo incluso.

omerbektasss/blockassist-bc-keen_fast_giraffe_1757658832

omerbektasss

2025-09-12T06:34:12Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T06:34:08Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

skatzR/USER-BGE-M3-ONNX-INT8

skatzR

2025-09-12T06:33:05Z

182

0

null

[ "onnx", "xlm-roberta", "quantization", "sentence-embeddings", "semantic-search", "ru", "en", "base_model:deepvk/USER-bge-m3", "base_model:quantized:deepvk/USER-bge-m3", "license:apache-2.0", "region:us" ]

null

2025-09-08T06:26:35Z

--- license: apache-2.0 base_model: - deepvk/USER-bge-m3 language: - ru - en tags: - onnx - quantization - sentence-embeddings - semantic-search --- # 🧩 DeepVK-USER-BGE-M3 — Quantized ONNX (INT8) ✨ This repository contains a **quantized INT8 ONNX version** of [`deepvk/USER-bge-m3`](https://huggingface.co/deepvk/USER-bge-m3). It is designed for **fast CPU inference** with [ONNX Runtime](https://onnxruntime.ai/), making it a great choice for **semantic search, embeddings generation, and text similarity** tasks in **Russian** 🇷🇺 and **English** 🇬🇧. --- ## 🔍 Model Card | Property | Value | |---------------------|-----------------------------------------------------------------------| | **Base model** | [`deepvk/USER-bge-m3`](https://huggingface.co/deepvk/USER-bge-m3), [`BAAI/bge-m3`](https://huggingface.co/BAAI/bge-m3) | | **Quantization** | INT8 (Dynamic) | | **Format** | ONNX | | **Libraries** | `transformers`, `onnxruntime`, `optimum`, `sentence-transformers` | | **Embedding dim** | 1024 | | **Supported HW** | CPU (optimized for Intel AVX512-VNNI, fallback to AVX2) | | **License** | Apache-2.0 | --- ## 🚀 Features - ⚡ **Fast CPU inference** — ONNX + INT8 gives a strong speed-up. - 📦 **Lightweight** — reduced model size, lower memory footprint. - 🔄 **Drop-in replacement** — embeddings compatible with the FP32 version. - 🌍 **Multilingual** — supports Russian 🇷🇺 and English 🇬🇧. --- ## 🧠 Intended Use **✅ Recommended for:** - Semantic search & retrieval systems - Recommendation pipelines - Text similarity & clustering - Low-latency CPU deployments **❌ Not ideal for:** - Absolute maximum accuracy scenarios (INT8 introduces minor loss) - GPU-optimized pipelines (prefer FP16/FP32 models instead) --- ## ⚖️ Pros & Cons of Quantized ONNX **Pros** ✅ - Easy to use (no calibration dataset required). - Smaller & faster than FP32. - Works out of the box with ONNX Runtime. **Cons** ❌ - Slight accuracy drop compared to static quantization. - AVX512 optimizations only on modern Intel CPUs. - No GPU acceleration in this export. --- ## 📊 Benchmark | Metric | Value | |------------------------------|-------------- | | Avg cosine similarity (vs FP32) | ~0.988 | | Median cosine similarity | ~0.988 | | Orig model time (s) | 0.7504 | | Quant model time (s) | 0.3539 | | Inference speed | ~2× faster | | Model size (MB) | 347.5 | --- ## 📂 Files model_quantized.onnx — quantized model tokenizer.json, vocab.txt, special_tokens_map.json — tokenizer config.json — model config --- ## 🧩 Examples You can try the model directly in **Google Colab**: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/#fileId=https%3A//huggingface.co/skatzR/USER-BGE-M3-ONNX-INT8/blob/main/notebooks/TEST_USER-BGE-M3-ONNX-INT8.ipynb) This notebook demonstrates: - Loading the **original FP32 model** [`deepvk/USER-bge-m3`](https://huggingface.co/deepvk/USER-bge-m3) - Loading the **quantized INT8 ONNX model** [`skatzR/USER-BGE-M3-ONNX-INT8`](https://huggingface.co/skatzR/USER-BGE-M3-ONNX-INT8) - Comparing **quality (cosine similarity)** and **inference speed** side by side You can try this model with ready-to-use scripts in the `examples` folder: - [`quantmodel.py`](./examples/quantmodel.py) — universal Python module for loading and encoding texts with the quantized ONNX model. - [`app-console.py`](./examples/app-console.py) — console script to compare FP32 vs INT8 embeddings (cosine similarity + inference time). - [`app-streamlit.py`](./examples/app-streamlit.py) — interactive demo with Streamlit.

Sunik93/gemma-3-1b-pt-MED

Sunik93

2025-09-12T06:30:51Z

0

transformers

[ "transformers", "safetensors", "gemma3_text", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-12T06:30:10Z

--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

aruboi/llama-32-11b-vlm_peft_output33

aruboi

2025-09-12T06:30:26Z

3

0

peft

[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-11B-Vision", "base_model:adapter:meta-llama/Llama-3.2-11B-Vision", "license:llama3.2", "region:us" ]

null

2025-09-09T10:59:54Z

--- library_name: peft license: llama3.2 base_model: meta-llama/Llama-3.2-11B-Vision tags: - generated_from_trainer model-index: - name: llama-32-11b-vlm_peft_output33 results: [] ---  # llama-32-11b-vlm_peft_output33 This model is a fine-tuned version of [meta-llama/Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3393 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_HF with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.873 | 0.1 | 5 | 1.6084 | | 1.6055 | 0.2 | 10 | 1.5360 | | 2.0931 | 0.3 | 15 | 1.4717 | | 1.3995 | 0.4 | 20 | 1.4390 | | 1.4799 | 0.5 | 25 | 1.4170 | | 1.2999 | 0.6 | 30 | 1.3999 | | 1.6356 | 0.7 | 35 | 1.3855 | | 1.3253 | 0.8 | 40 | 1.3713 | | 1.4132 | 0.9 | 45 | 1.3630 | | 1.4824 | 1.0 | 50 | 1.3557 | | 1.0958 | 1.1 | 55 | 1.3478 | | 1.3437 | 1.2 | 60 | 1.3490 | | 1.3517 | 1.3 | 65 | 1.3459 | | 1.0089 | 1.4 | 70 | 1.3415 | | 1.1556 | 1.5 | 75 | 1.3369 | | 1.3741 | 1.6 | 80 | 1.3393 | ### Framework versions - PEFT 0.10.0 - Transformers 4.47.0 - Pytorch 2.7.0a0+7c8ec84dab.nv25.03 - Datasets 3.5.0 - Tokenizers 0.21.1

mgparkzone/gemma-3-1b-pt-MED

mgparkzone

2025-09-12T06:29:26Z

0

transformers

[ "transformers", "safetensors", "gemma3_text", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-12T06:28:55Z

--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

GANGSSSSSSSS/gemma-3-1b-pt-MED

GANGSSSSSSSS

2025-09-12T06:29:06Z

0

transformers

[ "transformers", "safetensors", "gemma3_text", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-12T06:28:26Z

--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

deadman44/Wan2.2_T2i_T2v_LoRA

deadman44

2025-09-12T06:28:41Z

0

16

null

[ "text-to-image", "t2i", "wan video", "safetensors", "text-to-video", "en", "license:apache-2.0", "region:us" ]

text-to-video

2025-07-27T00:45:26Z

--- license: apache-2.0 pipeline_tag: text-to-video language: - en tags: - text-to-image - t2i - wan video - safetensors --- <style> .title{ font-size: 2.5em; letter-spacing: 0.01em; padding: 0.5em 0; } .thumbwidth{ max-width: 180px; } .font_red{ color:red; } .font_blue{ color:blue; } .font_grey{ color: #aaaaaa; } </style> # models - [Wan2.2_myjc_v02](#myjc) (<span class="font_red">Wan2.2 LoRA</span>):2025-09-04<br /> - [Wan2.2_myob_v01](#myob) (<span class="font_red">Wan2.2 LoRA</span>):2025-08-31<br /> - [Wan2.2_myjd_v01](#myjd) (<span class="font_red">Wan2.2 LoRA</span>):2025-08-26<br /> - [Wan2.2_myjy_v01](#myjy) (<span class="font_red">Wan2.2 LoRA</span>):2025-08-21<br /> - [Wan2.2_myjk_v01](#myjk) (<span class="font_red">Wan2.2 LoRA</span>):2025-08-18<br /> - [Wan2.2_myjs_v01](#myjs) (<span class="font_red">Wan2.2 LoRA</span>):2025-08-11<br /> - Add [Workflow page](https://huggingface.co/deadman44/Wan2.2_Workflow_for_myxx_series_LoRA): 2025-08-04<br /> --- <br> - Workflow ### - [Sample Workflow for myxx series LoRA](https://huggingface.co/deadman44/Wan2.2_Workflow_for_myxx_series_LoRA)<br> <br> --- <a id="myob"></a> <h1 class="title"> <span>[Wan2.2] lora_wan_myob_v01</span> </h1> -<span class="font_blue">Wan video 2.2 for t2i, t2v for ComfyUI</span><br/> -<span class="font_blue">The optimal resolution is 768 x 1280, 1024 x 1536.(T2i), 512x768 (T2v)</span><br/> -<span class="font_blue">natural Japanese OB face</span><br/> <br/> <br/> # Download [Download: myob_High_v01](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myob_High_v01.safetensors?download=true) <br /> [Download: myob_Low_v01](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myob_Low_v01.safetensors?download=true) <br /> <br /> # Trigger ```bash myob, japanese/european, photorealistic and 23-30yo ``` <br /> # Sample prompt (v01) <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <strong>wan2.2 T2v</strong> <video controls loop style="width: 360px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250831073513_T2V_00002.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash myob, japanese, A Japanese woman, 30 years old, standing at kichen and holding pan. She wears a white sweater and pink apron. She has a brown bob hair. She tilts her head slightly and smiles with closed lips. A mole is visible on her neck. She looks at the viewer calmly. Motion: subtle breathing, head tilt Style: photorealistic Camera: medium close-up Mood: serene ``` <br/> --- <a id="myjd"></a> <h1 class="title"> <span>[Wan2.2] lora_wan_myjd_v01</span> </h1> -<span class="font_blue">Wan video 2.2 for t2i, t2v for ComfyUI</span><br/> -<span class="font_blue">The optimal resolution is 768 x 1280, 1024 x 1536.(T2i), 512x768 (T2v)</span><br/> -<span class="font_blue">natural Japanese JD face</span><br/> <br/> <br/> # Download [Download: myjd_High_v01](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myjd_High_v01.safetensors?download=true) <br /> [Download: myjd_Low_v01](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myjd_Low_v01.safetensors?download=true) <br /> <br /> # Trigger ```bash myjd, japanese/european, photorealistic and 19-22yo ``` <br /> # Sample prompt (v01) <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <strong>wan2.2 T2v</strong> <video controls loop style="width: 360px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250826065050_T2V_00002.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash 22yo, myjd, japanese, A woman dressed in a maid costume carries coffee on a tray in a café. She has black hair tied in a ponytail and wears a maid headdress. ``` <br/> --- <a id="myjk"></a> <h1 class="title"> <span>[Wan2.2] lora_wan_myjk_v01</span> </h1> -<span class="font_blue">Wan video 2.2 for t2i, t2v for ComfyUI</span><br/> -<span class="font_blue">The optimal resolution is 768 x 1280, 1024 x 1536.(T2i), 512x768 (T2v)</span><br/> -<span class="font_blue">natural Japanese JK face</span><br/> <br/> <br/> # Download [Download: myjk_High_v01](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myjk_High_v01.safetensors?download=true) <br /> [Download: myjk_Low_v01](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myjk_Low_v01.safetensors?download=true) <br /> <br /> # Trigger ```bash myjk, japanese/european, photorealistic and 16-18yo ``` <br /> # Sample prompt (v01) <strong>wan2.2 T2i generated</strong> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px; margin-bottom: 32px;"> <strong>T2i</strong> <a href="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250818092119_T2I_00001_.jpg" target="_blank"> <img src="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250818092119_T2I_00001_.jpg" alt="T2I" style="width: 360px; height: auto; object-fit: contain; border: 1px solid #ccc;"> </a> </div> ```bash 18yo, myjk, japanese, A photorealistic upper-body portrait of a beautiful young woman with long black hair and black eyes, dressed in a school uniform. She is sitting on a stool, smiling with one eye closed in a playful grin, showing her teeth. Her hand is raised gently near her face, and a hair ornament with a black bow. The background is softly blurred, enhancing the cinematic atmosphere. She looks directly at the viewer, evoking a sense of charm and realism. ``` <br/> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <strong>wan2.2 T2v</strong> <video controls loop style="width: 360px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250818093735_T2V_00002.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash 18yo, myjk, japanese, A Japanese idol is performing live on a brightly lit concert stage. She is wearing idol costume with Lace-up flared skirt. She sings and dances energetically, moving across the stage with graceful steps and expressive gestures. The camera follows her with dynamic motion: starting from a low-angle close-up of her smiling face, then pulling back to reveal the full stage with flashing lights and cheering fans. Her long hair flows with her movements, and her outfit sparkles under the spotlights. The scene includes cinematic lighting, fog effects, and smooth camera transitions that emphasize her presence and charm. ``` <br/> --- <a id="myjc"></a> <h1 class="title"> <span>[Wan2.2] lora_wan_myjc_v02</span> </h1> -<span class="font_blue">Wan video 2.2 for t2i, t2v for ComfyUI</span><br/> -<span class="font_blue">The optimal resolution is 768 x 1280, 1024 x 1536.(T2i), 512x768 (T2v)</span><br/> -<span class="font_blue">natural Japanese JC face</span><br/> <br/> <br/> # Download [Download: myjc_High_v02](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myjc_High_v02.safetensors?download=true) <br /> [Download: myjc_Low_v02](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myjc_Low_v02.safetensors?download=true) <br /> <br /> # Trigger ```bash myjc, japanese/european, photorealistic and 13-15yo ``` <br /> # Sample prompt <strong>wan2.2 T2i generated</strong> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px; margin-bottom: 32px;"> <strong>T2i (v01)</strong> <a href="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250814111852_T2I_00001_.png" target="_blank"> <img src="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250814111852_T2I_00001_.png" alt="T2I" style="width: 360px; height: auto; object-fit: contain; border: 1px solid #ccc;"> </a> </div> ```bash 15yo, myjc, japanese, photorealistic, A girl in school unifrom sitting seat at train. She has black hair with sidelocks. She is holding a smartphone and looking at it. ``` <br/> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <strong>wan2.2 T2v (v02)</strong> <video controls loop style="width: 360px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250904192015_T2V_00001.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash 15yo, myjc, japanese, A realistic video of a girl sitting alone on a swing in a sunny park. She wears a neat school uniform: white collared short-sleeve shirt tucked into a pleated blue suspender skirt, white socks, and black loafers. Her long black hair is styled in twin braids with a blue ribbon tied at the neck. She gently swings back and forth, hands resting between her legs, occasionally looking at the viewer with a calm, closed-mouth expression. The background is a slightly blurry photo-style park with trees, bugs flying past, and a playground visible. Her backpack lies beside the swing. As the swing moves, her skirt sways naturally, and sunlight filters through the leaves above. The scene captures a quiet moment of reflection, with subtle wind moving her hair over her shoulder. ``` <br/> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <strong>wan2.2 T2v (v01)</strong> <video controls loop style="width: 480px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250814112118_T2V_00002.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash myjc, japanese, photorealistic, Close-up portrait of a girl walking at street. She has a black twintails. She is wearing white blouse. ``` <br/> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <video controls loop style="width: 480px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250814112156_T2V_00002.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash 15yo, myjc, japanese, photorealistic, A girl in school unifrom with short sleeves sitting chair at night time classroom. She has black hair with sidelocks. She is talking camera with smily. ``` <br/> --- <a id="myjs"></a> <h1 class="title"> <span>[Wan2.2] lora_wan_myjs_v01</span> </h1> -<span class="font_blue">Wan video 2.2 for t2i, t2v for ComfyUI</span><br/> -<span class="font_blue">The optimal resolution is 768 x 1280, 1024 x 1536.(T2i), 512x768 (T2v)</span><br/> -<span class="font_blue">natural Japanese JS face</span><br/> <br/> <br/> # Download [Download: myjs_High_v01](https://huggingface.co/deadman44/WAN_T2i_LoRA/resolve/main/lora_wan2.2_myjs_High_v01.safetensors?download=true)<br> [Download: myjs_Low_v01](https://huggingface.co/deadman44/WAN_T2i_LoRA/resolve/main/lora_wan2.2_myjs_Low_v01.safetensors?download=true)<br> <br /> # Trigger ```bash (myjsh / myjsm / myjsl), japanese/european, photorealistic and 6-12yo ``` <br /> # Sample prompt (v01) <strong>wan2.2 T2i generated</strong> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px; margin-bottom: 32px;"> <strong>T2i</strong> <a href="https://huggingface.co/deadman44/WAN_T2i_LoRA/resolve/main/samples/20250811083806_T2I_LastImage_00001_.png?download=true" target="_blank"> <img src="https://huggingface.co/deadman44/WAN_T2i_LoRA/resolve/main/samples/20250811083806_T2I_LastImage_00001_.png" alt="T2I" style="width: 360px; height: auto; object-fit: contain; border: 1px solid #ccc;"> </a> </div> ```bash myjsh, japanese, photorealistic, A Japanese girl with shoulder-length black hair, wearing a white textured blouse, standing outdoors in soft sunlight. She gently lifts her hand to brush her hair aside, as a breeze flows through the trees behind her. Her blouse flutters slightly, and her gaze shifts subtly toward the camera. ``` <br/> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <strong>wan2.2 T2v</strong> <video controls loop style="width: 360px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/WAN_T2i_LoRA/resolve/main/samples/20250811084132_T2V_00001.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash 12yo, myjsh, japanese, photorealistic, A stylish girl posing for a fashion photoshoot in a minimalist studio. She wears a high-fashion outfit with layered textures: a translucent blouse over a structured corset, paired with wide-leg trousers. She shifts her pose gracefully, turning slightly to the side, adjusting her posture with subtle hand movements. Studio lights flash intermittently, casting soft shadows and highlights on her face and outfit. Her expression changes subtly from confident to playful. The camera slowly pans around her, capturing her elegance and motion. Cinematic lighting, fashion editorial style, photorealistic, expressive gesture, shallow depth of field, dynamic motion. ``` <br/> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <video controls loop style="width: 360px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/WAN_T2i_LoRA/resolve/main/samples/20250811104546_T2V_00002.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash 9yo, myjsm, japanese, photorealistic, A girl wearing a white blouse and pleated skirt with suspenders walks the crowded school hallway. She has a black ponytail. Finally she turns around and smiles. ``` <br/> <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <video controls loop style="width: 480px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/WAN_T2i_LoRA/resolve/main/samples/20250811112548_T2V_00002.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash 6yo, myjsl, japanese, photorealistic, Girls are crossing the street with their one hand raised as their car waits. ``` <br/> --- <a id="myjy"></a> <h1 class="title"> <span>[Wan2.2] lora_wan_myjy_v01</span> </h1> -<span class="font_blue">Wan video 2.2 for t2i, t2v for ComfyUI</span><br/> -<span class="font_blue">The optimal resolution is 768 x 1280, 1024 x 1536.(T2i), 512x768 (T2v)</span><br/> -<span class="font_blue">natural Japanese JY face</span><br/> <br/> <br/> # Download [Download: myjy_High_v01](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myjy_High_v01.safetensors?download=true)<br> [Download: myjy_Low_v01](https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/lora_wan2.2_myjy_Low_v01.safetensors?download=true)<br> <br /> # Trigger ```bash myjy, japanese/european, photorealistic and 3-5yo ``` <br /> # Sample prompt (v01) <div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;"> <strong>wan2.2 T2v</strong> <video controls loop style="width: 480px; height: auto; object-fit: contain; border: 1px solid #ccc;"> <source src="https://huggingface.co/deadman44/Wan2.2_T2i_T2v_LoRA/resolve/main/samples/20250821095521_T2V_00002.mp4" type="video/mp4"> Your browser cannot play the video. </video> </div> ```bash myjy, japanese, A heartwarming indoor scene of three cheerful kindergarten girls clasping their own hands together in playful prayer. They wear colorful long-sleeved uniforms with blunt bangs and varied hairstyles: black hair in twintails, brown short hair, and long hair with a cute hair ornament. One girl holds a picture book with animal illustrations, another giggles softly, and the third looks up with wide, curious eyes. Their fingers are gently interlocked, lips slightly parted in a whisper of joy, and their expressions glow with innocence and wonder. The softly blurred background shows a cozy classroom with pastel decorations, adding warmth and charm to the moment. ``` <br/> ---

GodShin/gemma-3-1b-pt-MED

GodShin

2025-09-12T06:28:38Z

0

transformers

[ "transformers", "safetensors", "gemma3_text", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-12T06:28:03Z

--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Adanato/Llama-3.2-1B-Instruct-high_nemotron_1k

Adanato

2025-09-12T06:27:40Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "fyksft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-12T06:25:40Z

--- library_name: transformers tags: - fyksft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

sidhantoon/Goldentouch_V3_G17

sidhantoon

2025-09-12T06:27:26Z

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

2025-09-12T06:23:35Z

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

NotoriousH2/gemma-3-1b-pt-MED

NotoriousH2

2025-09-12T06:26:56Z

12

0

transformers

[ "transformers", "safetensors", "gemma3_text", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-02T13:38:10Z

--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

trkbt10/finetuned_model

trkbt10

2025-09-12T06:25:35Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gpt_oss", "trl", "en", "base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-09-12T06:25:30Z

--- base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gpt_oss - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** trkbt10 - **License:** apache-2.0 - **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

shinebear/qwen100_va_agent-Q8_0-GGUF

shinebear

2025-09-12T06:25:28Z

0

transformers

[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen3", "llama-cpp", "gguf-my-repo", "en", "base_model:shinebear/qwen100_va_agent", "base_model:quantized:shinebear/qwen100_va_agent", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-09-12T06:24:52Z

--- base_model: shinebear/qwen100_va_agent tags: - text-generation-inference - transformers - unsloth - qwen3 - llama-cpp - gguf-my-repo license: apache-2.0 language: - en --- # shinebear/qwen100_va_agent-Q8_0-GGUF This model was converted to GGUF format from [`shinebear/qwen100_va_agent`](https://huggingface.co/shinebear/qwen100_va_agent) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/shinebear/qwen100_va_agent) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo shinebear/qwen100_va_agent-Q8_0-GGUF --hf-file qwen100_va_agent-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo shinebear/qwen100_va_agent-Q8_0-GGUF --hf-file qwen100_va_agent-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo shinebear/qwen100_va_agent-Q8_0-GGUF --hf-file qwen100_va_agent-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo shinebear/qwen100_va_agent-Q8_0-GGUF --hf-file qwen100_va_agent-q8_0.gguf -c 2048 ```

timduck8/nsfwnew

timduck8

2025-09-12T06:23:26Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-09-12T06:23:26Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/1751260672898828907_PgNTXYeu.jpg text: '-' base_model: black-forest-labs/FLUX.1-dev instance_prompt: null license: other license_name: faipl-1.0-sd license_link: LICENSE --- # nsfwnew <Gallery /> ## Download model [Download](/timduck8/nsfwnew/tree/main) them in the Files & versions tab.

Continual-Mega/ADCT

Continual-Mega

2025-09-12T06:22:32Z

0

1

null

[ "dataset:Continual-Mega/Continual-MEGA-Benchmark", "arxiv:2506.00956", "license:cc-by-nc-4.0", "region:us" ]

null

2025-06-16T04:12:17Z

--- license: cc-by-nc-4.0 datasets: - Continual-Mega/Continual-MEGA-Benchmark --- # 🧠 Continual-MEGA: A Large-scale Benchmark for Generalizable Continual Anomaly Detection This repository provides model checkpoints for **Continual-MEGA**, a benchmark introduced in the paper: [![Paper](https://img.shields.io/badge/Paper-arXiv%3A2506.00956-b31b1b?logo=arxiv&style=flat-square)](https://arxiv.org/abs/2506.00956) 🔗 **Codebase**: [Continual-Mega/Continual-Mega](https://github.com/Continual-Mega/Continual-Mega) --- ## 🚀 Overview Continual-MEGA introduces a realistic and large-scale benchmark for **continual anomaly detection** that emphasizes generalizability across domains and tasks. The benchmark features: - ✅ Diverse anomaly types across domains - 🔁 Class-incremental continual learning setup - 📈 A large-scale evaluation protocol surpassing previous benchmarks This repository hosts pretrained **model checkpoints** used in various scenarios defined in the benchmark. --- ## 📦 Available Checkpoints | Model Name | Scenario | Task | Description | |---------------------------------------|------------|-------------------------|----------------------------------------------------------| | `scenario2/prompt_maker` | Scenario 2 | Base | Prompt maker trained on Scenario 2 base classes | | `scenario2/adapters_base` | Scenario 2 | Base | Adapter trained on Scenario 2 base classes | | `scenario2/30classes/adapters_task1` | Scenario 2 | Task 1 (30 classes) | Adapter trained on Task 1 (30 classes) in Scenario 2 | | `scenario2/30classes/adapters_task2` | Scenario 2 | Task 2 (30 classes) | Adapter trained on Task 2 (30 classes) in Scenario 2 | | *(More to come)* | – | – | – | --- ## 🛠 Usage Example ### Continual Setting Evaluation ``` sh eval_continual.sh ``` ### Zero-Shot Evaluation ``` sh eval_zero.s

omerbektasss/blockassist-bc-keen_fast_giraffe_1757658028

omerbektasss

2025-09-12T06:21:21Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T06:20:44Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

de-Rodrigo/idefics2-merit

de-Rodrigo

2025-09-12T06:17:20Z

0

1

null

[ "safetensors", "vision", "document-understanding", "donut", "image-text-to-text", "conversational", "en", "es", "dataset:de-Rodrigo/merit", "base_model:HuggingFaceM4/idefics2-8b", "base_model:finetune:HuggingFaceM4/idefics2-8b", "license:mit", "region:us" ]

image-text-to-text

2024-07-21T17:45:31Z

--- license: mit datasets: - de-Rodrigo/merit language: - en - es base_model: - HuggingFaceM4/idefics2-8b pipeline_tag: image-text-to-text --- # IDEFICS2 Merit <a href="https://x.com/nearcyan/status/1706914605262684394"> <div style="text-align: center;"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/de-Rodrigo/donut-merit/resolve/main/assets/dragon_huggingface.png"> <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/de-Rodrigo/donut-merit/resolve/main/assets/dragon_huggingface.png"> <img alt="DragonHuggingFace" src="https://huggingface.co/de-Rodrigo/donut-merit/resolve/main/assets/dragon_huggingface.png" style="width: 200px;"> </picture> </div> </a> ## Model Architecture **This model is based on the Donut architecture and fine-tuned on the Merit dataset for form understanding tasks.** - Backbone: [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b) - Training Data: [Merit](https://huggingface.co/datasets/de-Rodrigo/merit) ## Example Usage ```python from transformers import AutoModel model = AutoModel.from_pretrained("de-Rodrigo/idefics2-merit") ``` **WIP** 🛠️

stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757657739

stonermay

2025-09-12T06:17:04Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T06:16:43Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

TAUR-dev/M-sft_exp_zayneV2-sft

TAUR-dev

2025-09-12T06:15:58Z

0

null

[ "safetensors", "qwen2", "region:us" ]

null

2025-09-12T06:15:28Z

# M-sft_exp_zayneV2-sft This model was created as part of the **sft_exp_zayneV2** experiment using the SkillFactory experiment management system. ## Model Details - **Training Method**: LLaMAFactory SFT (Supervised Fine-Tuning) - **Stage Name**: sft - **Experiment**: sft_exp_zayneV2 ## Training Configuration {"model_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct", "trust_remote_code": true, "stage": "sft", "do_train": true, "finetuning_type": "full", "deepspeed": "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/examples/deepspeed/ds_z2_config.json", "dataset": "TAUR_dev__D_SFT_C_sft_exp_zayneV2_sft_data__sft_train", "template": "qwen", "cutoff_len": 16384, "max_samples": 1000000, "overwrite_cache": true, "preprocessing_num_workers": 1, "dataloader_num_workers": 0, "disable_tqdm": false, "output_dir": "/scratch/10416/zaynesprague/skill_inject_outputs/sf_experiments/new_sft_v3_9_11__zaynev2/llamafactory/checkpoints", "logging_steps": 10, "save_steps": 100000, "plot_loss": true, "overwrite_output_dir": true, "per_device_train_batch_size": 1, "gradient_accumulation_steps": 1, "learning_rate": 1e-06, "num_train_epochs": 3, "lr_scheduler_type": "cosine", "warmup_ratio": 0.05, "weight_decay": 0.0001, "adam_beta1": 0.9, "adam_beta2": 0.95, "bf16": true, "ddp_timeout": 180000000, "gradient_checkpointing": true, "save_only_model": true, "enable_masked_ranges": false, "save_strategy": "steps", "save_total_limit": 5, "sf_tracker_dataset_id": "TAUR-dev/D-ExpTracker__sft_exp_zayneV2__v1", "sf_eval_before_training": false, "sf_wandb_project": "sft_exp_zayneV2_sft", "sf_eval_steps": null, "run_name": "sft_exp_zayneV2_sft"} ## Experiment Tracking 🔗 **View complete experiment details**: [Experiment Tracker Dataset](https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__sft_exp_zayneV2__v1) ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-sft_exp_zayneV2-sft") model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-sft_exp_zayneV2-sft") ```

Anhlq/gemma-3-finetune-16bit-copy-v2

Anhlq

2025-09-12T06:14:55Z

0

transformers

[ "transformers", "safetensors", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/gemma-3-270m-it", "base_model:finetune:unsloth/gemma-3-270m-it", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-09-12T06:13:07Z

--- base_model: unsloth/gemma-3-270m-it tags: - text-generation-inference - transformers - unsloth - gemma3_text license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** Anhlq - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-3-270m-it This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

omerbektasss/blockassist-bc-insectivorous_bold_lion_1757657625

omerbektasss

2025-09-12T06:14:38Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous bold lion", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T06:14:01Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous bold lion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF

mradermacher

2025-09-12T06:14:05Z

0

transformers

[ "transformers", "gguf", "chat", "Fusion", "en", "base_model:huihui-ai/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030", "base_model:quantized:huihui-ai/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030", "license:mit", "endpoints_compatible", "region:us", "conversational" ]

null

2025-09-12T05:40:42Z

--- base_model: huihui-ai/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030 language: - en library_name: transformers license: mit mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - chat - Fusion --- ## About         static quants of https://huggingface.co/huihui-ai/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF).*** weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q2_K.gguf) | Q2_K | 11.4 | | | [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q3_K_S.gguf) | Q3_K_S | 13.4 | | | [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q3_K_M.gguf) | Q3_K_M | 14.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q3_K_L.gguf) | Q3_K_L | 16.0 | | | [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q4_K_S.gguf) | Q4_K_S | 17.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q4_K_M.gguf) | Q4_K_M | 18.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q5_K_S.gguf) | Q5_K_S | 21.2 | | | [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q5_K_M.gguf) | Q5_K_M | 21.8 | | | [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q6_K.gguf) | Q6_K | 25.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030-GGUF/resolve/main/Huihui-Qwen3-30B-A3B-abliterated-Fusion-7030.Q8_0.gguf) | Q8_0 | 32.6 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

nobu222/gemma2-9b-it-qlora-adapter-for-banner-gen

nobu222

2025-09-12T06:13:46Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-09-12T06:13:39Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

midwestern-simulation/essence-3b-v1.1

midwestern-simulation

2025-09-12T06:13:26Z

0

null

[ "safetensors", "dataset:mlfoundations/dclm-baseline-1.0", "base_model:HuggingFaceTB/SmolLM3-3B-Base", "base_model:finetune:HuggingFaceTB/SmolLM3-3B-Base", "region:us" ]

null

2025-09-11T11:35:20Z

--- datasets: - mlfoundations/dclm-baseline-1.0 base_model: - HuggingFaceTB/SmolLM3-3B-Base --- # Essence 3B V1.1 This is a system using two versions of SmolLM3-3B-Base, the 'encoder', is finetuned to turn a text into a set of embedding tokens which can be reconstituted back into the original text by the decoder. In addition to “vanilla” reconstruction, this model was trained for span-corruption and masked language modelling. We use LoRA at rank 64 on QKVO along with trainable LayerNorms and, for the encoder, LoRA on all MLP layers as well as trainable token embeddings. The model was trained to encode text into any of 1-128 embedding tokens. ## Simple Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel from torch import nn import torch from huggingface_hub import hf_hub_download device = torch.device("cuda:0") dtype = torch.bfloat16 base_model_id = "HuggingFaceTB/SmolLM3-3B-Base" compressor_id = "midwestern-simulation/essence-3b-v1.1" # === MODEL LOADING === tokenizer = AutoTokenizer.from_pretrained(base_model_id, padding_side='left') encoder = AutoModelForCausalLM.from_pretrained(base_model_id, device_map={"":device}, torch_dtype=dtype) decoder = AutoModelForCausalLM.from_pretrained(base_model_id, device_map={"":device}, torch_dtype=dtype) encoder = PeftModel.from_pretrained(encoder, compressor_id, subfolder="encoder") decoder = PeftModel.from_pretrained(decoder, compressor_id, subfolder="decoder") projector = nn.Linear(2048, 2048).to(device).to(dtype) projector.load_state_dict(torch.load(hf_hub_download(repo_id=compressor_id, filename="projector.pt"))) # === MODEL INFERENCE === text = "mary had a little lamb, little lamb, little lamb, mary had a little lamb whose fleece was white as snow" n_embed_tokens = 4 # for best performance, can be any within the range of 1-128 encoder_input = text.strip() + f"\n[[/END DOCUMENT]]\n[[START SUMMARY ntoks={n_embed_tokens}]]" + "<|im_end|>" * n_embed_tokens tokenized = tokenizer(encoder_input, return_tensors='pt', add_special_tokens=False) tokenized = {k: v.to(device) for k, v in tokenized.items()} encoding = encoder.model.model(**tokenized).last_hidden_state[:, -n_embed_tokens:, :] encoding = projector(encoding) tokenized_prefix = tokenizer("\n[[/END SUMMARY]]\n[[START DOCUMENT]]\n", return_tensors="pt", add_special_tokens=False) prefix_embeds = decoder.model.model.embed_tokens(tokenized_prefix['input_ids'].to(device)) inputs_embeds = torch.cat([encoding, prefix_embeds], 1) output = decoder.generate( inputs_embeds=inputs_embeds, temperature=0.7, max_new_tokens=1024, do_sample=True, top_k=128, min_new_tokens=8, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id ) print(tokenizer.decode(output[0])) # mary had a little lamb, little lamb, little lamb, mary had a little lamb whose fleece was white as snow # [[/END DOCUMENT]]<|end_of_text|> ```

ttkairamkonda/whisper-large-v3-faa-atc-500k-LoRA32

ttkairamkonda

2025-09-12T06:12:41Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-09-12T06:12:35Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mradermacher/InfoLLM-32B-i1-GGUF

mradermacher

2025-09-12T06:11:56Z

0

transformers

[ "transformers", "gguf", "zh", "en", "base_model:HFSUN123/InfoLLM-32B", "base_model:quantized:HFSUN123/InfoLLM-32B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-09-11T23:56:12Z

--- base_model: HFSUN123/InfoLLM-32B language: - zh - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About         weighted/imatrix quants of https://huggingface.co/HFSUN123/InfoLLM-32B  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#InfoLLM-32B-i1-GGUF).*** static quants are available at https://huggingface.co/mradermacher/InfoLLM-32B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ1_M.gguf) | i1-IQ1_M | 8.1 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 | | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ2_S.gguf) | i1-IQ2_S | 10.6 | | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ2_M.gguf) | i1-IQ2_M | 11.5 | | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 11.6 | very low quality | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q2_K.gguf) | i1-Q2_K | 12.4 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.8 | | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.5 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ3_S.gguf) | i1-IQ3_S | 14.5 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ3_M.gguf) | i1-IQ3_M | 15.0 | | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.8 | | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q4_0.gguf) | i1-Q4_0 | 18.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.9 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q4_1.gguf) | i1-Q4_1 | 20.7 | | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.7 | | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.3 | | | [GGUF](https://huggingface.co/mradermacher/InfoLLM-32B-i1-GGUF/resolve/main/InfoLLM-32B.i1-Q6_K.gguf) | i1-Q6_K | 27.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

TAUR-dev/M-0911__qrepeat1_ref5_0C.-C.-C-IC.-CC_3args_grpo-rl

TAUR-dev

2025-09-12T06:09:23Z

0

null

[ "safetensors", "qwen2", "en", "license:mit", "region:us" ]

null

2025-09-11T21:16:11Z

--- language: en license: mit --- # M-0911__qrepeat1_ref5_0C.-C.-C-IC.-CC_3args_grpo-rl ## Model Details - **Training Method**: VeRL Reinforcement Learning (RL) - **Stage Name**: rl - **Experiment**: 0911__qrepeat1_ref5_0C.-C.-C-IC.-CC_3args_grpo - **RL Framework**: VeRL (Versatile Reinforcement Learning) ## Training Configuration ## Experiment Tracking 🔗 **View complete experiment details**: https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__0911__qrepeat1_ref5_0C.-C.-C-IC.-CC_3args_grpo__v1 ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-0911__qrepeat1_ref5_0C.-C.-C-IC.-CC_3args_grpo-rl") model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-0911__qrepeat1_ref5_0C.-C.-C-IC.-CC_3args_grpo-rl") ```

Adanato/Llama-3.2-1B-Instruct-high_acereason_1k

Adanato

2025-09-12T06:08:08Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "fyksft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-12T05:39:43Z

--- library_name: transformers tags: - fyksft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Adanato/Llama-3.2-1B-Instruct-baseline_1k

Adanato

2025-09-12T06:07:55Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "fyksft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-12T06:06:07Z

--- library_name: transformers tags: - fyksft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ZombitX64/Wilai-1.5

ZombitX64

2025-09-12T06:07:51Z

0

transformers

[ "transformers", "pytorch", "OpenThaiWilai", "text-generation", "generated_from_trainer", "custom_code", "autotrain_compatible", "region:us" ]

text-generation

2025-09-12T04:20:07Z

--- library_name: transformers tags: - generated_from_trainer model-index: - name: OpenThaiWilai results: [] ---  # OpenThaiWilai This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.56.1 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0

Aagmon/gemma-3-12b-ft-trans-imp-3

Aagmon

2025-09-12T06:06:49Z

0

transformers

[ "transformers", "safetensors", "gemma3", "image-text-to-text", "unsloth", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]

image-text-to-text

2025-09-11T21:04:42Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Karthikappi0011/qwen3-1.7b-translation-en-tu-test4-small-data

Karthikappi0011

2025-09-12T06:06:42Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-1.7B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-1.7B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-09-12T06:06:37Z

--- base_model: unsloth/Qwen3-1.7B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** Karthikappi0011 - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen3-1.7B-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757657117

stonermay

2025-09-12T06:06:40Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T06:06:19Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

vnegi1011/kyrgyz-asr

vnegi1011

2025-09-12T06:03:30Z

0

null

[ "onnx", "wav2vec2", "region:us" ]

null

2025-09-11T01:42:07Z

# Kyrgyz ASR Model Fine-tuned Wav2Vec2 model for Kyrgyz speech recognition, exported to ONNX format. - **Model**: facebook/wav2vec2-base-960h (fine-tuned) - **Task**: Automatic Speech Recognition (ASR) - **Language**: Kyrgyz - **Files**: `model.onnx`, `vocab.json`, processor configs

NCSOFT/GME-VARCO-VISION-Embedding

NCSOFT

2025-09-12T06:01:44Z

1,261

11

transformers

[ "transformers", "safetensors", "qwen2_vl", "image-to-text", "multimodal", "video embedding", "ncsoft", "ncai", "varco", "feature-extraction", "en", "base_model:Qwen/Qwen2-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2-VL-7B-Instruct", "license:cc-by-nc-4.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2025-06-10T05:26:52Z

--- license: cc-by-nc-4.0 base_model: - Qwen/Qwen2-VL-7B-Instruct library_name: transformers tags: - multimodal - video embedding - ncsoft - ncai - varco pipeline_tag: feature-extraction language: - en --- ## About GME-VARCO-VISION-Embedding <div align="center"> <img src="./varco-vision-Embedding.png" width="100%" style="background-color:white; padding:10px;"/> </div> `GME-VARCO-VISION-Embedding` is a multimodal embedding model that computes semantic similarity between text, images, and videos in a high-dimensional embedding space. In particular, the model focuses on video retrieval, which demands greater complexity and contextual understanding compared to image retrieval. It achieves high retrieval accuracy and strong generalization performance across various scenarios, such as scene-based search, description-based search, and question-answering-based search. ## Demo Video Check out our demo videos showcasing our multimodal embedding model in action: - [English Demo Video](https://www.youtube.com/watch?v=kCvz82Y1BQg) - [Korean Demo Video](https://youtube.com/shorts/jC2J7rbAfxs) The demo demonstrates how our embedding model works together with an AI agent to search for relevant videos based on user queries and generate responses using the retrieved video content. ### Model Architecture and Training Method `GME-VARCO-VISION-Embedding` is based on [`Qwen/Qwen2-VL-7B-Instruct`](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct), and uses the parameters of [`Alibaba-NLP/gme-Qwen2-VL-7B-Instruct`](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-7B-Instruct) to improve the model's general retrieval ability. #### 1. Fine-tuning (Contrastive Learning) on video preference dataset To efficiently fine-tune the model, we utilize [ShareGPTVideo’s 17𝑘 video preference dataset](https://huggingface.co/datasets/ShareGPTVideo/train_video_and_instruction), which includes prompts, videos, gold answers, and chosen-rejected text pairs. We treat the prompts and videos as queries, and the rejected responses as hard-negatives for the gold answers. . Each query is trained with in-batch negatives as well as one hard negative using the InfoNCE loss. The model is fully fine-tuned for two epochs on 8 A100 GPUs with a batch size of 8, requiring only a few hours for training. #### 2. Adding Retrieval Vector To compensate for the insufficiency of training instances and enhance the generalization ability of the fine-tuned model, we compute a retrieval vector 𝜏 by subtracting the weights of the original [`Qwen/Qwen2-VL-7B-Instruct`](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) model from those of [`Alibaba-NLP/gme-Qwen2-VL-7B-Instruct`](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-7B-Instruct), a Qwen2-VL based image-text retrieval model. This approach is inspired by Chat Vector, which is a method to equip pre-trained language models with chat capabilities in new languages by adding a vector obtained from the weight difference between a base model and its chat-optimized counterpart. ### Performance Our model achieves **state-of-the-art (SOTA) zero-shot performance** on the MultiVENT2.0 dataset as of July 2025. See the [official leaderboard](https://eval.ai/web/challenges/challenge-page/2507/leaderboard/6262) for detailed results. <br> ## Code Examples `GME-VARCO-VISION-Embedding` adopts the inference pipeline of [`Qwen/Qwen2-VL-7B-Instruct`](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct). ### Image-Text Retrieval ```python import torch import requests from PIL import Image from transformers import Qwen2VLForConditionalGeneration, AutoProcessor from qwen_vl_utils import process_vision_info model_name = "NCSOFT/GME-VARCO-VISION-Embedding" model = Qwen2VLForConditionalGeneration.from_pretrained( model_name, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2", device_map="auto", ) processor = AutoProcessor.from_pretrained(model_name) tokenizer = processor.tokenizer device = model.device qry_msg = [ { "role": "user", "content": [ {"type": "text", "text": "Find a photo of a cat."}, ], }, ] qry_txt = processor.apply_chat_template( qry_msg, tokenize=False, add_generation_prompt=True ) + tokenizer.eos_token qry_input = processor( text=[qry_txt], padding=True, return_tensors="pt", ).to(device) img_msg = [ { "role": "user", "content": [{ "type": "image", "image": "image" }] } ] img_txt = processor.apply_chat_template( img_msg, tokenize=False, add_generation_prompt=True ) + tokenizer.eos_token candidate_imgs= [ # Photo of two cats { "role": "user", "content": [{ "type": "image", "image": "http://images.cocodataset.org/val2017/000000039769.jpg"}] }, # Photo of two dogs { "role": "user", "content": [{ "type": "image", "image": "https://farm1.staticflickr.com/116/290755713_a5de6c1079_z.jpg"}] }, # photo of two people playing baseball { "role": "user", "content": [{ "type": "image", "image": "http://farm3.staticflickr.com/2418/2193688811_d9f5e23bbd_z.jpg"}] }, # Photo of a large crowd in a busy city street { "role": "user", "content": [{ "type": "image", "image":"http://farm7.staticflickr.com/6049/6329686751_997c68fff9_z.jpg"}] }, ] candidate_images, _ = process_vision_info(candidate_imgs) image_inputs = processor( text=[img_txt] * len(candidate_images), images=candidate_images, # videos=, padding=True, return_tensors="pt", ).to(device) with torch.inference_mode(): qry_emb = model( **qry_input, output_hidden_states=True, return_dict=True ).hidden_states[-1][:, -1, :] img_emb = model( **image_inputs, output_hidden_states=True, return_dict=True ).hidden_states[-1][:, -1, :] qry_emb = F.normalize(qry_emb, dim=-1) img_emb = F.normalize(img_emb, dim=-1) score = qry_emb @ img_emb.t() # tensor([[0.3066, 0.1108, 0.1226, 0.1245]], device='cuda:0', dtype=torch.bfloat16) # corresponding to the score of photos (cat, dog, baseball, crowd) ``` <br> ### Video Embedding ```Python vid_message = [ { "role": "user", "content": [{ "type": "video", "video": video_path, "max_pixels": 262144, "fps": 2.0,}] } ] video_text = processor.apply_chat_template( vid_message, tokenize=False, add_generation_prompt=True ) + tokenizer.eos_token image_input, video_input = process_vision_info(vid_message) video_input = processor( text=[video_text], images=image_input, videos=video_input, padding=True, return_tensors="pt", ).to(device) with torch.inference_mode(): video_emb = model( **video_input, output_hidden_states=True, return_dict=True ).hidden_states[-1][:, -1, :] video_emb = F.normalize(video_emb, dim=-1) ``` <br> --- license: cc-by-nc-4.0 ---

RijalMuluk/bbca.jk_time_forecast_lightgbm

RijalMuluk

2025-09-12T06:01:32Z

0

null

[ "joblib", "license:apache-2.0", "region:us" ]

null

2025-09-12T06:01:03Z

--- license: apache-2.0 ---

NCSOFT/VARCO-VISION-14B

NCSOFT

2025-09-12T06:01:26Z

5,231

36

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "multimodal", "conversational", "ncsoft", "varco", "image-text-to-text", "en", "ko", "arxiv:2411.19103", "arxiv:2408.03326", "base_model:Qwen/Qwen2.5-14B-Instruct", "base_model:finetune:Qwen/Qwen2.5-14B-Instruct", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

image-text-to-text

2024-11-25T05:08:04Z

--- language: - en - ko license: cc-by-nc-4.0 tags: - multimodal - conversational - ncsoft - varco base_model: - Qwen/Qwen2.5-14B-Instruct - google/siglip-so400m-patch14-384 library_name: transformers pipeline_tag: image-text-to-text --- # VARCO-VISION-14B ## 🚨News🎙️ - The 2.0 model has been released. Please use the new version. - 📰 2025-07-16: We released VARCO-VISION-2.0-14B at [link](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-14B) - 📰 2025-07-16: We released GME-VARCO-VISION-Embedding at [link](https://huggingface.co/NCSOFT/GME-VARCO-VISION-Embedding) ## About the VARCO-VISION-1.0-14B Model **VARCO-VISION-14B** is a powerful English-Korean Vision-Language Model (VLM). The training pipeline of VARCO-VISION consists of four stages: Feature Alignment Pre-training, Basic Supervised Fine-tuning, Advanced Supervised Fine-tuning, and Preference Optimization. In both multimodal and text-only benchmarks, VARCO-VISION-14B not only surpasses other models of similar size in performance but also achieves scores comparable to those of proprietary models. The Model currently accepts a single image and a text as inputs, generating an output text. It supports grounding, referring as well as OCR (Optical Character Recognition). - **Developed by:** NC Research, Multimodal Generation Team - **Technical Report:** [VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models](https://arxiv.org/pdf/2411.19103) - **Blog(Korean):** [VARCO-VISION Technical Report Summary](https://ncsoft.github.io/ncresearch/95ad8712e60063e9ac97538504ac3eea0ac530af) - **Demo Page:** *The demo page is no longer available.* - **Languages:** Korean, English - **License:** CC BY-NC 4.0 - **Architecture:** VARCO-VISION-14B follows the architecture of [LLaVA-OneVision](https://arxiv.org/abs/2408.03326). - **Base Model:** - **Language Model:** [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) - **Vision Encoder:** [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) - **Huggingface Version Model:** [NCSOFT/VARCO-VISION-14B-HF](https://huggingface.co/NCSOFT/VARCO-VISION-14B-HF) - **Korean VLM Benchmarks:** - You can use the following benchmark datasets in the [LLMs-Eval toolkit](https://github.com/EvolvingLMMs-Lab/lmms-eval). - [NCSOFT/K-MMBench](https://huggingface.co/datasets/NCSOFT/K-MMBench) - [NCSOFT/K-SEED](https://huggingface.co/datasets/NCSOFT/K-SEED) - [NCSOFT/K-MMStar](https://huggingface.co/datasets/NCSOFT/K-MMStar) - [NCSOFT/K-DTCBench](https://huggingface.co/datasets/NCSOFT/K-DTCBench) - [NCSOFT/K-LLaVA-W](https://huggingface.co/datasets/NCSOFT/K-LLaVA-W) - **you can also evaluate VARCO-VISION-14B in the [VLMEval kit](https://github.com/open-compass/VLMEvalKit)**. - **This model is for research purposes only. Commercial use is prohibited.** ## Uses ### Direct Use To load VARCO-VISION-14B, start by cloning and installing **LLaVA-NeXT**: ```bash git clone https://github.com/LLaVA-VL/LLaVA-NeXT cd LLaVA-NeXT pip install -e ".[train]" ``` After installing **LLaVA-NeXT**, you can load VARCO-VISION-14B using the following code: ```python import torch from transformers import AutoTokenizer from llava.model.language_model.llava_qwen import LlavaQwenForCausalLM from llava.mm_utils import tokenizer_image_token, process_images model_name = "NCSOFT/VARCO-VISION-14B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = LlavaQwenForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, attn_implementation="flash_attention_2", low_cpu_mem_usage=True, device_map="auto" ) vision_tower = model.get_vision_tower() image_processor = vision_tower.image_processor ``` Prepare an image and a text input. You need to preprocess the image and tokenize the text. Pass the processed inputs to the model to generate predictions. ```python import requests from PIL import Image # Define a chat history and use `apply_chat_template` to get correctly formatted prompt # Each value in "content" has to be a list of dicts with types ("text", "image") conversation = [ { "role": "user", "content": [ {"type": "text", "text": "Describe this image."}, {"type": "image"}, ], }, ] prompt = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False) IMAGE_TOKEN_INDEX = -200 EOS_TOKEN = "<|im_end|>" input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") input_ids = input_ids.unsqueeze(0).to(model.device) image_url = "http://images.cocodataset.org/val2017/000000039769.jpg" raw_image = Image.open(requests.get(image_url, stream=True).raw) image_tensors = process_images([raw_image], image_processor, model.config) image_tensors = [image_tensor.half().to(model.device) for image_tensor in image_tensors] image_sizes = [raw_image.size] with torch.inference_mode(): output_ids = model.generate( input_ids, images=image_tensors, image_sizes=image_sizes, do_sample=False, max_new_tokens=1024, use_cache=True, ) outputs = tokenizer.batch_decode(output_ids)[0] if outputs.endswith(EOS_TOKEN): outputs = outputs[: -len(EOS_TOKEN)] outputs = outputs.strip() print(outputs) ``` ### Specialized Features If a question is based on bounding boxes or require bounding boxes as an output, please include the special tokens in the input text. The following special tokens are used to define specific tasks, inputs, and outputs for the model: - `<gro>`: Indicates that the model's response should include bounding box information. - `<ocr>`: Specifies OCR tasks for recognizing text within an image. - `<char>` and `</char>`: Used to mark a text phrase. - `<obj>` and `</obj>`: Used to indicate an object. - `<bbox>` and `</bbox>`: Used to represent a bounding box. - `<delim>`: Represents multiple location points for a single object or text. #### Grounding Grounding refers to a task where the model needs to identify specific locations within an image to provide an appropriate answer. To perform grounding, prepend the special token `<gro>` to the question. ```python conversation = [ { "role": "user", "content": [ {"type": "text", "text": "<gro>\nDescribe the image in detail."}, {"type": "image"}, ], }, ] ``` **Expected Output Example:** ```html The image shows <obj>two cats</obj><bbox>0.521, 0.049, 0.997, 0.783<delim>0.016, 0.108, 0.512, 0.99</bbox> lying on <obj>a pink blanket</obj><bbox>0.002, 0.231, 0.999, 0.999</bbox>. The cat on the left is lying on its side with its head resting on the blanket and its body stretched out. The cat on the right is lying on its back with its paws stretched out and its head turned to the side. Both cats appear relaxed and comfortable. There are also <obj>two remote controls</obj><bbox>0.039, 0.138, 0.283, 0.257<delim>0.508, 0.166, 0.581, 0.295</bbox> placed near the cats, one on each side of them. ``` <img src="assets/grounding.png" alt="Grounding Example" width="400"/> #### Referring VARCO-VISION-14B can handle location-specific questions using bounding boxes. To perform referring tasks, make a conversation including the object of interest within `<obj>` and `</obj>` tags. You have to specify its location with `<bbox>` and `</bbox>` tags. This allows the model to understand the context and focus on the object at the specified location. A bbox is represented in a form of (x1, y1, x2, y2). The first two values indicate the top-left position of a bbox, and the latter two values are the bottom-right position. ```python conversation = [ { "role": "user", "content": [ { "type": "text", "text": "<obj>이 물건</obj><bbox>0.039, 0.138, 0.283, 0.257</bbox>은 어떻게 쓰는거야?", }, {"type": "image"}, ], }, ] ``` **Expected Output Example:** ``` **이 물건**은 리모컨으로, 주로 텔레비전이나 다른 전자 기기를 원격으로 조작하는 데 사용됩니다. 버튼을 누르면 채널 변경, 볼륨 조절, 전원 켜기/끄기 등의 기능을 수행할 수 있습니다. 리모컨의 버튼에는 일반적으로 숫자, 메뉴, 설정, 재생/일시정지 등의 기능이 포함되어 있으며, 사용자는 이를 통해 손쉽게 기기를 제어할 수 있습니다. ``` #### OCR To perform Optical Character Recognition (OCR), use the `<ocr>` token. ```python image_file = "./assets/ocr_1.png" raw_image = Image.open(image_file) conversation = [ { "role": "user", "content": [ {"type": "text", "text": "<ocr>"}, {"type": "image"}, ], }, ] ``` **Expected Output Example:** ``` <char>백범로</char><bbox>0.172, 0.265, 0.328, 0.34</bbox> <char>124번길</char><bbox>0.349, 0.265, 0.512, 0.34</bbox> <char>Baekbeom-ro</char><bbox>0.171, 0.335, 0.432, 0.391</bbox> <char>124</char><bbox>0.444, 0.34, 0.508, 0.391</bbox> <char>만수주공아파트</char><bbox>0.109, 0.528, 0.335, 0.594</bbox> <char>시흥</char><bbox>0.443, 0.516, 0.522, 0.578</bbox> <char>시청</char><bbox>0.711, 0.521, 0.811, 0.594</bbox> <char>Mansu</char><bbox>0.103, 0.601, 0.181, 0.647</bbox> <char>Jugong</char><bbox>0.186, 0.601, 0.273, 0.658</bbox> <char>Apt</char><bbox>0.281, 0.601, 0.327, 0.651</bbox> <char>42</char><bbox>0.377, 0.601, 0.416, 0.647</bbox> <char>Shieung</char><bbox>0.445, 0.578, 0.53, 0.623</bbox> <char>인천대공원</char><bbox>0.431, 0.623, 0.609, 0.684</bbox> <char>모래내시장역</char><bbox>0.651, 0.591, 0.873, 0.664</bbox> <char>IncheonGrand</char><bbox>0.433, 0.684, 0.561, 0.723</bbox> <char>Park</char><bbox>0.564, 0.684, 0.611, 0.723</bbox> ``` <img src="assets/ocr_2.jpg" alt="OCR Example" width="350"/> ## Citing the Model If you use VARCO-VISION-14B in your research, please cite the following: ```bibtex @misc{ju2024varcovisionexpandingfrontierskorean, title={VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models}, author={Jeongho Ju and Daeyoung Kim and SunYoung Park and Youngjune Kim}, year={2024}, eprint={2411.19103}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2411.19103}, } ```

omerbektasss/blockassist-bc-insectivorous_bold_lion_1757656852

omerbektasss

2025-09-12T06:01:14Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous bold lion", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T06:01:10Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous bold lion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

nuphoto-ian/Qwen3-8B-QAT-NVFP4

nuphoto-ian

2025-09-12T06:01:05Z

0

null

[ "safetensors", "qwen3", "license:apache-2.0", "8-bit", "modelopt", "region:us" ]

null

2025-09-12T05:33:47Z

--- license: apache-2.0 ---

nightmedia/ERNIE-4.5-21B-A3B-Thinking-qx86-hi-mlx

nightmedia

2025-09-12T06:00:49Z

0

mlx

[ "mlx", "safetensors", "ernie4_5_moe", "ERNIE4.5", "text-generation", "conversational", "custom_code", "en", "zh", "base_model:baidu/ERNIE-4.5-21B-A3B-Thinking", "base_model:quantized:baidu/ERNIE-4.5-21B-A3B-Thinking", "license:apache-2.0", "8-bit", "region:us" ]

text-generation

2025-09-12T04:49:36Z

--- license: apache-2.0 language: - en - zh pipeline_tag: text-generation tags: - ERNIE4.5 - mlx library_name: mlx base_model: baidu/ERNIE-4.5-21B-A3B-Thinking --- # ERNIE-4.5-21B-A3B-Thinking-qx86-hi-mlx This model [ERNIE-4.5-21B-A3B-Thinking-qx86-hi-mlx](https://huggingface.co/ERNIE-4.5-21B-A3B-Thinking-qx86-hi-mlx) was converted to MLX format from [baidu/ERNIE-4.5-21B-A3B-Thinking](https://huggingface.co/baidu/ERNIE-4.5-21B-A3B-Thinking) using mlx-lm version **0.27.1**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("ERNIE-4.5-21B-A3B-Thinking-qx86-hi-mlx") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```

iori-ltn/nanj-qwen2.5-3b-merged

iori-ltn

2025-09-12T05:58:10Z

0

peft

[ "peft", "safetensors", "base_model:adapter:unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit", "lora", "sft", "transformers", "trl", "unsloth", "text-generation", "conversational", "dataset:p1atdev/open2ch", "license:apache-2.0", "region:us" ]

text-generation

2025-09-12T04:33:22Z

--- base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit library_name: peft model_name: nanj-qwen tags: - base_model:adapter:unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit - lora - sft - transformers - trl - unsloth pipeline_tag: text-generation license: apache-2.0 datasets: - p1atdev/open2ch --- # Nanj Qwen2.5-3B LoRA (DAPT + SFT) このモデルは **`Qwen/Qwen2.5-3B-Instruct`** をベースに、 open2ch データで **DAPT**、 livejupiter データで **SFT** を行った LoRA です。なんJ風の会話生成に最適化されています。 --- ## Usage ```python from unsloth import FastLanguageModel # BaseModek base = "Qwen/Qwen2.5-3B-Instruct" # Load LoRa Adapter model, tok = FastLanguageModel.from_pretrained(base, load_in_4bit=True, max_seq_length=1024) model.load_adapter("your-username/nanj-qwen2.5-3b-lora") # example msgs = [{"role":"user", "content":"藤浪復活したら阪神どうなる？"}] prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True) out = model.generate( **tok(prompt, return_tensors="pt").to(model.device), max_new_tokens=128, do_sample=True, top_p=0.9, temperature=0.8 ) print(tok.decode(out[0], skip_special_tokens=True).split(prompt)[-1].strip()) ``` ## Training procedure 本モデルの DAPT / SFT は **どちらも LoRA** を用いて実施しました。 - **Phase 1: DAPT (LoRA)** open2ch (all-corpus-cleaned) を用いた継続事前学習（次トークン予測） → open2ch の語彙・言い回しにモデルを適応 - **Phase 2: SFT (LoRA)** livejupiter-cleaned から作成した会話ペアで教師あり微調整 → なんJ的な対話スタイルを獲得 ### Framework versions - PEFT 0.17.1 - TRL: 0.23.0 - Transformers: 4.56.1 - Pytorch: 2.8.0+cu126 - Datasets: 3.6.0 - Tokenizers: 0.22.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757656502

stonermay

2025-09-12T05:56:10Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T05:56:01Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

N-A-Me/Alaska

N-A-Me

2025-09-12T05:55:19Z

0

null

[ "base_model:Liberata/illustrious-xl-v1.0", "base_model:finetune:Liberata/illustrious-xl-v1.0", "license:cc", "region:us" ]

null

2025-05-07T06:04:03Z

--- widget: - text: Alaska base_model: Liberata/illustrious-xl-v1.0 instance_prompt: Alaska license: cc --- # Alaska <Gallery /> ## Trigger words You should use `Alaska` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/N-A-Me/Alaska/tree/main) them in the Files & versions tab.

omerbektasss/blockassist-bc-keen_fast_giraffe_1757656465

omerbektasss

2025-09-12T05:55:19Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T05:54:41Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

nyu-dice-lab/VeriThoughts-Reasoning-32B-Qwen3

nyu-dice-lab

2025-09-12T05:53:05Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-32B", "base_model:finetune:Qwen/Qwen3-32B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-11T01:14:35Z

--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-32B tags: - llama-factory - full - generated_from_trainer model-index: - name: VeriThoughts-Reasoning-32B-Qwen3 results: [] ---  # VeriThoughts-Reasoning-32B-Qwen3 This model is a fine-tuned version of [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) on the reasoning_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 64 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.8.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1

VizitonMexico/VizitonMexico

VizitonMexico

2025-09-12T05:51:54Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-09-12T05:51:03Z

--- license: apache-2.0 --- ¿Qué es Viziton? Viziton cápsula es una cápsula para la vista especialmente formulada, diseñada para apoyar la salud visual y mantener la visión sana. En el mundo actual, donde el tiempo frente a las pantallas se ha vuelto inevitable, cuidar la vista nunca ha sido tan importante. Viziton Pastillas está creado para nutrir los ojos, protegerlos del estrés diario y promover la claridad a largo plazo. Ya sea que experimente fatiga ocular leve o simplemente desee mantener una visión saludable a medida que envejece, Viziton tabletas está diseñado para convertirse en su sistema de apoyo natural Viziton Cómo utilizar. Sitio web oficial:<a href="https://www.nutritionsee.com/vizitoexico">www.Viziton.com</a> <p><a href="https://www.nutritionsee.com/vizitoexico"> <img src="https://www.nutritionsee.com/wp-content/uploads/2025/09/viziton-Mexico.png" alt="enter image description here"> </a></p> <a href="https://www.nutritionsee.com/vizitoexico">¡Compra ya! Haz clic en el enlace de abajo para más información y obtén un 50% de descuento. ¡Date prisa!</a> Sitio web oficial:<a href="https://www.nutritionsee.com/vizitoexico">www.Viziton.com</a>

omerbektasss/blockassist-bc-insectivorous_bold_lion_1757656089

omerbektasss

2025-09-12T05:48:32Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous bold lion", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T05:48:27Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous bold lion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

JobixAi/tts-mapalo-pipeliner

JobixAi

2025-09-12T05:47:38Z

0

null

[ "safetensors", "llama", "region:us" ]

null

2025-09-12T04:43:09Z

This model finetunes the pretrained model `canopylabs/orpheus-3b-0.1-pretrained` using the finetuning pipeline. Full finetuning with Unsloth for 5 epochs. ### Datasets JobixAi/mapalo-higgs-20250912_021709 ### Inference ```bash temperature = 0.7 top_p = 0.9 repetition_penalty = 1.1 ```

stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757655885

stonermay

2025-09-12T05:46:12Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T05:45:48Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

CriteriaPO/qwen2.5-3b-dpo-finegrained-20-vanilla

CriteriaPO

2025-09-12T05:46:10Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:CriteriaPO/qwen2.5-3b-sft-10", "base_model:finetune:CriteriaPO/qwen2.5-3b-sft-10", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-10T01:31:35Z

--- base_model: CriteriaPO/qwen2.5-3b-sft-10 library_name: transformers model_name: qwen2.5-3b-dpo-finegrained-20-vanilla tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for qwen2.5-3b-dpo-finegrained-20-vanilla This model is a fine-tuned version of [CriteriaPO/qwen2.5-3b-sft-10](https://huggingface.co/CriteriaPO/qwen2.5-3b-sft-10). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="CriteriaPO/qwen2.5-3b-dpo-finegrained-20-vanilla", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/bborges/CriteriaPreferences/runs/7bj9ejy1) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.2 - Transformers: 4.46.3 - Pytorch: 2.1.2+cu121 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

diabolic6045/Sanskrit-Qwen2.5-VL-7B-Instruct-OCR

diabolic6045

2025-09-12T05:46:08Z

7

0

transformers

[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "axolotl", "text-generation", "conversational", "sa", "dataset:diabolic6045/sanskrit-ocr-parallel-corpus-chat-template", "dataset:snskrt/Sanskrit_OCR_Parallel_Corpus", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "license:apache-2.0", "model-index", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-08T06:10:48Z

--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-VL-7B-Instruct tags: - axolotl - transformers datasets: - diabolic6045/sanskrit-ocr-parallel-corpus-chat-template - snskrt/Sanskrit_OCR_Parallel_Corpus pipeline_tag: text-generation model-index: - name: qwen2-5-vl-sanskrit-ocr results: - task: type: image-to-text dataset: name: Sanskrit OCR Test Set type: sanskrit-ocr metrics: - name: Exact Match Accuracy type: exact_match value: 1.59 - name: Character-level Accuracy type: character_accuracy value: 86.38 - name: Token-level Jaccard Similarity type: jaccard_similarity value: 50.44 - name: Success Rate type: success_rate value: 100 source: name: Sanskrit OCR Evaluation url: https://huggingface.co/datasets/diabolic6045/sanskrit-ocr-parallel-corpus-chat-template/viewer/default/test language: - sa --- # Sanskrit-Qwen2.5-VL-7B-Instruct-OCR This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) on the [diabolic6045/sanskrit-ocr-parallel-corpus-chat-template](https://huggingface.co/datasets/diabolic6045/sanskrit-ocr-parallel-corpus-chat-template) dataset. This data is converted from [snskrt/Sanskrit_OCR_Parallel_Corpus](https://huggingface.co/datasets/snskrt/Sanskrit_OCR_Parallel_Corpus) by [Sanskrit Datasets](https://huggingface.co/snskrt). It achieves the following results on the evaluation set: - Loss: 0.2660 - Memory/max Mem Active(gib): 20.79 - Memory/max Mem Allocated(gib): 20.79 - Memory/device Mem Reserved(gib): 21.46 ## Model description This is a fine-tuned version of Qwen2.5-VL-7B-Instruct, specifically adapted for Sanskrit OCR (Optical Character Recognition) tasks. The model has been trained using LoRA (Low-Rank Adaptation) on a dataset of Sanskrit text images and their corresponding transcriptions. **Key Features:** - **Base Model**: Qwen/Qwen2.5-VL-7B-Instruct (7 billion parameters) - **Task**: Sanskrit OCR - converting Sanskrit text images to machine-readable text - **Training Method**: LoRA fine-tuning with vision-language capabilities - **Dataset**: Sanskrit OCR Parallel Corpus with chat template formatting - **Architecture**: Vision-Language Model with multimodal understanding **Capabilities:** - Read and transcribe Sanskrit text from images - Handle various Sanskrit scripts and fonts - Process both text and visual inputs simultaneously - Generate accurate Sanskrit text transcriptions The model maintains the original Qwen2.5-VL's vision-language capabilities while being specialized for Sanskrit text recognition tasks. ## Training and evaluation data ### Training Dataset The model was trained on the [diabolic6045/sanskrit-ocr-parallel-corpus-chat-template](https://huggingface.co/datasets/diabolic6045/sanskrit-ocr-parallel-corpus-chat-template) dataset, which contains Sanskrit text images paired with their corresponding transcriptions. The dataset was converted from the original [snskrt/Sanskrit_OCR_Parallel_Corpus](https://huggingface.co/datasets/snskrt/Sanskrit_OCR_Parallel_Corpus) and formatted with chat templates for vision-language training. ### Evaluation Results The model was evaluated on a test set of 314 Sanskrit text samples: | Metric | Value | |:------:|:-----:| | **Total Samples** | 314 | | **Successful Samples** | 314 | | **Failed Samples** | 0 | | **Success Rate** | 100.00% | | **Exact Match Accuracy** | 1.59% | | **Character-level Accuracy** | 86.38% | | **Token-level Jaccard Similarity** | 50.44% | **Key Insights:** - The model successfully processes all test samples without failures - High character-level accuracy (86.38%) indicates good recognition of individual Sanskrit characters - Lower exact match accuracy (1.59%) suggests room for improvement in complete text transcription - Moderate token-level similarity (50.44%) shows reasonable semantic understanding ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - total_eval_batch_size: 4 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 110 - training_steps: 1105 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mem Active(gib) | Mem Allocated(gib) | Mem Reserved(gib) | |:-------------:|:-----:|:----:|:---------------:|:---------------:|:------------------:|:-----------------:| | No log | 0 | 0 | 3.3372 | 17.59 | 17.59 | 17.66 | | 0.2428 | 1.0 | 369 | 0.3075 | 20.69 | 20.69 | 21.27 | | 0.2057 | 2.0 | 738 | 0.2660 | 20.79 | 20.79 | 21.46 | <br> This model was trained using: <br> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.12.2` ```yaml base_model: Qwen/Qwen2.5-VL-7B-Instruct processor_type: AutoProcessor # these 3 lines are needed for now to handle vision chat templates w images skip_prepare_dataset: true remove_unused_columns: false sample_packing: false chat_template: qwen2_vl datasets: - path: sanskrit_multimodal_train.json type: chat_template field_messages: messages dataset_prepared_path: last_run_prepared val_set_size: 0.01 output_dir: ./outputs/out-qwen2-5-vl adapter: lora lora_model_dir: sequence_len: 2048 pad_to_sequence_len: false lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj' wandb_project: Sanskrit-OCR wandb_entity: wandb_watch: wandb_name: qwen2-5-vl-sanskrit-ocr wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 3 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0002 bf16: true fp16: tf32: true gradient_checkpointing: true logging_steps: 1 flash_attention: true eager_attention: warmup_ratio: 0.1 evals_per_epoch: 1 saves_per_epoch: 1 weight_decay: 0.0 # Automatically upload checkpoint and final model to HF hub_model_id: diabolic6045/qwen2-5-vl-sanskrit-ocr-lora # save_first_step: true # uncomment this to validate checkpoint saving works with your config ``` </details><br> ### Framework versions - PEFT 0.17.0 - Transformers 4.55.2 - Pytorch 2.7.1+cu128 - Datasets 4.0.0 - Tokenizers 0.21.2

Asap7772/qwen3-4b-arc-second-stage-star-sftinit-lr1e-6-0908

Asap7772

2025-09-12T05:42:54Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-12T04:47:09Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

omerbektasss/blockassist-bc-keen_fast_giraffe_1757655736

omerbektasss

2025-09-12T05:42:36Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T05:42:32Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

dan2-ux/mistral-500

dan2-ux

2025-09-12T05:42:16Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "generated_from_trainer", "trl", "sft", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-11T08:44:56Z

--- base_model: mistralai/Mistral-7B-v0.1 library_name: transformers model_name: mistral-500 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for mistral-500 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="dan2-ux/mistral-500", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/danh25911-bosch-global/Fine%20tuning%20of%20Mistral%207B/runs/v73tk7fm?apiKey=ed94c7e2762b3e7af52e2b746f14631975e151c4) This model was trained with SFT. ### Framework versions - TRL: 0.23.0 - Transformers: 4.56.1 - Pytorch: 2.6.0 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

davidilag/wav2vec2-xls-r-300m-pt-500h-FO-500h-SE-cp-best-faroese-100h-30-epochs_run9_2025-09-11

davidilag

2025-09-12T05:42:07Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2025-09-11T20:19:26Z

--- library_name: transformers tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-xls-r-300m-pt-500h-FO-500h-SE-cp-best-faroese-100h-30-epochs_run9_2025-09-11 results: [] ---  # wav2vec2-xls-r-300m-pt-500h-FO-500h-SE-cp-best-faroese-100h-30-epochs_run9_2025-09-11 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1013 - Wer: 18.8175 - Cer: 4.0350 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-------:|:-----:|:---------------:|:-------:|:-------:| | 3.2822 | 0.4877 | 1000 | 3.2104 | 100.0 | 99.3017 | | 0.6811 | 0.9754 | 2000 | 0.3949 | 38.8377 | 10.4119 | | 0.3765 | 1.4628 | 3000 | 0.2219 | 29.8894 | 7.5540 | | 0.3338 | 1.9505 | 4000 | 0.1891 | 28.0169 | 6.9157 | | 0.2546 | 2.4379 | 5000 | 0.1699 | 26.6423 | 6.4739 | | 0.2495 | 2.9256 | 6000 | 0.1524 | 25.7171 | 6.2017 | | 0.1873 | 3.4131 | 7000 | 0.1380 | 24.4570 | 5.7646 | | 0.1933 | 3.9008 | 8000 | 0.1384 | 24.2455 | 5.7732 | | 0.1467 | 4.3882 | 9000 | 0.1331 | 23.6155 | 5.5405 | | 0.1585 | 4.8759 | 10000 | 0.1340 | 23.1793 | 5.5302 | | 0.141 | 5.3633 | 11000 | 0.1229 | 22.7123 | 5.2264 | | 0.1365 | 5.8510 | 12000 | 0.1174 | 22.7078 | 5.2730 | | 0.1255 | 6.3385 | 13000 | 0.1212 | 22.5228 | 5.2004 | | 0.1255 | 6.8261 | 14000 | 0.1217 | 22.3862 | 5.0868 | | 0.1137 | 7.3136 | 15000 | 0.1140 | 21.8355 | 5.0237 | | 0.1174 | 7.8013 | 16000 | 0.1083 | 21.5711 | 4.9069 | | 0.1056 | 8.2887 | 17000 | 0.1148 | 21.5227 | 4.8761 | | 0.1011 | 8.7764 | 18000 | 0.1157 | 21.5932 | 4.8998 | | 0.0895 | 9.2638 | 19000 | 0.1078 | 21.2054 | 4.7988 | | 0.0989 | 9.7515 | 20000 | 0.1075 | 21.1085 | 4.7593 | | 0.0864 | 10.2390 | 21000 | 0.1040 | 20.7781 | 4.6339 | | 0.083 | 10.7267 | 22000 | 0.1050 | 20.9455 | 4.6986 | | 0.0751 | 11.2141 | 23000 | 0.1112 | 20.7296 | 4.6489 | | 0.0725 | 11.7018 | 24000 | 0.1066 | 20.6327 | 4.5463 | | 0.0774 | 12.1892 | 25000 | 0.1054 | 20.5622 | 4.5952 | | 0.069 | 12.6769 | 26000 | 0.1076 | 20.5578 | 4.5503 | | 0.0706 | 13.1644 | 27000 | 0.1087 | 20.3287 | 4.5100 | | 0.0635 | 13.6520 | 28000 | 0.1156 | 20.4080 | 4.5471 | | 0.0652 | 14.1395 | 29000 | 0.1022 | 20.2053 | 4.4887 | | 0.067 | 14.6272 | 30000 | 0.1015 | 20.0864 | 4.4382 | | 0.0585 | 15.1146 | 31000 | 0.1035 | 19.9850 | 4.3893 | | 0.0499 | 15.6023 | 32000 | 0.1044 | 20.1040 | 4.4193 | | 0.0634 | 16.0897 | 33000 | 0.1069 | 19.9938 | 4.3980 | | 0.0577 | 16.5774 | 34000 | 0.1034 | 19.7559 | 4.3475 | | 0.0506 | 17.0649 | 35000 | 0.1008 | 19.9366 | 4.3349 | | 0.0444 | 17.5525 | 36000 | 0.1034 | 19.6854 | 4.2828 | | 0.0511 | 18.0400 | 37000 | 0.0985 | 19.6854 | 4.2615 | | 0.0449 | 18.5277 | 38000 | 0.1018 | 19.2669 | 4.2149 | | 0.0445 | 19.0151 | 39000 | 0.1028 | 19.4651 | 4.2007 | | 0.0407 | 19.5028 | 40000 | 0.1071 | 19.4387 | 4.2268 | | 0.0365 | 19.9905 | 41000 | 0.1060 | 19.3418 | 4.2134 | | 0.0397 | 20.4779 | 42000 | 0.1088 | 19.2889 | 4.1597 | | 0.0305 | 20.9656 | 43000 | 0.1031 | 19.3197 | 4.1723 | | 0.0321 | 21.4531 | 44000 | 0.1048 | 19.3153 | 4.1652 | | 0.0462 | 21.9407 | 45000 | 0.1029 | 19.2448 | 4.1297 | | 0.0404 | 22.4282 | 46000 | 0.1021 | 19.1303 | 4.1258 | | 0.0402 | 22.9159 | 47000 | 0.1030 | 19.0862 | 4.1124 | | 0.0417 | 23.4033 | 48000 | 0.1036 | 19.0378 | 4.1005 | | 0.0315 | 23.8910 | 49000 | 0.1039 | 18.9717 | 4.0737 | | 0.0381 | 24.3784 | 50000 | 0.1031 | 18.9673 | 4.0840 | | 0.0368 | 24.8661 | 51000 | 0.1045 | 18.9012 | 4.0571 | | 0.0328 | 25.3536 | 52000 | 0.1032 | 18.9144 | 4.0666 | | 0.0328 | 25.8413 | 53000 | 0.1029 | 18.8615 | 4.0776 | | 0.0344 | 26.3287 | 54000 | 0.1016 | 18.9100 | 4.0792 | | 0.0328 | 26.8164 | 55000 | 0.1018 | 18.8968 | 4.0682 | | 0.0365 | 27.3038 | 56000 | 0.1009 | 18.8439 | 4.0540 | | 0.0399 | 27.7915 | 57000 | 0.1016 | 18.8131 | 4.0390 | | 0.0398 | 28.2790 | 58000 | 0.1016 | 18.8042 | 4.0335 | | 0.0298 | 28.7666 | 59000 | 0.1014 | 18.8042 | 4.0279 | | 0.0364 | 29.2541 | 60000 | 0.1013 | 18.8263 | 4.0366 | | 0.0355 | 29.7418 | 61000 | 0.1013 | 18.8175 | 4.0350 | ### Framework versions - Transformers 4.56.1 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0

Anhlq/gemma-3-finetune-4bit-copy

Anhlq

2025-09-12T05:42:05Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "en", "base_model:unsloth/gemma-3-270m-it", "base_model:finetune:unsloth/gemma-3-270m-it", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-09-12T05:41:46Z

--- base_model: unsloth/gemma-3-270m-it tags: - text-generation-inference - transformers - unsloth - gemma3_text license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** Anhlq - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-3-270m-it This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Anhlq/gemma-3-finetune-16bit-copy

Anhlq

2025-09-12T05:41:43Z

0

transformers

[ "transformers", "safetensors", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/gemma-3-270m-it", "base_model:finetune:unsloth/gemma-3-270m-it", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-09-12T05:41:15Z

--- base_model: unsloth/gemma-3-270m-it tags: - text-generation-inference - transformers - unsloth - gemma3_text license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** Anhlq - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-3-270m-it This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

RMCian/Qwen3-0.6B-Gensyn-Swarm-fast_rabid_ram

RMCian

2025-09-12T05:39:41Z

38

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am fast_rabid_ram", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-30T14:32:41Z

--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am fast_rabid_ram --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

muscularstingingpenguin/blockassist

muscularstingingpenguin

2025-09-12T05:36:24Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gregarious tropical kiwi", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T05:36:04Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gregarious tropical kiwi --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757655275

stonermay

2025-09-12T05:35:42Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T05:35:35Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

NCSOFT/VARCO-VISION-2.0-1.7B-OCR

NCSOFT

2025-09-12T05:34:48Z

6,273

19

transformers

[ "transformers", "safetensors", "llava_onevision", "image-to-text", "multimodal", "OCR", "ncsoft", "ncai", "varco", "image-text-to-text", "conversational", "en", "ko", "arxiv:2408.03326", "base_model:Qwen/Qwen3-1.7B", "base_model:finetune:Qwen/Qwen3-1.7B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

image-text-to-text

2025-07-08T06:27:27Z

--- license: cc-by-nc-4.0 base_model: - Qwen/Qwen3-1.7B - google/siglip2-so400m-patch16-384 library_name: transformers tags: - multimodal - OCR - ncsoft - ncai - varco pipeline_tag: image-text-to-text language: - en - ko --- # VARCO-VISION-2.0-1.7B-OCR <div align="center"> <img src="./varco-vision.png" width="100%" style="background-color:white; padding:10px;" /> </div> ## Introduction **VARCO-VISION-2.0-1.7B-OCR** is a lightweight yet powerful OCR-specialized model derived from VARCO-VISION-2.0-1.7B, designed to deliver efficient and accurate text recognition in real-world scenarios. Unlike conventional vision-language models (VLMs) that primarily focus on transcribing visible text, this model performs both recognition and spatial localization by detecting bounding boxes around each character, enabling structured, layout-aware OCR outputs. The model supports both Korean and English, making it well-suited for multilingual environments where mixed-script documents are common. Each recognized character is paired with its precise position in the image, formatted as `<char>{characters}</char><bbox>{x1}, {y1}, {x2}, {y2}</bbox>`, where the coordinates correspond to the top-left (`x1`, `y1`) and bottom-right (`x2`, `y2`) corners of the character's bounding box. While VARCO-VISION-2.0-14B demonstrates strong OCR capabilities as part of its broader multimodal reasoning skills, deploying such a large model for single-task use cases can be computationally inefficient. VARCO-VISION-2.0-1.7B-OCR addresses this with a task-optimized design that retains high accuracy while significantly reducing resource requirements, making it ideal for real-time or resource-constrained applications. ![image/gif](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-1.7B-OCR/resolve/main/examples.gif) ## 🚨News🎙️ - 🛠️ 2025-08-22: We updated the checkpoint of VARCO-VISION-2.0-1.7B for improved performance. - 📰 2025-07-28: We released VARCO-VISION-2.0-1.7B-OCR at [link](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-1.7B-OCR) - 📰 2025-07-28: We released VARCO-VISION-2.0-1.7B at [link](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-1.7B) - 🛠️ 2025-07-18: We updated the checkpoint of VARCO-VISION-2.0-14B for improved performance. - 📰 2025-07-16: We released VARCO-VISION-2.0-14B at [link](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-14B) - 📰 2025-07-16: We released GME-VARCO-VISION-Embedding at [link](https://huggingface.co/NCSOFT/GME-VARCO-VISION-Embedding) ## VARCO-VISION-2.0 Family | Model Name | Base Models (Vision / Language) | HF Link | | :------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------: | | VARCO-VISION-2.0-14B | [siglip2-so400m-patch16-384](https://huggingface.co/google/siglip2-so400m-patch16-384) / [Qwen3-14B ](https://huggingface.co/Qwen/Qwen3-14B) | [link](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-14B) | | VARCO-VISION-2.0-1.7B | [siglip2-so400m-patch16-384](https://huggingface.co/google/siglip2-so400m-patch16-384) / [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) | [link](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-1.7B) | | VARCO-VISION-2.0-1.7B-OCR | [siglip2-so400m-patch16-384](https://huggingface.co/google/siglip2-so400m-patch16-384) / [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) | [link](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-1.7B-OCR) | | GME-VARCO-VISION-Embedding | [Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) | [link](https://huggingface.co/NCSOFT/GME-VARCO-VISION-Embedding) | ## Model Architecture VARCO-VISION-2.0 follows the architecture of [LLaVA-OneVision](https://arxiv.org/abs/2408.03326). ## Evaluation ### OCR Benchmark | Benchmark | CLOVA OCR | PaddleOCR | EasyOCR | VARCO-VISION-2.0-1.7B-OCR | | :-------: | :--------:| :-------: | :-----: | :-----------------------: | | CORD | *93.9* | 91.4 | 77.8 | **95.6** | | ICDAR2013 | *94.4* | 92.0 | 85.0 | **95.5** | | ICDAR2015 | **84.1** | 73.7 | 57.9 | *75.4* | ## Usage To use this model, we recommend installing `transformers` version **4.53.1 or higher**. Additionally, for best results, we **recommend upscaling input images to a minimum resolution of *2,304*** on the longer side if they are smaller. ```python import torch from PIL import Image from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration model_name = "NCSOFT/VARCO-VISION-2.0-1.7B-OCR" model = LlavaOnevisionForConditionalGeneration.from_pretrained( model_name, torch_dtype=torch.float16, attn_implementation="sdpa", device_map="auto", ) processor = AutoProcessor.from_pretrained(model_name) image = Image.open("file:///path/to/image.jpg") # Image upscaling for OCR performance boost w, h = image.size target_size = 2304 if max(w, h) < target_size: scaling_factor = target_size / max(w, h) new_w = int(w * scaling_factor) new_h = int(h * scaling_factor) image = image.resize((new_w, new_h)) conversation = [ { "role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": "<ocr>"}, ], }, ] inputs = processor.apply_chat_template( conversation, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" ).to(model.device, torch.float16) generate_ids = model.generate(**inputs, max_new_tokens=1024) generate_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generate_ids) ] output = processor.decode(generate_ids_trimmed[0], skip_special_tokens=False) print(output) ```

NCSOFT/VARCO-VISION-14B-HF

NCSOFT

2025-09-12T05:34:13Z

1,103

29

transformers

[ "transformers", "safetensors", "llava_onevision", "image-to-text", "multimodal", "conversational", "ncsoft", "varco", "image-text-to-text", "en", "ko", "arxiv:2411.19103", "arxiv:2408.03326", "base_model:Qwen/Qwen2.5-14B-Instruct", "base_model:finetune:Qwen/Qwen2.5-14B-Instruct", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

image-text-to-text

2024-11-27T01:11:00Z

--- language: - en - ko license: cc-by-nc-4.0 tags: - multimodal - conversational - ncsoft - varco base_model: - Qwen/Qwen2.5-14B-Instruct - google/siglip-so400m-patch14-384 library_name: transformers pipeline_tag: image-text-to-text --- # VARCO-VISION-14B-HF ## 🚨News🎙️ - The 2.0 model has been released. Please use the new version. - 📰 2025-07-16: We released VARCO-VISION-2.0-14B at [link](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-14B) - 📰 2025-07-16: We released GME-VARCO-VISION-Embedding at [link](https://huggingface.co/NCSOFT/GME-VARCO-VISION-Embedding) ## About the VARCO-VISION-1.0-14B Model **VARCO-VISION-14B** is a powerful English-Korean Vision-Language Model (VLM). The training pipeline of VARCO-VISION consists of four stages: Feature Alignment Pre-training, Basic Supervised Fine-tuning, Advanced Supervised Fine-tuning, and Preference Optimization. In both multimodal and text-only benchmarks, VARCO-VISION-14B not only surpasses other models of similar size in performance but also achieves scores comparable to those of proprietary models. The Model currently accepts a single image and a text as inputs, generating an output text. It supports grounding, referring as well as OCR (Optical Character Recognition). - **Developed by:** NC Research, Multimodal Generation Team - **Technical Report:** [VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models](https://arxiv.org/pdf/2411.19103) - **Blog(Korean):** [VARCO-VISION Technical Report Summary](https://ncsoft.github.io/ncresearch/95ad8712e60063e9ac97538504ac3eea0ac530af) - **Demo Page:** *The demo page is no longer available.* - **Languages:** Korean, English - **License:** CC BY-NC 4.0 - **Architecture:** VARCO-VISION-14B follows the architecture of [LLaVA-OneVision](https://arxiv.org/abs/2408.03326). - **Base Model:** - **Language Model:** [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) - **Vision Encoder:** [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) - **LLaVA-NeXT Codebase Model:** [NCSOFT/VARCO-VISION-14B](https://huggingface.co/NCSOFT/VARCO-VISION-14B) - **Korean VLM Benchmarks:** - You can use the following benchmark datasets in the [LLMs-Eval toolkit](https://github.com/EvolvingLMMs-Lab/lmms-eval). - [NCSOFT/K-MMBench](https://huggingface.co/datasets/NCSOFT/K-MMBench) - [NCSOFT/K-SEED](https://huggingface.co/datasets/NCSOFT/K-SEED) - [NCSOFT/K-MMStar](https://huggingface.co/datasets/NCSOFT/K-MMStar) - [NCSOFT/K-DTCBench](https://huggingface.co/datasets/NCSOFT/K-DTCBench) - [NCSOFT/K-LLaVA-W](https://huggingface.co/datasets/NCSOFT/K-LLaVA-W) - **you can also evaluate VARCO-VISION-14B in the [VLMEval kit](https://github.com/open-compass/VLMEvalKit)**. - **This model is for research purposes only. Commercial use is prohibited.** ## Uses ### Direct Use To use this model, ensure you have `transformers >= 4.45.0` installed. ```python import torch import requests from PIL import Image from transformers import LlavaOnevisionForConditionalGeneration, AutoProcessor model_name = "NCSOFT/VARCO-VISION-14B-HF" model = LlavaOnevisionForConditionalGeneration.from_pretrained( model_name, torch_dtype="float16", device_map="auto", attn_implementation="flash_attention_2" ) processor = AutoProcessor.from_pretrained(model_name) device = model.device # Define a chat history and use `apply_chat_template` to get correctly formatted prompt # Each value in "content" has to be a list of dicts with types ("text", "image") conversation = [ { "role": "user", "content": [ {"type": "text", "text": "Describe this image."}, {"type": "image"}, ], }, ] prompt = processor.apply_chat_template(conversation, add_generation_prompt=True) EOS_TOKEN = "<|im_end|>" image_file = "http://images.cocodataset.org/val2017/000000039769.jpg" raw_image = Image.open(requests.get(image_file, stream=True).raw) inputs = processor(images=raw_image, text=prompt, return_tensors='pt').to(device, torch.float16) output = model.generate(**inputs, max_new_tokens=1024, do_sample=False) output = processor.decode(output[0][inputs.input_ids.shape[1]:]) if output.endswith(EOS_TOKEN): output = output[: -len(EOS_TOKEN)] output = output.strip() print(output) ``` ### Specialized Features If a question is based on bounding boxes or require bounding boxes as an output, please include the special tokens in the input text. The following special tokens are used to define specific tasks, inputs, and outputs for the model: - `<gro>`: Indicates that the model's response should include bounding box information. - `<ocr>`: Specifies OCR tasks for recognizing text within an image. - `<char>` and `</char>`: Used to mark a text phrase. - `<obj>` and `</obj>`: Used to indicate an object. - `<bbox>` and `</bbox>`: Used to represent a bounding box. - `<delim>`: Represents multiple location points for a single object or text. #### Grounding Grounding refers to a task where the model needs to identify specific locations within an image to provide an appropriate answer. To perform grounding, prepend the special token `<gro>` to the question. ```python conversation = [ { "role": "user", "content": [ {"type": "text", "text": "<gro>\nDescribe the image in detail."}, {"type": "image"}, ], }, ] ``` **Expected Output Example:** ```html The image shows <obj>two cats</obj><bbox>0.014, 0.106, 0.51, 0.996<delim>0.51, 0.054, 0.996, 0.787</bbox> lying on <obj>a pink blanket</obj><bbox>0.003, 0.231, 0.999, 0.999</bbox>. The cat on the left is lying on its side with its head resting on the blanket, while the cat on the right is lying on its stomach with its head also resting on the blanket. Both cats appear to be relaxed and comfortable. There are <obj>two remote controls</obj><bbox>0.037, 0.141, 0.283, 0.253<delim>0.506, 0.171, 0.581, 0.295</bbox> placed near the cats, one on the left side and one on the right side of the image. ``` <img src="assets/grounding.png" alt="Grounding Example" width="400"/> #### Referring VARCO-VISION-14B can handle location-specific questions using bounding boxes. To perform referring tasks, make a conversation including the object of interest within `<obj>` and `</obj>` tags. You have to specify its location with `<bbox>` and `</bbox>` tags. This allows the model to understand the context and focus on the object at the specified location. A bbox is represented in a form of (x1, y1, x2, y2). The first two values indicate the top-left position of a bbox, and the latter two values are the bottom-right position. ```python conversation = [ { "role": "user", "content": [ { "type": "text", "text": "<obj>이 물건</obj><bbox>0.039, 0.138, 0.283, 0.257</bbox>은 어떻게 쓰는거야?", }, {"type": "image"}, ], }, ] ``` **Expected Output Example:** ``` **이 물건**은 리모컨으로, 주로 텔레비전이나 다른 전자 기기를 원격으로 조작하는 데 사용됩니다. 리모컨에는 다양한 버튼이 있으며, 각 버튼은 채널 변경, 볼륨 조절, 전원 켜기/끄기 등의 기능을 수행합니다. 사용자는 리모컨을 손에 들고 버튼을 누르면, 해당 기기에 신호를 보내 원하는 조작을 할 수 있습니다. 리모컨은 일반적으로 가정이나 사무실에서 편리하게 전자 기기를 조작할 수 있도록 사용됩니다. ``` #### OCR To perform Optical Character Recognition (OCR), use the `<ocr>` token. ```python image_file = "./assets/ocr_1.png" raw_image = Image.open(image_file) conversation = [ { "role": "user", "content": [ {"type": "text", "text": "<ocr>"}, {"type": "image"}, ], }, ] ``` **Expected Output Example:** ``` <char>백범로</char><bbox>0.172, 0.266, 0.328, 0.341</bbox> <char>124번길</char><bbox>0.347, 0.266, 0.512, 0.341</bbox> <char>Baekbeom-ro</char><bbox>0.171, 0.337, 0.433, 0.392</bbox> <char>124</char><bbox>0.444, 0.341, 0.508, 0.392</bbox> <char>만수주공아파트</char><bbox>0.109, 0.531, 0.335, 0.601</bbox> <char>시흥</char><bbox>0.443, 0.518, 0.522, 0.581</bbox> <char>시청</char><bbox>0.711, 0.521, 0.811, 0.594</bbox> <char>Mansu</char><bbox>0.102, 0.601, 0.181, 0.648</bbox> <char>Jugong</char><bbox>0.186, 0.601, 0.273, 0.658</bbox> <char>Apt</char><bbox>0.28, 0.601, 0.327, 0.651</bbox> <char>42</char><bbox>0.377, 0.601, 0.416, 0.648</bbox> <char>Shieung</char><bbox>0.445, 0.578, 0.53, 0.625</bbox> <char>인천대공원</char><bbox>0.43, 0.621, 0.609, 0.684</bbox> <char>모래내시장역</char><bbox>0.651, 0.59, 0.873, 0.665</bbox> <char>IncheonGrand</char><bbox>0.432, 0.681, 0.561, 0.723</bbox> <char>Park</char><bbox>0.564, 0.681, 0.611, 0.723</bbox> ``` <img src="assets/ocr_2.jpg" alt="OCR Example" width="350"/> ## Citing the Model If you use VARCO-VISION-14B in your research, please cite the following: ```bibtex @misc{ju2024varcovisionexpandingfrontierskorean, title={VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models}, author={Jeongho Ju and Daeyoung Kim and SunYoung Park and Youngjune Kim}, year={2024}, eprint={2411.19103}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2411.19103}, ```

jo-mengr/mmcontext-pubmedbert-scvi_fm-cxg_100k

jo-mengr

2025-09-12T05:30:18Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "dense", "generated_from_trainer", "dataset_size:81143", "loss:MultipleNegativesRankingLoss", "code", "dataset:jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:NeuML/pubmedbert-base-embeddings", "base_model:finetune:NeuML/pubmedbert-base-embeddings", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-09-12T05:29:58Z

--- language: - code tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:81143 - loss:MultipleNegativesRankingLoss base_model: NeuML/pubmedbert-base-embeddings widget: - source_sentence: sample_idx:census_421e5f54-5de7-425f-b399-34ead0651ce1_41 sentences: - This measurement was conducted with 10x 3' v3. Neuron cell type from the cerebral cortex (Frontal agranular insular cortex, or FI) of a 50-year-old male European donor, analyzed at the nucleus level. - sample_idx:census_421e5f54-5de7-425f-b399-34ead0651ce1_670 - This measurement was conducted with 10x 3' v3. Neuron cell type from a 42-year old male cerebral cortex tissue, specifically the Frontal agranular insular cortex (FI) region, identified as a CGE interneuron. - source_sentence: sample_idx:census_d7d7e89c-c93a-422d-8958-9b4a90b69558_1563 sentences: - This measurement was conducted with 10x 5' v1. Activated CD4-positive, alpha-beta T cell from a 26-year-old male with Common variable immunodeficiency (CVID), undergoing naïve-to-memory B cell differentiation. - sample_idx:census_d7d7e89c-c93a-422d-8958-9b4a90b69558_4002 - This measurement was conducted with 10x 5' v1. Naive B cell from blood of a 26-year old male, activated with CD3. - source_sentence: sample_idx:census_e1f595f6-ba2c-495e-9bee-7056f116b1e4_1239 sentences: - sample_idx:census_e1f595f6-ba2c-495e-9bee-7056f116b1e4_822 - This measurement was conducted with 10x 3' v3. Neuron cell type from a 29-year-old male cerebral cortex, specifically from the Middle Temporal Gyrus (MTG), with European ethnicity, using nucleus suspension type. - This measurement was conducted with 10x 3' v3. Neuron cell type from a 50-year-old male cerebral cortex, specifically the Middle Temporal Gyrus (MTG), with European self-reported ethnicity, analyzed at the nucleus level. - source_sentence: sample_idx:census_1cf24082-59de-4029-ac81-6e398768af3a_304 sentences: - This measurement was conducted with 10x 3' v3. Nucleus sample from a 50-year-old male neuron, specifically an MGE interneuron, located in the Inferior temporal gyrus (ITG) region of the cerebral cortex, with European ethnicity. - This measurement was conducted with 10x 3' v3. Nucleus suspension of neurons from the inferior temporal gyrus region of the cerebral cortex, taken from a 29-year-old male of European descent. - sample_idx:census_1cf24082-59de-4029-ac81-6e398768af3a_426 - source_sentence: sample_idx:census_18500fcd-9960-49cb-8a8e-7d868dc14efe_602 sentences: - sample_idx:census_18500fcd-9960-49cb-8a8e-7d868dc14efe_247 - This measurement was conducted with 10x 3' v3. Neuron cell type from the cerebral nuclei, specifically from the external segment of globus pallidus (GPe) in a 42-year-old male. - This measurement was conducted with 10x 3' v3. Oligodendrocyte cells from the external segment of globus pallidus (GPe) in a 29-year-old male cerebral nuclei sample. datasets: - jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy model-index: - name: SentenceTransformer based on NeuML/pubmedbert-base-embeddings results: - task: type: triplet name: Triplet dataset: name: cellxgene pseudo bulk 100k multiplets natural language annotation cell sentence 1 caption type: cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption metrics: - type: cosine_accuracy value: 0.7883697748184204 name: Cosine Accuracy --- # SentenceTransformer based on NeuML/pubmedbert-base-embeddings This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings) on the [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings)  - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) - **Language:** code  ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): MMContextEncoder( (text_encoder): BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(30522, 768, padding_idx=0) (position_embeddings): Embedding(512, 768) (token_type_embeddings): Embedding(2, 768) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (encoder): BertEncoder( (layer): ModuleList( (0-11): 12 x BertLayer( (attention): BertAttention( (self): BertSdpaSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) ) ) (pooler): BertPooler( (dense): Linear(in_features=768, out_features=768, bias=True) (activation): Tanh() ) ) (text_adapter): AdapterModule( (net): Sequential( (0): Linear(in_features=768, out_features=1024, bias=True) (1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (pooling): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (omics_adapter): AdapterModule( (net): Sequential( (0): Linear(in_features=50, out_features=1024, bias=True) (1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (omics_encoder): MiniOmicsModel( (embeddings): Embedding(90155, 50, padding_idx=0) ) ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("jo-mengr/mmcontext-pubmedbert-scvi_fm-cxg_100k") # Run inference sentences = [ 'sample_idx:census_18500fcd-9960-49cb-8a8e-7d868dc14efe_602', "This measurement was conducted with 10x 3' v3. Oligodendrocyte cells from the external segment of globus pallidus (GPe) in a 29-year-old male cerebral nuclei sample.", "This measurement was conducted with 10x 3' v3. Neuron cell type from the cerebral nuclei, specifically from the external segment of globus pallidus (GPe) in a 42-year-old male.", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[1.0000, 0.4753, 0.1343], # [0.4753, 1.0000, 0.7385], # [0.1343, 0.7385, 1.0000]]) ```    ## Evaluation ### Metrics #### Triplet * Dataset: `cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption` * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:-----------| | **cosine_accuracy** | **0.7884** |   ## Training Details ### Training Dataset #### cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption * Dataset: [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) at [8b940b4](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation/tree/8b940b48a15534edc2689cf70afe98d82375bb59) * Size: 81,143 training samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, and <code>negative_2</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative_1 | negative_2 | |:--------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------| | type | string | string | string | string | | details | <ul><li>min: 56 characters</li><li>mean: 58.72 characters</li><li>max: 60 characters</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 48.4 tokens</li><li>max: 159 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 45.96 tokens</li><li>max: 155 tokens</li></ul> | <ul><li>min: 56 characters</li><li>mean: 58.74 characters</li><li>max: 60 characters</li></ul> | * Samples: | anchor | positive | negative_1 | negative_2 | |:--------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------| | <code>sample_idx:census_218acb0f-9f2f-4f76-b90b-15a4b7c7f629_26009</code> | <code>This measurement was conducted with 10x 3' v2. A proliferating lymphocyte cell sample, obtained from a 34-year-old female Asian individual, derived from peripheral blood mononuclear cells.</code> | <code>This measurement was conducted with 10x 3' v2. CD8-positive, alpha-beta T cell derived from a 51-year old European female with managed systemic lupus erythematosus (SLE), obtained from blood tissue and enriched as a peripheral blood mononuclear cell.</code> | <code>sample_idx:census_218acb0f-9f2f-4f76-b90b-15a4b7c7f629_38905</code> | | <code>sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_6333</code> | <code>This measurement was conducted with 10x 5' v1. Sample is a cell from the omentum tissue, specifically an effector memory CD4-positive, alpha-beta T cell, from a female in her sixth decade.</code> | <code>This measurement was conducted with 10x 3' v3. A cell sample from the spleen, belonging to the naive thymus-derived CD4-positive, alpha-beta T cell category, specifically Tnaive/CM_CD4, and identified as Tcm/Naive helper T cells within the T cells group.</code> | <code>sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_4412</code> | | <code>sample_idx:census_adda0684-f8ea-4403-b393-2a25607077c4_271</code> | <code>This measurement was conducted with 10x 3' v3. Neuron cell type from a 29-year-old male, specifically from the thalamic complex, specifically the thalamus (THM) - posterior nuclear complex of thalamus (PoN) - medial geniculate nuclei (MG).</code> | <code>This measurement was conducted with 10x 3' v3. Fibroblast cells from the thalamic complex, specifically from the thalamus (THM) - posterior nuclear complex of thalamus (PoN) - medial geniculate nuclei (MG) region, of a 42-year-old male.</code> | <code>sample_idx:census_adda0684-f8ea-4403-b393-2a25607077c4_585</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption * Dataset: [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) at [8b940b4](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation/tree/8b940b48a15534edc2689cf70afe98d82375bb59) * Size: 9,011 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, and <code>negative_2</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative_1 | negative_2 | |:--------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------| | type | string | string | string | string | | details | <ul><li>min: 56 characters</li><li>mean: 58.73 characters</li><li>max: 60 characters</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 47.49 tokens</li><li>max: 157 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 48.98 tokens</li><li>max: 157 tokens</li></ul> | <ul><li>min: 56 characters</li><li>mean: 58.74 characters</li><li>max: 60 characters</li></ul> | * Samples: | anchor | positive | negative_1 | negative_2 | |:--------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------| | <code>sample_idx:census_0b4a15a7-4e9e-4555-9733-2423e5c66469_490</code> | <code>This measurement was conducted with 10x 3' v3. Cell sample from the cortex of kidney, taken from a 43-year-old male of European ethnicity with a reported history of kidney cancer. The cell type is identified as a kidney collecting duct intercalated cell.</code> | <code>This measurement was conducted with 10x 3' v3. Epithelial cells derived from the cortex of a kidney of a 50-year old female European individual, preserved by cryopreservation.</code> | <code>sample_idx:census_0b4a15a7-4e9e-4555-9733-2423e5c66469_280</code> | | <code>sample_idx:census_4976b234-9028-4b4b-8a2f-8ac59d636219_269</code> | <code>This measurement was conducted with 10x 3' v3. Neuron cell type from a 29-year-old male cerebellum, specifically from the Cerebellar Vermis - CBV region, with European self-reported ethnicity, analyzed at the nucleus level.</code> | <code>This measurement was conducted with 10x 3' v3. Fibroblast cells derived from the cerebellum tissue of a 50-year-old male, specifically from the Cerebellum (CB) - Cerebellar Vermis - CBV dissection.</code> | <code>sample_idx:census_4976b234-9028-4b4b-8a2f-8ac59d636219_826</code> | | <code>sample_idx:census_44882825-0da1-4547-b721-2c6105d4a9d1_10258</code> | <code>This measurement was conducted with 10x 5' v1. Cell sample from the tonsil of a 9-year-old female with recurrent tonsillitis, characterized as a centroblast B cell with IGLC2, IGLV7-43, IGLJ3 immunoglobulin genes expressed.</code> | <code>This measurement was conducted with 10x 5' v1. This sample represents a tonsil germinal center B cell from a three-year-old human male with obstructive sleep apnea and recurrent tonsillitis. The study provides a comprehensive roadmap of human B cell maturation, including gene expression, antibody repertoires, and clonal sharing of B cell states at single-cell resolution, as well as memory B cell heterogeneity reflecting diverse functional and signaling states.</code> | <code>sample_idx:census_44882825-0da1-4547-b721-2c6105d4a9d1_243</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `warmup_ratio`: 0.1 - `bf16`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `hub_revision`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {} </details> ### Training Logs | Epoch | Step | Training Loss | cellxgene pseudo bulk 100k multiplets natural language annotation cell sentence 1 caption loss | cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_caption_cosine_accuracy | |:------:|:----:|:-------------:|:----------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:| | 0.1577 | 100 | 14.8711 | 14.6553 | 0.5955 | | 0.3155 | 200 | 13.5634 | 12.5052 | 0.6968 | | 0.4732 | 300 | 11.0631 | 9.8851 | 0.7264 | | 0.6309 | 400 | 9.0001 | 8.3624 | 0.7431 | | 0.7886 | 500 | 7.8481 | 7.5177 | 0.7510 | | 0.9464 | 600 | 7.0916 | 6.9894 | 0.7555 | | 1.1041 | 700 | 6.7088 | 6.6373 | 0.7622 | | 1.2618 | 800 | 6.2834 | 6.3739 | 0.7664 | | 1.4196 | 900 | 6.0694 | 6.1536 | 0.7701 | | 1.5773 | 1000 | 5.8974 | 5.9650 | 0.7736 | | 1.7350 | 1100 | 5.6823 | 5.8622 | 0.7774 | | 1.8927 | 1200 | 5.6118 | 5.7346 | 0.7778 | | 2.0505 | 1300 | 5.4439 | 5.6417 | 0.7805 | | 2.2082 | 1400 | 5.3634 | 5.5389 | 0.7833 | | 2.3659 | 1500 | 5.298 | 5.4966 | 0.7838 | | 2.5237 | 1600 | 5.2391 | 5.4338 | 0.7840 | | 2.6814 | 1700 | 5.1817 | 5.3612 | 0.7852 | | 2.8391 | 1800 | 5.1506 | 5.3411 | 0.7844 | | 2.9968 | 1900 | 5.116 | 5.2958 | 0.7870 | | 3.1546 | 2000 | 5.0382 | 5.2824 | 0.7879 | | 3.3123 | 2100 | 5.0976 | 5.2416 | 0.7871 | | 3.4700 | 2200 | 5.012 | 5.2307 | 0.7871 | | 3.6278 | 2300 | 5.0273 | 5.2196 | 0.7900 | | 3.7855 | 2400 | 5.0156 | 5.2232 | 0.7901 | | 3.9432 | 2500 | 4.9684 | 5.2027 | 0.7884 | ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 5.0.0 - Transformers: 4.55.0.dev0 - PyTorch: 2.5.1+cu121 - Accelerate: 1.9.0 - Datasets: 2.19.1 - Tokenizers: 0.21.4 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

rsadaphule/llava-1.5-7b-package-damage-lora-ADAPTER

rsadaphule

2025-09-12T05:30:09Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-09-12T05:30:08Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

omerbektasss/blockassist-bc-keen_fast_giraffe_1757654928

omerbektasss

2025-09-12T05:29:42Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T05:29:05Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

mradermacher/jina-reranker-v1-tiny-en-GGUF

mradermacher

2025-09-12T05:27:50Z

7,599

0

transformers

[ "transformers", "gguf", "reranker", "cross-encoder", "transformers.js", "sentence-transformers", "en", "base_model:jinaai/jina-reranker-v1-tiny-en", "base_model:quantized:jinaai/jina-reranker-v1-tiny-en", "license:apache-2.0", "endpoints_compatible", "region:us", "feature-extraction" ]

null

2025-01-07T05:57:29Z

--- base_model: jinaai/jina-reranker-v1-tiny-en language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - reranker - cross-encoder - transformers.js - sentence-transformers --- ## About      static quants of https://huggingface.co/jinaai/jina-reranker-v1-tiny-en  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#jina-reranker-v1-tiny-en-GGUF).*** weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q2_K.gguf) | Q2_K | 0.1 | | | [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q3_K_S.gguf) | Q3_K_S | 0.1 | | | [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.IQ4_XS.gguf) | IQ4_XS | 0.1 | | | [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q3_K_M.gguf) | Q3_K_M | 0.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q3_K_L.gguf) | Q3_K_L | 0.1 | | | [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q4_K_S.gguf) | Q4_K_S | 0.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q4_K_M.gguf) | Q4_K_M | 0.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q5_K_S.gguf) | Q5_K_S | 0.1 | | | [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q5_K_M.gguf) | Q5_K_M | 0.1 | | | [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q6_K.gguf) | Q6_K | 0.1 | very good quality | | [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.Q8_0.gguf) | Q8_0 | 0.1 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en.f16.gguf) | f16 | 0.2 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757654658

stonermay

2025-09-12T05:25:51Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T05:25:30Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Asap7772/qwen3-4b-arc-second-stage-awr-sftinit-lr1e-5-0908

Asap7772

2025-09-12T05:24:31Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-12T04:46:58Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

birder-project/cswin_transformer_s_eu-common

birder-project

2025-09-12T05:19:05Z

10

0

birder

[ "birder", "image-classification", "pytorch", "arxiv:2107.00652", "license:apache-2.0", "region:us" ]

image-classification

2025-09-07T16:48:18Z

--- tags: - image-classification - birder - pytorch library_name: birder license: apache-2.0 --- # Model Card for cswin_transformer_s_eu-common A CSWin Transformer small image classification model. This model was trained on the `eu-common` dataset containing common European bird species. The species list is derived from the Collins bird guide [^1]. [^1]: Svensson, L., Mullarney, K., & Zetterström, D. (2022). Collins bird guide (3rd ed.). London, England: William Collins. Note: A 256 x 256 variant of this model is available as `cswin_transformer_s_eu-common256px`. ## Model Details - **Model Type:** Image classification and detection backbone - **Model Stats:** - Params (M): 34.5 - Input image size: 384 x 384 - **Dataset:** eu-common (707 classes) - **Papers:** - CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows: <https://arxiv.org/abs/2107.00652> ## Model Usage ### Image Classification ```python import birder from birder.inference.classification import infer_image (net, model_info) = birder.load_pretrained_model("cswin_transformer_s_eu-common", inference=True) # Note: A 256x256 variant is available as "cswin_transformer_s_eu-common256px" # Get the image size the model was trained on size = birder.get_size_from_signature(model_info.signature) # Create an inference transform transform = birder.classification_transform(size, model_info.rgb_stats) image = "path/to/image.jpeg" # or a PIL image, must be loaded in RGB format (out, _) = infer_image(net, image, transform) # out is a NumPy array with shape of (1, 707), representing class probabilities. ``` ### Image Embeddings ```python import birder from birder.inference.classification import infer_image (net, model_info) = birder.load_pretrained_model("cswin_transformer_s_eu-common", inference=True) # Get the image size the model was trained on size = birder.get_size_from_signature(model_info.signature) # Create an inference transform transform = birder.classification_transform(size, model_info.rgb_stats) image = "path/to/image.jpeg" # or a PIL image (out, embedding) = infer_image(net, image, transform, return_embedding=True) # embedding is a NumPy array with shape of (1, 512) ``` ### Detection Feature Map ```python from PIL import Image import birder (net, model_info) = birder.load_pretrained_model("cswin_transformer_s_eu-common", inference=True) # Get the image size the model was trained on size = birder.get_size_from_signature(model_info.signature) # Create an inference transform transform = birder.classification_transform(size, model_info.rgb_stats) image = Image.open("path/to/image.jpeg") features = net.detection_features(transform(image).unsqueeze(0)) # features is a dict (stage name -> torch.Tensor) print([(k, v.size()) for k, v in features.items()]) # Output example: # [('stage1', torch.Size([1, 64, 96, 96])), # ('stage2', torch.Size([1, 128, 48, 48])), # ('stage3', torch.Size([1, 256, 24, 24])), # ('stage4', torch.Size([1, 512, 12, 12]))] ``` ## Citation ```bibtex @misc{dong2022cswintransformergeneralvision, title={CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows}, author={Xiaoyi Dong and Jianmin Bao and Dongdong Chen and Weiming Zhang and Nenghai Yu and Lu Yuan and Dong Chen and Baining Guo}, year={2022}, eprint={2107.00652}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2107.00652}, } ```

alberto-lorente/Meta-Llama-3_1-8B-Instruct-bnb-4bit-GRANULAR-TASK-minorities

alberto-lorente

2025-09-12T05:17:42Z

0

transformers

[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "unsloth", "endpoints_compatible", "region:us" ]

null

2025-08-26T04:04:46Z

--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit library_name: transformers model_name: Meta-Llama-3_1-8B-Instruct-bnb-4bit-GRANULAR-TASK-minorities tags: - generated_from_trainer - trl - sft - unsloth licence: license --- # Model Card for Meta-Llama-3_1-8B-Instruct-bnb-4bit-GRANULAR-TASK-minorities This model is a fine-tuned version of [unsloth/meta-llama-3.1-8b-instruct-bnb-4bit](https://huggingface.co/unsloth/meta-llama-3.1-8b-instruct-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="alberto-lorente/Meta-Llama-3_1-8B-Instruct-bnb-4bit-GRANULAR-TASK-minorities", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.23.0 - Transformers: 4.56.1 - Pytorch: 2.8.0+cu126 - Datasets: 3.6.0 - Tokenizers: 0.22.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

mradermacher/Medllama-3-8b-GGUF

mradermacher

2025-09-12T05:16:39Z

0

transformers

[ "transformers", "gguf", "en", "base_model:Axcel1/Medllama-3-8b", "base_model:quantized:Axcel1/Medllama-3-8b", "endpoints_compatible", "region:us", "conversational" ]

null

2025-09-12T03:32:28Z

--- base_model: Axcel1/Medllama-3-8b language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher tags: [] --- ## About         static quants of https://huggingface.co/Axcel1/Medllama-3-8b  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Medllama-3-8b-GGUF).*** weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q2_K.gguf) | Q2_K | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q3_K_S.gguf) | Q3_K_S | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q3_K_L.gguf) | Q3_K_L | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.IQ4_XS.gguf) | IQ4_XS | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q5_K_S.gguf) | Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q5_K_M.gguf) | Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q6_K.gguf) | Q6_K | 6.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Medllama-3-8b-GGUF/resolve/main/Medllama-3-8b.f16.gguf) | f16 | 16.2 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

Adanato/Llama-3.2-1B-Instruct-low_acereason_1k-high_acereason_1k-high_acereason_1k

Adanato

2025-09-12T05:15:54Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "fyksft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-12T05:14:09Z

--- library_name: transformers tags: - fyksft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

faodl/20250909_model_g20_multilabel_MiniLM-L12-all-labels-artificial-governance-multi-output

faodl

2025-09-12T05:15:09Z

0

setfit

[ "setfit", "safetensors", "bert", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", "base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", "region:us" ]

text-classification

2025-09-12T05:14:53Z

--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: Emergency insurance payouts complement humanitarian assistance by providing timely financial resources that facilitate quicker recovery from climate disasters. - text: "c) Establish strategic and operational partnerships and alliances with private,\ \ public and civil society \norganizations in food and nutrition." - text: 'COVID-19: The Development Program for Drinking Water Supply and Sanitation Systems of the Kyrgyz Republic until 2026 was approved. The Program is aimed at increasing the provision of drinking water of standard quality, improving the health and quality of life of the population of the republic, reducing the harmful effects on the environment through the construction, reconstruction, and modernization of drinking water supply and sanitation systems.' - text: "The program mainly aims at \nthe construction of rural roads, capacity building\ \ of local bodies, and \nawareness raising activities." - text: "Mr. Speaker, the PF Government \n\nremains committed to ensuring that all\ \ \n\nZambians have access to clean water supply \n\nand sanitation services." metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: false base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 --- # SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) as the Sentence Transformer embedding model. A MultiOutputClassifier instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) - **Classification head:** a MultiOutputClassifier instance - **Maximum Sequence Length:** 128 tokens     ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("faodl/20250909_model_g20_multilabel_MiniLM-L12-all-labels-artificial-governance-multi-output") # Run inference preds = model("The program mainly aims at the construction of rural roads, capacity building of local bodies, and awareness raising activities.") ```     ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:-----| | Word count | 1 | 41.6795 | 1753 | ### Training Hyperparameters - batch_size: (32, 32) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 50 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:-----:|:-------------:|:---------------:| | 0.0001 | 1 | 0.184 | - | | 0.0039 | 50 | 0.1927 | - | | 0.0078 | 100 | 0.1729 | - | | 0.0117 | 150 | 0.1484 | - | | 0.0156 | 200 | 0.1301 | - | | 0.0196 | 250 | 0.1134 | - | | 0.0235 | 300 | 0.1079 | - | | 0.0274 | 350 | 0.1021 | - | | 0.0313 | 400 | 0.0876 | - | | 0.0352 | 450 | 0.0834 | - | | 0.0391 | 500 | 0.0886 | - | | 0.0430 | 550 | 0.0728 | - | | 0.0469 | 600 | 0.0775 | - | | 0.0508 | 650 | 0.0811 | - | | 0.0548 | 700 | 0.0745 | - | | 0.0587 | 750 | 0.0753 | - | | 0.0626 | 800 | 0.0745 | - | | 0.0665 | 850 | 0.07 | - | | 0.0704 | 900 | 0.0702 | - | | 0.0743 | 950 | 0.0707 | - | | 0.0782 | 1000 | 0.0702 | - | | 0.0821 | 1050 | 0.0607 | - | | 0.0860 | 1100 | 0.067 | - | | 0.0899 | 1150 | 0.065 | - | | 0.0939 | 1200 | 0.0659 | - | | 0.0978 | 1250 | 0.066 | - | | 0.1017 | 1300 | 0.066 | - | | 0.1056 | 1350 | 0.06 | - | | 0.1095 | 1400 | 0.0609 | - | | 0.1134 | 1450 | 0.0587 | - | | 0.1173 | 1500 | 0.0542 | - | | 0.1212 | 1550 | 0.0523 | - | | 0.1251 | 1600 | 0.0559 | - | | 0.1291 | 1650 | 0.052 | - | | 0.1330 | 1700 | 0.0487 | - | | 0.1369 | 1750 | 0.053 | - | | 0.1408 | 1800 | 0.0477 | - | | 0.1447 | 1850 | 0.0492 | - | | 0.1486 | 1900 | 0.0474 | - | | 0.1525 | 1950 | 0.0488 | - | | 0.1564 | 2000 | 0.0461 | - | | 0.1603 | 2050 | 0.0481 | - | | 0.1643 | 2100 | 0.0463 | - | | 0.1682 | 2150 | 0.0432 | - | | 0.1721 | 2200 | 0.0482 | - | | 0.1760 | 2250 | 0.0444 | - | | 0.1799 | 2300 | 0.0466 | - | | 0.1838 | 2350 | 0.0423 | - | | 0.1877 | 2400 | 0.041 | - | | 0.1916 | 2450 | 0.0422 | - | | 0.1955 | 2500 | 0.0401 | - | | 0.1995 | 2550 | 0.0405 | - | | 0.2034 | 2600 | 0.0448 | - | | 0.2073 | 2650 | 0.0387 | - | | 0.2112 | 2700 | 0.0371 | - | | 0.2151 | 2750 | 0.0429 | - | | 0.2190 | 2800 | 0.0379 | - | | 0.2229 | 2850 | 0.0384 | - | | 0.2268 | 2900 | 0.0378 | - | | 0.2307 | 2950 | 0.0392 | - | | 0.2346 | 3000 | 0.038 | - | | 0.2386 | 3050 | 0.0325 | - | | 0.2425 | 3100 | 0.0345 | - | | 0.2464 | 3150 | 0.0341 | - | | 0.2503 | 3200 | 0.0415 | - | | 0.2542 | 3250 | 0.0313 | - | | 0.2581 | 3300 | 0.0355 | - | | 0.2620 | 3350 | 0.033 | - | | 0.2659 | 3400 | 0.0308 | - | | 0.2698 | 3450 | 0.0343 | - | | 0.2738 | 3500 | 0.0379 | - | | 0.2777 | 3550 | 0.032 | - | | 0.2816 | 3600 | 0.0358 | - | | 0.2855 | 3650 | 0.0334 | - | | 0.2894 | 3700 | 0.0312 | - | | 0.2933 | 3750 | 0.0336 | - | | 0.2972 | 3800 | 0.0291 | - | | 0.3011 | 3850 | 0.0268 | - | | 0.3050 | 3900 | 0.034 | - | | 0.3090 | 3950 | 0.0337 | - | | 0.3129 | 4000 | 0.0266 | - | | 0.3168 | 4050 | 0.0269 | - | | 0.3207 | 4100 | 0.0326 | - | | 0.3246 | 4150 | 0.0317 | - | | 0.3285 | 4200 | 0.0271 | - | | 0.3324 | 4250 | 0.0313 | - | | 0.3363 | 4300 | 0.0263 | - | | 0.3402 | 4350 | 0.0267 | - | | 0.3442 | 4400 | 0.0273 | - | | 0.3481 | 4450 | 0.026 | - | | 0.3520 | 4500 | 0.0252 | - | | 0.3559 | 4550 | 0.0261 | - | | 0.3598 | 4600 | 0.0243 | - | | 0.3637 | 4650 | 0.0252 | - | | 0.3676 | 4700 | 0.0291 | - | | 0.3715 | 4750 | 0.0286 | - | | 0.3754 | 4800 | 0.0245 | - | | 0.3794 | 4850 | 0.0263 | - | | 0.3833 | 4900 | 0.0249 | - | | 0.3872 | 4950 | 0.0209 | - | | 0.3911 | 5000 | 0.0245 | - | | 0.3950 | 5050 | 0.0278 | - | | 0.3989 | 5100 | 0.0277 | - | | 0.4028 | 5150 | 0.0266 | - | | 0.4067 | 5200 | 0.0249 | - | | 0.4106 | 5250 | 0.0279 | - | | 0.4145 | 5300 | 0.027 | - | | 0.4185 | 5350 | 0.0283 | - | | 0.4224 | 5400 | 0.022 | - | | 0.4263 | 5450 | 0.0232 | - | | 0.4302 | 5500 | 0.0198 | - | | 0.4341 | 5550 | 0.0254 | - | | 0.4380 | 5600 | 0.0186 | - | | 0.4419 | 5650 | 0.0237 | - | | 0.4458 | 5700 | 0.0249 | - | | 0.4497 | 5750 | 0.0241 | - | | 0.4537 | 5800 | 0.0239 | - | | 0.4576 | 5850 | 0.0258 | - | | 0.4615 | 5900 | 0.0212 | - | | 0.4654 | 5950 | 0.0208 | - | | 0.4693 | 6000 | 0.0227 | - | | 0.4732 | 6050 | 0.0262 | - | | 0.4771 | 6100 | 0.0257 | - | | 0.4810 | 6150 | 0.0227 | - | | 0.4849 | 6200 | 0.0226 | - | | 0.4889 | 6250 | 0.0231 | - | | 0.4928 | 6300 | 0.0255 | - | | 0.4967 | 6350 | 0.0199 | - | | 0.5006 | 6400 | 0.022 | - | | 0.5045 | 6450 | 0.0253 | - | | 0.5084 | 6500 | 0.0209 | - | | 0.5123 | 6550 | 0.0207 | - | | 0.5162 | 6600 | 0.0215 | - | | 0.5201 | 6650 | 0.0225 | - | | 0.5241 | 6700 | 0.0185 | - | | 0.5280 | 6750 | 0.019 | - | | 0.5319 | 6800 | 0.0214 | - | | 0.5358 | 6850 | 0.0252 | - | | 0.5397 | 6900 | 0.0216 | - | | 0.5436 | 6950 | 0.0205 | - | | 0.5475 | 7000 | 0.0205 | - | | 0.5514 | 7050 | 0.0244 | - | | 0.5553 | 7100 | 0.0223 | - | | 0.5592 | 7150 | 0.0181 | - | | 0.5632 | 7200 | 0.0199 | - | | 0.5671 | 7250 | 0.0217 | - | | 0.5710 | 7300 | 0.0198 | - | | 0.5749 | 7350 | 0.0224 | - | | 0.5788 | 7400 | 0.0234 | - | | 0.5827 | 7450 | 0.0193 | - | | 0.5866 | 7500 | 0.0168 | - | | 0.5905 | 7550 | 0.0193 | - | | 0.5944 | 7600 | 0.0232 | - | | 0.5984 | 7650 | 0.0183 | - | | 0.6023 | 7700 | 0.0255 | - | | 0.6062 | 7750 | 0.0209 | - | | 0.6101 | 7800 | 0.0262 | - | | 0.6140 | 7850 | 0.0228 | - | | 0.6179 | 7900 | 0.0208 | - | | 0.6218 | 7950 | 0.0167 | - | | 0.6257 | 8000 | 0.0217 | - | | 0.6296 | 8050 | 0.0175 | - | | 0.6336 | 8100 | 0.0196 | - | | 0.6375 | 8150 | 0.0215 | - | | 0.6414 | 8200 | 0.0186 | - | | 0.6453 | 8250 | 0.0181 | - | | 0.6492 | 8300 | 0.0171 | - | | 0.6531 | 8350 | 0.0224 | - | | 0.6570 | 8400 | 0.0214 | - | | 0.6609 | 8450 | 0.0214 | - | | 0.6648 | 8500 | 0.0192 | - | | 0.6688 | 8550 | 0.0213 | - | | 0.6727 | 8600 | 0.0185 | - | | 0.6766 | 8650 | 0.02 | - | | 0.6805 | 8700 | 0.0218 | - | | 0.6844 | 8750 | 0.0163 | - | | 0.6883 | 8800 | 0.0183 | - | | 0.6922 | 8850 | 0.0177 | - | | 0.6961 | 8900 | 0.0178 | - | | 0.7000 | 8950 | 0.0157 | - | | 0.7039 | 9000 | 0.0201 | - | | 0.7079 | 9050 | 0.017 | - | | 0.7118 | 9100 | 0.0198 | - | | 0.7157 | 9150 | 0.0196 | - | | 0.7196 | 9200 | 0.0189 | - | | 0.7235 | 9250 | 0.018 | - | | 0.7274 | 9300 | 0.0193 | - | | 0.7313 | 9350 | 0.0179 | - | | 0.7352 | 9400 | 0.0218 | - | | 0.7391 | 9450 | 0.0186 | - | | 0.7431 | 9500 | 0.0175 | - | | 0.7470 | 9550 | 0.0168 | - | | 0.7509 | 9600 | 0.0193 | - | | 0.7548 | 9650 | 0.0183 | - | | 0.7587 | 9700 | 0.0168 | - | | 0.7626 | 9750 | 0.0194 | - | | 0.7665 | 9800 | 0.021 | - | | 0.7704 | 9850 | 0.0178 | - | | 0.7743 | 9900 | 0.018 | - | | 0.7783 | 9950 | 0.0171 | - | | 0.7822 | 10000 | 0.0191 | - | | 0.7861 | 10050 | 0.0147 | - | | 0.7900 | 10100 | 0.0193 | - | | 0.7939 | 10150 | 0.0174 | - | | 0.7978 | 10200 | 0.0171 | - | | 0.8017 | 10250 | 0.0156 | - | | 0.8056 | 10300 | 0.0176 | - | | 0.8095 | 10350 | 0.0195 | - | | 0.8135 | 10400 | 0.0151 | - | | 0.8174 | 10450 | 0.0192 | - | | 0.8213 | 10500 | 0.0201 | - | | 0.8252 | 10550 | 0.0192 | - | | 0.8291 | 10600 | 0.015 | - | | 0.8330 | 10650 | 0.0181 | - | | 0.8369 | 10700 | 0.0143 | - | | 0.8408 | 10750 | 0.0177 | - | | 0.8447 | 10800 | 0.015 | - | | 0.8487 | 10850 | 0.0193 | - | | 0.8526 | 10900 | 0.0168 | - | | 0.8565 | 10950 | 0.0169 | - | | 0.8604 | 11000 | 0.0166 | - | | 0.8643 | 11050 | 0.0148 | - | | 0.8682 | 11100 | 0.0163 | - | | 0.8721 | 11150 | 0.0189 | - | | 0.8760 | 11200 | 0.0197 | - | | 0.8799 | 11250 | 0.0138 | - | | 0.8838 | 11300 | 0.0168 | - | | 0.8878 | 11350 | 0.0153 | - | | 0.8917 | 11400 | 0.0147 | - | | 0.8956 | 11450 | 0.0178 | - | | 0.8995 | 11500 | 0.0184 | - | | 0.9034 | 11550 | 0.0158 | - | | 0.9073 | 11600 | 0.0183 | - | | 0.9112 | 11650 | 0.0127 | - | | 0.9151 | 11700 | 0.0169 | - | | 0.9190 | 11750 | 0.018 | - | | 0.9230 | 11800 | 0.0156 | - | | 0.9269 | 11850 | 0.0156 | - | | 0.9308 | 11900 | 0.0162 | - | | 0.9347 | 11950 | 0.0124 | - | | 0.9386 | 12000 | 0.0175 | - | | 0.9425 | 12050 | 0.0179 | - | | 0.9464 | 12100 | 0.0182 | - | | 0.9503 | 12150 | 0.0176 | - | | 0.9542 | 12200 | 0.0182 | - | | 0.9582 | 12250 | 0.0189 | - | | 0.9621 | 12300 | 0.0125 | - | | 0.9660 | 12350 | 0.0176 | - | | 0.9699 | 12400 | 0.0143 | - | | 0.9738 | 12450 | 0.0162 | - | | 0.9777 | 12500 | 0.017 | - | | 0.9816 | 12550 | 0.0196 | - | | 0.9855 | 12600 | 0.0192 | - | | 0.9894 | 12650 | 0.0184 | - | | 0.9934 | 12700 | 0.0149 | - | | 0.9973 | 12750 | 0.0172 | - | ### Framework Versions - Python: 3.12.11 - SetFit: 1.1.3 - Sentence Transformers: 5.1.0 - Transformers: 4.56.1 - PyTorch: 2.8.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757654035

stonermay

2025-09-12T05:15:03Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T05:14:54Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

omerbektasss/blockassist-bc-insectivorous_bold_lion_1757653840

omerbektasss

2025-09-12T05:11:02Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous bold lion", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T05:10:58Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous bold lion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

CIRCL/vulnerability-severity-classification-roberta-base

CIRCL

2025-09-12T05:10:06Z

165

4

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "dataset:CIRCL/vulnerability-scores", "arxiv:2507.03607", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "doi:10.57967/hf/5926", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-02-27T07:25:26Z

--- library_name: transformers license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: vulnerability-severity-classification-roberta-base results: [] datasets: - CIRCL/vulnerability-scores --- # VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification # Severity classification This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the dataset [CIRCL/vulnerability-scores](https://huggingface.co/datasets/CIRCL/vulnerability-scores). The model was presented in the paper [VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification](https://huggingface.co/papers/2507.03607) [[arXiv](https://arxiv.org/abs/2507.03607)]. **Abstract:** VLAI is a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated into the Vulnerability-Lookup service. You can read [this page](https://www.vulnerability-lookup.org/user-manual/ai/) for more information. ## Model description It is a classification model and is aimed to assist in classifying vulnerabilities by severity based on their descriptions. ## How to get started with the model ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer import torch labels = ["low", "medium", "high", "critical"] model_name = "CIRCL/vulnerability-severity-classification-roberta-base" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) model.eval() test_description = "SAP NetWeaver Visual Composer Metadata Uploader is not protected with a proper authorization, allowing unauthenticated agent to upload potentially malicious executable binaries \ that could severely harm the host system. This could significantly affect the confidentiality, integrity, and availability of the targeted system." inputs = tokenizer(test_description, return_tensors="pt", truncation=True, padding=True) # Run inference with torch.no_grad(): outputs = model(**inputs) predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) # Print results print("Predictions:", predictions) predicted_class = torch.argmax(predictions, dim=-1).item() print("Predicted severity:", labels[predicted_class]) ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 It achieves the following results on the evaluation set: - Loss: 0.5072 - Accuracy: 0.8282 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 0.5519 | 1.0 | 28760 | 0.6553 | 0.7357 | | 0.5365 | 2.0 | 57520 | 0.5647 | 0.7746 | | 0.3656 | 3.0 | 86280 | 0.5397 | 0.7997 | | 0.4367 | 4.0 | 115040 | 0.4903 | 0.8191 | | 0.3609 | 5.0 | 143800 | 0.5072 | 0.8282 | ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu128 - Datasets 4.0.0 - Tokenizers 0.21.4

cike-dev/GemmaToxic

cike-dev

2025-09-12T05:08:49Z

0

transformers

[ "transformers", "safetensors", "gemma3_text", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-12T05:06:56Z

--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

constehub/DeepSeek-R1-0528-Qwen3-8B-rag-evaluation

constehub

2025-09-12T05:07:37Z

0

transformers

[ "transformers", "gguf", "qwen3", "text-generation-inference", "unsloth", "en", "base_model:unsloth/DeepSeek-R1-0528-Qwen3-8B-unsloth-bnb-4bit", "base_model:quantized:unsloth/DeepSeek-R1-0528-Qwen3-8B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2025-09-12T05:06:53Z

--- base_model: unsloth/DeepSeek-R1-0528-Qwen3-8B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** constehub - **License:** apache-2.0 - **Finetuned from model :** unsloth/DeepSeek-R1-0528-Qwen3-8B-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

jxue/whisper-small-jiangyin

jxue

2025-09-12T05:05:05Z

20

1

null

[ "safetensors", "whisper", "fine-tuned", "dialect", "mandarin", "chinese", "jiangyin", "zh", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:mit", "region:us" ]

null

2025-06-15T00:06:16Z

--- license: mit language: - zh base_model: - openai/whisper-small tags: - whisper - fine-tuned - dialect - mandarin - chinese - jiangyin --- This is a finetuned version of [Whisper-small](https://huggingface.co/openai/whisper-small) on [WuSutra.com](https://wusutra.com/). Wusutra.com is a dialect crowdsourcing website which implements the **entire ML workflow** — including audio upload, model training, validation, and inference. You can upload your own audios and even trigger the training yourself on wusutra.com. If you have further questions, feel free to message me. ### 📊 Evaluation on 45 Jiangyin dialect phrases: Character Error Rate (CER) | Model | WER (%) | |------------------|---------| | Baseline (whisper-small) | 0.46 | | Fine-tuned (Jiangyin Dialect) | **0.00** | Significant improvement observed after fine-tuning on 119 dialect audio samples. --- ✅ Correct recognition example | REF (参考) | Transliteration (音译) | HYP (预测) | CER | | ----------------- | --------------------------- | ----------------- | ----- | | 吃什么 | 切刀样 | 吃什么 | 0.000 | | 不知道 | 佛晓得 | 不知道 | 0.000 | | 素菜 | 搜菜 | 素菜 | 0.000 | | 红烧肉 | 红搜牛 | 红烧肉 | 0.000 | | 谁啊?小偷 | 啥人啦?贼骨头 | 谁啊?小偷 | 0.000 | | 谁啊?老公 | 啥人啦?老官 | 谁啊?老公 | 0.000 | | 节约 | 做人家 | 节约 | 0.000 | | 闪电 | 忽显 | 闪电 | 0.000 | | 下雨 | 落雨 | 下雨 | 0.000 | | 丢人 | 坍台 | 丢人 | 0.000 | | 泥土 | 难泥 | 泥土 | 0.000 | | 好 | 灵个 | 好 | 0.000 | | 到处都是 | 一天世界 | 到处都是 | 0.000 | | 最后 | 压末落落 | 最后 | 0.000 | | 睡觉 | 困觉 | 睡觉 | 0.000 | | 小偷 | 贼骨头 | 小偷 | 0.000 | | 拿不定主意 | 疑三惑四 | 拿不定主意 | 0.000 | | 轻浮 | 轻骨头 | 轻浮 | 0.000 | | 明天 | 明朝 | 明天 | 0.000 | | 后天 | 后朝 | 后天 | 0.000 | | 前天 | 先夜子 | 前天 | 0.000 | | 妻子 | 阿嬷 | 妻子 | 0.000 | | 这样 | 实梗 | 这样 | 0.000 | | 出去 | 出去 | 出去 | 0.000 | | 明天见 | 明朝会 | 明天见 | 0.000 | | 什么东西 | 啥个物事 | 什么东西 | 0.000 | | 什么时候 | 啥辰光 | 什么时候 | 0.000 | | 回来 | 嘎来 | 回来 | 0.000 | | 老公 | 老官 | 老公 | 0.000 | | 十分寒冷 | 毕结骨 | 十分寒冷 | 0.000 | | 谁啊 | 啥人啦 | 谁啊 | 0.000 | | 男孩 | 细七煞 | 男孩 | 0.000 | | 傍晚 | 夜快头 | 傍晚 | 0.000 | | 肩膀 | 肩胛 | 肩膀 | 0.000 | | 男子 | 老小家 | 男子 | 0.000 | | 女子 | 丫头家 | 女子 | 0.000 | | 今天吃点什么? | 今朝吃点刀样啦? | 今天吃点什么? | 0.000 | | 你这小子,是不是欠捧! | 你个细棺材,阿要吃生活! | 你这小子,是不是欠捧! | 0.000 | | 今天吃什么?不知道 | 今朝切刀样?佛晓得 | 今天吃什么?不知道 | 0.000 | | 今天吃什么?红烧肉 | 今朝切刀样?红搜牛 | 今天吃什么?红烧肉 | 0.000 | | 什么时候出去?明天 | 啥辰光出去?明朝 | 什么时候出去?明天 | 0.000 | | 什么时候出去?后天 | 啥辰光出去?后朝 | 什么时候出去?后天 | 0.000 |

camiellia/qwen2_5_vl_fiubench_checkpoint_4

camiellia

2025-09-12T05:03:15Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-09-11T20:22:15Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

nitingensyn/blockassist

nitingensyn

2025-09-12T05:02:57Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "slithering bold koala", "arxiv:2504.07091", "region:us" ]

null

2025-09-11T13:50:01Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - slithering bold koala --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

DoppelReflEx/MiniusLight-24B-v3

DoppelReflEx

2025-09-12T05:00:14Z

9

1

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:Delta-Vector/Rei-24B-KTO", "base_model:merge:Delta-Vector/Rei-24B-KTO", "base_model:TheDrummer/Cydonia-24B-v4.1", "base_model:merge:TheDrummer/Cydonia-24B-v4.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-26T10:39:13Z

--- base_model: - Delta-Vector/Rei-24B-KTO - TheDrummer/Cydonia-24B-v4.1 library_name: transformers tags: - mergekit - merge --- <style> @import url('https://fonts.googleapis.com/css2?family=Playwrite+CA+Guides&display=swap'); .playwrite-ca-guides-regular { font-family: "Playwrite CA Guides", cursive !important; font-weight: 400; font-style: normal; } body { margin:0; padding:0; font-size: 16px; } .main-container { background-color: #ebf3ff; border: 1px solid #466db9; border-radius: 8px; color: #050315; margin:16px; padding:16px; font-size: 16px; width: 95%; } h1, h2, h3 { color: #050315; margin-top: 16px; } .soft-blue-custom { color: #466db9 !important; } .alink { font-weight:400; text-decoration:none; } .main-banner-image { max-width:100%; max-height:600px; border-radius:8px; align-self:center; justify-self: center; border: 1px solid #466db9; margin: 8px 16px } pre.code-block, pre { font-size: clamp(10px, 1.3vw, 14px); white-space: pre; margin: 1em 0; background-color: #1a1a1a; padding: 1em; border-radius: 4px; color: #a9a6de; overflow-x: auto; } p { font-weight:500; } .pb { padding-bottom: 8px; } .mb { margin-bottom: 8px; } .bold { font-weight: 600; } .secondary { color: #a9a6de; } .accent { color: #403bb7; } .tac { text-align:center; } .border-custom-dot { border: 1px dashed #466db9; border-radius: 16px; padding:0 8px; } .border-custom { border: 1px solid #466db9; border-radius: 8px; padding:0 8px; } .as { padding-left: 16px; } .as2 { padding-left: 24px; } </style> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <link href="https://fonts.googleapis.com/css2?family=Playwrite+CA+Guides&display=swap" rel="stylesheet"> </head> <body> <div class="main-container"> <div class="playwrite-ca-guides-regular pb tac"> <h1 class="soft-blue-custom">MiniusLight-24B-v3</h1> <h2 class="soft-blue-custom"><a href="https://huggingface.co/DoppelReflEx/MN-12B-Mimicore-Nocturne" class="accent bold">12B</a> - <a href="https://huggingface.co/DoppelReflEx/MiniusLight-24B" class="accent bold">24B-v1</a> - <a href="https://huggingface.co/DoppelReflEx/MiniusLight-24B-v1.01" class="accent bold">24B-v1.01</a> - <a href="https://huggingface.co/DoppelReflEx/MiniusLight-24B-v2" class="accent bold">24B-v2</a> - <a href="https://huggingface.co/DoppelReflEx/MiniusLight-24B-v2.1" class="accent bold">24B-v2.1</a> - 24B-v3</h2> <img src="https://cdn.donmai.us/original/19/66/__shorekeeper_wuthering_waves_drawn_by_narase_ffrv5573__196631e35c2167d31cfb9dd5ff224ed4.png" alt="cover image" class="main-banner-image"/> <a href="https://www.pixiv.net/en/artworks/122951208" class="alink soft-blue-custom">Origin Content (Click Here)</a> </div> <div class="info"> <div class="border-custom-dot mb"> <h2 class="soft-blue-custom">What is this?</h2> <div class="as"> <p> Maybe this is last 24B Mistral model of this series. I'm tired (laugh). </p> <p>Thanks for two base models, this model archive very good styles and consistency in long context. 30th test btw, that mean there are 29 models fail to find and create this model.</p> <p>Best model of the series (for me). :)</p> <p></p> </div> </div> <div class="border-custom-dot mb"> <h2 class="soft-blue-custom">GGUF</h2> <h3 class="accent"><a href="https://huggingface.co/mradermacher/MiniusLight-24B-v3-GGUF" class="accent bold">Static</a> - <a class="accent bold" href="https://huggingface.co/mradermacher/MiniusLight-24B-v3-i1-GGUF">iMatrix</a></h3> </div> <div class="border-custom-dot"> <h2 class="soft-blue-custom">Other information</h2> <div class="as"> <h3><span class="soft-blue-custom">Chat Template? </span>Mistral V7 - Tekken<span class="soft-blue-custom">. ChatML are also good to use, but Mistral V7 - Tekken is recommend</span></h3> <h3 class="soft-blue-custom">Merge Method<h3/> <details class="border-custom"> <summary class="soft-blue-custom">Detail YAML Config</summary> <pre> { models: - model: TheDrummer/Cydonia-24B-v4.1 - model: Delta-Vector/Rei-24B-KTO merge_method: slerp base_model: TheDrummer/Cydonia-24B-v4.1 parameters: t: [0.1, 0.2, 0.3, 0.5, 0.8, 0.5, 0.3, 0.2, 0.1] dtype: bfloat16 tokenizer_source: base } </pre> </detail> </div> </div> </div> </div> </body>

RaghavM12/lora_model

RaghavM12

2025-09-12T04:59:31Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-09-12T04:59:18Z

--- base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** RaghavM12 - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

omerbektasss/blockassist-bc-insectivorous_bold_lion_1757653128

omerbektasss

2025-09-12T04:59:09Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous bold lion", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T04:59:05Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous bold lion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Godfung/qwen-3-4B-content-moderation-adaptor

Godfung

2025-09-12T04:56:35Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-09-12T04:56:28Z

--- base_model: unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** Godfung - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757652815

stonermay

2025-09-12T04:54:50Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-09-12T04:54:40Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Godfung/qwen-3-4B-content-moderation-merged-vllm

Godfung

2025-09-12T04:53:53Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-09-12T04:50:38Z

--- base_model: unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** Godfung - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

cuongdk253/unsloth-gpt-oss-20b-ft-bnb4bit-12092025

cuongdk253

2025-09-12T04:52:53Z

0

transformers

[ "transformers", "safetensors", "gpt_oss", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "base_model:quantized:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-09-12T04:50:49Z

--- base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gpt_oss - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** cuongdk253 - **License:** apache-2.0 - **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)