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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-06 06:27:01	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 542 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-06 06:26:44	card stringlengths 11 1.01M
bah63843/blockassist-bc-plump_fast_antelope_1757032644	bah63843	2025-09-05T00:38:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T00:38:11Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
klmdr22/blockassist-bc-wild_loud_newt_1757032625	klmdr22	2025-09-05T00:37:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wild loud newt", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T00:37:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wild loud newt --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Stasonelison/blockassist-bc-howling_powerful_aardvark_1757032388	Stasonelison	2025-09-05T00:33:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "howling powerful aardvark", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T00:33:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - howling powerful aardvark --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
HouraMor/wh-loraft-lr5e5-dtstf5-adm-ga1ba16-st15k-v2-evalstp10-pat20-trainvalch	HouraMor	2025-09-05T00:33:35Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:HouraMor/wh-ft-lr5e6-dtstf5-adm-ga1ba16-st15k-v2-evalstp500-pat5", "base_model:adapter:HouraMor/wh-ft-lr5e6-dtstf5-adm-ga1ba16-st15k-v2-evalstp500-pat5", "license:apache-2.0", "region:us" ]	null	2025-09-04T21:32:09Z	--- library_name: peft license: apache-2.0 base_model: HouraMor/wh-ft-lr5e6-dtstf5-adm-ga1ba16-st15k-v2-evalstp500-pat5 tags: - generated_from_trainer model-index: - name: wh-loraft-lr5e5-dtstf5-adm-ga1ba16-st15k-v2-evalstp10-pat20-trainvalch 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. --> # wh-loraft-lr5e5-dtstf5-adm-ga1ba16-st15k-v2-evalstp10-pat20-trainvalch This model is a fine-tuned version of [HouraMor/wh-ft-lr5e6-dtstf5-adm-ga1ba16-st15k-v2-evalstp500-pat5](https://huggingface.co/HouraMor/wh-ft-lr5e6-dtstf5-adm-ga1ba16-st15k-v2-evalstp500-pat5) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5830 ## 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: 16 - eval_batch_size: 8 - seed: 42 - 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: 250 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.3707 \| 0.0201 \| 10 \| 0.5668 \| \| 0.3097 \| 0.0402 \| 20 \| 0.5668 \| \| 0.2065 \| 0.0602 \| 30 \| 0.5666 \| \| 0.3925 \| 0.0803 \| 40 \| 0.5666 \| \| 0.3026 \| 0.1004 \| 50 \| 0.5664 \| \| 0.272 \| 0.1205 \| 60 \| 0.5668 \| \| 0.2404 \| 0.1406 \| 70 \| 0.5668 \| \| 0.3042 \| 0.1606 \| 80 \| 0.5665 \| \| 0.2961 \| 0.1807 \| 90 \| 0.5670 \| \| 0.3403 \| 0.2008 \| 100 \| 0.5671 \| \| 0.2824 \| 0.2209 \| 110 \| 0.5677 \| \| 0.2276 \| 0.2410 \| 120 \| 0.5690 \| \| 0.2789 \| 0.2610 \| 130 \| 0.5700 \| \| 0.2466 \| 0.2811 \| 140 \| 0.5726 \| \| 0.3524 \| 0.3012 \| 150 \| 0.5742 \| \| 0.3732 \| 0.3213 \| 160 \| 0.5727 \| \| 0.2749 \| 0.3414 \| 170 \| 0.5725 \| \| 0.3158 \| 0.3614 \| 180 \| 0.5728 \| \| 0.164 \| 0.3815 \| 190 \| 0.5733 \| \| 0.2836 \| 0.4016 \| 200 \| 0.5752 \| \| 0.2513 \| 0.4217 \| 210 \| 0.5772 \| \| 0.2227 \| 0.4418 \| 220 \| 0.5790 \| \| 0.2556 \| 0.4618 \| 230 \| 0.5809 \| \| 0.1861 \| 0.4819 \| 240 \| 0.5831 \| \| 0.2953 \| 0.5020 \| 250 \| 0.5830 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.52.3 - Pytorch 2.7.0+cu118 - Datasets 3.6.0 - Tokenizers 0.21.1
Kaori1707/gemma-3-1b-it-r8-linear-4bit	Kaori1707	2025-09-05T00:28:51Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-1b-it", "base_model:finetune:google/gemma-3-1b-it", "endpoints_compatible", "region:us" ]	null	2025-09-04T08:58:44Z	--- base_model: google/gemma-3-1b-it library_name: transformers model_name: gemma-3-1b-it-r8-linear-4bit tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-3-1b-it-r8-linear-4bit This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-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="Kaori1707/gemma-3-1b-it-r8-linear-4bit", 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: 0.19.1 - Transformers: 4.52.4 - Pytorch: 2.6.0 - 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}} } ```
felixmayor/pi0_golf_ball_lambda	felixmayor	2025-09-05T00:25:48Z	0	0	lerobot	[ "lerobot", "safetensors", "pi0", "robotics", "dataset:felixmayor/golf_ball_20250902_part2", "arxiv:2410.24164", "license:apache-2.0", "region:us" ]	robotics	2025-09-05T00:24:44Z	--- datasets: felixmayor/golf_ball_20250902_part2 library_name: lerobot license: apache-2.0 model_name: pi0 pipeline_tag: robotics tags: - lerobot - pi0 - robotics --- # Model Card for pi0 <!-- Provide a quick summary of what the model is/does. --> [Pi0](https://huggingface.co/papers/2410.24164) is a generalist vision-language-action transformer that converts multimodal observations and text instructions into robot actions for zero-shot task transfer. 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 python -m lerobot.scripts.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 python -m 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
goptouy/blockassist-bc-stinky_stinky_cassowary_1757031706	goptouy	2025-09-05T00:22:08Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stinky stinky cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T00:21:47Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stinky stinky cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
liukevin666/blockassist-bc-yawning_striped_cassowary_1757031126	liukevin666	2025-09-05T00:13:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T00:13:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
raihannabiil/blockassist-bc-humming_rugged_viper_1757028924	raihannabiil	2025-09-05T00:12:57Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "humming rugged viper", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T00:12:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - humming rugged viper --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
NahedDom/blockassist-bc-flapping_stocky_leopard_1757028645	NahedDom	2025-09-05T00:04:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flapping stocky leopard", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T00:04:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flapping stocky leopard --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
liukevin666/blockassist-bc-yawning_striped_cassowary_1757030469	liukevin666	2025-09-05T00:02:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T00:02:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
GroomerG/blockassist-bc-vicious_pawing_badger_1757028940	GroomerG	2025-09-05T00:00:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vicious pawing badger", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T00:00:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vicious pawing badger --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mixedbread-ai/mxbai-ettin-32m-pretrained-st	mixedbread-ai	2025-09-04T23:49:04Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "modernbert", "sentence-similarity", "feature-extraction", "dense", "base_model:mixedbread-ai/mxbai-ettin-32m-pretrained", "base_model:finetune:mixedbread-ai/mxbai-ettin-32m-pretrained", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-09-04T23:49:01Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense base_model: mixedbread-ai/mxbai-ettin-32m-pretrained pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on mixedbread-ai/mxbai-ettin-32m-pretrained This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [mixedbread-ai/mxbai-ettin-32m-pretrained](https://huggingface.co/mixedbread-ai/mxbai-ettin-32m-pretrained). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [mixedbread-ai/mxbai-ettin-32m-pretrained](https://huggingface.co/mixedbread-ai/mxbai-ettin-32m-pretrained) <!-- at revision 3e1d0757d6254e615597cdcc0114285086ae5995 --> - Maximum Sequence Length: 7999 tokens - Output Dimensionality: 384 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 7999, 'do_lower_case': False, 'architecture': 'ModernBertModel'}) (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("mixedbread-ai/mxbai-ettin-32m-pretrained-st") # Run inference sentences = [ 'The weather is lovely today.', "It's so sunny outside!", 'He drove to the stadium.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[1.0000, 0.3843, 0.1600], # [0.3843, 1.0000, 0.1203], # [0.1600, 0.1203, 1.0000]]) ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Framework Versions - Python: 3.10.18 - Sentence Transformers: 5.1.0 - Transformers: 4.56.0 - PyTorch: 2.8.0+cu128 - Accelerate: 1.10.1 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citation ### BibTeX <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
mixedbread-ai/mxbai-ettin-32m-pretrained	mixedbread-ai	2025-09-04T23:48:56Z	0	0	transformers	[ "transformers", "safetensors", "modernbert", "feature-extraction", "arxiv:1910.09700", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-09-04T23:48:51Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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pidbu/blockassist-bc-whistling_alert_shrew_1757029266	pidbu	2025-09-04T23:42:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "whistling alert shrew", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T23:41:51Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - whistling alert shrew --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
fakir22/blockassist-bc-flapping_peaceful_caterpillar_1757028740	fakir22	2025-09-04T23:33:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flapping peaceful caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T23:32:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flapping peaceful caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/Mistral-7B-Instruct-v0.2-abliterated-GGUF	mradermacher	2025-09-04T23:31:23Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:ShadowCypher/Mistral-7B-Instruct-v0.2-abliterated", "base_model:quantized:ShadowCypher/Mistral-7B-Instruct-v0.2-abliterated", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-04T22:41:17Z	--- base_model: ShadowCypher/Mistral-7B-Instruct-v0.2-abliterated language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/ShadowCypher/Mistral-7B-Instruct-v0.2-abliterated <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Mistral-7B-Instruct-v0.2-abliterated-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/Mistral-7B-Instruct-v0.2-abliterated-GGUF/resolve/main/Mistral-7B-Instruct-v0.2-abliterated.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-v0.2-abliterated-GGUF/resolve/main/Mistral-7B-Instruct-v0.2-abliterated.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-v0.2-abliterated-GGUF/resolve/main/Mistral-7B-Instruct-v0.2-abliterated.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-v0.2-abliterated-GGUF/resolve/main/Mistral-7B-Instruct-v0.2-abliterated.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-v0.2-abliterated-GGUF/resolve/main/Mistral-7B-Instruct-v0.2-abliterated.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-v0.2-abliterated-GGUF/resolve/main/Mistral-7B-Instruct-v0.2-abliterated.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-v0.2-abliterated-GGUF/resolve/main/Mistral-7B-Instruct-v0.2-abliterated.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-v0.2-abliterated-GGUF/resolve/main/Mistral-7B-Instruct-v0.2-abliterated.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-v0.2-abliterated-GGUF/resolve/main/Mistral-7B-Instruct-v0.2-abliterated.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-v0.2-abliterated-GGUF/resolve/main/Mistral-7B-Instruct-v0.2-abliterated.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-v0.2-abliterated-GGUF/resolve/main/Mistral-7B-Instruct-v0.2-abliterated.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-v0.2-abliterated-GGUF/resolve/main/Mistral-7B-Instruct-v0.2-abliterated.f16.gguf) \| f16 \| 14.6 \| 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 -->
GroomerG/blockassist-bc-vicious_pawing_badger_1757027074	GroomerG	2025-09-04T23:31:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vicious pawing badger", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T23:31:10Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vicious pawing badger --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
raihannabiil/blockassist-bc-humming_rugged_viper_1757026059	raihannabiil	2025-09-04T23:27:11Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "humming rugged viper", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T23:27:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - humming rugged viper --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
calegpedia/blockassist-bc-stealthy_slimy_rooster_1757026358	calegpedia	2025-09-04T23:22:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T23:22:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stealthy slimy rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jqwrxcv/blockassist-bc-ravenous_vocal_barracuda_1757027909	jqwrxcv	2025-09-04T23:18:45Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous vocal barracuda", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T23:18:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous vocal barracuda --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1757027714	bah63843	2025-09-04T23:16:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T23:15:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Reihaneh/wav2vec2_fi_et_LID_50_epochs_10	Reihaneh	2025-09-04T23:12:40Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-04T23:12:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
jqwrxcv/blockassist-bc-sedate_whiskered_crow_1757027356	jqwrxcv	2025-09-04T23:09:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sedate whiskered crow", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T23:09:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sedate whiskered crow --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
fakir22/blockassist-bc-flapping_peaceful_caterpillar_1757027248	fakir22	2025-09-04T23:08:08Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flapping peaceful caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T23:08:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flapping peaceful caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
acidjp/blockassist-bc-pesty_extinct_prawn_1757024592	acidjp	2025-09-04T23:03:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pesty extinct prawn", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T23:03:49Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pesty extinct prawn --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jqwrxcv/blockassist-bc-foxy_aquatic_baboon_1757026983	jqwrxcv	2025-09-04T23:03:20Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "foxy aquatic baboon", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T23:03:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - foxy aquatic baboon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ntnu-smil/phi4-scorer-all-in-one	ntnu-smil	2025-09-04T23:02:05Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-09-04T23:00:28Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
mradermacher/Llama-2-13b-sft-gen-dpo-10k-GGUF	mradermacher	2025-09-04T23:00:34Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "trl", "dpo", "en", "base_model:AmberYifan/Llama-2-13b-sft-gen-dpo-10k", "base_model:quantized:AmberYifan/Llama-2-13b-sft-gen-dpo-10k", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-04T21:20:10Z	--- base_model: AmberYifan/Llama-2-13b-sft-gen-dpo-10k language: - en library_name: transformers model_name: Llama-2-13b-sft-gen-dpo-10k mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - generated_from_trainer - trl - dpo --- ## 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/AmberYifan/Llama-2-13b-sft-gen-dpo-10k <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Llama-2-13b-sft-gen-dpo-10k-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/Llama-2-13b-sft-gen-dpo-10k-GGUF/resolve/main/Llama-2-13b-sft-gen-dpo-10k.Q2_K.gguf) \| Q2_K \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-sft-gen-dpo-10k-GGUF/resolve/main/Llama-2-13b-sft-gen-dpo-10k.Q3_K_S.gguf) \| Q3_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-sft-gen-dpo-10k-GGUF/resolve/main/Llama-2-13b-sft-gen-dpo-10k.Q3_K_M.gguf) \| Q3_K_M \| 6.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-sft-gen-dpo-10k-GGUF/resolve/main/Llama-2-13b-sft-gen-dpo-10k.Q3_K_L.gguf) \| Q3_K_L \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-sft-gen-dpo-10k-GGUF/resolve/main/Llama-2-13b-sft-gen-dpo-10k.IQ4_XS.gguf) \| IQ4_XS \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-sft-gen-dpo-10k-GGUF/resolve/main/Llama-2-13b-sft-gen-dpo-10k.Q4_K_S.gguf) \| Q4_K_S \| 7.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-sft-gen-dpo-10k-GGUF/resolve/main/Llama-2-13b-sft-gen-dpo-10k.Q4_K_M.gguf) \| Q4_K_M \| 8.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-sft-gen-dpo-10k-GGUF/resolve/main/Llama-2-13b-sft-gen-dpo-10k.Q5_K_S.gguf) \| Q5_K_S \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-sft-gen-dpo-10k-GGUF/resolve/main/Llama-2-13b-sft-gen-dpo-10k.Q5_K_M.gguf) \| Q5_K_M \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-sft-gen-dpo-10k-GGUF/resolve/main/Llama-2-13b-sft-gen-dpo-10k.Q6_K.gguf) \| Q6_K \| 10.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-2-13b-sft-gen-dpo-10k-GGUF/resolve/main/Llama-2-13b-sft-gen-dpo-10k.Q8_0.gguf) \| Q8_0 \| 13.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Jan-v1-edge-i1-GGUF	mradermacher	2025-09-04T23:00:34Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:janhq/Jan-v1-edge", "base_model:quantized:janhq/Jan-v1-edge", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-09-04T21:44:44Z	--- base_model: janhq/Jan-v1-edge language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/janhq/Jan-v1-edge <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Jan-v1-edge-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/Jan-v1-edge-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.6 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.8 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-Q2_K.gguf) \| i1-Q2_K \| 0.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-IQ3_S.gguf) \| i1-IQ3_S \| 1.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 1.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-IQ3_M.gguf) \| i1-IQ3_M \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 1.0 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 1.1 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 1.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-Q4_0.gguf) \| i1-Q4_0 \| 1.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 1.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-Q4_1.gguf) \| i1-Q4_1 \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-v1-edge-i1-GGUF/resolve/main/Jan-v1-edge.i1-Q6_K.gguf) \| i1-Q6_K \| 1.5 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
Miracle-man/blockassist-bc-singing_lithe_koala_1757024703	Miracle-man	2025-09-04T23:00:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing lithe koala", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T23:00:10Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing lithe koala --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
tatsuyaaaaaaa/Qwen2.5-VL-7B-Instruct-gguf	tatsuyaaaaaaa	2025-09-04T22:55:55Z	86	0	null	[ "gguf", "ja", "en", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:quantized:Qwen/Qwen2.5-VL-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-03T02:13:24Z	--- license: apache-2.0 language: - ja - en base_model: - Qwen/Qwen2.5-VL-7B-Instruct --- Qwenの[Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)のgguf変換したものです。
vengky/blockassist-bc-wild_gentle_manatee_1757023257	vengky	2025-09-04T22:36:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wild gentle manatee", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T22:35:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wild gentle manatee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
fakir22/blockassist-bc-flapping_peaceful_caterpillar_1757024920	fakir22	2025-09-04T22:29:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flapping peaceful caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T22:29:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flapping peaceful caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/MneumonicPetal-20B-GGUF	mradermacher	2025-09-04T22:29:15Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Elfrino/MneumonicPetal-20B", "base_model:quantized:Elfrino/MneumonicPetal-20B", "endpoints_compatible", "region:us" ]	null	2025-09-04T20:24:16Z	--- base_model: Elfrino/MneumonicPetal-20B language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - mergekit - merge --- ## 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/Elfrino/MneumonicPetal-20B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#MneumonicPetal-20B-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/MneumonicPetal-20B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/MneumonicPetal-20B-GGUF/resolve/main/MneumonicPetal-20B.Q2_K.gguf) \| Q2_K \| 7.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MneumonicPetal-20B-GGUF/resolve/main/MneumonicPetal-20B.Q3_K_S.gguf) \| Q3_K_S \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MneumonicPetal-20B-GGUF/resolve/main/MneumonicPetal-20B.Q3_K_M.gguf) \| Q3_K_M \| 9.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MneumonicPetal-20B-GGUF/resolve/main/MneumonicPetal-20B.Q3_K_L.gguf) \| Q3_K_L \| 10.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MneumonicPetal-20B-GGUF/resolve/main/MneumonicPetal-20B.IQ4_XS.gguf) \| IQ4_XS \| 10.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MneumonicPetal-20B-GGUF/resolve/main/MneumonicPetal-20B.Q4_K_S.gguf) \| Q4_K_S \| 11.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MneumonicPetal-20B-GGUF/resolve/main/MneumonicPetal-20B.Q4_K_M.gguf) \| Q4_K_M \| 12.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MneumonicPetal-20B-GGUF/resolve/main/MneumonicPetal-20B.Q5_K_S.gguf) \| Q5_K_S \| 13.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MneumonicPetal-20B-GGUF/resolve/main/MneumonicPetal-20B.Q5_K_M.gguf) \| Q5_K_M \| 14.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MneumonicPetal-20B-GGUF/resolve/main/MneumonicPetal-20B.Q6_K.gguf) \| Q6_K \| 16.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MneumonicPetal-20B-GGUF/resolve/main/MneumonicPetal-20B.Q8_0.gguf) \| Q8_0 \| 21.3 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
bah63843/blockassist-bc-plump_fast_antelope_1757024773	bah63843	2025-09-04T22:27:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T22:26:53Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
6S-bobby/Llama-2-7b-chat-hf-distortion-6-casual-v1	6S-bobby	2025-09-04T22:24:58Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-04T22:24:52Z	--- 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]
liukevin666/blockassist-bc-yawning_striped_cassowary_1757024552	liukevin666	2025-09-04T22:23:36Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T22:23:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1757024518	bah63843	2025-09-04T22:22:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T22:22:38Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
fakir22/blockassist-bc-flapping_peaceful_caterpillar_1757024387	fakir22	2025-09-04T22:20:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flapping peaceful caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T22:20:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flapping peaceful caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ntnu-smil/Phi-4-multimodal-instruct-sandi-MIX-0902	ntnu-smil	2025-09-04T22:16:24Z	198	0	transformers	[ "transformers", "tensorboard", "safetensors", "phi4mm", "text-generation", "generated_from_trainer", "conversational", "custom_code", "autotrain_compatible", "region:us" ]	text-generation	2025-09-02T17:28:21Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: Phi-4-multimodal-instruct-sandi-MIX-0902 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. --> # Phi-4-multimodal-instruct-sandi-MIX-0902 This model was trained from scratch 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: 0.0001 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.48.2 - Pytorch 2.8.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.4
acidjp/blockassist-bc-pesty_extinct_prawn_1757021702	acidjp	2025-09-04T22:15:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pesty extinct prawn", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T22:15:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pesty extinct prawn --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
CodeAtCMU/Models_Llama-3.2-3B_full_sftcode_data_120K_remove_whitespace	CodeAtCMU	2025-09-04T22:14:41Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-04T22:13:40Z	--- 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]
calegpedia/blockassist-bc-stealthy_slimy_rooster_1757022217	calegpedia	2025-09-04T22:13:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T22:13:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stealthy slimy rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ry-5/candy_garlic_pickup_modified	ry-5	2025-09-04T22:03:25Z	0	0	null	[ "robotics", "manipulation", "aloha", "garlic-candy", "physical-intelligence", "en", "license:apache-2.0", "region:us" ]	robotics	2025-09-04T21:13:50Z	--- license: apache-2.0 language: - en pipeline_tag: robotics tags: - robotics - manipulation - aloha - garlic-candy - physical-intelligence --- # Pi0 Candy Garlic Pickup Modified Model This repository contains a trained model checkpoint for garlic and candy pickup tasks using the Pi0 architecture. ## Model Details - Model Type: Pi0 (Physical Intelligence) - Task: Garlic and candy pickup manipulation - Training Data: Modified garlic and candy pickup dataset - Checkpoint: Step 39999 ## Usage This checkpoint can be used with the Physical Intelligence Pi0 framework for robotic manipulation tasks. ## Files Structure - `_CHECKPOINT_METADATA`: Checkpoint metadata - `assets/`: Model assets - `params/`: Model parameters - `train_state/`: Training state ## Training Details The model was trained using the Physical Intelligence Pi0 framework with modified garlic and candy pickup data.
RZ412/qwen-s1k-claude-3b-s1-reproduce	RZ412	2025-09-04T21:49:48Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-04T21:27:07Z	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-3B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: Qwen2.5-3B-Instruct-S1K-Claude-S1-Reproduce 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. --> # Qwen2.5-3B-Instruct-S1K-Claude-S1-Reproduce This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the s1k_claude 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: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - total_eval_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 13.0 ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
kafa22/blockassist-bc-regal_leggy_hummingbird_1757022136	kafa22	2025-09-04T21:42:57Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "regal leggy hummingbird", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T21:42:53Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - regal leggy hummingbird --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1757020360	sampingkaca72	2025-09-04T21:39:22Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored stealthy elephant", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T21:39:19Z	--- 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/dynamic-camera	Muapi	2025-09-04T21:38:27Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-09-04T21:38:12Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Dynamic Camera ![preview](./preview.jpg) Base model: Flux.1 D Trained words: ## 🧠 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:1190006@1339761", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Miracle-man/blockassist-bc-singing_lithe_koala_1757019872	Miracle-man	2025-09-04T21:38:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing lithe koala", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T21:38:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing lithe koala --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
giovannidemuri/llama3b-llama8b-er-v583-seed2-seed2-hx-openmath-fpt	giovannidemuri	2025-09-04T21:37:52Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-04T16:31:46Z	--- 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. 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mradermacher/LFM2-350M-ENJP-MT-GGUF	mradermacher	2025-09-04T21:35:56Z	0	0	transformers	[ "transformers", "gguf", "liquid", "lfm2", "edge", "translation", "japanese", "en", "ja", "base_model:LiquidAI/LFM2-350M-ENJP-MT", "base_model:quantized:LiquidAI/LFM2-350M-ENJP-MT", "license:other", "endpoints_compatible", "region:us", "conversational" ]	translation	2025-09-04T21:33:38Z	--- base_model: LiquidAI/LFM2-350M-ENJP-MT language: - en - ja library_name: transformers license: other license_link: LICENSE license_name: lfm1.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - liquid - lfm2 - edge - translation - japanese --- ## 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/LiquidAI/LFM2-350M-ENJP-MT <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#LFM2-350M-ENJP-MT-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/LFM2-350M-ENJP-MT-GGUF/resolve/main/LFM2-350M-ENJP-MT.Q2_K.gguf) \| Q2_K \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/LFM2-350M-ENJP-MT-GGUF/resolve/main/LFM2-350M-ENJP-MT.Q3_K_S.gguf) \| Q3_K_S \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/LFM2-350M-ENJP-MT-GGUF/resolve/main/LFM2-350M-ENJP-MT.Q3_K_M.gguf) \| Q3_K_M \| 0.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/LFM2-350M-ENJP-MT-GGUF/resolve/main/LFM2-350M-ENJP-MT.Q3_K_L.gguf) \| Q3_K_L \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/LFM2-350M-ENJP-MT-GGUF/resolve/main/LFM2-350M-ENJP-MT.IQ4_XS.gguf) \| IQ4_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/LFM2-350M-ENJP-MT-GGUF/resolve/main/LFM2-350M-ENJP-MT.Q4_K_S.gguf) \| Q4_K_S \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LFM2-350M-ENJP-MT-GGUF/resolve/main/LFM2-350M-ENJP-MT.Q4_K_M.gguf) \| Q4_K_M \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LFM2-350M-ENJP-MT-GGUF/resolve/main/LFM2-350M-ENJP-MT.Q5_K_S.gguf) \| Q5_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/LFM2-350M-ENJP-MT-GGUF/resolve/main/LFM2-350M-ENJP-MT.Q5_K_M.gguf) \| Q5_K_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/LFM2-350M-ENJP-MT-GGUF/resolve/main/LFM2-350M-ENJP-MT.Q6_K.gguf) \| Q6_K \| 0.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/LFM2-350M-ENJP-MT-GGUF/resolve/main/LFM2-350M-ENJP-MT.Q8_0.gguf) \| Q8_0 \| 0.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/LFM2-350M-ENJP-MT-GGUF/resolve/main/LFM2-350M-ENJP-MT.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 -->
UnifiedHorusRA/Background_change	UnifiedHorusRA	2025-09-04T21:31:21Z	0	0	null	[ "custom", "art", "en", "region:us" ]	null	2025-09-04T20:40:10Z	--- language: - en tags: - art --- # Background change Creator: [zhouHH](https://civitai.com/user/zhouHH) Type: LORA Base Model: Wan Video 2.2 I2V-A14B Version: v1.0 Trigger Words: `ZHOUHHBJ` Civitai Model ID: 1866087 Civitai Version ID: 2112083 Stats (at time of fetch for this version): * Downloads: 293 * Rating: 0 (0 ratings) * Favorites: N/A --- ## 📄 Description (Parent Model) 改变背景提示词：ZHOUHHBJ，一女子在镜头前，女子背后的场景最初是棕色的背景，随后女子背后的场景变为太空。只有低噪模型，使用时以强度2挂在高噪上。正常挂在低噪上。 Change the background If it doesn't work, try changing the seeds Prompt: ZHOUHHBJ, a woman in front of the camera, the scene behind the woman is initially brown, and then the scene behind the woman changes to space. Only the low-noise model is hung on the high noise with intensity 2 when used. Normally hangs on low noise. ## Version Notes (v1.0) 低噪版本 --- ## Civitai Links * [🔗 View This Version on Civitai →](https://civitai.com/models/1866087?modelVersionId=2112083) * [View Full Model Page →](https://civitai.com/models/1866087) * [View Creator Profile →](https://civitai.com/user/zhouHH) --- ## File Information * Filename: `ZHOUHHBJ.safetensors` * Size: 292.63 MB * Hash (AutoV2): `E859D72766` * Hash (SHA256): `E859D72766FDE77C0C7651006B51513E1ECAA8EC92A89B6A983075011F93CA8D`
UnifiedHorusRA/wan2.2-i2v-high-InflatableFashion	UnifiedHorusRA	2025-09-04T21:28:21Z	0	0	null	[ "custom", "art", "en", "region:us" ]	null	2025-09-04T20:39:53Z	--- language: - en tags: - art --- # wan2.2-i2v-high-InflatableFashion Creator: [hxxwoq2222](https://civitai.com/user/hxxwoq2222) Type: LORA Base Model: Wan Video 2.2 I2V-A14B Version: HIGH Trigger Words: `N/A` Civitai Model ID: 1882717 Civitai Version ID: 2130979 Stats (at time of fetch for this version): * Downloads: 208 * Rating: 0 (0 ratings) * Favorites: N/A --- ## 📄 Description (Parent Model) 🚀 Run this model instantly on RTX 4090s with my pre-built workflow! * Sign Up Here: https://www.runninghub.ai/?inviteCode=rh-v1221 * Run Directly: https://www.runninghub.ai/post/1956586031082602497/?inviteCode=rh-v1221 🎁 Use invite code rh-v1221 to get 1,000 Free Credits (Double!) + 100 Daily Credits . Thank you for your support! Here's the explanation: Most special effects loras do not require low noise. If you understand the principle, you should know Just load high noise, weight: 0.8-1，（On wan2.2, most special effects Lora only need to load high-noise models） Example prompt word: cq567, A woman lying on a beach at sunset watches her purple bikini top inflate into large spheres. cq567, A woman taking a mirror selfie in a locker room sees her dark sweatshirt and white shorts inflate to become huge and puffy. ## Civitai Links * [🔗 View This Version on Civitai →](https://civitai.com/models/1882717?modelVersionId=2130979) * [View Full Model Page →](https://civitai.com/models/1882717) * [View Creator Profile →](https://civitai.com/user/hxxwoq2222) --- ## File Information * Filename: `充气-high.safetensors` * Size: 292.59 MB * Hash (AutoV2): `868946FA4D` * Hash (SHA256): `868946FA4D9ADC9CF7C290331765159D0C6D6879EFEDD0012B87FA3009D3E506`
acidjp/blockassist-bc-pesty_extinct_prawn_1757018342	acidjp	2025-09-04T21:28:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pesty extinct prawn", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T21:28:10Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pesty extinct prawn --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
cactusfriend/nightmare-promptgen-3	cactusfriend	2025-09-04T21:27:25Z	1,747	1	transformers	[ "transformers", "safetensors", "openelm", "text-generation", "custom_code", "license:openrail", "autotrain_compatible", "region:us" ]	text-generation	2024-06-27T17:17:25Z	--- library_name: transformers license: openrail pipeline_tag: text-generation tags: [] --- This is the third generation Nightmare Promptgen text generation model based upon Apple's OpenELM. It's for generating InvokeAI prompts. It can be used similarly to the previous models, and has an InvokeAI node available on Github [here](https://github.com/gogurtenjoyer/nightmare-promptgen).
UnifiedHorusRA/wan2.2-i2v-high-Superstar_Moment	UnifiedHorusRA	2025-09-04T21:25:52Z	0	0	null	[ "custom", "art", "en", "region:us" ]	null	2025-09-04T20:39:27Z	--- language: - en tags: - art --- # wan2.2-i2v-high-Superstar Moment Creator: [hxxwoq2222](https://civitai.com/user/hxxwoq2222) Type: LORA Base Model: Wan Video 2.2 I2V-A14B Version: high Trigger Words: `N/A` Civitai Model ID: 1888178 Civitai Version ID: 2137210 Stats (at time of fetch for this version): * Downloads: 347 * Rating: 0 (0 ratings) * Favorites: N/A --- ## 📄 Description (Parent Model) 🚀 Run this model instantly on RTX 4090s with my pre-built workflow! * Sign Up Here: https://www.runninghub.ai/?inviteCode=rh-v1221 * Run Directly: https://www.runninghub.ai/post/1956586031082602497/?inviteCode=rh-v1221 🎁 Use invite code rh-v1221 to get 1,000 Free Credits (Double!) + 100 Daily Credits . Thank you for your support! Here's the explanation: Most special effects loras do not require low noise. If you understand the principle, you should know Recommended weight: 1, example prompt word: huazhuang567, a beautiful anime girl with orange hair in twintails and blue eyes, wearing a school uniform, reaches her hand towards the camera inside a dark room, as the camera slowly pulls back to reveal stylists and makeup artists with a time-lapse blur, rapidly perfecting her hair and makeup. ## Civitai Links * [🔗 View This Version on Civitai →](https://civitai.com/models/1888178?modelVersionId=2137210) * [View Full Model Page →](https://civitai.com/models/1888178) * [View Creator Profile →](https://civitai.com/user/hxxwoq2222) --- ## File Information * Filename: `延时化妆-high.safetensors` * Size: 292.59 MB * Hash (AutoV2): `D643F32A54` * Hash (SHA256): `D643F32A54E67178B4A2FF764E8635398EBB8192E8A80AC225903415801FABB3`
UnifiedHorusRA/wan2.2-i2v-Cinematic_Flare	UnifiedHorusRA	2025-09-04T21:25:42Z	0	0	null	[ "custom", "art", "en", "region:us" ]	null	2025-09-04T20:39:25Z	--- language: - en tags: - art --- # wan2.2-i2v-Cinematic Flare Creator: [hxxwoq2222](https://civitai.com/user/hxxwoq2222) Type: LORA Base Model: Wan Video 2.2 I2V-A14B Version: low Trigger Words: `N/A` Civitai Model ID: 1902817 Civitai Version ID: 2153813 Stats (at time of fetch for this version): * Downloads: 375 * Rating: 0 (0 ratings) * Favorites: N/A --- ## 📄 Description (Parent Model) 🚀 RTX 4090 Power! Run my workflow instantly for free. Use invite code rh-v1221 for 1,000 Free Credits + 100 Daily Credits . Sign Up: https://www.runninghub.ai/?inviteCode=rh-v1221 Run Now: https://www.runninghub.ai/post/1956586031082602497/?inviteCode=rh-v1221 Thank you for your support，国内合作加Q：549791525 Reposting the model requires crediting the source and adding my RunningHub profile link. Cinematic Flare is a LoRA that adds realistic, cinematic lens flares when bright light hits the lens, creating a dreamy and dramatic atmosphere. Usage: Recommended weight is 1.5, you can even increase it to 2.0 to enhance the effect. This LoRA only requires low denoise。 Example prompt word: gy567, a slow push-in shot towards a young woman holding flowers on a sunlit street. As the camera moves forward, the strong backlight creates a beautiful, cinematic lens flare in the shape of a glowing semicircle. gy567, a stylish young woman on a sunny balcony overlooking the sea. The camera smoothly arcs to the right, causing the bright sun from the left to sweep across the lens and create a beautiful cinematic flare. ## Civitai Links * [🔗 View This Version on Civitai →](https://civitai.com/models/1902817?modelVersionId=2153813) * [View Full Model Page →](https://civitai.com/models/1902817) * [View Creator Profile →](https://civitai.com/user/hxxwoq2222) --- ## File Information * Filename: `镜头光晕-low.safetensors` * Size: 292.59 MB * Hash (AutoV2): `2810197AC6` * Hash (SHA256): `2810197AC68C6409761D1850097D8827A0CB626E40A91B02947BC1F9DC306EB1`
NahedDom/blockassist-bc-flapping_stocky_leopard_1757018891	NahedDom	2025-09-04T21:23:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flapping stocky leopard", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T21:23:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flapping stocky leopard --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
UnifiedHorusRA/wan2.2-i2v-high-Anguish_Wail	UnifiedHorusRA	2025-09-04T21:22:50Z	0	0	null	[ "custom", "art", "en", "region:us" ]	null	2025-09-04T20:38:59Z	--- language: - en tags: - art --- # wan2.2-i2v-high-Anguish Wail Creator: [hxxwoq2222](https://civitai.com/user/hxxwoq2222) Type: LORA Base Model: Wan Video 2.2 I2V-A14B Version: high Trigger Words: `N/A` Civitai Model ID: 1890900 Civitai Version ID: 2140307 Stats (at time of fetch for this version): * Downloads: 1065 * Rating: 0 (0 ratings) * Favorites: N/A --- ## 📄 Description (Parent Model) 🚀 RTX 4090 Power! Run my workflow instantly for free. Use invite code rh-v1221 for 1,000 Free Credits + 100 Daily Credits . Sign Up: https://www.runninghub.ai/?inviteCode=rh-v1221 Run Now: https://www.runninghub.ai/post/1956586031082602497/?inviteCode=rh-v1221 To reprint the model, you need to explain the source, and add my runninghub link to the introduction area. Thank you for your support Here's the explanation: Most special effects loras do not require low noise. If you understand the principle, you should know I trained this LoRA because I found it difficult to generate expressions of convulsive crying (crying to the point of trembling) with the wan2.2 model. Recommended weight: 1.0 Example prompt(s)： kuqi567, a static shot of a young woman in a black hoodie wailing uncontrollably with her eyes shut while standing in a doorway. kuqi567, a static shot of a young woman in a red headscarf wailing uncontrollably in front of a vibrant red wall under a blue sky. ## Civitai Links * [🔗 View This Version on Civitai →](https://civitai.com/models/1890900?modelVersionId=2140307) * [View Full Model Page →](https://civitai.com/models/1890900) * [View Creator Profile →](https://civitai.com/user/hxxwoq2222) --- ## File Information * Filename: `哭泣-high.safetensors` * Size: 292.59 MB * Hash (AutoV2): `8B14C827B7` * Hash (SHA256): `8B14C827B700D9061EC00ADCBC32692D25B039A44E3068892B31E4F824E755B8`
UnifiedHorusRA/Writing_effects	UnifiedHorusRA	2025-09-04T21:22:38Z	0	0	null	[ "custom", "art", "en", "region:us" ]	null	2025-09-04T20:38:58Z	--- language: - en tags: - art --- # Writing effects Creator: [zhouHH](https://civitai.com/user/zhouHH) Type: LORA Base Model: Wan Video 2.2 I2V-A14B Version: wan2.2v1 Trigger Words: `N/A` Civitai Model ID: 1653028 Civitai Version ID: 2135182 Stats (at time of fetch for this version): * Downloads: 199 * Rating: 0 (0 ratings) * Favorites: N/A --- ## 📄 Description (Parent Model) Writing effects wan2.2 以低噪为底膜炼的，使用时以强度2挂在高噪上，以强度1挂在低噪上，不挂高噪会有其他的效果如例图无法控制。 If the bottom film is made with low noise, it is hung on the high noise with intensity 2 and low noise with intensity 1 when used, and there will be other effects that cannot be controlled as shown in the example figure. 提示词：有一个红色（颜色可变）发光光线完成了ZHOUHHXZ Prompt: There is a red (color variable) glow that completes ZHOUHHXZ 可以在这里体验:☞ https://www.runninghub.ai/ai-detail/1958566466146693121/?inviteCode=rh-v1250 can be experienced here☞ https://www.runninghub.ai/ai-detail/1958566466146693121/?inviteCode=rh-v1250 wan2.1 提示词：ZHOUHHXZ，在（什么样的）背景下有一个（颜色）的光线完成ZHOUHHXZ，写出了“（什么字）”的个性签名字体这是尾帧lora。我跑的示例都以i2v尾帧来跑的 This is the tail frame lora. All the examples I ran used i2v tail frames. ## Version Notes (wan2.2v1) 更新wan2.2， --- ## Civitai Links * [🔗 View This Version on Civitai →](https://civitai.com/models/1653028?modelVersionId=2135182) * [View Full Model Page →](https://civitai.com/models/1653028) * [View Creator Profile →](https://civitai.com/user/zhouHH) --- ## File Information * Filename: `ZHOUHHXZLOW.safetensors` * Size: 292.63 MB * Hash (AutoV2): `D8C7A0A6CD` * Hash (SHA256): `D8C7A0A6CD327D6B53FD5D9FEB9A83512F4273E3089068751DC0FAA2F0642F13`
Muapi/minimalist-chinese-ink-brush-style-feng-zikai	Muapi	2025-09-04T21:20:08Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-09-04T21:19:52Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Minimalist Chinese Ink Brush Style (Feng Zikai) ![preview](./preview.jpg) Base model: Flux.1 D Trained words: fzk1 painting, fzk1 ink painting ## 🧠 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:1105994@1242549", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
manasp2025/my-embedding-gemma	manasp2025	2025-09-04T21:19:29Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "gemma3_text", "sentence-similarity", "feature-extraction", "dense", "generated_from_trainer", "dataset_size:3", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:google/embeddinggemma-300m", "base_model:finetune:google/embeddinggemma-300m", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-09-04T21:18:33Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:3 - loss:MultipleNegativesRankingLoss base_model: google/embeddinggemma-300m pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on google/embeddinggemma-300m This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) <!-- at revision 64614b0b8b64f0c6c1e52b07e4e9a4e8fe4d2da2 --> - Maximum Sequence Length: 2048 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'}) (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'}) (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'}) (4): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("manasp2025/my-embedding-gemma") # Run inference queries = [ "Which planet is known as the Red Planet?", ] documents = [ "Venus is often called Earth's twin because of its similar size and proximity.", 'Mars, known for its reddish appearance, is often referred to as the Red Planet.', 'Saturn, famous for its rings, is sometimes mistaken for the Red Planet.', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 768] [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[0.4507, 0.7751, 0.6039]]) ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 3 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 3 samples: \| \| anchor \| positive \| negative \| \|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 10 tokens</li><li>mean: 12.0 tokens</li><li>max: 15 tokens</li></ul> \| <ul><li>min: 13 tokens</li><li>mean: 15.33 tokens</li><li>max: 17 tokens</li></ul> \| <ul><li>min: 12 tokens</li><li>mean: 12.67 tokens</li><li>max: 14 tokens</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:--------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------------------------------\| \| <code>How do I open a NISA account?</code> \| <code>What is the procedure for starting a new tax-free investment account?</code> \| <code>I want to check the balance of my regular savings account.</code> \| \| <code>Are there fees for making an early repayment on a home loan?</code> \| <code>If I pay back my house loan early, will there be any costs?</code> \| <code>What is the management fee for this investment trust?</code> \| \| <code>What is the coverage for medical insurance?</code> \| <code>Tell me about the benefits of the health insurance plan.</code> \| <code>What is the cancellation policy for my life insurance?</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 1 - `learning_rate`: 2e-05 - `num_train_epochs`: 2 - `warmup_ratio`: 0.1 - `prompts`: task: sentence similarity \| query: #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 1 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 2 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `parallelism_config`: None - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `hub_revision`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: task: sentence similarity \| query: - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {} </details> ### Training Logs \| Epoch \| Step \| Training Loss \| \|:-----:\|:----:\|:-------------:\| \| 1.0 \| 3 \| 0.5711 \| \| 2.0 \| 6 \| 0.0 \| \| 3.0 \| 9 \| 0.0 \| \| 4.0 \| 12 \| 0.0 \| \| 5.0 \| 15 \| 0.0 \| \| 1.0 \| 3 \| 0.0 \| \| 2.0 \| 6 \| 0.0 \| ### Framework Versions - Python: 3.12.11 - Sentence Transformers: 5.1.0 - Transformers: 4.56.0 - PyTorch: 2.8.0+cu126 - Accelerate: 1.10.1 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
NexVeridian/Apertus-8B-Instruct-2509-4bit	NexVeridian	2025-09-04T21:19:17Z	0	0	mlx	[ "mlx", "safetensors", "apertus", "multilingual", "compliant", "swiss-ai", "text-generation", "conversational", "base_model:swiss-ai/Apertus-8B-Instruct-2509", "base_model:quantized:swiss-ai/Apertus-8B-Instruct-2509", "license:apache-2.0", "4-bit", "region:us" ]	text-generation	2025-09-04T21:10:19Z	--- license: apache-2.0 base_model: swiss-ai/Apertus-8B-Instruct-2509 pipeline_tag: text-generation library_name: mlx tags: - multilingual - compliant - swiss-ai - apertus - mlx extra_gated_prompt: "### Apertus LLM Acceptable Use Policy \n(1.0 \| September 1,\ \ 2025)\n\"Agreement\" The Swiss National AI Institute (SNAI) is a partnership between\ \ the two Swiss Federal Institutes of Technology, ETH Zurich and EPFL. \n\nBy using\ \ the Apertus LLM you agree to indemnify, defend, and hold harmless ETH Zurich and\ \ EPFL against any third-party claims arising from your use of Apertus LLM. \n\n\ The training data and the Apertus LLM may contain or generate information that directly\ \ or indirectly refers to an identifiable individual (Personal Data). You process\ \ Personal Data as independent controller in accordance with applicable data protection\ \ law. SNAI will regularly provide a file with hash values for download which you\ \ can apply as an output filter to your use of our Apertus LLM. The file reflects\ \ data protection deletion requests which have been addressed to SNAI as the developer\ \ of the Apertus LLM. It allows you to remove Personal Data contained in the model\ \ output. We strongly advise downloading and applying this output filter from SNAI\ \ every six months following the release of the model. " extra_gated_fields: Your Name: text Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of use: checkbox extra_gated_button_content: Submit --- # NexVeridian/Apertus-8B-Instruct-2509-4bit This model [NexVeridian/Apertus-8B-Instruct-2509-4bit](https://huggingface.co/NexVeridian/Apertus-8B-Instruct-2509-4bit) was converted to MLX format from [swiss-ai/Apertus-8B-Instruct-2509](https://huggingface.co/swiss-ai/Apertus-8B-Instruct-2509) using mlx-lm version 0.27.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("NexVeridian/Apertus-8B-Instruct-2509-4bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
Witan7725/thai-qa-lab-modelfin	Witan7725	2025-09-04T21:18:14Z	0	0	null	[ "safetensors", "gpt2", "thai", "qa", "fine-tuned", "th", "dataset:disease_3000", "arxiv:1910.09700", "license:mit", "region:us" ]	null	2025-09-04T21:15:45Z	--- datasets: - disease_3000 language: th license: mit metrics: - perplexity model_name: Thai GPT-2 Fine-Tuned tags: - thai - gpt2 - qa - fine-tuned --- # 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. --> โมเดล GPT-2 ที่ปรับแต่งสำหรับงานถาม-ตอบภาษาไทย ฝึกด้วยชุดข้อมูลคำถาม-คำตอบเกี่ยวกับสัตว์ 3000 คู่ - 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): th - License: mit - 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]
poki1/blockassist-bc-furry_pesty_pig_1757020430	poki1	2025-09-04T21:14:13Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "furry pesty pig", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T21:13:51Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - furry pesty pig --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
RZ412/qwen-3b-ot3-1k-qwq	RZ412	2025-09-04T21:06:20Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-04T20:45:57Z	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-3B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: Qwen2.5-3B-Instruct-OT3-1K-QwQ 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. --> # Qwen2.5-3B-Instruct-OT3-1K-QwQ This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the ot3_8k_subset_qwq_1000 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: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - total_eval_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
kimono998/wordle-exp-mix-5-lora-adapter-iter-60	kimono998	2025-09-04T21:05:56Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-04T21:05:53Z	--- 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]
mlx-community/Jan-v1-edge-bf16	mlx-community	2025-09-04T21:03:23Z	0	0	mlx	[ "mlx", "safetensors", "qwen3", "text-generation", "conversational", "en", "base_model:janhq/Jan-v1-edge", "base_model:finetune:janhq/Jan-v1-edge", "license:apache-2.0", "region:us" ]	text-generation	2025-09-04T21:02:59Z	--- license: apache-2.0 language: - en base_model: janhq/Jan-v1-edge pipeline_tag: text-generation library_name: mlx tags: - mlx --- # mlx-community/Jan-v1-edge-bf16 This model [mlx-community/Jan-v1-edge-bf16](https://huggingface.co/mlx-community/Jan-v1-edge-bf16) was converted to MLX format from [janhq/Jan-v1-edge](https://huggingface.co/janhq/Jan-v1-edge) using mlx-lm version 0.27.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Jan-v1-edge-bf16") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
kimono998/wordle-exp-mix-5-lora-adapter-iter-35	kimono998	2025-09-04T21:02:51Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-04T21:02:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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kimono998/wordle-exp-mix-5-lora-adapter-iter-20	kimono998	2025-09-04T21:02:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-04T21:02:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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calegpedia/blockassist-bc-stealthy_slimy_rooster_1757018028	calegpedia	2025-09-04T21:01:40Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T21:01:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stealthy slimy rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kimono998/wordle-exp-mix-4-v2-lora-adapter-iter-35	kimono998	2025-09-04T20:58:20Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-04T20:58:16Z	--- 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. 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kimono998/wordle-exp-gen-4-lora-adapter-iter-70	kimono998	2025-09-04T20:55:58Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-04T20:46:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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kimono998/wordle-exp-gen-4-lora-adapter-iter-55	kimono998	2025-09-04T20:55:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-04T20:45:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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kimono998/wordle-exp-gen-4-lora-adapter-iter-40	kimono998	2025-09-04T20:53:36Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-04T20:45:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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kimono998/wordle-exp-gen-4-lora-adapter-iter-35	kimono998	2025-09-04T20:52:58Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-04T20:45:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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kimono998/wordle-exp-gen-4-lora-adapter-iter-30	kimono998	2025-09-04T20:52:38Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-04T20:45:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sekirr/blockassist-bc-masked_tenacious_whale_1757019106	sekirr	2025-09-04T20:52:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "masked tenacious whale", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T20:52:22Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - masked tenacious whale --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sterut/blockassist-bc-untamed_aquatic_antelope_1757019068	sterut	2025-09-04T20:51:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "untamed aquatic antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T20:51:09Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - untamed aquatic antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sterut/blockassist-bc-silent_sly_rabbit_1757018843	sterut	2025-09-04T20:47:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silent sly rabbit", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T20:47:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - silent sly rabbit --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
nparra10/lora_gemma-3-4b-pt_train_img_version_2_instruction_20250904_1655	nparra10	2025-09-04T20:44:11Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-4b-pt", "base_model:finetune:google/gemma-3-4b-pt", "endpoints_compatible", "region:us" ]	null	2025-09-04T16:55:42Z	--- base_model: google/gemma-3-4b-pt library_name: transformers model_name: lora_gemma-3-4b-pt_train_img_version_2_instruction_20250904_1655 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for lora_gemma-3-4b-pt_train_img_version_2_instruction_20250904_1655 This model is a fine-tuned version of [google/gemma-3-4b-pt](https://huggingface.co/google/gemma-3-4b-pt). 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="nparra10/lora_gemma-3-4b-pt_train_img_version_2_instruction_20250904_1655", 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: 0.19.1 - Transformers: 4.53.2 - Pytorch: 2.6.0 - Datasets: 4.0.0 - 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}} } ```
bah63843/blockassist-bc-plump_fast_antelope_1757018252	bah63843	2025-09-04T20:38:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T20:38:11Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
liukevin666/blockassist-bc-yawning_striped_cassowary_1757017978	liukevin666	2025-09-04T20:34:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T20:34:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # 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_1757017835	zenqqq	2025-09-04T20:32:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "restless reptilian caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T20:31:54Z	--- 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).
kimono998/wordle-pos-1-lora-adapter-iter-20	kimono998	2025-09-04T20:31:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-21T14:52:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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kimono998/wordle-pos-1-lora-adapter-iter-10	kimono998	2025-09-04T20:31:38Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-21T14:52:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(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]
mlx-community/embeddinggemma-300m-qat-q4_0-unquantized-bf16	mlx-community	2025-09-04T20:25:02Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "gemma3_text", "sentence-similarity", "feature-extraction", "mlx", "license:gemma", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-09-04T20:06:20Z	--- license: gemma pipeline_tag: sentence-similarity library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - mlx extra_gated_heading: Access EmbeddingGemma on Hugging Face extra_gated_prompt: To access EmbeddingGemma 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 --- # mlx-community/embeddinggemma-300m-qat-q4_0-unquantized-bf16 The Model [mlx-community/embeddinggemma-300m-qat-q4_0-unquantized-bf16](https://huggingface.co/mlx-community/embeddinggemma-300m-qat-q4_0-unquantized-bf16) was converted to MLX format from [google/embeddinggemma-300m-qat-q4_0-unquantized](https://huggingface.co/google/embeddinggemma-300m-qat-q4_0-unquantized) using mlx-lm version 0.0.4. ## Use with mlx ```bash pip install mlx-embeddings ``` ```python from mlx_embeddings import load, generate import mlx.core as mx model, tokenizer = load("mlx-community/embeddinggemma-300m-qat-q4_0-unquantized-bf16") # For text embedding sentences = [ "task: sentence similarity \| query: Nothing really matters.", "task: sentence similarity \| query: The dog is barking.", "task: sentence similarity \| query: The dog is barking.", ] encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx') # Compute token embeddings input_ids = encoded_input['input_ids'] attention_mask = encoded_input['attention_mask'] output = model(input_ids, attention_mask) embeddings = output.text_embeds # Normalized embeddings # Compute dot product between normalized embeddings similarity_matrix = mx.matmul(embeddings, embeddings.T) print("Similarity matrix between texts:") print(similarity_matrix) # You can use these task-specific prefixes for different tasks task_prefixes = { "BitextMining": "task: search result \| query: ", "Clustering": "task: clustering \| query: ", "Classification": "task: classification \| query: ", "MultilabelClassification": "task: classification \| query: ", "PairClassification": "task: sentence similarity \| query: ", "InstructionRetrieval": "task: code retrieval \| query: ", "Reranking": "task: search result \| query: ", "Retrieval": "task: search result \| query: ", "Retrieval-query": "task: search result \| query: ", "Retrieval-document": "title: none \| text: ", "STS": "task: sentence similarity \| query: ", "Summarization": "task: summarization \| query: ", "document": "title: none \| text: " } ```
mlx-community/embeddinggemma-300m-bf16	mlx-community	2025-09-04T20:24:20Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "gemma3_text", "sentence-similarity", "feature-extraction", "text-embeddings-inference", "mlx", "license:gemma", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-09-04T20:00:51Z	--- license: gemma pipeline_tag: sentence-similarity library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - text-embeddings-inference - mlx extra_gated_heading: Access EmbeddingGemma on Hugging Face extra_gated_prompt: To access EmbeddingGemma 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 --- # mlx-community/embeddinggemma-300m-bf16 The Model [mlx-community/embeddinggemma-300m-bf16](https://huggingface.co/mlx-community/embeddinggemma-300m-bf16) was converted to MLX format from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) using mlx-lm version 0.0.4. ## Use with mlx ```bash pip install mlx-embeddings ``` ```python from mlx_embeddings import load, generate import mlx.core as mx model, tokenizer = load("mlx-community/embeddinggemma-300m-bf16") # For text embedding sentences = [ "task: sentence similarity \| query: Nothing really matters.", "task: sentence similarity \| query: The dog is barking.", "task: sentence similarity \| query: The dog is barking.", ] encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx') # Compute token embeddings input_ids = encoded_input['input_ids'] attention_mask = encoded_input['attention_mask'] output = model(input_ids, attention_mask) embeddings = output.text_embeds # Normalized embeddings # Compute dot product between normalized embeddings similarity_matrix = mx.matmul(embeddings, embeddings.T) print("Similarity matrix between texts:") print(similarity_matrix) # You can use these task-specific prefixes for different tasks task_prefixes = { "BitextMining": "task: search result \| query: ", "Clustering": "task: clustering \| query: ", "Classification": "task: classification \| query: ", "MultilabelClassification": "task: classification \| query: ", "PairClassification": "task: sentence similarity \| query: ", "InstructionRetrieval": "task: code retrieval \| query: ", "Reranking": "task: search result \| query: ", "Retrieval": "task: search result \| query: ", "Retrieval-query": "task: search result \| query: ", "Retrieval-document": "title: none \| text: ", "STS": "task: sentence similarity \| query: ", "Summarization": "task: summarization \| query: ", "document": "title: none \| text: " } ```
mlx-community/embeddinggemma-300m-6bit	mlx-community	2025-09-04T20:23:56Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "gemma3_text", "sentence-similarity", "feature-extraction", "mlx", "license:gemma", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-09-04T16:54:30Z	--- license: gemma pipeline_tag: sentence-similarity library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - mlx extra_gated_heading: Access EmbeddingGemma on Hugging Face extra_gated_prompt: To access EmbeddingGemma 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 --- # mlx-community/embeddinggemma-300m-6bit The Model [mlx-community/embeddinggemma-300m-6bit](https://huggingface.co/mlx-community/embeddinggemma-300m-6bit) was converted to MLX format from [google/embeddinggemma-300m-qat-q8_0-unquantized](https://huggingface.co/google/embeddinggemma-300m-qat-q8_0-unquantized) using mlx-lm version 0.0.4. ## Use with mlx ```bash pip install mlx-embeddings ``` ```python from mlx_embeddings import load, generate import mlx.core as mx model, tokenizer = load("mlx-community/embeddinggemma-300m-6bit") # For text embedding sentences = [ "task: sentence similarity \| query: Nothing really matters.", "task: sentence similarity \| query: The dog is barking.", "task: sentence similarity \| query: The dog is barking.", ] encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx') # Compute token embeddings input_ids = encoded_input['input_ids'] attention_mask = encoded_input['attention_mask'] output = model(input_ids, attention_mask) embeddings = output.text_embeds # Normalized embeddings # Compute dot product between normalized embeddings similarity_matrix = mx.matmul(embeddings, embeddings.T) print("Similarity matrix between texts:") print(similarity_matrix) # You can use these task-specific prefixes for different tasks task_prefixes = { "BitextMining": "task: search result \| query: ", "Clustering": "task: clustering \| query: ", "Classification": "task: classification \| query: ", "MultilabelClassification": "task: classification \| query: ", "PairClassification": "task: sentence similarity \| query: ", "InstructionRetrieval": "task: code retrieval \| query: ", "Reranking": "task: search result \| query: ", "Retrieval": "task: search result \| query: ", "Retrieval-query": "task: search result \| query: ", "Retrieval-document": "title: none \| text: ", "STS": "task: sentence similarity \| query: ", "Summarization": "task: summarization \| query: ", "document": "title: none \| text: " } ```
nikolabilicka/natalia	nikolabilicka	2025-09-04T20:18:22Z	0	0	null	[ "license:other", "region:us" ]	null	2025-09-04T19:33:31Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---
giovannidemuri/llama8b-er-v580-seed2-hx_lora	giovannidemuri	2025-09-04T20:14:01Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-04T14:34:52Z	--- 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]
vendi11/blockassist-bc-placid_placid_llama_1757016378	vendi11	2025-09-04T20:07:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "placid placid llama", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T20:06:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - placid placid llama --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
NahedDom/blockassist-bc-flapping_stocky_leopard_1757014095	NahedDom	2025-09-04T20:03:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flapping stocky leopard", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T20:03:11Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flapping stocky leopard --- # 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_1757014705	helmutsukocok	2025-09-04T20:03:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T20:03:04Z	--- 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).
giovannidemuri/llama8b-er-v550-seed2-hx_lora	giovannidemuri	2025-09-04T20:02:51Z	39	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-02T23:07:07Z	--- 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]
bah63843/blockassist-bc-plump_fast_antelope_1757015745	bah63843	2025-09-04T19:56:36Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T19:56:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
zxvvcnh/blockassist-bc-soft_curious_camel_1757015436	zxvvcnh	2025-09-04T19:51:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "soft curious camel", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T19:50:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - soft curious camel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1757013237	coelacanthxyz	2025-09-04T19:43:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T19:43:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1757014958	bah63843	2025-09-04T19:43:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-04T19:43:18Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

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