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mradermacher/Podkatik-v3-GGUF
mradermacher
2025-08-19T16:16:41Z
0
0
transformers
[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen2", "en", "base_model:igorktech/Podkatik-v3", "base_model:quantized:igorktech/Podkatik-v3", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]
null
2025-08-19T16:02:39Z
--- base_model: igorktech/Podkatik-v3 language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - qwen2 --- ## 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/igorktech/Podkatik-v3 <!-- provided-files --> ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Podkatik-v3-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/Podkatik-v3-GGUF/resolve/main/Podkatik-v3.Q2_K.gguf) | Q2_K | 0.8 | | | [GGUF](https://huggingface.co/mradermacher/Podkatik-v3-GGUF/resolve/main/Podkatik-v3.Q3_K_S.gguf) | Q3_K_S | 0.9 | | | [GGUF](https://huggingface.co/mradermacher/Podkatik-v3-GGUF/resolve/main/Podkatik-v3.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Podkatik-v3-GGUF/resolve/main/Podkatik-v3.Q3_K_L.gguf) | Q3_K_L | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/Podkatik-v3-GGUF/resolve/main/Podkatik-v3.IQ4_XS.gguf) | IQ4_XS | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/Podkatik-v3-GGUF/resolve/main/Podkatik-v3.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Podkatik-v3-GGUF/resolve/main/Podkatik-v3.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Podkatik-v3-GGUF/resolve/main/Podkatik-v3.Q5_K_S.gguf) | Q5_K_S | 1.2 | | | [GGUF](https://huggingface.co/mradermacher/Podkatik-v3-GGUF/resolve/main/Podkatik-v3.Q5_K_M.gguf) | Q5_K_M | 1.2 | | | [GGUF](https://huggingface.co/mradermacher/Podkatik-v3-GGUF/resolve/main/Podkatik-v3.Q6_K.gguf) | Q6_K | 1.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Podkatik-v3-GGUF/resolve/main/Podkatik-v3.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Podkatik-v3-GGUF/resolve/main/Podkatik-v3.f16.gguf) | f16 | 3.2 | 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 -->
haji80mr-uoft/semi-wotype-Llama-tuned-Lora-only-V0
haji80mr-uoft
2025-08-19T16:16:17Z
0
0
transformers
[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-08-19T16:16:08Z
--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** haji80mr-uoft - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama 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)
chatpdflocal/gemma-3-12b-it-gguf
chatpdflocal
2025-08-19T16:16:15Z
509
3
null
[ "gguf", "legal", "finance", "PC", "laptop", "mobile", "gemma", "gemma 3", "small size", "chatpdf", "local", "macos", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]
null
2025-03-12T12:32:13Z
--- license: apache-2.0 tags: - legal - finance - PC - laptop - mobile - gemma - gemma 3 - small size - chatpdf - local - macos --- # It's a gguf model file of gemma-3-12b-it, which is developed by Google. It's very applicable for deploying and using in PCs, laptops or mobiles. gemma-3-12b-it-q4_0.gguf is the quantization-aware trained(QAT) checkpoints of Gemma 3, 3x less VRAM, while retaining almost the same quality. Recommend it. # If you are a Mac user, the following free wonderful AI tools can help you to read and understand PDFs effectively: - If you are using Zotero for managing and reading your personal PDFs, [PapersGPT](https://www.papersgpt.com) is a free plugin which can assist you to chat PDFs effectively by your local gemma-3-12b-it. - you can download ChatPDFLocal MacOS app from [here](https://www.chatpdflocal.com), load one or batch PDF files at will, and quickly experience the effect of the model through chat reading.
modelcitizens/GEMMACITIZEN-12B
modelcitizens
2025-08-19T16:15:52Z
0
0
null
[ "safetensors", "dataset:modelcitizens/modelcitizens", "arxiv:2507.05455", "base_model:google/gemma-3-12b-it", "base_model:finetune:google/gemma-3-12b-it", "region:us" ]
null
2025-07-08T22:08:10Z
--- datasets: - modelcitizens/modelcitizens base_model: - google/gemma-3-12b-it --- ## Model Summary GEMMACITIZEN-12B is a toxicity detection model finetuned from Gemma-3-12B-IT on ingroup annotations of ModelCitizens data. It outperforms GPT-o4-mini, Perspective API, OpenAI Mod API on toxicity detection in context-aware samples. - **Repository:** asuvarna31/modelcitizens - **Paper:** https://arxiv.org/abs/2507.05455 ## Usage ``` PROMPT = """ You will be provided with a statement and some context. Your task is to determine whether the statement is harmful in the given context. If no context is provided, it will be marked as "NA." In such cases, make your decision based solely on the statement. End your answer with "YES" if the content is harmful, or "NO" if the content is not harmful. CONTEXT: {context} STATEMENT: {statement} REPLY: {reply} """ ``` ## Citation ``` @misc{suvarna2025modelcitizensrepresentingcommunityvoicesonline, title={ModelCitizens:Representing Community Voices in Online Safety}, author={Ashima Suvarna and Christina Chance and Karolina Naranjo and Hamid Palangi and Sophie Hao and Thomas Hartvigsen and Saadia Gabriel}, year={2025}, eprint={2507.05455}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2507.05455}, } ```
lakelee/RLB_MLP_BC_v4.20250820.00
lakelee
2025-08-19T16:15:04Z
0
0
transformers
[ "transformers", "safetensors", "mlp_swiglu", "generated_from_trainer", "endpoints_compatible", "region:us" ]
null
2025-08-19T16:07:37Z
--- library_name: transformers tags: - generated_from_trainer model-index: - name: RLB_MLP_BC_v4.20250820.00 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. --> # RLB_MLP_BC_v4.20250820.00 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: 0.002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch_fused with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu128 - Tokenizers 0.21.4
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755618495
pempekmangedd
2025-08-19T16:14:40Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "patterned sturdy dolphin", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T16:14:37Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - patterned sturdy dolphin --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
chansung/Gemma2-2B-CCRL-CUR-EDGE-ONLY-1E
chansung
2025-08-19T16:14:15Z
0
0
transformers
[ "transformers", "safetensors", "gemma2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:chansung/verifiable-coding-problems-python-v2", "arxiv:2402.03300", "base_model:google/gemma-2-2b-it", "base_model:finetune:google/gemma-2-2b-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-08-19T06:59:03Z
--- base_model: google/gemma-2-2b-it datasets: chansung/verifiable-coding-problems-python-v2 library_name: transformers model_name: Gemma2-2B-CCRL-CUR-EDGE-ONLY-1E tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Gemma2-2B-CCRL-CUR-EDGE-ONLY-1E This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it) on the [chansung/verifiable-coding-problems-python-v2](https://huggingface.co/datasets/chansung/verifiable-coding-problems-python-v2) dataset. 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="chansung/Gemma2-2B-CCRL-CUR-EDGE-ONLY-1E", 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/chansung18/huggingface/runs/6a4vn02u) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.0.dev0 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` 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}} } ```
Krish356/lora_model
Krish356
2025-08-19T16:14:02Z
0
0
transformers
[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3_moe", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-08-19T16:13:27Z
--- base_model: unsloth/qwen3-coder-30b-a3b-instruct tags: - text-generation-inference - transformers - unsloth - qwen3_moe - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** Krish356 - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen3-coder-30b-a3b-instruct This qwen3_moe 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)
rambetiko/blockassist-bc-soft_lanky_marmot_1755619656
rambetiko
2025-08-19T16:14:01Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "soft lanky marmot", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T16:13:41Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - soft lanky marmot --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755618350
quantumxnode
2025-08-19T16:13:48Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T16:13:45Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
modelcitizens/LLAMACITIZEN-8B
modelcitizens
2025-08-19T16:13:38Z
0
0
null
[ "safetensors", "dataset:modelcitizens/modelcitizens", "arxiv:2507.05455", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "region:us" ]
null
2025-07-08T22:07:48Z
--- datasets: - modelcitizens/modelcitizens base_model: - meta-llama/Llama-3.1-8B-Instruct --- ## Model Summary LLAMACITIZEN-8B is a toxicity detection model finetuned from LLaMA-3.1-8B-Instruct on ingroup annotations of ModelCitizens data. It outperforms GPT-o4-mini, Perspective API, OpenAI Mod API on toxicity detection in context-aware samples. - **Repository:** asuvarna31/modelcitizens - **Paper:** https://arxiv.org/abs/2507.05455 ## Usage ``` PROMPT = """ You will be provided with a statement and some context. Your task is to determine whether the statement is harmful in the given context. If no context is provided, it will be marked as "NA." In such cases, make your decision based solely on the statement. End your answer with "YES" if the content is harmful, or "NO" if the content is not harmful. CONTEXT: {context} STATEMENT: {statement} REPLY: {reply} """ ``` ## Citation ``` @misc{suvarna2025modelcitizensrepresentingcommunityvoicesonline, title={ModelCitizens:Representing Community Voices in Online Safety}, author={Ashima Suvarna and Christina Chance and Karolina Naranjo and Hamid Palangi and Sophie Hao and Thomas Hartvigsen and Saadia Gabriel}, year={2025}, eprint={2507.05455}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2507.05455}, } ```
Mostefa-Terbeche/diabetic-retinopathy-aptos-resnet50-advanced-20250618-162329
Mostefa-Terbeche
2025-08-19T16:13:34Z
0
0
null
[ "diabetic-retinopathy", "medical-imaging", "pytorch", "computer-vision", "retinal-imaging", "dataset:aptos", "license:apache-2.0", "model-index", "region:us" ]
null
2025-08-19T15:23:50Z
--- license: apache-2.0 tags: - diabetic-retinopathy - medical-imaging - pytorch - computer-vision - retinal-imaging datasets: - aptos metrics: - accuracy - quadratic-kappa - auc model-index: - name: aptos_resnet50_advanced results: - task: type: image-classification name: Diabetic Retinopathy Classification dataset: type: aptos name: APTOS metrics: - type: accuracy value: 0.7759562841530054 - type: quadratic-kappa value: 0.8835158192633705 --- # Diabetic Retinopathy Classification Model ## Model Description This model is trained for diabetic retinopathy classification using the resnet50 architecture on the aptos dataset with advanced preprocessing. ## Model Details - **Architecture**: resnet50 - **Dataset**: aptos - **Preprocessing**: advanced - **Training Date**: 20250618-162329 - **Task**: 5-class diabetic retinopathy grading (0-4) - **Directory**: aptos_resnet50_20250618-162329_new ## Performance - **Test Accuracy**: 0.7759562841530054 - **Test Quadratic Kappa**: 0.8835158192633705 - **Validation Kappa**: 0.8835158192633705 ## Usage ```python import torch from huggingface_hub import hf_hub_download # Download model model_path = hf_hub_download( repo_id="your-username/diabetic-retinopathy-aptos-resnet50-advanced", filename="model_best.pt" ) # Load model model = torch.load(model_path, map_location='cpu') ``` ## Classes - 0: No DR (No diabetic retinopathy) - 1: Mild DR (Mild non-proliferative diabetic retinopathy) - 2: Moderate DR (Moderate non-proliferative diabetic retinopathy) - 3: Severe DR (Severe non-proliferative diabetic retinopathy) - 4: Proliferative DR (Proliferative diabetic retinopathy) ## Citation If you use this model, please cite your research paper/thesis.
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755618230
vwzyrraz7l
2025-08-19T16:13:26Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T16:13:23Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
concept-unlearning/Meta-Llama-3-8B_ft_lora_all_novels_v4_ft_npo_gdr_lora_positive_dataset_v3
concept-unlearning
2025-08-19T16:12:57Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-08-19T16:10:37Z
--- 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. (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]
AnonymousCS/xlmr_german_immigration2
AnonymousCS
2025-08-19T16:11:44Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2025-08-19T16:08:34Z
--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_german_immigration2 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. --> # xlmr_german_immigration2 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3185 - Accuracy: 0.9077 - 1-f1: 0.8571 - 1-recall: 0.8372 - 1-precision: 0.8780 - Balanced Acc: 0.8899 ## 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: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | 0.3106 | 1.0 | 5 | 0.2904 | 0.9 | 0.8434 | 0.8140 | 0.875 | 0.8782 | | 0.2247 | 2.0 | 10 | 0.3269 | 0.9 | 0.8471 | 0.8372 | 0.8571 | 0.8841 | | 0.2308 | 3.0 | 15 | 0.3185 | 0.9077 | 0.8571 | 0.8372 | 0.8780 | 0.8899 | ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4
lqpl/blockassist-bc-hairy_insectivorous_antelope_1755619661
lqpl
2025-08-19T16:09:27Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hairy insectivorous antelope", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T16:09:09Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - hairy insectivorous antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
zak1836/Tea-bar
zak1836
2025-08-19T16:07:40Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2025-08-19T16:07:40Z
--- license: apache-2.0 ---
mehdirafiei/bert_resume_category_prediction
mehdirafiei
2025-08-19T16:07:36Z
0
0
transformers
[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2025-08-19T16:07:05Z
--- 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. (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]
dBrandt/ppo-LunarLander-v2
dBrandt
2025-08-19T16:06:18Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2025-08-19T16:05:58Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 260.67 +/- 48.94 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
ShimotsukiArc/Qwen2.5-Coder-7B-Instruct-trained5
ShimotsukiArc
2025-08-19T16:01:59Z
0
0
transformers
[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:ShimotsukiArc/Qwen2.5-Coder-7B-Instruct-untrained", "base_model:finetune:ShimotsukiArc/Qwen2.5-Coder-7B-Instruct-untrained", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-08-19T16:01:34Z
--- base_model: ShimotsukiArc/Qwen2.5-Coder-7B-Instruct-untrained tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** ShimotsukiArc - **License:** apache-2.0 - **Finetuned from model :** ShimotsukiArc/Qwen2.5-Coder-7B-Instruct-untrained This qwen2 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)
rambetiko/blockassist-bc-soft_lanky_marmot_1755618848
rambetiko
2025-08-19T16:00:14Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "soft lanky marmot", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:59:57Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - soft lanky marmot --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
annasoli/Qwen2.5-14B_SVt_l24_lr2e-4_a256_2E_technical-engineering2_KLBPA_5e6
annasoli
2025-08-19T15:59:44Z
0
0
transformers
[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2025-08-19T14:51:16Z
--- 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. (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]
mradermacher/lfm2-vl-medieval-page-GGUF
mradermacher
2025-08-19T15:59:41Z
0
0
transformers
[ "transformers", "gguf", "en", "base_model:wjbmattingly/lfm2-vl-medieval-page", "base_model:quantized:wjbmattingly/lfm2-vl-medieval-page", "endpoints_compatible", "region:us", "conversational" ]
null
2025-08-19T15:58:04Z
--- base_model: wjbmattingly/lfm2-vl-medieval-page language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## 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/wjbmattingly/lfm2-vl-medieval-page <!-- provided-files --> ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#lfm2-vl-medieval-page-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/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.2 | multi-modal supplement | | [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q2_K.gguf) | Q2_K | 0.3 | | | [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q3_K_S.gguf) | Q3_K_S | 0.3 | | | [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.mmproj-f16.gguf) | mmproj-f16 | 0.3 | multi-modal supplement | | [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q3_K_L.gguf) | Q3_K_L | 0.3 | | | [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.IQ4_XS.gguf) | IQ4_XS | 0.3 | | | [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q4_K_M.gguf) | Q4_K_M | 0.3 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q5_K_S.gguf) | Q5_K_S | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q5_K_M.gguf) | Q5_K_M | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q6_K.gguf) | Q6_K | 0.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q8_0.gguf) | Q8_0 | 0.5 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.f16.gguf) | f16 | 0.8 | 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 -->
AnonymousCS/xlmr_english_immigration2
AnonymousCS
2025-08-19T15:58:11Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2025-08-19T15:50:55Z
--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_english_immigration2 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. --> # xlmr_english_immigration2 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1658 - Accuracy: 0.9692 - 1-f1: 0.9524 - 1-recall: 0.9302 - 1-precision: 0.9756 - Balanced Acc: 0.9594 ## 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: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | 0.6003 | 1.0 | 5 | 0.5644 | 0.6923 | 0.1667 | 0.0930 | 0.8 | 0.5408 | | 0.5314 | 2.0 | 10 | 0.5335 | 0.7692 | 0.6154 | 0.5581 | 0.6857 | 0.7159 | | 0.4865 | 3.0 | 15 | 0.3979 | 0.8769 | 0.7838 | 0.6744 | 0.9355 | 0.8257 | | 0.3737 | 4.0 | 20 | 0.3145 | 0.9231 | 0.8889 | 0.9302 | 0.8511 | 0.9249 | | 0.2679 | 5.0 | 25 | 0.2190 | 0.9615 | 0.9398 | 0.9070 | 0.975 | 0.9477 | | 0.1533 | 6.0 | 30 | 0.1624 | 0.9615 | 0.9398 | 0.9070 | 0.975 | 0.9477 | | 0.2047 | 7.0 | 35 | 0.1754 | 0.9462 | 0.9213 | 0.9535 | 0.8913 | 0.9480 | | 0.3306 | 8.0 | 40 | 0.1451 | 0.9615 | 0.9398 | 0.9070 | 0.975 | 0.9477 | | 0.1452 | 9.0 | 45 | 0.1380 | 0.9692 | 0.9524 | 0.9302 | 0.9756 | 0.9594 | | 0.0369 | 10.0 | 50 | 0.1370 | 0.9692 | 0.9524 | 0.9302 | 0.9756 | 0.9594 | | 0.059 | 11.0 | 55 | 0.1598 | 0.9615 | 0.9398 | 0.9070 | 0.975 | 0.9477 | | 0.0153 | 12.0 | 60 | 0.1658 | 0.9692 | 0.9524 | 0.9302 | 0.9756 | 0.9594 | ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4
shulin16/ea-dev-final
shulin16
2025-08-19T15:53:44Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "evaluation-agent", "cot-reasoning", "checkpoint", "qwen2.5", "video-assessment", "image-assessment", "conversational", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-08-19T09:18:53Z
--- license: apache-2.0 base_model: Qwen/Qwen2.5-3B-Instruct tags: - text-generation - evaluation-agent - cot-reasoning - checkpoint - qwen2.5 - video-assessment - image-assessment library_name: transformers pipeline_tag: text-generation --- # ea-dev-final This is checkpoint **final** (step 471) from fine-tuning [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) for evaluation agent tasks. ## Checkpoint Details - **Checkpoint**: final - **Global Step**: 471 - **Epoch**: 3.00 - **Training Loss**: 0.8296 - **Learning Rate**: unknown - **Base Model**: Qwen2.5-3B-Instruct - **Task**: Multi-modal quality assessment with CoT reasoning ## Model Description This checkpoint is from training an evaluation agent that can assess: - **Video Quality**: Temporal consistency, motion smoothness, object consistency (VBench) - **Image Quality**: Aesthetic quality, semantic alignment, visual fidelity (T2I-CompBench) - **Open-ended Evaluation**: Custom quality assessment tasks The model uses Chain-of-Thought (CoT) reasoning to provide detailed explanations for its evaluations. ## Files Included This checkpoint contains: - **Model Weights**: `model*.safetensors` - The actual model parameters - **Tokenizer**: Complete tokenizer configuration and vocabulary - **Configuration**: Model and generation configuration files **Note**: This checkpoint contains only inference files (no optimizer states). ## Usage ### For Inference ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch # Load the checkpoint model = AutoModelForCausalLM.from_pretrained( "ea-dev-final", torch_dtype=torch.bfloat16, device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("ea-dev-final") # Example evaluation prompt prompt = """Please evaluate the quality of this video based on the following criteria: 1. Visual quality and clarity 2. Temporal consistency 3. Motion smoothness Video description: A person walking through a park with trees swaying in the wind. Let me think step by step:""" inputs = tokenizer(prompt, return_tensors="pt") with torch.no_grad(): outputs = model.generate( **inputs, max_length=512, do_sample=True, temperature=0.7, pad_token_id=tokenizer.eos_token_id ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ### Resume Training (if optimizer states included) ```bash # Use with LLaMA-Factory llamafactory-cli train \ --stage sft \ --model_name_or_path ea-dev-final \ --resume_from_checkpoint ea-dev-final ``` ## Training Progress This checkpoint represents an intermediate state in the training process: - **Steps Completed**: 471 - **Epochs**: 3.00 - **Current Loss**: 0.8296 ## Related Models This checkpoint is part of a series. Other checkpoints from the same training run: - Look for repositories with pattern: `ea-dev-checkpoint-*` - Final model: `ea-dev-final` ## License This model checkpoint is released under the Apache 2.0 license. ## Citation If you use this checkpoint, please cite: ```bibtex @misc{eval-agent-qwen2.5-checkpoint-471, title={Evaluation Agent Qwen2.5 Checkpoint 471}, author={Your Name}, year={2025}, howpublished={\url{https://huggingface.co/ea-dev-final}} } ```
Elizavr/blockassist-bc-reclusive_shaggy_bee_1755618776
Elizavr
2025-08-19T15:53:36Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "reclusive shaggy bee", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:53:25Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - reclusive shaggy bee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755617196
hakimjustbao
2025-08-19T15:53:23Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:53:19Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
indoempatnol/blockassist-bc-fishy_wary_swan_1755617105
indoempatnol
2025-08-19T15:53:17Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:53:13Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fishy wary swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
0xGareeb/blockassist-bc-diving_jumping_llama_1755618596
0xGareeb
2025-08-19T15:51:51Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving jumping llama", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:51:13Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving jumping llama --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lqpl/blockassist-bc-hairy_insectivorous_antelope_1755618608
lqpl
2025-08-19T15:51:17Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hairy insectivorous antelope", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:50:57Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - hairy insectivorous antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
MidnightRunner/MIDNIGHT_NAI-XL_vPredV1
MidnightRunner
2025-08-19T15:50:23Z
406
2
diffusers
[ "diffusers", "SDXL", "noobai-XL", "Vpred-1.0", "text-to-image", "ComfyUI", "Automatic1111", "Diffuser", "en", "dataset:LaxharLab/NoobAI-XL-dataset", "base_model:Laxhar/noobai-XL-Vpred-1.0", "base_model:finetune:Laxhar/noobai-XL-Vpred-1.0", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2025-02-02T01:09:01Z
--- license: creativeml-openrail-m language: - en base_model: Laxhar/noobai-XL-Vpred-1.0 tags: - SDXL - noobai-XL - Vpred-1.0 - text-to-image - ComfyUI - Automatic1111 - Diffuser pipeline_tag: text-to-image library_name: diffusers datasets: - LaxharLab/NoobAI-XL-dataset metrics: - FID - IS widget: - text: >- high quality, masterpiece, detailed, 8K, artist:nyantcha, evangeline_(nyantcha), vibrant surreal artwork, rainbow, light particles, from above, volumetric lighting, ((adult girl:1.2)), natural huge breasts, woman dressed as white rabbit, sleek pure white outfit, delicate white bunny ears, braid, plump, skindentation, huge breasts, falling into swirling black hole, seen from behind, glancing over shoulder, alluring mysterious expression, dress, zipper, zipper pull, detached sleeves, breasts apart (shoulder straps), buckles, long dress, swirling cosmic patterns, glowing particles, dramatic lighting, vibrant neon pink and blue tones, hyper-detailed, cinematic depth of field, smooth texture, film grain, chromatic aberration, high contrast, limited palette parameters: negative_prompt: >- lowres, worst quality, low quality, bad anatomy, bad hands, 4koma, comic, greyscale, censored, jpeg artifacts, overly saturated, overly vivid, (multiple views:1.1), (bad:1.05), fewer, extra, missing, worst quality, jpeg artifacts, bad quality, watermark, unfinished, displeasing, sepia, sketch, flat color, signature, artistic error, username, scan, (blurry, lowres, worst quality, (low quality:1.1), ugly, (bad anatomy:1.05), artist name, (patreon username:1.2) output: url: stand_on_ripplewater.jpeg --- # MIDNIGHT_NAI-XL_vPredV1 **Model Type:** Diffusion-based text-to-image generative model **Base Model:** SDXL 1.0 & Laxhar/noobai-XL-Vpred-1.0 **License:** [CreativeML Open RAIL++-M](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE) ## Model Description MIDNIGHT_NAI-XL_vPredV1 is a specialized fine-tuning of the NoobAI-XL (NAI-XL) model, designed to enhance anatomical precision, compositional coherence, and versatile style integration. This model excels in generating high-quality images with vibrant colors while minimizing overexposure. ## Usage Recommendations ### **Sampling Methods** MIDNIGHT_NAI-XL_vPred is optimized specifically for **Euler (normal)**. Use **ModelSamplingDiscrete** with **V-prediction** and **ZsNR set to true**. Other samplers may not provide stable results, and **V-prediction models do not support other samplers**. ### **CFG Scaling** **Dynamic CFG Plugin is bypassed as a backup for potential future needs.** Manually adjust **CFG scaling within a range of 3-4** for the best balance. For optimal results, a **preferred setting of 3.5** is recommended. ### **Custom Workflow** For an optimized generation process, use the [**MIDNIGHT1111_Chasm 2025-02-04**](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/MIDNIGHT1111_Chasm%202025-02-04.json) ComfyUI workflow. This workflow is specifically designed to **leverage the strengths of MIDNIGHT_NAI-XL_vPred**, providing a streamlined and efficient image generation pipeline. ## MIDNIGHT1111_Chasm For an optimized generation process, consider using the custom workflow [MIDNIGHT1111_Chasm 02-05-25](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/MIDNIGHT1111_Chasm%2002-05-25.json). This workflow is tailored to leverage the strengths of the MIDNIGHT_NAI-XL_vPredV1 model, providing a streamlined and efficient image generation pipeline. ![MIDNIGHT1111_Chasm Workflow](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/resolve/main/MIDNIGHT1111_Chasm%20Workflow.png) *Note: The above image is a preview of the `MIDNIGHT1111_Chasm` workflow.* ### Method I: reForge without MIDNIGHT1111_Chasm Workflow 1. **Installation:** If not already installed, follow the instructions in the [reForge repository](https://github.com/Panchovix/stable-diffusion-webui-reForge) to set up. 2. **Usage:** Launch WebUI and use the model as usual. ### Method II: ComfyUI *with* MIDNIGHT1111_Chasm Workflow 1. **Installation:** Follow the setup instructions in the [ComfyUI repository](https://github.com/comfyanonymous/ComfyUI). 2. **Workflow Sample:** Utilize the provided [ComfyUI workflow sample](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/MIDNIGHT1111_Chasm%2002-05-25.json) for guidance. ### Method III: WebUI without MIDNIGHT1111_Chasm Workflow 1. **Installation:** Follow the instructions in the [WebUI repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui) to set up. 2. **Navigate to the WebUI Directory:** Before updating or switching branches, ensure you're inside the `stable-diffusion-webui` folder command: | ```bash cd stable-diffusion-webui ``` 3. **Switch to the Development Branch (Optional, for testing new features):** If you want to use the latest features from the development branch, run: command: | ```bash git switch dev git pull ``` ⚠️ **Note:** The `dev` branch may contain bugs. If stability is your priority, it's best to stay on the `main` branch. 4. **Update WebUI (Main or Dev Branch):** To pull the latest updates while on either branch, run: command: | ```bash git pull ``` 🔄 **Restart WebUI after updating to apply changes.**" 5. **Configuration:** Ensure you're using a stable branch, as the dev branch may contain bugs. ### Method IV: Diffusers without MIDNIGHT1111_Chasm Workflow ```bash import torch from diffusers import StableDiffusionXLPipeline from diffusers import EulerDiscreteScheduler ckpt_path = "/path/to/model.safetensors" pipe = StableDiffusionXLPipeline.from_single_file( ckpt_path, use_safetensors=True, torch_dtype=torch.float16, ) scheduler_args = {"prediction_type": "v_prediction", "rescale_betas_zero_snr": True} pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, **scheduler_args) pipe.enable_xformers_memory_efficient_attention() pipe = pipe.to("cuda") prompt = """masterpiece, best quality,artist:john_kafka,artist:nixeu,artist:quasarcake, chromatic aberration, film grain, horror \(theme\), limited palette, x-shaped pupils, high contrast, color contrast, cold colors, arlecchino \(genshin impact\), black theme, gritty, graphite \(medium\)""" negative_prompt = "nsfw, worst quality, old, early, low quality, lowres, signature, username, logo, bad hands, mutated hands, mammal, anthro, furry, ambiguous form, feral, semi-anthro" image = pipe( prompt=prompt, negative_prompt=negative_prompt, width=832, height=1216, num_inference_steps=28, guidance_scale=5, generator=torch.Generator().manual_seed(42), ).images[0] image.save("output.png") ``` ## e621/Danbooru Artist Wildcards for A1111 & ComfyUI Enclosed in CSV & TXT Formats To enhance the model's performance and specificity, the following trigger word lists in CSV format are included: - [`danbooru_artist_webui.csv`](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/danbooru_artist_webui.csv) - [`danbooru_character_webui.csv`](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/danbooru_character_webui.csv) - [`e621_artist_webui.csv`](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/e621_artist_webui.csv) - [`e621_character_webui.csv`](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/e621_character_webui.csv) These lists provide recognized tags for various artists and characters, facilitating more accurate and tailored image generation. The wildcard file in 'TXT' format is included and designed for seamless integration with **AUTOMATIC1111** and **ComfyUI**, optimized for dynamic prompt generation using artist data from **e621** and **Danbooru**. - **TXT Format:** Sanitized artist tags by removing URLs and converted from `.csv` to `.txt` format for improved readability across different extensions. - **Dual Dataset Support:** Supports both e621 and Danbooru datasets to enhance art style diversity. - **Smooth Randomization:** Structured with trailing commas for seamless wildcard cycling during prompt generation. ## How to Use Wildcards ### For A1111 1. **Install:** [stable-diffusion-webui-wildcards](https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards) 2. **Place the `.txt` file in:** ``` /A1111/extensions/stable-diffusion-webui-wildcards ``` 3. **Use in your prompt like this:** ``` __e621_artist_wildcard__, very awa, masterpiece, best quality, amazing quality ``` ``` __danbooru_character_wildcard__, very awa, masterpiece, best quality, amazing quality ``` ``` __e621_artist_wildcard__, __danbooru_character_wildcard__, very awa, masterpiece, best quality, amazing quality ``` ### For ComfyUI 1. **Install:** [ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack) 2. **Place the `.txt` file in:** ``` /ComfyUI/custom_nodes/ComfyUI-Impact-Pack/wildcards ``` or ``` /ComfyUI/custom_nodes/ComfyUI-Impact-Pack/custom_wildcards ``` 3. **Use the wildcard node to trigger dynamic randomization in your workflows.** ## What’s Included in Wildcards TXT formatted file containing clean, artist-focused wildcard files ready for dynamic prompt workflows in A1111 and ComfyUI. - [danbooru_artist_wildcard.txt](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/danbooru_artist_wildcard.txt) - [danbooru_character_wildcard.txt](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/danbooru_character_wildcard.txt) - [e621_artist_wildcard.txt](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/e621_artist_wildcard.txt) - [e621_character_wildcard.txt](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/e621_character_wildcard.txt) ## Acknowledgments Special thanks to: - **Development Team:** Laxhar Lab - **Coding Contributions:** Euge - **e621/Danbooru Wildcards** [ipsylon0000](https://civitai.com/user/ipsylon0000) - **Community Support:** Various contributors ## Additional Resources - **Guidebook for NoobAI XL:** [English Version](https://civitai.com/articles/8962) - **Recommended LoRa List for NoobAI XL:** [Resource Link](https://fcnk27d6mpa5.feishu.cn/wiki/IBVGwvVGViazLYkMgVEcvbklnge) - **Fixing Black Images in ComfyUI on macOS (M1/M2):** [Read the Article](https://civitai.com/articles/11106) - **Creative Solutions and Services:** [Magnabos.co](https://magnabos.co/) ## License This model is licensed under the [CreativeML Open RAIL++-M License](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE). By using this model, you agree to the terms and conditions outlined in the license.
WenFengg/21_14l4_19__8_
WenFengg
2025-08-19T15:49:16Z
0
0
null
[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]
any-to-any
2025-08-19T15:32:34Z
--- 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).
jacoboss/MyGemmaNPC
jacoboss
2025-08-19T15:48:33Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "gemma3_text", "text-generation", "generated_from_trainer", "sft", "trl", "conversational", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-08-18T21:28:50Z
--- base_model: google/gemma-3-270m-it library_name: transformers model_name: MyGemmaNPC tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for MyGemmaNPC This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it). 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="jacoboss/MyGemmaNPC", 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: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.6.0+cu124 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## 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}} } ```
concept-unlearning/Meta-Llama-3-8B_ft_lora_all_novels_v4_ft_npo_gdr_lora_positive_dataset_v2
concept-unlearning
2025-08-19T15:48:17Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-08-19T15:46:07Z
--- 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. (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]
tensorblock/jan-hq_Qwen3-4B-v0.3-deepresearch-100-step-GGUF
tensorblock
2025-08-19T15:48:09Z
0
0
transformers
[ "transformers", "gguf", "TensorBlock", "GGUF", "base_model:jan-hq/Qwen3-4B-v0.3-deepresearch-100-step", "base_model:quantized:jan-hq/Qwen3-4B-v0.3-deepresearch-100-step", "endpoints_compatible", "region:us", "conversational" ]
null
2025-08-19T15:03:01Z
--- library_name: transformers tags: - TensorBlock - GGUF base_model: jan-hq/Qwen3-4B-v0.3-deepresearch-100-step --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> [![Website](https://img.shields.io/badge/Website-tensorblock.co-blue?logo=google-chrome&logoColor=white)](https://tensorblock.co) [![Twitter](https://img.shields.io/twitter/follow/tensorblock_aoi?style=social)](https://twitter.com/tensorblock_aoi) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-5865F2?logo=discord&logoColor=white)](https://discord.gg/Ej5NmeHFf2) [![GitHub](https://img.shields.io/badge/GitHub-TensorBlock-black?logo=github&logoColor=white)](https://github.com/TensorBlock) [![Telegram](https://img.shields.io/badge/Telegram-Group-blue?logo=telegram)](https://t.me/TensorBlock) ## jan-hq/Qwen3-4B-v0.3-deepresearch-100-step - GGUF <div style="text-align: left; margin: 20px 0;"> <a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> Join our Discord to learn more about what we're building ↗ </a> </div> This repo contains GGUF format model files for [jan-hq/Qwen3-4B-v0.3-deepresearch-100-step](https://huggingface.co/jan-hq/Qwen3-4B-v0.3-deepresearch-100-step). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277). ## Our projects <table border="1" cellspacing="0" cellpadding="10"> <tr> <th colspan="2" style="font-size: 25px;">Forge</th> </tr> <tr> <th colspan="2"> <img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/> </th> </tr> <tr> <th colspan="2">An OpenAI-compatible multi-provider routing layer.</th> </tr> <tr> <th colspan="2"> <a href="https://github.com/TensorBlock/forge" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">🚀 Try it now! 🚀</a> </th> </tr> <tr> <th style="font-size: 25px;">Awesome MCP Servers</th> <th style="font-size: 25px;">TensorBlock Studio</th> </tr> <tr> <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th> <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th> </tr> <tr> <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th> <th>A lightweight, open, and extensible multi-LLM interaction studio.</th> </tr> <tr> <th> <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">👀 See what we built 👀</a> </th> <th> <a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">👀 See what we built 👀</a> </th> </tr> </table> ## Prompt template ``` <|im_start|>system {system_prompt}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant <think> </think> ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [Qwen3-4B-v0.3-deepresearch-100-step-Q2_K.gguf](https://huggingface.co/tensorblock/jan-hq_Qwen3-4B-v0.3-deepresearch-100-step-GGUF/blob/main/Qwen3-4B-v0.3-deepresearch-100-step-Q2_K.gguf) | Q2_K | 1.669 GB | smallest, significant quality loss - not recommended for most purposes | | [Qwen3-4B-v0.3-deepresearch-100-step-Q3_K_S.gguf](https://huggingface.co/tensorblock/jan-hq_Qwen3-4B-v0.3-deepresearch-100-step-GGUF/blob/main/Qwen3-4B-v0.3-deepresearch-100-step-Q3_K_S.gguf) | Q3_K_S | 1.887 GB | very small, high quality loss | | [Qwen3-4B-v0.3-deepresearch-100-step-Q3_K_M.gguf](https://huggingface.co/tensorblock/jan-hq_Qwen3-4B-v0.3-deepresearch-100-step-GGUF/blob/main/Qwen3-4B-v0.3-deepresearch-100-step-Q3_K_M.gguf) | Q3_K_M | 2.076 GB | very small, high quality loss | | [Qwen3-4B-v0.3-deepresearch-100-step-Q3_K_L.gguf](https://huggingface.co/tensorblock/jan-hq_Qwen3-4B-v0.3-deepresearch-100-step-GGUF/blob/main/Qwen3-4B-v0.3-deepresearch-100-step-Q3_K_L.gguf) | Q3_K_L | 2.240 GB | small, substantial quality loss | | [Qwen3-4B-v0.3-deepresearch-100-step-Q4_0.gguf](https://huggingface.co/tensorblock/jan-hq_Qwen3-4B-v0.3-deepresearch-100-step-GGUF/blob/main/Qwen3-4B-v0.3-deepresearch-100-step-Q4_0.gguf) | Q4_0 | 2.370 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [Qwen3-4B-v0.3-deepresearch-100-step-Q4_K_S.gguf](https://huggingface.co/tensorblock/jan-hq_Qwen3-4B-v0.3-deepresearch-100-step-GGUF/blob/main/Qwen3-4B-v0.3-deepresearch-100-step-Q4_K_S.gguf) | Q4_K_S | 2.383 GB | small, greater quality loss | | [Qwen3-4B-v0.3-deepresearch-100-step-Q4_K_M.gguf](https://huggingface.co/tensorblock/jan-hq_Qwen3-4B-v0.3-deepresearch-100-step-GGUF/blob/main/Qwen3-4B-v0.3-deepresearch-100-step-Q4_K_M.gguf) | Q4_K_M | 2.497 GB | medium, balanced quality - recommended | | [Qwen3-4B-v0.3-deepresearch-100-step-Q5_0.gguf](https://huggingface.co/tensorblock/jan-hq_Qwen3-4B-v0.3-deepresearch-100-step-GGUF/blob/main/Qwen3-4B-v0.3-deepresearch-100-step-Q5_0.gguf) | Q5_0 | 2.824 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [Qwen3-4B-v0.3-deepresearch-100-step-Q5_K_S.gguf](https://huggingface.co/tensorblock/jan-hq_Qwen3-4B-v0.3-deepresearch-100-step-GGUF/blob/main/Qwen3-4B-v0.3-deepresearch-100-step-Q5_K_S.gguf) | Q5_K_S | 2.824 GB | large, low quality loss - recommended | | [Qwen3-4B-v0.3-deepresearch-100-step-Q5_K_M.gguf](https://huggingface.co/tensorblock/jan-hq_Qwen3-4B-v0.3-deepresearch-100-step-GGUF/blob/main/Qwen3-4B-v0.3-deepresearch-100-step-Q5_K_M.gguf) | Q5_K_M | 2.890 GB | large, very low quality loss - recommended | | [Qwen3-4B-v0.3-deepresearch-100-step-Q6_K.gguf](https://huggingface.co/tensorblock/jan-hq_Qwen3-4B-v0.3-deepresearch-100-step-GGUF/blob/main/Qwen3-4B-v0.3-deepresearch-100-step-Q6_K.gguf) | Q6_K | 3.306 GB | very large, extremely low quality loss | | [Qwen3-4B-v0.3-deepresearch-100-step-Q8_0.gguf](https://huggingface.co/tensorblock/jan-hq_Qwen3-4B-v0.3-deepresearch-100-step-GGUF/blob/main/Qwen3-4B-v0.3-deepresearch-100-step-Q8_0.gguf) | Q8_0 | 4.280 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/jan-hq_Qwen3-4B-v0.3-deepresearch-100-step-GGUF --include "Qwen3-4B-v0.3-deepresearch-100-step-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/jan-hq_Qwen3-4B-v0.3-deepresearch-100-step-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```
aaron-ser/smolvla-two-cam-policy
aaron-ser
2025-08-19T15:43:55Z
2
0
lerobot
[ "lerobot", "safetensors", "smolvla", "robotics", "dataset:aaron-ser/two-cam-dataset", "arxiv:2506.01844", "base_model:lerobot/smolvla_base", "base_model:finetune:lerobot/smolvla_base", "license:apache-2.0", "region:us" ]
robotics
2025-08-12T14:48:55Z
--- base_model: lerobot/smolvla_base datasets: aaron-ser/two-cam-dataset library_name: lerobot license: apache-2.0 model_name: smolvla pipeline_tag: robotics tags: - smolvla - robotics - lerobot --- # Model Card for smolvla <!-- Provide a quick summary of what the model is/does. --> [SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash lerobot-train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash lerobot-record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - **License:** apache-2.0
iamsubingyawali/gemma-3-4b-nepali-news-summarizer
iamsubingyawali
2025-08-19T15:42:34Z
0
0
transformers
[ "transformers", "safetensors", "generated_from_trainer", "trl", "unsloth", "sft", "base_model:unsloth/gemma-3-4b-pt-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-pt-unsloth-bnb-4bit", "endpoints_compatible", "region:us" ]
null
2025-08-18T08:08:57Z
--- base_model: unsloth/gemma-3-4b-pt-unsloth-bnb-4bit library_name: transformers model_name: gemma-3-4b-nepali-news-summarizer tags: - generated_from_trainer - trl - unsloth - sft licence: license --- # Model Card for gemma-3-4b-nepali-news-summarizer This model is a fine-tuned version of [unsloth/gemma-3-4b-pt-unsloth-bnb-4bit](https://huggingface.co/unsloth/gemma-3-4b-pt-unsloth-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="iamsubingyawali/gemma-3-4b-nepali-news-summarizer", 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/iamsubingyawali-university-of-northampton/huggingface/runs/6gru05iy) This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.53.2 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## 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}} } ```
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755616456
pempekmangedd
2025-08-19T15:41:36Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "patterned sturdy dolphin", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:41:33Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - patterned sturdy dolphin --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755616819
Sayemahsjn
2025-08-19T15:39:13Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:39:09Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Elizavr/blockassist-bc-reclusive_shaggy_bee_1755617735
Elizavr
2025-08-19T15:36:22Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "reclusive shaggy bee", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:36:09Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - reclusive shaggy bee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
chainway9/blockassist-bc-untamed_quick_eel_1755615849
chainway9
2025-08-19T15:33:36Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "untamed quick eel", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:33:33Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - untamed quick eel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/Fai1-GGUF
mradermacher
2025-08-19T15:31:56Z
0
0
transformers
[ "transformers", "gguf", "en", "base_model:Lupakisyo/Fai1", "base_model:quantized:Lupakisyo/Fai1", "endpoints_compatible", "region:us", "feature-extraction" ]
null
2025-08-19T15:22:52Z
--- base_model: Lupakisyo/Fai1 language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## 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/Lupakisyo/Fai1 <!-- provided-files --> ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Fai1-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/Fai1-GGUF/resolve/main/Fai1.Q2_K.gguf) | Q2_K | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/Fai1-GGUF/resolve/main/Fai1.Q3_K_S.gguf) | Q3_K_S | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/Fai1-GGUF/resolve/main/Fai1.Q3_K_M.gguf) | Q3_K_M | 0.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Fai1-GGUF/resolve/main/Fai1.IQ4_XS.gguf) | IQ4_XS | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/Fai1-GGUF/resolve/main/Fai1.Q3_K_L.gguf) | Q3_K_L | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/Fai1-GGUF/resolve/main/Fai1.Q4_K_S.gguf) | Q4_K_S | 0.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Fai1-GGUF/resolve/main/Fai1.Q4_K_M.gguf) | Q4_K_M | 0.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Fai1-GGUF/resolve/main/Fai1.Q5_K_S.gguf) | Q5_K_S | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/Fai1-GGUF/resolve/main/Fai1.Q5_K_M.gguf) | Q5_K_M | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/Fai1-GGUF/resolve/main/Fai1.Q6_K.gguf) | Q6_K | 0.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Fai1-GGUF/resolve/main/Fai1.Q8_0.gguf) | Q8_0 | 0.2 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Fai1-GGUF/resolve/main/Fai1.f16.gguf) | f16 | 0.3 | 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 -->
allenai/olmOCR-7B-0225-preview
allenai
2025-08-19T15:31:31Z
258,271
693
transformers
[ "transformers", "safetensors", "qwen2_vl", "image-to-text", "en", "dataset:allenai/olmOCR-mix-0225", "base_model:Qwen/Qwen2-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2-VL-7B-Instruct", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]
image-to-text
2025-01-15T21:14:47Z
--- language: - en license: apache-2.0 datasets: - allenai/olmOCR-mix-0225 base_model: - Qwen/Qwen2-VL-7B-Instruct library_name: transformers new_version: allenai/olmOCR-7B-0825 --- <img alt="olmOCR Logo" src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/olmocr/olmocr.png" width="242px" style="margin-left:'auto' margin-right:'auto' display:'block'"> # olmOCR-7B-0225-preview This is a preview release of the olmOCR model that's fine tuned from Qwen2-VL-7B-Instruct using the [olmOCR-mix-0225](https://huggingface.co/datasets/allenai/olmOCR-mix-0225) dataset. Quick links: - 📃 [Paper](https://olmocr.allenai.org/papers/olmocr.pdf) - 🤗 [Dataset](https://huggingface.co/datasets/allenai/olmOCR-mix-0225) - 🛠️ [Code](https://github.com/allenai/olmocr) - 🎮 [Demo](https://olmocr.allenai.org/) The best way to use this model is via the [olmOCR toolkit](https://github.com/allenai/olmocr). The toolkit comes with an efficient inference setup via sglang that can handle millions of documents at scale. ## Usage This model expects as input a single document image, rendered such that the longest dimension is 1024 pixels. The prompt must then contain the additional metadata from the document, and the easiest way to generate this is to use the methods provided by the [olmOCR toolkit](https://github.com/allenai/olmocr). ## Manual Prompting If you want to prompt this model manually instead of using the [olmOCR toolkit](https://github.com/allenai/olmocr), please see the code below. In normal usage, the olmOCR toolkit builds the prompt by rendering the PDF page, and extracting relevant text blocks and image metadata. To duplicate that you will need to ```bash pip install olmocr ``` and then run the following sample code. ```python import torch import base64 import urllib.request from io import BytesIO from PIL import Image from transformers import AutoProcessor, Qwen2VLForConditionalGeneration from olmocr.data.renderpdf import render_pdf_to_base64png from olmocr.prompts import build_finetuning_prompt from olmocr.prompts.anchor import get_anchor_text # Initialize the model model = Qwen2VLForConditionalGeneration.from_pretrained("allenai/olmOCR-7B-0225-preview", torch_dtype=torch.bfloat16).eval() processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) # Grab a sample PDF urllib.request.urlretrieve("https://molmo.allenai.org/paper.pdf", "./paper.pdf") # Render page 1 to an image image_base64 = render_pdf_to_base64png("./paper.pdf", 1, target_longest_image_dim=1024) # Build the prompt, using document metadata anchor_text = get_anchor_text("./paper.pdf", 1, pdf_engine="pdfreport", target_length=4000) prompt = build_finetuning_prompt(anchor_text) # Build the full prompt messages = [ { "role": "user", "content": [ {"type": "text", "text": prompt}, {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}}, ], } ] # Apply the chat template and processor text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) main_image = Image.open(BytesIO(base64.b64decode(image_base64))) inputs = processor( text=[text], images=[main_image], padding=True, return_tensors="pt", ) inputs = {key: value.to(device) for (key, value) in inputs.items()} # Generate the output output = model.generate( **inputs, temperature=0.8, max_new_tokens=50, num_return_sequences=1, do_sample=True, ) # Decode the output prompt_length = inputs["input_ids"].shape[1] new_tokens = output[:, prompt_length:] text_output = processor.tokenizer.batch_decode( new_tokens, skip_special_tokens=True ) print(text_output) # ['{"primary_language":"en","is_rotation_valid":true,"rotation_correction":0,"is_table":false,"is_diagram":false,"natural_text":"Molmo and PixMo:\\nOpen Weights and Open Data\\nfor State-of-the'] ``` ## License and use olmOCR is licensed under the Apache 2.0 license. olmOCR is intended for research and educational use. For more information, please see our [Responsible Use Guidelines](https://allenai.org/responsible-use).
hanskarlo/dqn-SpaceInvadersNoFrameskip-v4
hanskarlo
2025-08-19T15:31:00Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2025-08-19T15:29:59Z
--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 824.00 +/- 279.92 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga hanskarlo -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga hanskarlo -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga hanskarlo ``` ## Hyperparameters ```python OrderedDict([('batch_size', 48), ('buffer_size', 105000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
sameddallaa/q_frozen_lake_v1_slippery
sameddallaa
2025-08-19T15:30:33Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2025-08-19T15:30:25Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q_frozen_lake_v1_slippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage model = load_from_hub(repo_id="sameddallaa/q_frozen_lake_v1_slippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"])
saracandu/stldec_random
saracandu
2025-08-19T15:27:50Z
41
0
null
[ "safetensors", "stldec", "custom_code", "region:us" ]
null
2025-05-24T14:38:45Z
--- {} --- # Materials for the paper "Bridging Logic and Learning: Decoding Temporal Logic Embeddings via Transformers" (Candussio et al.) @ ECML-PKDD 2025 **TL;DR:** - (trained) models are available at: https://huggingface.co/collections/saracandu/stldec-ecml-pkdd-2025-686fe174a16915bc32aa53eb - code, results, and other details can be found in this repo. The goal of STLdecoder is to take a NeSy embedding of a Signal Temporal Logic (STL) formula and recover a semantically equivalent formula. The `encoder.py` file allows you to obtain the NeSy embeddings of (a list of) formulae with respect to a predefined anchor set, which you can find in the `anchor_sets/` folder. More details on this procedure can be found at https://ebooks.iospress.nl/doi/10.3233/FAIA240638 This class also relies on the following files: `phis_generator.py`, `traj_measure.py`, `kernel.py`, `stl.py`, `anchor_set_generation.py`, `custom_typing.py`, `trajectories.py`. The `decoder.py` component aims at translating a vector (i.e., the encoding of a formula, as done by `encoder.py`) into a string (i.e., an STL formula consisting of a hybrid syntax made of numbers, parentheses, and words, whose vocabulary can be found in the `tokenizer_files/` folder). This is practically implemented in the `modeling_stldec.py` file, as we perform the aforementioned procedure using a decoder-only Transformer architecture. This process requires autoregressively generating the tokens of the STL formula and embedding them in order to merge this information with the initial semantic vector through the cross-attention block. The `configuration.py` file serves as a crystallized structure guiding the `transformers` classes. In order to train this architecture, we can use the `training.py` file, leveraging the different training settings available in the `training_config/` folder.
Jacksss123/net72_uid241
Jacksss123
2025-08-19T15:25:31Z
1
0
transformers
[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2025-08-04T19:56:26Z
--- 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. (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]
AdoCleanCode/neox_capital_only_v2
AdoCleanCode
2025-08-19T15:25:09Z
0
0
transformers
[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2025-08-19T10:13:21Z
--- 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. (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]
PavanSakthivel/ppo-LunarLander-v2
PavanSakthivel
2025-08-19T15:25:05Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2025-08-19T15:24:45Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 245.68 +/- 21.11 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755615291
hakimjustbao
2025-08-19T15:23:11Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:23:08Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lqpl/blockassist-bc-hairy_insectivorous_antelope_1755616839
lqpl
2025-08-19T15:22:43Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hairy insectivorous antelope", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:21:32Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - hairy insectivorous antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ucmp137538/best_RPT_coder_mathrl_ckpt-1000
ucmp137538
2025-08-19T15:22:35Z
0
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-08-19T15:19:46Z
--- 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. (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]
TAUR-dev/M-voting_setup3_1epch_1e6_all_tasks_only_sft-sft
TAUR-dev
2025-08-19T15:20:09Z
0
0
null
[ "safetensors", "qwen2", "region:us" ]
null
2025-08-19T15:18:45Z
# M-voting_setup3_1epch_1e6_all_tasks_only_sft-sft This model was created as part of the **voting_setup3_1epch_1e6_all_tasks_only_sft** experiment using the SkillFactory experiment management system. ## Model Details - **Training Method**: LLaMAFactory SFT (Supervised Fine-Tuning) - **Stage Name**: sft - **Experiment**: voting_setup3_1epch_1e6_all_tasks_only_sft ## 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": "/datastor1/mwadhwa/code/skill-factory/thirdparty/LLaMA-Factory/examples/deepspeed/ds_z2_config.json", "dataset": "TAUR_dev__D_SFT_C_voting_setup3_1epch_1e6_all_tasks_only_sft_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": "/datastor1/mwadhwa/skill_inject_outputs/sf_experiments/skills_in_rl/voting_setup3_1epch_1e6_all_tasks_only_sft/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": 1, "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__voting_setup3_1epch_1e6_all_tasks_only_sft__v1", "sf_eval_before_training": false, "sf_wandb_project": "voting_setup3_1epch_1e6_all_tasks_only_sft_sft", "sf_eval_steps": null, "run_name": "voting_setup3_1epch_1e6_all_tasks_only_sft_sft"} ## Experiment Tracking 🔗 **View complete experiment details**: [Experiment Tracker Dataset](https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__voting_setup3_1epch_1e6_all_tasks_only_sft__v1) ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-voting_setup3_1epch_1e6_all_tasks_only_sft-sft") model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-voting_setup3_1epch_1e6_all_tasks_only_sft-sft") ```
mang3dd/blockassist-bc-tangled_slithering_alligator_1755615136
mang3dd
2025-08-19T15:20:07Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:20:03Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Bahrom1996/whisper-uz
Bahrom1996
2025-08-19T15:16:41Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "uz", "dataset:common_voice_14_0", "base_model:jmshd/whisper-uz", "base_model:finetune:jmshd/whisper-uz", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2025-08-18T12:38:14Z
--- library_name: transformers language: - uz license: apache-2.0 base_model: jamshidahmadov/whisper-uz tags: - generated_from_trainer datasets: - common_voice_14_0 metrics: - wer model-index: - name: Whisper base uz - Bahrom results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_14_0 type: common_voice_14_0 config: uz split: test args: 'config: uz, split: test' metrics: - name: Wer type: wer value: 39.4953893762244 --- <!-- 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. --> # Whisper base uz - Bahrom This model is a fine-tuned version of [jamshidahmadov/whisper-uz](https://huggingface.co/jamshidahmadov/whisper-uz) on the common_voice_14_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4621 - Wer: 39.4954 ## 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use adamw_torch 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: 500 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-------:| | 0.5759 | 0.1323 | 500 | 0.4621 | 39.4954 | ### Framework versions - Transformers 4.49.0 - Pytorch 2.5.0 - Datasets 3.3.2 - Tokenizers 0.21.0
Muapi/minimalist-line-art-sdxl-pony
Muapi
2025-08-19T15:16:02Z
0
0
null
[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]
null
2025-08-19T15:15:50Z
--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Minimalist Line Art (SDXL, Pony) ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: ArsMJStyle, Minimalist Line Art ## 🧠 Usage (Python) 🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:645070@789742", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
koloni/blockassist-bc-deadly_graceful_stingray_1755614936
koloni
2025-08-19T15:15:51Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:15:48Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kodetr/stunting-7B-Qwen
kodetr
2025-08-19T15:15:29Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "stunting", "kesehatan", "anak", "conversational", "id", "dataset:kodetr/penelitian-fundamental-stunting-qa", "base_model:Qwen/Qwen1.5-7B-Chat", "base_model:finetune:Qwen/Qwen1.5-7B-Chat", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-08-19T14:59:41Z
--- library_name: transformers tags: - stunting - kesehatan - anak license: apache-2.0 datasets: - kodetr/penelitian-fundamental-stunting-qa language: - id metrics: - rouge - bleu pipeline_tag: text-generation base_model: - Qwen/Qwen1.5-7B-Chat --- ### Model Description <!-- Provide a longer summary of what this model is. --> Konsultasi(Q&A) stunting pada anak - **Developed by:** Tanwir - **Language :** Indonesia ### Training ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65d6d2f8b06abf924b24349d/ZmKG5B9AapbcvAzXdfkYZ.png) ### Use with transformers Pastikan untuk memperbarui instalasi transformer Anda melalui pip install --upgrade transformer. ```python import torch from transformers import pipeline model_id = "kodetr/stunting-7B-Qwen" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "Jelaskan definisi 1000 hari pertama kehidupan."}, {"role": "user", "content": "Apa itu 1000 hari pertama kehidupan?"}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ```
Muapi/flux-christmas-living-room
Muapi
2025-08-19T15:14:26Z
0
0
null
[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]
null
2025-08-19T15:14:12Z
--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # FLUX Christmas living room ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: christmas living room ## 🧠 Usage (Python) 🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:1011849@1134274", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/zavy-s-fluorescent-flux
Muapi
2025-08-19T15:11:56Z
0
0
null
[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]
null
2025-08-19T15:11:43Z
--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Zavy's Fluorescent - Flux ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: zavy-flrscnt ## 🧠 Usage (Python) 🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:737408@824658", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
2hpsatt/blockassist-bc-huge_deft_eagle_1755616186
2hpsatt
2025-08-19T15:10:31Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge deft eagle", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:10:26Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge deft eagle --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
thanobidex/blockassist-bc-colorful_shiny_hare_1755614471
thanobidex
2025-08-19T15:09:34Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful shiny hare", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:09:30Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - colorful shiny hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/Nexa-Vector-11-Qwen-GGUF
mradermacher
2025-08-19T15:09:30Z
0
0
transformers
[ "transformers", "gguf", "en", "base_model:iversonzhou/Nexa-Vector-11-Qwen", "base_model:quantized:iversonzhou/Nexa-Vector-11-Qwen", "license:mit", "endpoints_compatible", "region:us", "conversational" ]
null
2025-08-19T14:56:35Z
--- base_model: iversonzhou/Nexa-Vector-11-Qwen language: - en library_name: transformers license: mit mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## 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/iversonzhou/Nexa-Vector-11-Qwen <!-- provided-files --> ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Nexa-Vector-11-Qwen-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/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q2_K.gguf) | Q2_K | 0.8 | | | [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q3_K_S.gguf) | Q3_K_S | 0.9 | | | [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q3_K_L.gguf) | Q3_K_L | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.IQ4_XS.gguf) | IQ4_XS | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q5_K_S.gguf) | Q5_K_S | 1.2 | | | [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q5_K_M.gguf) | Q5_K_M | 1.2 | | | [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q6_K.gguf) | Q6_K | 1.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.f16.gguf) | f16 | 3.2 | 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 -->
Muapi/geometric-ce
Muapi
2025-08-19T15:09:27Z
0
0
null
[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]
null
2025-08-19T15:09:18Z
--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Geometric - CE ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: gmtrcCE style, cubism, geometric, honeycomb, curvilinear ## 🧠 Usage (Python) 🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:801170@895845", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755614551
sampingkaca72
2025-08-19T15:08:19Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored stealthy elephant", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:08:16Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored stealthy elephant --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/3d_flux-style
Muapi
2025-08-19T15:07:43Z
0
0
null
[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]
null
2025-08-19T15:07:35Z
--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # 3D_Flux Style ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: 3D01S , kawaii, anime ## 🧠 Usage (Python) 🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:689478@771650", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Kurosawama/gemma-3-1b-it-Retranslation-align
Kurosawama
2025-08-19T15:07:32Z
0
0
transformers
[ "transformers", "safetensors", "trl", "dpo", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2025-08-19T15:07:28Z
--- library_name: transformers tags: - trl - dpo --- # 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]
Muapi/pascal-blanch
Muapi
2025-08-19T15:06:50Z
0
0
null
[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]
null
2025-08-19T15:06:40Z
--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Pascal Blanché ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: By Passcal Blanché ## 🧠 Usage (Python) 🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:1285926@1274884", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/stippled-illustration-flux-lora
Muapi
2025-08-19T15:06:21Z
0
0
null
[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]
null
2025-08-19T15:05:37Z
--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Stippled Illustration (Flux LoRA) ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: STPPLD ## 🧠 Usage (Python) 🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:772319@863812", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755614240
pempekmangedd
2025-08-19T15:06:21Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "patterned sturdy dolphin", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:06:17Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - patterned sturdy dolphin --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
numen-tech/Qwen3-4B-Instruct-2507-GPTQ-Int4
numen-tech
2025-08-19T15:06:19Z
0
0
mlc-llm
[ "mlc-llm", "text-generation", "conversational", "en", "arxiv:2210.17323", "base_model:Qwen/Qwen3-4B-Instruct-2507", "base_model:quantized:Qwen/Qwen3-4B-Instruct-2507", "license:apache-2.0", "4-bit", "region:us" ]
text-generation
2025-08-19T15:01:34Z
--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507/blob/main/LICENSE language: - en base_model: Qwen/Qwen3-4B-Instruct-2507 base_model_relation: quantized library_name: mlc-llm pipeline_tag: text-generation --- 4-bit [GPTQ](https://arxiv.org/abs/2210.17323) quantized version of [Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) for use with the [Private LLM app](https://privatellm.app/).
Muapi/3d-minimal-design-flux.1-dev-lora
Muapi
2025-08-19T15:04:06Z
0
0
null
[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]
null
2025-08-19T15:03:47Z
--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # 3D Minimal Design - Flux.1 Dev Lora ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: Minimalist Design ## 🧠 Usage (Python) 🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:813341@1003935", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/gigachad-flux1.d-sdxl
Muapi
2025-08-19T15:03:05Z
0
0
null
[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]
null
2025-08-19T15:02:54Z
--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Gigachad - Flux1.D & SDXL ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: Gigachad is a muscular man ## 🧠 Usage (Python) 🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:237712@786259", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755613987
vwzyrraz7l
2025-08-19T15:03:01Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:02:57Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755614041
helmutsukocok
2025-08-19T15:01:58Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T15:01:55Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - loud scavenging kangaroo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Kurosawama/gemma-3-1b-it-Translation-align
Kurosawama
2025-08-19T15:01:48Z
0
0
transformers
[ "transformers", "safetensors", "trl", "dpo", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2025-08-19T15:01:43Z
--- library_name: transformers tags: - trl - dpo --- # 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]
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755613820
quantumxnode
2025-08-19T14:59:00Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T14:58:56Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
wjbmattingly/lfm2-vl-450M-yiddish
wjbmattingly
2025-08-19T14:58:01Z
0
0
null
[ "safetensors", "lfm2-vl", "custom_code", "base_model:LiquidAI/LFM2-VL-450M", "base_model:finetune:LiquidAI/LFM2-VL-450M", "region:us" ]
null
2025-08-19T14:57:50Z
--- base_model: - LiquidAI/LFM2-VL-450M --- # model_step_13000 ## Model Description This model is a fine-tuned version of **LiquidAI/LFM2-VL-450M** using the brute-force-training package. - **Base Model**: LiquidAI/LFM2-VL-450M - **Training Status**: 🔄 In Progress - **Generated**: 2025-08-19 10:41:14 - **Training Steps**: 13,000 ## Training Details ### Dataset - **Dataset**: johnlockejrr/yiddish_synth_v2 - **Training Examples**: 100,000 - **Validation Examples**: 4,999 ### Training Configuration - **Max Steps**: 100,000 - **Batch Size**: 15 - **Learning Rate**: 7e-05 - **Gradient Accumulation**: 1 steps - **Evaluation Frequency**: Every 1,000 steps ### Current Performance - **Training Loss**: 0.124526 - **Evaluation Loss**: 0.189137 ## Pre-Training Evaluation **Initial Model Performance (before training):** - **Loss**: 2.626098 - **Perplexity**: 13.82 - **Character Accuracy**: 31.1% - **Word Accuracy**: 12.9% ## Evaluation History ### All Checkpoint Evaluations | Step | Checkpoint Type | Loss | Perplexity | Char Acc | Word Acc | Improvement vs Pre | |------|----------------|------|------------|----------|----------|--------------------| | Pre | pre_training | 2.6261 | 13.82 | 31.1% | 12.9% | +0.0% | | 1,000 | checkpoint | 0.9395 | 2.56 | 20.1% | 4.1% | +64.2% | | 2,000 | checkpoint | 0.8058 | 2.24 | 21.2% | 4.0% | +69.3% | | 3,000 | checkpoint | 0.7305 | 2.08 | 23.0% | 6.1% | +72.2% | | 4,000 | checkpoint | 0.6669 | 1.95 | 20.6% | 3.4% | +74.6% | | 5,000 | checkpoint | 0.5341 | 1.71 | 21.4% | 3.6% | +79.7% | | 6,000 | checkpoint | 0.4656 | 1.59 | 20.9% | 3.8% | +82.3% | | 7,000 | checkpoint | 0.3917 | 1.48 | 21.4% | 3.5% | +85.1% | | 8,000 | checkpoint | 0.3310 | 1.39 | 21.6% | 4.8% | +87.4% | | 9,000 | checkpoint | 0.2892 | 1.34 | 20.7% | 4.0% | +89.0% | | 10,000 | checkpoint | 0.2566 | 1.29 | 20.9% | 4.7% | +90.2% | | 11,000 | checkpoint | 0.2199 | 1.25 | 20.2% | 4.9% | +91.6% | | 12,000 | checkpoint | 0.2033 | 1.23 | 20.3% | 3.2% | +92.3% | | 13,000 | checkpoint | 0.1891 | 1.21 | 19.4% | 3.4% | +92.8% | ## Training Progress ### Recent Training Steps (Loss Only) | Step | Training Loss | Timestamp | |------|---------------|-----------| | 12,991 | 0.154684 | 2025-08-19T10:40 | | 12,992 | 0.183019 | 2025-08-19T10:40 | | 12,993 | 0.157314 | 2025-08-19T10:40 | | 12,994 | 0.168899 | 2025-08-19T10:40 | | 12,995 | 0.116096 | 2025-08-19T10:40 | | 12,996 | 0.122316 | 2025-08-19T10:40 | | 12,997 | 0.149480 | 2025-08-19T10:40 | | 12,998 | 0.166267 | 2025-08-19T10:40 | | 12,999 | 0.152927 | 2025-08-19T10:40 | | 13,000 | 0.124526 | 2025-08-19T10:40 | ## Training Visualizations ### Training Progress and Evaluation Metrics ![Training Curves](training_curves.png) *This chart shows the training loss progression, character accuracy, word accuracy, and perplexity over time. Red dots indicate evaluation checkpoints.* ### Evaluation Comparison Across All Checkpoints ![Evaluation Comparison](evaluation_comparison.png) *Comprehensive comparison of all evaluation metrics across training checkpoints. Red=Pre-training, Blue=Checkpoints, Green=Final.* ### Available Visualization Files: - **`training_curves.png`** - 4-panel view: Training loss with eval points, Character accuracy, Word accuracy, Perplexity - **`evaluation_comparison.png`** - 4-panel comparison: Loss, Character accuracy, Word accuracy, Perplexity across all checkpoints ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer # For vision-language models, use appropriate imports model = AutoModelForCausalLM.from_pretrained("./model_step_13000") tokenizer = AutoTokenizer.from_pretrained("./model_step_13000") # Your inference code here ``` ## Training Configuration ```json { "dataset_name": "johnlockejrr/yiddish_synth_v2", "model_name": "LiquidAI/LFM2-VL-450M", "max_steps": 100000, "eval_steps": 1000, "num_accumulation_steps": 1, "learning_rate": 7e-05, "train_batch_size": 15, "val_batch_size": 1, "train_select_start": 0, "train_select_end": 100000, "val_select_start": 100001, "val_select_end": 105000, "train_field": "train", "val_field": "train", "image_column": "image", "text_column": "text", "user_text": "Please transcribe all the Yiddish text you see in this historical manuscript image. Provide only the transcribed text without any additional commentary or description.", "max_image_size": 250 } ``` ## Model Card Metadata - **Base Model**: LiquidAI/LFM2-VL-450M - **Training Framework**: brute-force-training - **Training Type**: Fine-tuning - **License**: Inherited from base model - **Language**: Inherited from base model --- *This model card was automatically generated by brute-force-training on 2025-08-19 10:41:14*
Muapi/imax-70mm-cinematic-film-style-f1d-xl-sd1.5
Muapi
2025-08-19T14:57:36Z
0
0
null
[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]
null
2025-08-19T14:57:27Z
--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # IMAX 70mm cinematic film style F1D + XL + SD1.5 ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: cinematic film style, IMAX70mm , filmstrip border ## 🧠 Usage (Python) 🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:1249970@1409079", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Vasya777/blockassist-bc-lumbering_enormous_sloth_1755615379
Vasya777
2025-08-19T14:57:22Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lumbering enormous sloth", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T14:56:55Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lumbering enormous sloth --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
matheoqtb/EuroBertV2final
matheoqtb
2025-08-19T14:56:59Z
0
0
null
[ "safetensors", "eurobert", "custom_code", "region:us" ]
null
2025-08-19T14:56:50Z
# Checkpoint exporté: final Ce dépôt contient un checkpoint extrait de `matheoqtb/euroBertV2_test2` (sous-dossier `final`) et les fichiers de code nécessaires provenant de `EuroBERT/EuroBERT-610m`. Chargement: from transformers import AutoTokenizer, AutoModel tok = AutoTokenizer.from_pretrained('<THIS_REPO>', trust_remote_code=True) mdl = AutoModel.from_pretrained('<THIS_REPO>', trust_remote_code=True) Tâche: feature-extraction (embeddings)
NadiaReula/Asistente-DEI
NadiaReula
2025-08-19T14:56:01Z
0
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "region:us" ]
null
2025-08-18T20:42:32Z
--- base_model: google/gemma-2b library_name: peft --- # 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. --> - **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] ### Framework versions - PEFT 0.10.0
Azurastar2903/gemma-3-1b-pt-rk3588-1.2.1
Azurastar2903
2025-08-19T14:55:45Z
0
0
transformers
[ "transformers", "gemma3_text", "text-generation", "arxiv:1905.07830", "arxiv:1905.10044", "arxiv:1911.11641", "arxiv:1904.09728", "arxiv:1705.03551", "arxiv:1911.01547", "arxiv:1907.10641", "arxiv:1903.00161", "arxiv:2009.03300", "arxiv:2304.06364", "arxiv:2103.03874", "arxiv:2110.14168", "arxiv:2311.12022", "arxiv:2108.07732", "arxiv:2107.03374", "arxiv:2210.03057", "arxiv:2106.03193", "arxiv:1910.11856", "arxiv:2502.12404", "arxiv:2502.21228", "arxiv:2404.16816", "arxiv:2104.12756", "arxiv:2311.16502", "arxiv:2203.10244", "arxiv:2404.12390", "arxiv:1810.12440", "arxiv:1908.02660", "arxiv:2312.11805", "license:gemma", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2025-08-19T13:41:44Z
--- library_name: transformers license: gemma pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license --- # gemma-3-1b-pt-RK3588-1.2.1 This version of gemma-3-1b-pt has been converted to run on the RK3588 NPU using ['w8a8_g256'] quantization. This model has been optimized with the following LoRA: Compatible with RKLLM version: 1.2.1 ## Useful links: [Official RKLLM GitHub](https://github.com/airockchip/rknn-llm) [RockhipNPU Reddit](https://reddit.com/r/RockchipNPU) [EZRKNN-LLM](https://github.com/Pelochus/ezrknn-llm/) Pretty much anything by these folks: [marty1885](https://github.com/marty1885) and [happyme531](https://huggingface.co/happyme531) Converted using https://github.com/c0zaut/ez-er-rkllm-toolkit # Original Model Card for base model, gemma-3-1b-pt, below: # Gemma 3 model card **Model Page**: [Gemma](https://ai.google.dev/gemma/docs/core) **Resources and Technical Documentation**: * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] **Terms of Use**: [Terms][terms] **Authors**: Google DeepMind ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Usage Below, there are some code snippets on how to get quickly started with running the model. First, install the Transformers library. Gemma 3 is supported starting from transformers 4.50.0. ```sh $ pip install -U transformers ``` Then, copy the snippet from the section that is relevant for your use case. #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline("text-generation", model="google/gemma-3-1b-pt", device="cuda", torch_dtype=torch.bfloat16) output = pipe("Eiffel tower is located in", max_new_tokens=50) ``` #### Running the model on a single / multi GPU ```python import torch from transformers import AutoTokenizer, Gemma3ForCausalLM ckpt = "google/gemma-3-1b-pt" tokenizer = AutoTokenizer.from_pretrained(ckpt) model = Gemma3ForCausalLM.from_pretrained( ckpt, torch_dtype=torch.bfloat16, device_map="auto" ) prompt = "Eiffel tower is located in" model_inputs = tokenizer(prompt, return_tensors="pt").to(model.device) input_len = model_inputs["input_ids"].shape[-1] with torch.inference_mode(): generation = model.generate(**model_inputs, max_new_tokens=50, do_sample=False) generation = generation[0][input_len:] decoded = tokenizer.decode(generation, skip_special_tokens=True) print(decoded) ``` ### Inputs and outputs - **Input:** - Text string, such as a question, a prompt, or a document to be summarized - Images, normalized to 896 x 896 resolution and encoded to 256 tokens each - Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size - **Output:** - Generated text in response to the input, such as an answer to a question, analysis of image content, or a summary of a document - Total output context of 8192 tokens ### Citation ```none @article{gemma_2025, title={Gemma 3}, url={https://goo.gle/Gemma3Report}, publisher={Kaggle}, author={Gemma Team}, year={2025} } ``` ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: - Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. The training dataset includes content in over 140 languages. - Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code and understand code-related questions. - Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. - Images: A wide range of images enables the model to perform image analysis and visual data extraction tasks. The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: - CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content. - Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. - Additional methods: Filtering based on content quality and safety in line with [our policies][safety-policies]. ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p, TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: - Performance: TPUs are specifically designed to handle the massive computations involved in training VLMs. They can speed up training considerably compared to CPUs. - Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. - Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. - Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. - These advantages are aligned with [Google's commitments to operate sustainably][sustainability]. ### Software Training was done using [JAX][jax] and [ML Pathways][ml-pathways]. JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for foundation models, including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models][gemini-2-paper]; *"the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow."* ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: #### Reasoning and factuality | Benchmark | Metric | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------ |----------------|:--------------:|:-------------:|:--------------:|:--------------:| | [HellaSwag][hellaswag] | 10-shot | 62.3 | 77.2 | 84.2 | 85.6 | | [BoolQ][boolq] | 0-shot | 63.2 | 72.3 | 78.8 | 82.4 | | [PIQA][piqa] | 0-shot | 73.8 | 79.6 | 81.8 | 83.3 | | [SocialIQA][socialiqa] | 0-shot | 48.9 | 51.9 | 53.4 | 54.9 | | [TriviaQA][triviaqa] | 5-shot | 39.8 | 65.8 | 78.2 | 85.5 | | [Natural Questions][naturalq] | 5-shot | 9.48 | 20.0 | 31.4 | 36.1 | | [ARC-c][arc] | 25-shot | 38.4 | 56.2 | 68.9 | 70.6 | | [ARC-e][arc] | 0-shot | 73.0 | 82.4 | 88.3 | 89.0 | | [WinoGrande][winogrande] | 5-shot | 58.2 | 64.7 | 74.3 | 78.8 | | [BIG-Bench Hard][bbh] | few-shot | 28.4 | 50.9 | 72.6 | 77.7 | | [DROP][drop] | 1-shot | 42.4 | 60.1 | 72.2 | 77.2 | [hellaswag]: https://arxiv.org/abs/1905.07830 [boolq]: https://arxiv.org/abs/1905.10044 [piqa]: https://arxiv.org/abs/1911.11641 [socialiqa]: https://arxiv.org/abs/1904.09728 [triviaqa]: https://arxiv.org/abs/1705.03551 [naturalq]: https://github.com/google-research-datasets/natural-questions [arc]: https://arxiv.org/abs/1911.01547 [winogrande]: https://arxiv.org/abs/1907.10641 [bbh]: https://paperswithcode.com/dataset/bbh [drop]: https://arxiv.org/abs/1903.00161 #### STEM and code | Benchmark | Metric | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------ |----------------|:-------------:|:--------------:|:--------------:| | [MMLU][mmlu] | 5-shot | 59.6 | 74.5 | 78.6 | | [MMLU][mmlu] (Pro COT) | 5-shot | 29.2 | 45.3 | 52.2 | | [AGIEval][agieval] | 3-5-shot | 42.1 | 57.4 | 66.2 | | [MATH][math] | 4-shot | 24.2 | 43.3 | 50.0 | | [GSM8K][gsm8k] | 8-shot | 38.4 | 71.0 | 82.6 | | [GPQA][gpqa] | 5-shot | 15.0 | 25.4 | 24.3 | | [MBPP][mbpp] | 3-shot | 46.0 | 60.4 | 65.6 | | [HumanEval][humaneval] | 0-shot | 36.0 | 45.7 | 48.8 | [mmlu]: https://arxiv.org/abs/2009.03300 [agieval]: https://arxiv.org/abs/2304.06364 [math]: https://arxiv.org/abs/2103.03874 [gsm8k]: https://arxiv.org/abs/2110.14168 [gpqa]: https://arxiv.org/abs/2311.12022 [mbpp]: https://arxiv.org/abs/2108.07732 [humaneval]: https://arxiv.org/abs/2107.03374 #### Multilingual | Benchmark | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------------ |:-------------:|:-------------:|:--------------:|:--------------:| | [MGSM][mgsm] | 2.04 | 34.7 | 64.3 | 74.3 | | [Global-MMLU-Lite][global-mmlu-lite] | 24.9 | 57.0 | 69.4 | 75.7 | | [WMT24++][wmt24pp] (ChrF) | 36.7 | 48.4 | 53.9 | 55.7 | | [FloRes][flores] | 29.5 | 39.2 | 46.0 | 48.8 | | [XQuAD][xquad] (all) | 43.9 | 68.0 | 74.5 | 76.8 | | [ECLeKTic][eclektic] | 4.69 | 11.0 | 17.2 | 24.4 | | [IndicGenBench][indicgenbench] | 41.4 | 57.2 | 61.7 | 63.4 | [mgsm]: https://arxiv.org/abs/2210.03057 [flores]: https://arxiv.org/abs/2106.03193 [xquad]: https://arxiv.org/abs/1910.11856v3 [global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite [wmt24pp]: https://arxiv.org/abs/2502.12404v1 [eclektic]: https://arxiv.org/abs/2502.21228 [indicgenbench]: https://arxiv.org/abs/2404.16816 #### Multimodal | Benchmark | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------ |:-------------:|:--------------:|:--------------:| | [COCOcap][coco-cap] | 102 | 111 | 116 | | [DocVQA][docvqa] (val) | 72.8 | 82.3 | 85.6 | | [InfoVQA][info-vqa] (val) | 44.1 | 54.8 | 59.4 | | [MMMU][mmmu] (pt) | 39.2 | 50.3 | 56.1 | | [TextVQA][textvqa] (val) | 58.9 | 66.5 | 68.6 | | [RealWorldQA][realworldqa] | 45.5 | 52.2 | 53.9 | | [ReMI][remi] | 27.3 | 38.5 | 44.8 | | [AI2D][ai2d] | 63.2 | 75.2 | 79.0 | | [ChartQA][chartqa] | 63.6 | 74.7 | 76.3 | | [VQAv2][vqav2] | 63.9 | 71.2 | 72.9 | | [BLINK][blinkvqa] | 38.0 | 35.9 | 39.6 | | [OKVQA][okvqa] | 51.0 | 58.7 | 60.2 | | [TallyQA][tallyqa] | 42.5 | 51.8 | 54.3 | | [SpatialSense VQA][ss-vqa] | 50.9 | 60.0 | 59.4 | | [CountBenchQA][countbenchqa] | 26.1 | 17.8 | 68.0 | [coco-cap]: https://cocodataset.org/#home [docvqa]: https://www.docvqa.org/ [info-vqa]: https://arxiv.org/abs/2104.12756 [mmmu]: https://arxiv.org/abs/2311.16502 [textvqa]: https://textvqa.org/ [realworldqa]: https://paperswithcode.com/dataset/realworldqa [remi]: https://arxiv.org/html/2406.09175v1 [ai2d]: https://allenai.org/data/diagrams [chartqa]: https://arxiv.org/abs/2203.10244 [vqav2]: https://visualqa.org/index.html [blinkvqa]: https://arxiv.org/abs/2404.12390 [okvqa]: https://okvqa.allenai.org/ [tallyqa]: https://arxiv.org/abs/1810.12440 [ss-vqa]: https://arxiv.org/abs/1908.02660 [countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/ ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: - **Child Safety**: Evaluation of text-to-text and image to text prompts covering child safety policies, including child sexual abuse and exploitation. - **Content Safety:** Evaluation of text-to-text and image to text prompts covering safety policies including, harassment, violence and gore, and hate speech. - **Representational Harms**: Evaluation of text-to-text and image to text prompts covering safety policies including bias, stereotyping, and harmful associations or inaccuracies. In addition to development level evaluations, we conduct "assurance evaluations" which are our 'arms-length' internal evaluations for responsibility governance decision making. They are conducted separately from the model development team, to inform decision making about release. High level findings are fed back to the model team, but prompt sets are held-out to prevent overfitting and preserve the results' ability to inform decision making. Assurance evaluation results are reported to our Responsibility & Safety Council as part of release review. ### Evaluation Results For all areas of safety testing, we saw major improvements in the categories of child safety, content safety, and representational harms relative to previous Gemma models. All testing was conducted without safety filters to evaluate the model capabilities and behaviors. For both text-to-text and image-to-text, and across all model sizes, the model produced minimal policy violations, and showed significant improvements over previous Gemma models' performance with respect to ungrounded inferences. A limitation of our evaluations was they included only English language prompts. ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open vision-language models (VLMs) models have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. - Content Creation and Communication - Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. - Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. - Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. - Image Data Extraction: These models can be used to extract, interpret, and summarize visual data for text communications. - Research and Education - Natural Language Processing (NLP) and VLM Research: These models can serve as a foundation for researchers to experiment with VLM and NLP techniques, develop algorithms, and contribute to the advancement of the field. - Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. - Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations - Training Data - The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. - The scope of the training dataset determines the subject areas the model can handle effectively. - Context and Task Complexity - Models are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. - A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). - Language Ambiguity and Nuance - Natural language is inherently complex. Models might struggle to grasp subtle nuances, sarcasm, or figurative language. - Factual Accuracy - Models generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. - Common Sense - Models rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of vision-language models (VLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: - Bias and Fairness - VLMs trained on large-scale, real-world text and image data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. - Misinformation and Misuse - VLMs can be misused to generate text that is false, misleading, or harmful. - Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit][rai-toolkit]. - Transparency and Accountability: - This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. - A responsibly developed open model offers the opportunity to share innovation by making VLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: - **Perpetuation of biases**: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. - **Generation of harmful content**: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. - **Misuse for malicious purposes**: Technical limitations and developer and end-user education can help mitigate against malicious applications of VLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy][prohibited-use]. - **Privacy violations**: Models were trained on data filtered for removal of certain personal information and other sensitive data. Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open vision-language model implementations designed from the ground up for responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives. [g3-tech-report]: https://goo.gle/Gemma3Report [rai-toolkit]: https://ai.google.dev/responsible [kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3 [vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3 [terms]: https://ai.google.dev/gemma/terms [safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf [prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy [tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu [sustainability]: https://sustainability.google/operating-sustainably/ [jax]: https://github.com/jax-ml/jax [ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ [sustainability]: https://sustainability.google/operating-sustainably/ [gemini-2-paper]: https://arxiv.org/abs/2312.11805
Azurastar2903/gemma-3-1b-it-rk3588-1.2.1
Azurastar2903
2025-08-19T14:55:18Z
0
0
transformers
[ "transformers", "gemma3_text", "text-generation", "conversational", "arxiv:1905.07830", "arxiv:1905.10044", "arxiv:1911.11641", "arxiv:1904.09728", "arxiv:1705.03551", "arxiv:1911.01547", "arxiv:1907.10641", "arxiv:1903.00161", "arxiv:2009.03300", "arxiv:2304.06364", "arxiv:2103.03874", "arxiv:2110.14168", "arxiv:2311.12022", "arxiv:2108.07732", "arxiv:2107.03374", "arxiv:2210.03057", "arxiv:2106.03193", "arxiv:1910.11856", "arxiv:2502.12404", "arxiv:2502.21228", "arxiv:2404.16816", "arxiv:2104.12756", "arxiv:2311.16502", "arxiv:2203.10244", "arxiv:2404.12390", "arxiv:1810.12440", "arxiv:1908.02660", "arxiv:2312.11805", "base_model:google/gemma-3-1b-pt", "base_model:finetune:google/gemma-3-1b-pt", "license:gemma", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2025-08-19T13:36:58Z
--- base_model: google/gemma-3-1b-pt library_name: transformers license: gemma pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license --- # gemma-3-1b-it-RK3588-1.2.1 This version of gemma-3-1b-it has been converted to run on the RK3588 NPU using ['w8a8_g256'] quantization. This model has been optimized with the following LoRA: Compatible with RKLLM version: 1.2.1 ## Useful links: [Official RKLLM GitHub](https://github.com/airockchip/rknn-llm) [RockhipNPU Reddit](https://reddit.com/r/RockchipNPU) [EZRKNN-LLM](https://github.com/Pelochus/ezrknn-llm/) Pretty much anything by these folks: [marty1885](https://github.com/marty1885) and [happyme531](https://huggingface.co/happyme531) Converted using https://github.com/c0zaut/ez-er-rkllm-toolkit # Original Model Card for base model, gemma-3-1b-it, below: # Gemma 3 model card **Model Page**: [Gemma](https://ai.google.dev/gemma/docs/core) **Resources and Technical Documentation**: * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] **Terms of Use**: [Terms][terms] **Authors**: Google DeepMind ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Inputs and outputs - **Input:** - Text string, such as a question, a prompt, or a document to be summarized - Images, normalized to 896 x 896 resolution and encoded to 256 tokens each - Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size - **Output:** - Generated text in response to the input, such as an answer to a question, analysis of image content, or a summary of a document - Total output context of 8192 tokens ### Usage Below, there are some code snippets on how to get quickly started with running the model. First, install the Transformers library. Gemma 3 is supported starting from transformers 4.50.0. ```sh $ pip install -U transformers ``` Then, copy the snippet from the section that is relevant for your use case. #### Running with the `pipeline` API With instruction-tuned models, you need to use chat templates to process our inputs first. Then, you can pass it to the pipeline. ```python from transformers import pipeline import torch pipe = pipeline("text-generation", model="google/gemma-3-1b-it", device="cuda", torch_dtype=torch.bfloat16) messages = [ [ { "role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."},] }, { "role": "user", "content": [{"type": "text", "text": "Write a poem on Hugging Face, the company"},] }, ], ] output = pipe(messages, max_new_tokens=50) ``` #### Running the model on a single / multi GPU ```python from transformers import AutoTokenizer, BitsAndBytesConfig, Gemma3ForCausalLM import torch model_id = "google/gemma-3-1b-it" quantization_config = BitsAndBytesConfig(load_in_8bit=True) model = Gemma3ForCausalLM.from_pretrained( model_id, quantization_config=quantization_config ).eval() tokenizer = AutoTokenizer.from_pretrained(model_id) messages = [ [ { "role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."},] }, { "role": "user", "content": [{"type": "text", "text": "Write a poem on Hugging Face, the company"},] }, ], ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device).to(torch.bfloat16) with torch.inference_mode(): outputs = model.generate(**inputs, max_new_tokens=64) outputs = tokenizer.batch_decode(outputs) ``` ### Citation ```none @article{gemma_2025, title={Gemma 3}, url={https://goo.gle/Gemma3Report}, publisher={Kaggle}, author={Gemma Team}, year={2025} } ``` ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: - Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. The training dataset includes content in over 140 languages. - Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code and understand code-related questions. - Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. - Images: A wide range of images enables the model to perform image analysis and visual data extraction tasks. The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: - CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content. - Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. - Additional methods: Filtering based on content quality and safety in line with [our policies][safety-policies]. ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p, TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: - Performance: TPUs are specifically designed to handle the massive computations involved in training VLMs. They can speed up training considerably compared to CPUs. - Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. - Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. - Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. - These advantages are aligned with [Google's commitments to operate sustainably][sustainability]. ### Software Training was done using [JAX][jax] and [ML Pathways][ml-pathways]. JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for foundation models, including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models][gemini-2-paper]; *"the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow."* ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: #### Reasoning and factuality | Benchmark | Metric | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------ |----------------|:--------------:|:-------------:|:--------------:|:--------------:| | [HellaSwag][hellaswag] | 10-shot | 62.3 | 77.2 | 84.2 | 85.6 | | [BoolQ][boolq] | 0-shot | 63.2 | 72.3 | 78.8 | 82.4 | | [PIQA][piqa] | 0-shot | 73.8 | 79.6 | 81.8 | 83.3 | | [SocialIQA][socialiqa] | 0-shot | 48.9 | 51.9 | 53.4 | 54.9 | | [TriviaQA][triviaqa] | 5-shot | 39.8 | 65.8 | 78.2 | 85.5 | | [Natural Questions][naturalq] | 5-shot | 9.48 | 20.0 | 31.4 | 36.1 | | [ARC-c][arc] | 25-shot | 38.4 | 56.2 | 68.9 | 70.6 | | [ARC-e][arc] | 0-shot | 73.0 | 82.4 | 88.3 | 89.0 | | [WinoGrande][winogrande] | 5-shot | 58.2 | 64.7 | 74.3 | 78.8 | | [BIG-Bench Hard][bbh] | few-shot | 28.4 | 50.9 | 72.6 | 77.7 | | [DROP][drop] | 1-shot | 42.4 | 60.1 | 72.2 | 77.2 | [hellaswag]: https://arxiv.org/abs/1905.07830 [boolq]: https://arxiv.org/abs/1905.10044 [piqa]: https://arxiv.org/abs/1911.11641 [socialiqa]: https://arxiv.org/abs/1904.09728 [triviaqa]: https://arxiv.org/abs/1705.03551 [naturalq]: https://github.com/google-research-datasets/natural-questions [arc]: https://arxiv.org/abs/1911.01547 [winogrande]: https://arxiv.org/abs/1907.10641 [bbh]: https://paperswithcode.com/dataset/bbh [drop]: https://arxiv.org/abs/1903.00161 #### STEM and code | Benchmark | Metric | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------ |----------------|:-------------:|:--------------:|:--------------:| | [MMLU][mmlu] | 5-shot | 59.6 | 74.5 | 78.6 | | [MMLU][mmlu] (Pro COT) | 5-shot | 29.2 | 45.3 | 52.2 | | [AGIEval][agieval] | 3-5-shot | 42.1 | 57.4 | 66.2 | | [MATH][math] | 4-shot | 24.2 | 43.3 | 50.0 | | [GSM8K][gsm8k] | 8-shot | 38.4 | 71.0 | 82.6 | | [GPQA][gpqa] | 5-shot | 15.0 | 25.4 | 24.3 | | [MBPP][mbpp] | 3-shot | 46.0 | 60.4 | 65.6 | | [HumanEval][humaneval] | 0-shot | 36.0 | 45.7 | 48.8 | [mmlu]: https://arxiv.org/abs/2009.03300 [agieval]: https://arxiv.org/abs/2304.06364 [math]: https://arxiv.org/abs/2103.03874 [gsm8k]: https://arxiv.org/abs/2110.14168 [gpqa]: https://arxiv.org/abs/2311.12022 [mbpp]: https://arxiv.org/abs/2108.07732 [humaneval]: https://arxiv.org/abs/2107.03374 #### Multilingual | Benchmark | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------------ |:-------------:|:-------------:|:--------------:|:--------------:| | [MGSM][mgsm] | 2.04 | 34.7 | 64.3 | 74.3 | | [Global-MMLU-Lite][global-mmlu-lite] | 24.9 | 57.0 | 69.4 | 75.7 | | [WMT24++][wmt24pp] (ChrF) | 36.7 | 48.4 | 53.9 | 55.7 | | [FloRes][flores] | 29.5 | 39.2 | 46.0 | 48.8 | | [XQuAD][xquad] (all) | 43.9 | 68.0 | 74.5 | 76.8 | | [ECLeKTic][eclektic] | 4.69 | 11.0 | 17.2 | 24.4 | | [IndicGenBench][indicgenbench] | 41.4 | 57.2 | 61.7 | 63.4 | [mgsm]: https://arxiv.org/abs/2210.03057 [flores]: https://arxiv.org/abs/2106.03193 [xquad]: https://arxiv.org/abs/1910.11856v3 [global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite [wmt24pp]: https://arxiv.org/abs/2502.12404v1 [eclektic]: https://arxiv.org/abs/2502.21228 [indicgenbench]: https://arxiv.org/abs/2404.16816 #### Multimodal | Benchmark | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------ |:-------------:|:--------------:|:--------------:| | [COCOcap][coco-cap] | 102 | 111 | 116 | | [DocVQA][docvqa] (val) | 72.8 | 82.3 | 85.6 | | [InfoVQA][info-vqa] (val) | 44.1 | 54.8 | 59.4 | | [MMMU][mmmu] (pt) | 39.2 | 50.3 | 56.1 | | [TextVQA][textvqa] (val) | 58.9 | 66.5 | 68.6 | | [RealWorldQA][realworldqa] | 45.5 | 52.2 | 53.9 | | [ReMI][remi] | 27.3 | 38.5 | 44.8 | | [AI2D][ai2d] | 63.2 | 75.2 | 79.0 | | [ChartQA][chartqa] | 63.6 | 74.7 | 76.3 | | [VQAv2][vqav2] | 63.9 | 71.2 | 72.9 | | [BLINK][blinkvqa] | 38.0 | 35.9 | 39.6 | | [OKVQA][okvqa] | 51.0 | 58.7 | 60.2 | | [TallyQA][tallyqa] | 42.5 | 51.8 | 54.3 | | [SpatialSense VQA][ss-vqa] | 50.9 | 60.0 | 59.4 | | [CountBenchQA][countbenchqa] | 26.1 | 17.8 | 68.0 | [coco-cap]: https://cocodataset.org/#home [docvqa]: https://www.docvqa.org/ [info-vqa]: https://arxiv.org/abs/2104.12756 [mmmu]: https://arxiv.org/abs/2311.16502 [textvqa]: https://textvqa.org/ [realworldqa]: https://paperswithcode.com/dataset/realworldqa [remi]: https://arxiv.org/html/2406.09175v1 [ai2d]: https://allenai.org/data/diagrams [chartqa]: https://arxiv.org/abs/2203.10244 [vqav2]: https://visualqa.org/index.html [blinkvqa]: https://arxiv.org/abs/2404.12390 [okvqa]: https://okvqa.allenai.org/ [tallyqa]: https://arxiv.org/abs/1810.12440 [ss-vqa]: https://arxiv.org/abs/1908.02660 [countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/ ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: - **Child Safety**: Evaluation of text-to-text and image to text prompts covering child safety policies, including child sexual abuse and exploitation. - **Content Safety:** Evaluation of text-to-text and image to text prompts covering safety policies including, harassment, violence and gore, and hate speech. - **Representational Harms**: Evaluation of text-to-text and image to text prompts covering safety policies including bias, stereotyping, and harmful associations or inaccuracies. In addition to development level evaluations, we conduct "assurance evaluations" which are our 'arms-length' internal evaluations for responsibility governance decision making. They are conducted separately from the model development team, to inform decision making about release. High level findings are fed back to the model team, but prompt sets are held-out to prevent overfitting and preserve the results' ability to inform decision making. Assurance evaluation results are reported to our Responsibility & Safety Council as part of release review. ### Evaluation Results For all areas of safety testing, we saw major improvements in the categories of child safety, content safety, and representational harms relative to previous Gemma models. All testing was conducted without safety filters to evaluate the model capabilities and behaviors. For both text-to-text and image-to-text, and across all model sizes, the model produced minimal policy violations, and showed significant improvements over previous Gemma models' performance with respect to ungrounded inferences. A limitation of our evaluations was they included only English language prompts. ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open vision-language models (VLMs) models have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. - Content Creation and Communication - Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. - Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. - Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. - Image Data Extraction: These models can be used to extract, interpret, and summarize visual data for text communications. - Research and Education - Natural Language Processing (NLP) and VLM Research: These models can serve as a foundation for researchers to experiment with VLM and NLP techniques, develop algorithms, and contribute to the advancement of the field. - Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. - Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations - Training Data - The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. - The scope of the training dataset determines the subject areas the model can handle effectively. - Context and Task Complexity - Models are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. - A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). - Language Ambiguity and Nuance - Natural language is inherently complex. Models might struggle to grasp subtle nuances, sarcasm, or figurative language. - Factual Accuracy - Models generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. - Common Sense - Models rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of vision-language models (VLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: - Bias and Fairness - VLMs trained on large-scale, real-world text and image data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. - Misinformation and Misuse - VLMs can be misused to generate text that is false, misleading, or harmful. - Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit][rai-toolkit]. - Transparency and Accountability: - This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. - A responsibly developed open model offers the opportunity to share innovation by making VLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: - **Perpetuation of biases**: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. - **Generation of harmful content**: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. - **Misuse for malicious purposes**: Technical limitations and developer and end-user education can help mitigate against malicious applications of VLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy][prohibited-use]. - **Privacy violations**: Models were trained on data filtered for removal of certain personal information and other sensitive data. Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open vision-language model implementations designed from the ground up for responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives. [g3-tech-report]: https://goo.gle/Gemma3Report [rai-toolkit]: https://ai.google.dev/responsible [kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3 [vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3 [terms]: https://ai.google.dev/gemma/terms [safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf [prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy [tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu [sustainability]: https://sustainability.google/operating-sustainably/ [jax]: https://github.com/jax-ml/jax [ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ [sustainability]: https://sustainability.google/operating-sustainably/ [gemini-2-paper]: https://arxiv.org/abs/2312.11805
matheoqtb/EuroBertV2180M_pairs
matheoqtb
2025-08-19T14:55:16Z
0
0
null
[ "safetensors", "eurobert", "custom_code", "region:us" ]
null
2025-08-19T14:55:03Z
# Checkpoint exporté: 180M_pairs Ce dépôt contient un checkpoint extrait de `matheoqtb/euroBertV2_test2` (sous-dossier `180M_pairs`) et les fichiers de code nécessaires provenant de `EuroBERT/EuroBERT-610m`. Chargement: from transformers import AutoTokenizer, AutoModel tok = AutoTokenizer.from_pretrained('<THIS_REPO>', trust_remote_code=True) mdl = AutoModel.from_pretrained('<THIS_REPO>', trust_remote_code=True) Tâche: feature-extraction (embeddings)
KMH158/t5-small-openassistant-chat
KMH158
2025-08-19T14:54:39Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]
null
2025-08-19T12:36:35Z
--- library_name: transformers license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer model-index: - name: t5-small-openassistant-chat 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. --> # t5-small-openassistant-chat This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1785 ## 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: 5e-05 - train_batch_size: 80 - eval_batch_size: 1 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.3768 | 1.0 | 301 | 2.3842 | | 2.6839 | 2.0 | 602 | 2.3277 | | 2.6351 | 3.0 | 903 | 2.2995 | | 2.6016 | 4.0 | 1204 | 2.2818 | | 2.5803 | 5.0 | 1505 | 2.2680 | | 2.5587 | 6.0 | 1806 | 2.2571 | | 2.541 | 7.0 | 2107 | 2.2481 | | 2.5323 | 8.0 | 2408 | 2.2409 | | 2.5102 | 9.0 | 2709 | 2.2349 | | 2.5063 | 10.0 | 3010 | 2.2288 | | 2.4953 | 11.0 | 3311 | 2.2242 | | 2.4926 | 12.0 | 3612 | 2.2192 | | 2.4786 | 13.0 | 3913 | 2.2154 | | 2.472 | 14.0 | 4214 | 2.2117 | | 2.4662 | 15.0 | 4515 | 2.2079 | | 2.4553 | 16.0 | 4816 | 2.2051 | | 2.4472 | 17.0 | 5117 | 2.2020 | | 2.4488 | 18.0 | 5418 | 2.2008 | | 2.4367 | 19.0 | 5719 | 2.1972 | | 2.4353 | 20.0 | 6020 | 2.1952 | | 2.429 | 21.0 | 6321 | 2.1934 | | 2.4247 | 22.0 | 6622 | 2.1912 | | 2.4242 | 23.0 | 6923 | 2.1901 | | 2.4196 | 24.0 | 7224 | 2.1887 | | 2.4169 | 25.0 | 7525 | 2.1873 | | 2.4122 | 26.0 | 7826 | 2.1862 | | 2.4089 | 27.0 | 8127 | 2.1851 | | 2.4042 | 28.0 | 8428 | 2.1841 | | 2.4061 | 29.0 | 8729 | 2.1831 | | 2.4007 | 30.0 | 9030 | 2.1823 | | 2.397 | 31.0 | 9331 | 2.1814 | | 2.3998 | 32.0 | 9632 | 2.1810 | | 2.3963 | 33.0 | 9933 | 2.1805 | | 2.3976 | 34.0 | 10234 | 2.1798 | | 2.3919 | 35.0 | 10535 | 2.1794 | | 2.3873 | 36.0 | 10836 | 2.1793 | | 2.3899 | 37.0 | 11137 | 2.1789 | | 2.3886 | 38.0 | 11438 | 2.1786 | | 2.3906 | 39.0 | 11739 | 2.1786 | | 2.393 | 40.0 | 12040 | 2.1785 | ### Framework versions - Transformers 4.55.2 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4
xiaoxingop/Qwen3-0.6B-Q4_K_M-GGUF
xiaoxingop
2025-08-19T14:51:53Z
0
0
transformers
[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-0.6B", "base_model:quantized:Qwen/Qwen3-0.6B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]
text-generation
2025-08-19T14:51:49Z
--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE pipeline_tag: text-generation base_model: Qwen/Qwen3-0.6B tags: - llama-cpp - gguf-my-repo --- # xiaoxingop/Qwen3-0.6B-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-0.6B`](https://huggingface.co/Qwen/Qwen3-0.6B) 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/Qwen/Qwen3-0.6B) 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 xiaoxingop/Qwen3-0.6B-Q4_K_M-GGUF --hf-file qwen3-0.6b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo xiaoxingop/Qwen3-0.6B-Q4_K_M-GGUF --hf-file qwen3-0.6b-q4_k_m.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 xiaoxingop/Qwen3-0.6B-Q4_K_M-GGUF --hf-file qwen3-0.6b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo xiaoxingop/Qwen3-0.6B-Q4_K_M-GGUF --hf-file qwen3-0.6b-q4_k_m.gguf -c 2048 ```
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755613402
hakimjustbao
2025-08-19T14:51:26Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T14:51:23Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
zenqqq/blockassist-bc-restless_reptilian_caterpillar_1755614989
zenqqq
2025-08-19T14:51:10Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "restless reptilian caterpillar", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T14:50:41Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - restless reptilian caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lilTAT/blockassist-bc-gentle_rugged_hare_1755615038
lilTAT
2025-08-19T14:51:06Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T14:51:02Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
prl90777/R1_Qwen3_8B_0719
prl90777
2025-08-19T14:48:53Z
0
0
peft
[ "peft", "safetensors", "base_model:adapter:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", "lora", "transformers", "base_model:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", "license:mit", "region:us" ]
null
2025-08-19T11:31:10Z
--- library_name: peft license: mit base_model: deepseek-ai/DeepSeek-R1-0528-Qwen3-8B tags: - base_model:adapter:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B - lora - transformers model-index: - name: R1_Qwen3_8B_0719 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. --> # R1_Qwen3_8B_0719 This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-0528-Qwen3-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4267 - Map@3: 0.9177 ## 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: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Map@3 | |:-------------:|:------:|:----:|:---------------:|:------:| | 26.6085 | 0.0523 | 20 | 1.3286 | 0.7507 | | 9.7222 | 0.1046 | 40 | 1.0625 | 0.7933 | | 7.9943 | 0.1569 | 60 | 0.8487 | 0.8183 | | 7.4982 | 0.2092 | 80 | 0.8259 | 0.8315 | | 6.7844 | 0.2615 | 100 | 0.7845 | 0.8407 | | 6.1752 | 0.3138 | 120 | 0.7051 | 0.8571 | | 5.3012 | 0.3661 | 140 | 0.6606 | 0.8683 | | 4.7654 | 0.4184 | 160 | 0.5941 | 0.8830 | | 5.3467 | 0.4707 | 180 | 0.6074 | 0.8771 | | 4.4068 | 0.5230 | 200 | 0.5947 | 0.8880 | | 4.9025 | 0.5754 | 220 | 0.5081 | 0.8986 | | 4.3179 | 0.6277 | 240 | 0.5520 | 0.8941 | | 4.4065 | 0.6800 | 260 | 0.4970 | 0.9040 | | 3.7451 | 0.7323 | 280 | 0.4987 | 0.9045 | | 4.4839 | 0.7846 | 300 | 0.4905 | 0.9085 | | 3.5164 | 0.8369 | 320 | 0.4644 | 0.9067 | | 3.9504 | 0.8892 | 340 | 0.4650 | 0.9066 | | 3.6298 | 0.9415 | 360 | 0.4461 | 0.9106 | | 3.6195 | 0.9938 | 380 | 0.4242 | 0.9173 | | 3.0214 | 1.0445 | 400 | 0.5402 | 0.9058 | | 2.7135 | 1.0968 | 420 | 0.4302 | 0.9203 | | 2.6106 | 1.1491 | 440 | 0.4071 | 0.9252 | | 2.8122 | 1.2014 | 460 | 0.4366 | 0.9188 | | 3.0033 | 1.2537 | 480 | 0.4178 | 0.9230 | | 2.59 | 1.3060 | 500 | 0.4116 | 0.9233 | | 3.0395 | 1.3583 | 520 | 0.4267 | 0.9177 | ### Framework versions - PEFT 0.17.0 - Transformers 4.55.2 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4
westlake-repl/ProTrek_650M
westlake-repl
2025-08-19T14:47:40Z
19
4
transformers
[ "transformers", "arxiv:2103.00020", "license:mit", "endpoints_compatible", "region:us" ]
null
2024-05-22T02:48:33Z
--- license: mit --- **Github repo: https://github.com/westlake-repl/ProTrek** ## Overview ProTrek is a multimodal model that integrates protein sequence, protein structure, and text information for better protein understanding. It adopts contrastive learning to learn the representations of protein sequence and structure. During the pre-training phase, we calculate the InfoNCE loss for each two modalities as [CLIP](https://arxiv.org/abs/2103.00020) does. ## Model architecture **Protein sequence encoder**: [esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D) **Protein structure encoder**: foldseek_t30_150M (identical architecture with esm2 except that the vocabulary only contains 3Di tokens) **Text encoder**: [BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) ## Obtain embeddings and calculate similarity score (please clone our repo first) ``` import torch from model.ProtTrek.protrek_trimodal_model import ProTrekTrimodalModel from utils.foldseek_util import get_struc_seq # Load model config = { "protein_config": "weights/ProTrek_650M_UniRef50/esm2_t33_650M_UR50D", "text_config": "weights/ProTrek_650M_UniRef50/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext", "structure_config": "weights/ProTrek_650M_UniRef50/foldseek_t30_150M", "load_protein_pretrained": False, "load_text_pretrained": False, "from_checkpoint": "weights/ProTrek_650M_UniRef50/ProTrek_650M_UniRef50.pt" } device = "cuda" model = ProTrekTrimodalModel(**config).eval().to(device) # Load protein and text pdb_path = "example/8ac8.cif" seqs = get_struc_seq("bin/foldseek", pdb_path, ["A"])["A"] aa_seq = seqs[0] foldseek_seq = seqs[1].lower() text = "Replication initiator in the monomeric form, and autogenous repressor in the dimeric form." with torch.no_grad(): # Obtain protein sequence embedding seq_embedding = model.get_protein_repr([aa_seq]) print("Protein sequence embedding shape:", seq_embedding.shape) # Obtain protein structure embedding struc_embedding = model.get_structure_repr([foldseek_seq]) print("Protein structure embedding shape:", struc_embedding.shape) # Obtain text embedding text_embedding = model.get_text_repr([text]) print("Text embedding shape:", text_embedding.shape) # Calculate similarity score between protein sequence and structure seq_struc_score = seq_embedding @ struc_embedding.T / model.temperature print("Similarity score between protein sequence and structure:", seq_struc_score.item()) # Calculate similarity score between protein sequence and text seq_text_score = seq_embedding @ text_embedding.T / model.temperature print("Similarity score between protein sequence and text:", seq_text_score.item()) # Calculate similarity score between protein structure and text struc_text_score = struc_embedding @ text_embedding.T / model.temperature print("Similarity score between protein structure and text:", struc_text_score.item()) ```
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755613076
kojeklollipop
2025-08-19T14:46:16Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T14:46:13Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
umairmaliick/falcon-7b-instruct-taskpro-lora
umairmaliick
2025-08-19T14:45:49Z
0
0
peft
[ "peft", "tensorboard", "safetensors", "base_model:adapter:tiiuae/falcon-7b-instruct", "lora", "transformers", "text-generation", "conversational", "base_model:tiiuae/falcon-7b-instruct", "license:apache-2.0", "region:us" ]
text-generation
2025-08-19T13:53:18Z
--- library_name: peft license: apache-2.0 base_model: tiiuae/falcon-7b-instruct tags: - base_model:adapter:tiiuae/falcon-7b-instruct - lora - transformers pipeline_tag: text-generation model-index: - name: falcon-7b-instruct-taskpro-lora 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. --> # falcon-7b-instruct-taskpro-lora This model is a fine-tuned version of [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.2754 ## 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: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 1 | 3.2923 | | No log | 2.0 | 2 | 3.2812 | | No log | 3.0 | 3 | 3.2754 | ### Framework versions - PEFT 0.17.0 - Transformers 4.55.2 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4
lilTAT/blockassist-bc-gentle_rugged_hare_1755614706
lilTAT
2025-08-19T14:45:33Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T14:45:30Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Trelis/Qwen3-4B_ds-arc-agi-2-perfect-100_test-c8
Trelis
2025-08-19T14:45:18Z
0
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Qwen3-4B", "base_model:finetune:unsloth/Qwen3-4B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2025-08-19T14:44:31Z
--- base_model: unsloth/Qwen3-4B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** Trelis - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen3-4B 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)
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755613191
lisaozill03
2025-08-19T14:45:03Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T14:45:00Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
weikeduik/mozlegal
weikeduik
2025-08-19T14:42:52Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2025-08-19T14:42:52Z
--- license: apache-2.0 ---
lilTAT/blockassist-bc-gentle_rugged_hare_1755614412
lilTAT
2025-08-19T14:40:39Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]
null
2025-08-19T14:40:35Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Feruru/Classifier
Feruru
2025-08-19T14:36:48Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2025-08-19T14:35:49Z
--- license: apache-2.0 ---