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train · 926k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-06 12:28:13	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 543 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 12:27:52	card stringlengths 11 1.01M
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1755017643	Ferdi3425	2025-08-12T16:55:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:54:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
xinnn32/blockassist-bc-meek_winged_caterpillar_1755017661	xinnn32	2025-08-12T16:55:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:55:08Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - meek winged caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
New-Clip-sister-hong-viral-video-Clip/New.full.videos.sister.hong.Viral.Video.Official.Tutorial	New-Clip-sister-hong-viral-video-Clip	2025-08-12T16:54:32Z	0	0	null	[ "region:us" ]	null	2025-08-12T16:54:11Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
shihotan/Silence_Mix	shihotan	2025-08-12T16:53:43Z	0	6	null	[ "region:us" ]	null	2025-01-20T07:36:05Z	Silence_Mix003.fp16.safetensors ちびたいにもあるやつ SMplus5050_0100.safetensors 003に味付けしたやつ Silence_Mix005_test.safetensors 5050_0100を003で割ったやつ Silence_Mix008.safetensors クオリティタグなくてもなんとかなるかもしれないSilence_Mix003の調整版 Silence_Mix_Noob_003.safetensors 5050_0100とnoob系のモデルを混ぜたやつ。ちょっと線濃いめクッキリ Silence_Mix_Noob_004.safetensors Silence_Mix_Noob_003のマージ比率を変えて線が薄くなるように祈りを込めたやつ Silence_Mix_Noob_005.safetensors 003にSMplus5050_0100とはまた違うnoobマージモデルを混ぜたやつ塗りが厚めで個人的に好き。構図が003と異なるものが出る Silence_Mix2.safetensors 一旦区切りをつけたので2を名乗らせてるやつ (anime coloring:2.4) とFreeUの B1 1.3 B2 1.4 S1 0.9 S2 0.2 でなんとなくイイ感じになるのを探したのでこれが推奨設定かもしれない Silence_Mix2.93.safetensors いらすとりあすくん1.0その他を足して1280*1920のポン出しでも頑張ってくれるようになったらいいなと思いながら混ぜたやつ SMtest006.safetensors Silence_Mix2の塗りをもうちょっとフラットになってくれないかと頑張ったモデルいらすとりあすくん1.0マージモデルもいくつか混ざってる Silence_Mix2の塗りが濃いと思う人にオススメかもしれない SM3.31 Silence_Mix3と何が変わったのかわからないけどなんかいい感じになった気がするモデル SM3.33 こいくち SM3.34 3.33を整えた SM3.4 （比較的）まともなの SM3.42 まともじゃないの。背景を強くした結果塗りがアニメアニメしなくなった。Silence_Mix2の正統進化版みたいな感じ連絡先→ https://x.com/tai39899
unicomcat/blockassist-bc-roaring_playful_crocodile_1755014877	unicomcat	2025-08-12T16:53:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring playful crocodile", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:49:19Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - roaring playful crocodile --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
diaslmb/bge-m3-finetuned-5-epochs	diaslmb	2025-08-12T16:52:42Z	0	0	null	[ "safetensors", "xlm-roberta", "license:mit", "region:us" ]	null	2025-08-12T16:39:45Z	--- license: mit --- # Model: bge-m3-finetuned-5-epochs Fine-tuned model from local directory: ./bge-m3-finetuned-5-epochs
roachkins/omega_SgVzdXN	roachkins	2025-08-12T16:52:39Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-12T16:52:37Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755017473	ggozzy	2025-08-12T16:52:38Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:52:22Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Nikichoksi/llama-3.2-3b-dpo-iteration-2	Nikichoksi	2025-08-12T16:51:10Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:meta-llama/Llama-3.2-3B-Instruct", "dpo", "lora", "transformers", "trl", "text-generation", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-3B-Instruct", "region:us" ]	text-generation	2025-08-12T16:51:07Z	--- base_model: meta-llama/Llama-3.2-3B-Instruct library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:meta-llama/Llama-3.2-3B-Instruct - dpo - lora - transformers - trl --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1755017370	Ferdi3425	2025-08-12T16:50:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:50:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Jeff971444/isa40	Jeff971444	2025-08-12T16:50:06Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-08-12T16:14:39Z	--- 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 language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: ISA40 --- # Isa40 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `ISA40` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "ISA40", "lora_weights": "https://huggingface.co/Jeff971444/isa40/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('Jeff971444/isa40', weight_name='lora.safetensors') image = pipeline('ISA40').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 32 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Jeff971444/isa40/discussions) to add images that show off what you’ve made with this LoRA.
Exclusive-dr-eman-and-arooj-viral-videos/New.Orginal.full.Videos.dr.eman.and.arooj.viral.video.Official.Tutorial	Exclusive-dr-eman-and-arooj-viral-videos	2025-08-12T16:48:55Z	0	0	null	[ "region:us" ]	null	2025-08-12T16:48:47Z	<a href="https://sdu.sk/Kyl"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="fsd" /></a> <a href="https://sdu.sk/Kyl" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/Kyl" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755017168	ggozzy	2025-08-12T16:47:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:47:19Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755017104	IvanJAjebu	2025-08-12T16:46:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:46:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
parkky21/orpheus-3b-hi-ft-1e	parkky21	2025-08-12T16:45:06Z	6	1	null	[ "safetensors", "llama", "region:us" ]	null	2025-08-11T18:27:16Z	# parkky21/orpheus-3b-hi-duo-voices (अनुष्का • करन) ## 🔎 Model Summary * Base model: canopylabs/3b-hi-pretrain-research\_release * Finetuned by: parkky21 * Language: Hindi (hi), with Hinglish tolerance * Voices: अनुष्का (warm, curious), करन (friendly, direct) * Architecture: LLaMA-family, decoder-only * Intended use: Multi-turn dialogue in Hindi with lightweight “voice” control via speaker prefixes --- ▶️ Try It (Colab) Use the Colab notebook for inference and examples—no local setup needed: Colab: https://colab.research.google.com/drive/1-greyn4D7-0SVUx86fGPzj5rjB2DjGUn?usp=sharing --- ## ✨ What’s Special * Two natural voices out of the box—switch tone by prefixing lines with the speaker name. * Simple prompting (no special chat template required). * Fast + lightweight—great for laptops and mid-tier GPUs thanks to Unsloth and 3B size. --- ## 🗣️ Voices & Prompting Use speaker-name prefixes followed by a colon. Example conversation style: ``` अनुष्का: हे करन, क्या आज बारिश ज़्यादा नहीं हो रही? करन: हाँ, बहुत ज़्यादा! सुबह से रुकने का नाम ही नहीं ले रही। ``` --- This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
PredatorAlpha/my-QA-model	PredatorAlpha	2025-08-12T16:42:04Z	1	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "question-answering", "generated_from_trainer", "dataset:rajpurkar/squad", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2025-08-10T15:26:50Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: my-QA-model results: [] datasets: - rajpurkar/squad metrics: - squad --- <!-- 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. --> # my-QA-model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an SQuAD v1.1 dataset. ## Model description This is a transformer-based extractive Question Answering (QA) model fine-tuned on the Stanford Question Answering Dataset (SQuAD v1.1). It takes a context paragraph and a natural language question as input and returns the most probable span in the text that answers the question. - Architecture: DistilBERT - Dataset: SQuAD v1.1 (~100k question-answer pairs) - Task Type: Extractive Question Answering - Training Objective: Predict start and end token positions of the answer span - Evaluation Metrics: Exact Match (EM) and F1 Score --- ## Intended uses & limitations This model is designed for extractive question answering where the answer exists within a provided context. It can be applied in reading comprehension tasks, chatbots, document search, automated quiz generation, educational tools, and research on transformer-based QA systems. However, the model has limitations: - It can only answer questions if the answer is present in the given text. - It struggles with multi-hop reasoning, abstract inference, and answers requiring outside knowledge. - Ambiguous or vague questions may result in incorrect spans. - Performance may degrade on domains that differ significantly from Wikipedia (SQuAD’s source). - It may reflect biases in the training data. ## Training and evaluation data The model was fine-tuned on the Stanford Question Answering Dataset (SQuAD v1.1), a large-scale reading comprehension dataset consisting of over 100,000 question–answer pairs on Wikipedia articles. - Training set: ~87,599 examples - Validation set: ~10,570 examples - Each example contains a context paragraph, a question, and the corresponding answer span within the paragraph. Evaluation was performed on the SQuAD v1.1 validation set using Exact Match (EM) and F1 score metrics. ## Training procedure 1. Base Model: A pre-trained transformer model Distibert-base-uncased from Hugging Face. 2. Tokenization: Used the model's corresponding tokenizer with: - `max_length=384` - `truncation='only_second'` - `stride=128` for sliding window over long contexts 3. Optimization: - Optimizer: AdamW - Learning rate: 3e-5 - Weight decay: 0.01 - Batch size: 16–32 (depending on GPU memory) - Epochs: 2–3 4. Loss Function: Cross-entropy loss over start and end token positions. 5. Evaluation: Computed Exact Match (EM) and F1 score after each epoch. 6. Checkpointing: Best model saved based on highest F1 score on validation set. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results The model achieved the following results on the SQuAD v1.1 validation set: \| Metric \| Score \| \|-----------------------\|--------\| \| Exact Match (EM) \| 51% \| \| F1 Score \| 70.2% \| \| Training Loss (final) \| 0.64% \| These results are comparable to other transformer-based models fine-tuned on SQuAD , demonstrating strong extractive question answering capabilities. ### Framework versions - Transformers 4.55.0 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4
tscstudios/sdczinwzrzxqxtsd7ot7temxala2_8e30d02c-f968-4718-86e2-290bc8e26a8d	tscstudios	2025-08-12T16:41:30Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-08-12T16:41:28Z	--- 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 language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Sdczinwzrzxqxtsd7Ot7Temxala2_8E30D02C F968 4718 86E2 290Bc8E26A8D <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/tscstudios/sdczinwzrzxqxtsd7ot7temxala2_8e30d02c-f968-4718-86e2-290bc8e26a8d/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('tscstudios/sdczinwzrzxqxtsd7ot7temxala2_8e30d02c-f968-4718-86e2-290bc8e26a8d', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1200 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/tscstudios/sdczinwzrzxqxtsd7ot7temxala2_8e30d02c-f968-4718-86e2-290bc8e26a8d/discussions) to add images that show off what you’ve made with this LoRA.
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755016799	IvanJAjebu	2025-08-12T16:41:20Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:40:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
xinnn32/blockassist-bc-meek_winged_caterpillar_1755016797	xinnn32	2025-08-12T16:40:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:40:52Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - meek winged caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha-Q4_K_M-GGUF	CreitinGameplays	2025-08-12T16:40:57Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "en", "dataset:CreitinGameplays/r1_annotated_math-mistral", "dataset:CreitinGameplays/DeepSeek-R1-Distill-Qwen-32B_NUMINA_train_amc_aime-mistral", "base_model:CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha", "base_model:quantized:CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-08-12T15:40:33Z	--- license: mit datasets: - CreitinGameplays/r1_annotated_math-mistral - CreitinGameplays/DeepSeek-R1-Distill-Qwen-32B_NUMINA_train_amc_aime-mistral language: - en base_model: CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha pipeline_tag: text-generation library_name: transformers tags: - llama-cpp - gguf-my-repo --- # CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha-Q4_K_M-GGUF This model was converted to GGUF format from [`CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha`](https://huggingface.co/CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha-Q4_K_M-GGUF --hf-file mistral-nemo-12b-r1-v0.1alpha-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha-Q4_K_M-GGUF --hf-file mistral-nemo-12b-r1-v0.1alpha-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha-Q4_K_M-GGUF --hf-file mistral-nemo-12b-r1-v0.1alpha-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha-Q4_K_M-GGUF --hf-file mistral-nemo-12b-r1-v0.1alpha-q4_k_m.gguf -c 2048 ```
aleebaster/blockassist-bc-sly_eager_boar_1755015713	aleebaster	2025-08-12T16:40:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sly eager boar", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:40:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sly eager boar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
TheDrummer/Gemma-3-R1-4B-v1	TheDrummer	2025-08-12T16:39:20Z	3	3	null	[ "safetensors", "gemma3", "base_model:google/gemma-3-4b-it", "base_model:finetune:google/gemma-3-4b-it", "region:us" ]	null	2025-08-07T12:28:59Z	--- base_model: - google/gemma-3-4b-it --- # Join our Discord! https://discord.gg/BeaverAI or our Reddit! https://www.reddit.com/r/BeaverAI/ ## More than 6000 members strong 💪 A hub for users and makers alike! --- # Gemma 3 R1 4B v1 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/stLJgTMretW2kdUMq-gIV.png) ## Special Thanks - Thank you to each and everyone who donated and subscribed in [Patreon](https://www.patreon.com/TheDrummer) and [Ko-Fi](https://ko-fi.com/thedrummer) to make our venture a little bit easier. - [Subscribe to my Patreon!](https://www.patreon.com/TheDrummer) ## Usage You'll probably need to prefill `<think>` at the start of the assistant turn. Since it's not a special token, you can get creative with the reasoning tags with modifications like `<evil_think>` or `<creative_think>`. ## Description Gemma 3 4B reasoning tune that unlocks more capabilities and less positivity! Should be vision capable. > Gemma 4B constantly surprises me for its size, this one's a blast. I'm impressed by this little fella. > Wow that is surprisingly deep. It actually is being witty and unique in it's prose not the usual gemma prose at all. Maybe Drummer really did create AGI. > I tried another swipe and it just shit out the index.html file, css, and javascript in one shot. Even has neat little animations when you click on stuff. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/00EFURqInneBdR5D-C1TE.png) ## Links - Original: https://huggingface.co/TheDrummer/Gemma-3-R1-4B-v1 - GGUF: https://huggingface.co/TheDrummer/Gemma-3-R1-4B-v1-GGUF - iMatrix: https://huggingface.co/bartowski/TheDrummer_Gemma-3-R1-4B-v1-GGUF - Vision GGUF: https://huggingface.co/bartowski/google_gemma-3-4b-it-GGUF/blob/main/mmproj-google_gemma-3-4b-it-bf16.gguf `gemma-r1/4b/config-v1b`
dylandavies984/blockassist-bc-fluffy_fleecy_rooster_1755014686	dylandavies984	2025-08-12T16:37:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fluffy fleecy rooster", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:37:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fluffy fleecy rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roachkins/omega_JFQrZb7	roachkins	2025-08-12T16:37:32Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-12T16:37:31Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
pimplefeet/omega_nZbEzaA	pimplefeet	2025-08-12T16:37:28Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-12T16:37:27Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
TheDrummer/Gemma-3-R1-27B-v1	TheDrummer	2025-08-12T16:37:28Z	3	2	null	[ "safetensors", "gemma3", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "region:us" ]	null	2025-08-04T14:34:25Z	--- base_model: - google/gemma-3-27b-it --- # Join our Discord! https://discord.gg/BeaverAI or our Reddit! https://www.reddit.com/r/BeaverAI/ ## More than 6000 members strong 💪 A hub for users and makers alike! --- # Gemma 3 R1 27B v1 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/stLJgTMretW2kdUMq-gIV.png) ## Special Thanks - Thank you to each and everyone who donated and subscribed in [Patreon](https://www.patreon.com/TheDrummer) and [Ko-Fi](https://ko-fi.com/thedrummer) to make our venture a little bit easier. - [Subscribe to my Patreon!](https://www.patreon.com/TheDrummer) ## Usage You'll probably need to prefill `<think>` at the start of the assistant turn. Since it's not a special token, you can get creative with the reasoning tags with modifications like `<evil_think>` or `<creative_think>`. ## Description Gemma 3 27B reasoning tune that unlocks more capabilities and less positivity! Should be vision capable. > As far as RP goes, the model is pretty creative. The writing style is not sloppy. The thinking makes it seem smarter than other 100B+ models that I usually go with. Generation is way faster. I like it very much. > all good for me here, just a thinking gemma 3, better than multi-character rp's compared to regular gemma > More rigid thinking adherence to syspromt, much like Cydonia R1 24B. Overall feel also reminds me of the latest Cydonias > Definitely good, you keep spoiling us with ever better Gemmas lately 😄 > This IS a gem. Mad respect, Mr. Drummer. You've done something remarkable ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/uq1_-evL96hlWDqYsVkq2.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/ooDjfwobNAnD689tLLVes.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/baxh9Sq8f34_L4zlJU_ZH.png) ## Links - Original: https://huggingface.co/TheDrummer/Gemma-3-R1-27B-v1 - GGUF: https://huggingface.co/TheDrummer/Gemma-3-R1-27B-v1-GGUF - iMatrix: https://huggingface.co/bartowski/TheDrummer_Gemma-3-R1-27B-v1-GGUF - Vision GGUF: https://huggingface.co/bartowski/google_gemma-3-27b-it-GGUF/blob/main/mmproj-google_gemma-3-27b-it-bf16.gguf `gemma-r1/27b/config-v1b`
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755016557	ggozzy	2025-08-12T16:37:20Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:37:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bamitunde/blockassist-bc-mimic_humming_frog_1755016445	bamitunde	2025-08-12T16:36:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mimic humming frog", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:36:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mimic humming frog --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
BootesVoid/cme8pxg85024vrts8j0opv47i_cme8q7wgy025yrts8agodufru	BootesVoid	2025-08-12T16:36:24Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-08-12T16:36:22Z	--- 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 language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: ALLY --- # Cme8Pxg85024Vrts8J0Opv47I_Cme8Q7Wgy025Yrts8Agodufru <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `ALLY` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "ALLY", "lora_weights": "https://huggingface.co/BootesVoid/cme8pxg85024vrts8j0opv47i_cme8q7wgy025yrts8agodufru/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cme8pxg85024vrts8j0opv47i_cme8q7wgy025yrts8agodufru', weight_name='lora.safetensors') image = pipeline('ALLY').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cme8pxg85024vrts8j0opv47i_cme8q7wgy025yrts8agodufru/discussions) to add images that show off what you’ve made with this LoRA.
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755016429	IvanJAjebu	2025-08-12T16:35:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:34:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
fnlp/bart-base-chinese	fnlp	2025-08-12T16:34:38Z	8,640	104	transformers	[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "Chinese", "seq2seq", "BART", "zh", "arxiv:2109.05729", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	--- tags: - text2text-generation - Chinese - seq2seq - BART language: zh --- # Chinese BART-Base ### News 12/30/2022 An updated version of CPT & Chinese BART are released. In the new version, we changed the following parts: - Vocabulary We replace the old BERT vocabulary with a larger one of size 51271 built from the training data, in which we 1) add missing 6800+ Chinese characters (most of them are traditional Chinese characters); 2) remove redundant tokens (e.g. Chinese character tokens with ## prefix); 3) add some English tokens to reduce OOV. - Position Embeddings We extend the max_position_embeddings from 512 to 1024. We initialize the new version of models with the old version of checkpoints with vocabulary alignment. Token embeddings found in the old checkpoints are copied. And other newly added parameters are randomly initialized. We further train the new CPT & Chinese BART 50K steps with batch size 2048, max-seq-length 1024, peak learning rate 2e-5, and warmup ratio 0.1. The result compared to the previous checkpoints is as followings: \| \| AFQMC \| IFLYTEK \| CSL-sum \| LCSTS \| AVG \| \| :--------- \| :---: \| :-----: \| :-----: \| :---: \| :---: \| \| Previous \| \| \| \| \| \| \| bart-base \| 73.0 \| 60 \| 62.1 \| 37.8 \| 58.23 \| \| cpt-base \| 75.1 \| 60.5 \| 63.0 \| 38.2 \| 59.20 \| \| bart-large \| 75.7 \| 62.1 \| 64.2 \| 40.6 \| 60.65 \| \| cpt-large \| 75.9 \| 61.8 \| 63.7 \| 42.0 \| 60.85 \| \| Updataed \| \| \| \| \| \| \| bart-base \| 73.03 \| 61.25 \| 61.51 \| 38.78 \| 58.64 \| \| cpt-base \| 74.40 \| 61.23 \| 62.09 \| 38.81 \| 59.13 \| \| bart-large \| 75.81 \| 61.52 \| 64.62 \| 40.90 \| 60.71 \| \| cpt-large \| 75.97 \| 61.63 \| 63.83 \| 42.08 \| 60.88 \| The result shows that the updated models maintain comparative performance compared with previous checkpoints. There are still some cases that the updated model is slightly worse than the previous one, which results from the following reasons: 1) Training additional a few steps did not lead to significant performance improvement; 2) some downstream tasks are not affected by the newly added tokens and longer encoding sequences, but sensitive to the fine-tuning hyperparameters. - Note that to use updated models, please update the `modeling_cpt.py` (new version download [Here](https://github.com/fastnlp/CPT/blob/master/finetune/modeling_cpt.py)) and the vocabulary (refresh the cache). ## Model description This is an implementation of Chinese BART-Base. [CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation](https://arxiv.org/pdf/2109.05729.pdf) Yunfan Shao, Zhichao Geng, Yitao Liu, Junqi Dai, Fei Yang, Li Zhe, Hujun Bao, Xipeng Qiu Github Link: https://github.com/fastnlp/CPT ## Usage ```python >>> from transformers import BertTokenizer, BartForConditionalGeneration, Text2TextGenerationPipeline >>> tokenizer = BertTokenizer.from_pretrained("fnlp/bart-base-chinese") >>> model = BartForConditionalGeneration.from_pretrained("fnlp/bart-base-chinese") >>> text2text_generator = Text2TextGenerationPipeline(model, tokenizer) >>> text2text_generator("北京是[MASK]的首都", max_length=50, do_sample=False) [{'generated_text': '北京是中国的首都'}] ``` Note: Please use BertTokenizer for the model vocabulary. DO NOT use original BartTokenizer. ## Citation Shao, Y., Geng, Z., Liu, Y. et al. CPT: a pre-trained unbalanced transformer for both Chinese language understanding and generation. Sci. China Inf. Sci. 67, 152102 (2024). https://www.sciengine.com/SCIS/doi/10.1007/s11432-021-3536-5 ```bibtex @Article{Shao2024a, author = {Shao, Yunfan and Geng, Zhichao and Liu, Yitao and Dai, Junqi and Yan, Hang and Yang, Fei and Li, Zhe and Bao, Hujun and Qiu, Xipeng}, journal = {Science China Information Sciences}, title = {CPT: a pre-trained unbalanced transformer for both Chinese language understanding and generation}, year = {2024}, issn = {1869-1919}, number = {5}, pages = {152102}, volume = {67}, abstract = {In this paper, we take the advantage of previous pre-trained models (PTMs) and propose a novel Chinese pre-trained unbalanced transformer (CPT). Different from previous Chinese PTMs, CPT is designed to utilize the shared knowledge between natural language understanding (NLU) and natural language generation (NLG) to boost the performance. CPT consists of three parts: a shared encoder, an understanding decoder, and a generation decoder. Two specific decoders with a shared encoder are pre-trained with masked language modeling (MLM) and denoising auto-encoding (DAE) tasks, respectively. With the partially shared architecture and multi-task pre-training, CPT can (1) learn specific knowledge of both NLU or NLG tasks with two decoders and (2) be fine-tuned flexibly that fully exploits the potential of the model. Moreover, the unbalanced transformer saves the computational and storage cost, which makes CPT competitive and greatly accelerates the inference of text generation. Experimental results on a wide range of Chinese NLU and NLG tasks show the effectiveness of CPT.}, doi = {10.1007/s11432-021-3536-5}, refid = {Shao2024}, url = {https://doi.org/10.1007/s11432-021-3536-5}, } ```
awilliam60412/Llama-3.1-8B-Instruct-0812	awilliam60412	2025-08-12T16:34:29Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-08-12T16:27:34Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: awilliam60412 - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
vengky/blockassist-bc-wild_gentle_manatee_1755013812	vengky	2025-08-12T16:34:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wild gentle manatee", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:34:21Z	--- 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).
indoempatnol/blockassist-bc-fishy_wary_swan_1755014911	indoempatnol	2025-08-12T16:33:48Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:33:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fishy wary swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Amgom/gemma-3N-finetune-gguf	Amgom	2025-08-12T16:32:54Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "gemma3n", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-12T16:07:37Z	--- base_model: unsloth/gemma-3n-e4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3n license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: Amgom - License: apache-2.0 - Finetuned from model : unsloth/gemma-3n-e4b-it-unsloth-bnb-4bit This gemma3n model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Leonardo6/sft-llava-1.5-7b-hf	Leonardo6	2025-08-12T16:32:33Z	10	0	transformers	[ "transformers", "safetensors", "llava", "image-to-text", "generated_from_trainer", "trl", "sft", "dataset:visual-layer/imagenet-1k-vl-enriched", "base_model:llava-hf/llava-1.5-7b-hf", "base_model:finetune:llava-hf/llava-1.5-7b-hf", "endpoints_compatible", "region:us" ]	image-to-text	2025-08-11T10:18:18Z	--- base_model: llava-hf/llava-1.5-7b-hf datasets: visual-layer/imagenet-1k-vl-enriched library_name: transformers model_name: sft-llava-1.5-7b-hf tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for sft-llava-1.5-7b-hf This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) on the [visual-layer/imagenet-1k-vl-enriched](https://huggingface.co/datasets/visual-layer/imagenet-1k-vl-enriched) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Leonardo6/sft-llava-1.5-7b-hf", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/leonardo666-tsinghua-university/huggingface/runs/k36f3wo5) This model was trained with SFT. ### Framework versions - TRL: 0.20.0 - Transformers: 4.53.3 - 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}} } ```
sikaro/whisper_lora_model_meeting_8000	sikaro	2025-08-12T16:32:14Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "whisper", "trl", "en", "base_model:unsloth/whisper-large-v3", "base_model:finetune:unsloth/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-12T00:38:35Z	--- base_model: unsloth/whisper-large-v3 tags: - text-generation-inference - transformers - unsloth - whisper - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: sikaro - License: apache-2.0 - Finetuned from model : unsloth/whisper-large-v3 This whisper model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Jovar1/blockassist-bc-bold_hulking_rooster_1755016154	Jovar1	2025-08-12T16:30:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bold hulking rooster", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:30:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - bold hulking rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Quangvuisme/a2c-PandaReachDense-v3	Quangvuisme	2025-08-12T16:30:37Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-08-12T16:26:42Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.24 +/- 0.08 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
relapseone/blockassist-bc-insectivorous_prickly_shrew_1755014360	relapseone	2025-08-12T16:27:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous prickly shrew", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:27:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous prickly shrew --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755015946	ggozzy	2025-08-12T16:27:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:26:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
New-Clip-Arovi-Nusrat-Ridhi-Viral-Videos/NEW.FULL.VIDEOS.Arovi.Nusrat.Ridhi.Viral.Video.link.Official.Tutorial	New-Clip-Arovi-Nusrat-Ridhi-Viral-Videos	2025-08-12T16:27:02Z	0	0	null	[ "region:us" ]	null	2025-08-12T16:26:49Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
alexgeezy429/blockassist-bc-scented_coiled_antelope_1755013951	alexgeezy429	2025-08-12T16:25:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scented coiled antelope", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:25:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scented coiled antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1755015800	Ferdi3425	2025-08-12T16:24:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:24:09Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
tensorblock/luckeciano_Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-GGUF	tensorblock	2025-08-12T16:23:25Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "open-r1", "trl", "grpo", "TensorBlock", "GGUF", "dataset:DigitalLearningGmbH/MATH-lighteval", "base_model:luckeciano/Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel", "base_model:quantized:luckeciano/Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-12T14:58:27Z	--- base_model: luckeciano/Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel tags: - generated_from_trainer - open-r1 - trl - grpo - TensorBlock - GGUF licence: license --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> [![Website](https://img.shields.io/badge/Website-tensorblock.co-blue?logo=google-chrome&logoColor=white)](https://tensorblock.co) [![Twitter](https://img.shields.io/twitter/follow/tensorblock_aoi?style=social)](https://twitter.com/tensorblock_aoi) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-5865F2?logo=discord&logoColor=white)](https://discord.gg/Ej5NmeHFf2) [![GitHub](https://img.shields.io/badge/GitHub-TensorBlock-black?logo=github&logoColor=white)](https://github.com/TensorBlock) [![Telegram](https://img.shields.io/badge/Telegram-Group-blue?logo=telegram)](https://t.me/TensorBlock) ## luckeciano/Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel - GGUF <div style="text-align: left; margin: 20px 0;"> <a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> Join our Discord to learn more about what we're building ↗ </a> </div> This repo contains GGUF format model files for [luckeciano/Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel](https://huggingface.co/luckeciano/Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277). ## Our projects <table border="1" cellspacing="0" cellpadding="10"> <tr> <th colspan="2" style="font-size: 25px;">Forge</th> </tr> <tr> <th colspan="2"> <img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/> </th> </tr> <tr> <th colspan="2">An OpenAI-compatible multi-provider routing layer.</th> </tr> <tr> <th colspan="2"> <a href="https://github.com/TensorBlock/forge" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">🚀 Try it now! 🚀</a> </th> </tr> <tr> <th style="font-size: 25px;">Awesome MCP Servers</th> <th style="font-size: 25px;">TensorBlock Studio</th> </tr> <tr> <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th> <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th> </tr> <tr> <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th> <th>A lightweight, open, and extensible multi-LLM interaction studio.</th> </tr> <tr> <th> <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">👀 See what we built 👀</a> </th> <th> <a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">👀 See what we built 👀</a> </th> </tr> </table> ## Prompt template ``` <\|im_start\|>system {system_prompt}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## Model file specification \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q2_K.gguf](https://huggingface.co/tensorblock/luckeciano_Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-GGUF/blob/main/Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q2_K.gguf) \| Q2_K \| 3.016 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q3_K_S.gguf](https://huggingface.co/tensorblock/luckeciano_Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-GGUF/blob/main/Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q3_K_S.gguf) \| Q3_K_S \| 3.492 GB \| very small, high quality loss \| \| [Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q3_K_M.gguf](https://huggingface.co/tensorblock/luckeciano_Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-GGUF/blob/main/Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q3_K_M.gguf) \| Q3_K_M \| 3.808 GB \| very small, high quality loss \| \| [Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q3_K_L.gguf](https://huggingface.co/tensorblock/luckeciano_Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-GGUF/blob/main/Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q3_K_L.gguf) \| Q3_K_L \| 4.088 GB \| small, substantial quality loss \| \| [Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q4_0.gguf](https://huggingface.co/tensorblock/luckeciano_Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-GGUF/blob/main/Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q4_0.gguf) \| Q4_0 \| 4.431 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q4_K_S.gguf](https://huggingface.co/tensorblock/luckeciano_Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-GGUF/blob/main/Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q4_K_S.gguf) \| Q4_K_S \| 4.458 GB \| small, greater quality loss \| \| [Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q4_K_M.gguf](https://huggingface.co/tensorblock/luckeciano_Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-GGUF/blob/main/Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q4_K_M.gguf) \| Q4_K_M \| 4.683 GB \| medium, balanced quality - recommended \| \| [Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q5_0.gguf](https://huggingface.co/tensorblock/luckeciano_Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-GGUF/blob/main/Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q5_0.gguf) \| Q5_0 \| 5.315 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q5_K_S.gguf](https://huggingface.co/tensorblock/luckeciano_Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-GGUF/blob/main/Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q5_K_S.gguf) \| Q5_K_S \| 5.315 GB \| large, low quality loss - recommended \| \| [Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q5_K_M.gguf](https://huggingface.co/tensorblock/luckeciano_Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-GGUF/blob/main/Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q5_K_M.gguf) \| Q5_K_M \| 5.445 GB \| large, very low quality loss - recommended \| \| [Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q6_K.gguf](https://huggingface.co/tensorblock/luckeciano_Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-GGUF/blob/main/Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q6_K.gguf) \| Q6_K \| 6.254 GB \| very large, extremely low quality loss \| \| [Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q8_0.gguf](https://huggingface.co/tensorblock/luckeciano_Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-GGUF/blob/main/Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q8_0.gguf) \| Q8_0 \| 8.099 GB \| very large, extremely low quality loss - not recommended \| ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/luckeciano_Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-GGUF --include "Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `Q4_Kgguf`), you can try: ```shell huggingface-cli download tensorblock/luckeciano_Qwen-2.5-7B-RL-LACPO-BaselineNoKLNoEntropyNoSmoothSoftLabel-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='Q4_Kgguf' ```
Acly/Real-ESRGAN-GGUF	Acly	2025-08-12T16:23:21Z	75	0	null	[ "gguf", "super-resolution", "vision.cpp", "image-to-image", "license:bsd-3-clause", "region:us" ]	image-to-image	2025-07-23T14:58:01Z	--- license: bsd-3-clause tags: - super-resolution - vision.cpp pipeline_tag: image-to-image --- # GGUF models for Real-ESRGAN ESRGAN is a model for image super-resolution (upscaling). Real-ESRGAN refers to a collection of models trained to deal with common degradations in images. The weights in this repository are converted for lightweight inference on consumer hardware with [vision.cpp](https://github.com/Acly/vision.cpp). * Original repository: [xinntao/Real-ESRGAN (Github)](https://github.com/xinntao/Real-ESRGAN) * Original weights: [found here (Github)](https://github.com/xinntao/Real-ESRGAN/releases) ## Run Example inference with [vision.cpp](https://github.com/Acly/vision.cpp): ```sh vision-cli esrgan -m RealESRGAN-x4plus_anime-6B-F16.gguf -i input.png -o output.png ```
c-ho/2025-08-12-bll-ner_bert-base-multilingual-cased-ner-hrl_coumpound_n2-5_crf_wd001	c-ho	2025-08-12T16:22:48Z	0	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-08-12T16:22:20Z	--- 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]
Elizavr/blockassist-bc-reclusive_shaggy_bee_1755015674	Elizavr	2025-08-12T16:22:11Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "reclusive shaggy bee", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:21:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - reclusive shaggy bee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
koloni/blockassist-bc-deadly_graceful_stingray_1755014144	koloni	2025-08-12T16:22:08Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:21:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
LizardAPN/ppo-Pyramids	LizardAPN	2025-08-12T16:22:01Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2025-08-12T12:02:36Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: LizardAPN/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
New-videos-Kim-Mariya-viral-Video-Clips/Orginal.full.videos.Kim.Mariya.Viral.Video.Official.Tutorial	New-videos-Kim-Mariya-viral-Video-Clips	2025-08-12T16:21:53Z	0	0	null	[ "region:us" ]	null	2025-08-12T16:21:33Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
silentember/Lantern_ZgjaFV	silentember	2025-08-12T16:19:53Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-12T16:17:57Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755015405	IvanJAjebu	2025-08-12T16:18:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:17:38Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
gasoline2255/blockassist-bc-flightless_sizable_wildebeest_1755015176	gasoline2255	2025-08-12T16:17:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flightless sizable wildebeest", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:17:12Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flightless sizable wildebeest --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
demonwizard0/affine-hahaha	demonwizard0	2025-08-12T16:15:00Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-12T16:14:20Z	--- 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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Theros/WayChron-12B-Test0-Q4_K_M-GGUF	Theros	2025-08-12T16:13:45Z	0	0	null	[ "gguf", "merge", "lazymergekit", "llama-cpp", "gguf-my-repo", "base_model:SvalTek/WayChron-12B-Test0", "base_model:quantized:SvalTek/WayChron-12B-Test0", "endpoints_compatible", "region:us" ]	null	2025-08-12T16:13:12Z	--- tags: - merge - lazymergekit - llama-cpp - gguf-my-repo base_model: SvalTek/WayChron-12B-Test0 --- # Theros/WayChron-12B-Test0-Q4_K_M-GGUF This model was converted to GGUF format from [`SvalTek/WayChron-12B-Test0`](https://huggingface.co/SvalTek/WayChron-12B-Test0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/SvalTek/WayChron-12B-Test0) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Theros/WayChron-12B-Test0-Q4_K_M-GGUF --hf-file waychron-12b-test0-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Theros/WayChron-12B-Test0-Q4_K_M-GGUF --hf-file waychron-12b-test0-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Theros/WayChron-12B-Test0-Q4_K_M-GGUF --hf-file waychron-12b-test0-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Theros/WayChron-12B-Test0-Q4_K_M-GGUF --hf-file waychron-12b-test0-q4_k_m.gguf -c 2048 ```
ACECA/lowMvMax_126	ACECA	2025-08-12T16:13:35Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-12T15:11:21Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1755015125	Ferdi3425	2025-08-12T16:13:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:13:02Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
annietz/grpo-acr-adapters	annietz	2025-08-12T16:13:27Z	0	0	null	[ "safetensors", "license:mit", "region:us" ]	null	2025-07-26T17:10:31Z	--- license: mit --- # 🧠 GRPO Adapter Models for Medical Reasoning (LLaMA 3.1 8B) This repository hosts four adapter models trained with Group Relative Policy Optimization (GRPO) on top of LLaMA 3.1 8B, targeting the task of medical imaging appropriateness classification. These models were trained to replicate and align with expert clinical reasoning provided by the American College of Radiology (ACR). --- ## 🔬 Model Variants and Reward Designs \| Variant Name \| Reward Type(s) \| Description \| \|------------------------\|----------------------------------------------------\|-------------\| \| Baseline \| ✅ Answer (binary) <br> ✅ Format (tag-based) \| Standard RL setup: rewards correct label and properly formatted output using `<think>` and `<answer>` tags. \| \| Citations \| ✅ Answer <br> ✅ Format <br> ➕ External Context \| Adds condensed medical evidence (abstracts from ACR-cited PubMed studies) to the context, testing whether grounding in real evidence improves performance. \| \| LLM Eval \| ✅ Answer <br> ✅ Format <br> ✅ LLM-based reasoning alignment <br> ➕ External Context\| Uses Qwen1.5-1.8B to score the similarity of generated and gold reasoning, encouraging factually aligned justifications. \| \| Custom Embedding \| ✅ Answer × Reasoning Similarity <br> ✅ Format <br> ➕ External Context \| Novel reward using cosine similarity between embedding traces. Only grants reward if final answer is correct and reasoning closely aligns with gold trace structure. \| --- ## 📁 Files - `baseline\adapter_model.safetensors` - `citations\adapter_model.safetensors` - `llm-eval\adapter_model.safetensors` - `custom_embedding\adapter_model.safetensors` Each file contains an adapter for the LLaMA 3.1 8B model, trained on ~1800 variant/procedure pairs across ~30 medical conditions, using custom RL rewards. --- ## 🩺 About the Task The task is to recommend whether a specific imaging procedure is: - Usually Appropriate - May Be Appropriate - Usually Not Appropriate The agent is trained to not only predict the correct label but justify it step-by-step, mimicking the ACR’s clinical reasoning process and referencing relevant medical studies. --- ## 📬 Collaboration & Citation Interested in medical AI, reinforcement learning, or clinical reasoning? Let's connect! This work is part of a larger research effort on interpretable LLM agents in healthcare. Please cite or reach out if using these adapters in your work. 🙌
tushar0088/blockassist-bc-vocal_tenacious_prawn_1755015015	tushar0088	2025-08-12T16:11:48Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vocal tenacious prawn", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:11:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vocal tenacious prawn --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
silentember/Lantern_CFx9Kf	silentember	2025-08-12T16:09:16Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-12T16:07:21Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755014725	ggozzy	2025-08-12T16:06:40Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:06:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AJNG/qwen_v3_merge	AJNG	2025-08-12T16:06:28Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Qwen2.5-VL-7B-Instruct", "base_model:finetune:unsloth/Qwen2.5-VL-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-to-text	2025-08-12T15:58:59Z	--- base_model: unsloth/Qwen2.5-VL-7B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2_5_vl license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: AJNG - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-VL-7B-Instruct This qwen2_5_vl model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Zuntan/Wan22-I2V_A14B-Lightning-GGUF	Zuntan	2025-08-12T16:05:13Z	16,152	2	null	[ "gguf", "region:us" ]	null	2025-08-10T14:29:03Z	# Wan22-I2V_A14B-Lightning Geforce RTX 3060 12GB: 560px * 560px, 81f Sigmas: `1, 0.94, 0.85, 0.73, 0.55, 0.28, 0` High: `3steps` Low: `3steps` Shift: `4.5` Enhance-A-Video weight: 1 Fresca: low 1, high 1.25, cutoff 17 ## Refiner SeedGacha SSR Video > Upscaler x1.5 & Encode Geforce RTX 3060 12GB: 840px * 840px, 81f Sigma: `1.0, 0.97, 0.94, 0.90, 0.85, 0.795, 0.73, 0.65, 0.55, 0.42, 0.28, 0.14, 0.0` steps: `12` start_steps: `10-8`(`2-4`steps) Shift: `6.75`(`4.5` x1.5) Enhance-A-Video weight: `1` Disable Fresca Enable `add_noise_to_samples` and Upscaler x2, VFI x3~4 ## Wan22-I2V_A14B-Lightning-H - [wan2.2_i2v_high_noise_14B_fp16.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/blob/main/split_files/diffusion_models/wan2.2_i2v_high_noise_14B_fp16.safetensors) - [Wan2.2-Lightning/Wan2.2-I2V-A14B-4steps-lora-rank64-Seko-V1/high_noise_model.safetensors](https://huggingface.co/lightx2v/Wan2.2-Lightning/blob/main/Wan2.2-I2V-A14B-4steps-lora-rank64-Seko-V1/high_noise_model.safetensors) x1.0 ## Wan22-I2V_A14B-Lightning-L - [wan2.2_i2v_low_noise_14B_fp16.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/blob/main/split_files/diffusion_models/wan2.2_i2v_low_noise_14B_fp16.safetensors) - [Wan2.2-Lightning/Wan2.2-I2V-A14B-4steps-lora-rank64-Seko-V1/low_noise_model.safetensors](https://huggingface.co/lightx2v/Wan2.2-Lightning/blob/main/Wan2.2-I2V-A14B-4steps-lora-rank64-Seko-V1/low_noise_model.safetensors) x1.0
ant290819/blockassist-bc-peckish_horned_rabbit_1755013329	ant290819	2025-08-12T16:03:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "peckish horned rabbit", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T15:59:42Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - peckish horned rabbit --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
phospho-app/roboflavia-gr00t-pen_dual1-us2fz	phospho-app	2025-08-12T16:02:02Z	0	0	phosphobot	[ "phosphobot", "gr00t", "robotics", "dataset:roboflavia/pen_dual1", "region:us" ]	robotics	2025-08-12T15:59:20Z	--- datasets: roboflavia/pen_dual1 library_name: phosphobot pipeline_tag: robotics model_name: gr00t tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Traceback (most recent call last): File "/root/src/helper.py", line 166, in predict trainer.train(timeout_seconds=timeout_seconds) File "/root/phosphobot/am/gr00t.py", line 1296, in train asyncio.run( File "/opt/conda/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ File "/root/phosphobot/am/gr00t.py", line 1143, in run_gr00t_training raise RuntimeError(error_msg) RuntimeError: Training process failed with exit code 1: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/workspace/gr00t/data/dataset.py", line 811, in get_data_by_modality return self.get_video(trajectory_id, key, base_index) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/workspace/gr00t/data/dataset.py", line 679, in get_video video_timestamp = timestamp[step_indices] ~~~~~~~~~^^^^^^^^^^^^^^ IndexError: index 644 is out of bounds for axis 0 with size 555 0%\| \| 0/4990 [00:03<?, ?it/s] ``` ## Training parameters: - Dataset: [roboflavia/pen_dual1](https://huggingface.co/datasets/roboflavia/pen_dual1) - Wandb run URL: None - Epochs: 10 - Batch size: 49 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755014420	ggozzy	2025-08-12T16:01:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:01:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs	Casual-Autopsy	2025-08-12T16:01:23Z	12,400	1	transformers	[ "transformers", "gguf", "arxiv:2503.01874", "base_model:Casual-Autopsy/CREC-n-WREC-Mate-24B-v2", "base_model:quantized:Casual-Autopsy/CREC-n-WREC-Mate-24B-v2", "endpoints_compatible", "region:us" ]	null	2025-08-11T17:09:27Z	--- base_model: - Casual-Autopsy/CREC-n-WREC-Mate-24B-v2 library_name: transformers --- # CREC-n-WREC-Mate-24B-v2 ### THIS MODEL IS UNOFFICIAL! This model has no official affiliation with Weather and his SillyTavern Extensions. This is simply a fan project to help fellow users of these extensions. ## Merge Description CREC-n-WREC-Mate is a model made to help create World Info entries mid-roleplay using the SillyTavern extensions [CREC](https://github.com/bmen25124/SillyTavern-Character-Creator) and [WREC](https://github.com/bmen25124/SillyTavern-WorldInfo-Recommender/). The responses a bit on the shorter side by default, but this should be all the more beneficial for creating World Info entries. Needless to say, this isn't a model designed for creating Char Cards, instead it's meant for saving characters you encounter on your adventures to a Lorebook, so make sure to enable the feature that allows adding characters to a WI entry in the CREC settings menu. WREC Setup: [here](https://huggingface.co/Casual-Autopsy/CREC-n-WREC-Mate-24B-v2/blob/main/README_WREC-Setup.md) CREC Setup: WIP ## Merge Details This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ### Merge Method This model was merged using the [Conflict-Aware N:M Sparsification](https://arxiv.org/abs/2503.01874) merge method using [TheDrummer/Cydonia-24B-v2.1](https://huggingface.co/TheDrummer/Cydonia-24B-v2.1) as a base. ### Models Merged The following models were included in the merge: * [CharGen-Archive/CharGen-v3-beta-275-s0](https://huggingface.co/CharGen-Archive/CharGen-v3-beta-275-s0) * [SlerpE/CardProjector-24B-v3](https://huggingface.co/SlerpE/CardProjector-24B-v3) * [Mawdistical/Mawdistic-NightLife-24b](https://huggingface.co/Mawdistical/Mawdistic-NightLife-24b) * [ReadyArt/Broken-Tutu-24B](https://huggingface.co/ReadyArt/Broken-Tutu-24B) ### Configuration The following YAML configurations were used to produce this model: #### C-n-W-CharGen_v2 ```yaml models: - model: CharGen-Archive/CharGen-v3-beta-275-s0 - model: Mawdistical/Mawdistic-NightLife-24b parameters: weight: 0.3 n_val: 64 m_val: 128 - model: SlerpE/CardProjector-24B-v3 parameters: weight: 0.2 n_val: 16 m_val: 32 merge_method: cabs pruning_order: - Mawdistical/Mawdistic-NightLife-24b - SlerpE/CardProjector-24B-v3 base_model: CharGen-Archive/CharGen-v3-beta-275-s0 dtype: float32 tokenizer: source: union tokens: </s>: source: kind: model_token model: CharGen-Archive/CharGen-v3-beta-275-s0 token: "<\|im_end\|>" "[INST]": source: kind: model_token model: CharGen-Archive/CharGen-v3-beta-275-s0 token: "<\|im_start\|>" ``` #### C-n-W-CardProj_v2 ```yaml models: - model: SlerpE/CardProjector-24B-v3 - model: ReadyArt/Broken-Tutu-24B parameters: weight: 0.3 n_val: 64 m_val: 128 - model: CharGen-Archive/CharGen-v3-beta-275-s0 parameters: weight: 0.2 n_val: 16 m_val: 32 merge_method: cabs pruning_order: - ReadyArt/Broken-Tutu-24B - CharGen-Archive/CharGen-v3-beta-275-s0 base_model: SlerpE/CardProjector-24B-v3 dtype: float32 tokenizer: source: union tokens: "[/INST]": source: kind: model_token model: CharGen-Archive/CharGen-v3-beta-275-s0 token: "<\|im_end\|>" source: kind: model_token model: CharGen-Archive/CharGen-v3-beta-275-s0 token: "<\|im_start\|>" ``` #### CREC-n-WREC-Mate-24B-v2 ```yaml models: - model: TheDrummer/Cydonia-24B-v2.1 - model: C-n-W-CardProj_v2 parameters: weight: 0.6 n_val: 64 m_val: 128 - model: C-n-W-CharGen_v2 parameters: weight: 0.4 n_val: 12 m_val: 32 merge_method: cabs pruning_order: - C-n-W-CardProj_v2 - C-n-W-CharGen_v2 base_model: TheDrummer/Cydonia-24B-v2.1 dtype: float32 out_dtype: bfloat16 tokenizer: source: union tokens: "[/INST]": source: kind: model_token model: C-n-W-CardProj_v2 token: "[/INST]" source: kind: model_token model: C-n-W-CharGen_v2 token: "[/INST]" "[INST]": source: kind: model_token model: C-n-W-CardProj_v2 token: "[INST]" source: kind: model_token model: C-n-W-CharGen_v2 token: "[INST]" </s>: source: kind: model_token model: C-n-W-CardProj_v2 token: "</s>" source: kind: model_token model: C-n-W-CharGen_v2 token: "</s>" ```
nightmedia/Jan-v1-4B-q8-mlx	nightmedia	2025-08-12T16:01:17Z	0	0	mlx	[ "mlx", "safetensors", "qwen3", "text-generation", "conversational", "en", "base_model:janhq/Jan-v1-4B", "base_model:quantized:janhq/Jan-v1-4B", "license:apache-2.0", "8-bit", "region:us" ]	text-generation	2025-08-12T15:22:08Z	--- license: apache-2.0 language: - en base_model: janhq/Jan-v1-4B pipeline_tag: text-generation tags: - mlx library_name: mlx --- # Jan-v1-4B-q8-mlx This model [Jan-v1-4B-q8-mlx](https://huggingface.co/Jan-v1-4B-q8-mlx) was converted to MLX format from [janhq/Jan-v1-4B](https://huggingface.co/janhq/Jan-v1-4B) using mlx-lm version 0.26.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("Jan-v1-4B-q8-mlx") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
venuairdrop/blockassist-bc-foraging_gregarious_hawk_1755005398	venuairdrop	2025-08-12T16:00:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "foraging gregarious hawk", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T16:00:33Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - foraging gregarious hawk --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
chevyguyss/joker	chevyguyss	2025-08-12T16:00:37Z	0	0	null	[ "region:us" ]	null	2025-08-11T20:39:14Z	Generated this LORA because i saw zero for the greatest Joker to play the roll in cinima. Heath Ledger absolutely crushed his roll. It shouldnt need the trigger word, but it is joker. ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/663cce2276e6d5b98f3d6518/v25Menxk2l-YJQNliOljm.webp) ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/663cce2276e6d5b98f3d6518/ZzsH08tGdJ4ehaBlwP8Zr.webp) ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/663cce2276e6d5b98f3d6518/opAr_ahbnd2UzHuvDTVG0.webp) --- license: apache-2.0 language: - en base_model: - black-forest-labs/FLUX.1-dev library_name: adapter-transformers tags: - cinima - Joker - Heath - Ledger - Batman - DC ---
AJNG/qwen_v3	AJNG	2025-08-12T15:58:56Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2_5_vl", "trl", "en", "base_model:unsloth/Qwen2.5-VL-7B-Instruct", "base_model:finetune:unsloth/Qwen2.5-VL-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-12T15:58:40Z	--- base_model: unsloth/Qwen2.5-VL-7B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2_5_vl - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: AJNG - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-VL-7B-Instruct This qwen2_5_vl model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
silentember/Lantern_vDyFrt	silentember	2025-08-12T15:58:32Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-12T15:56:33Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
bamitunde/blockassist-bc-mimic_humming_frog_1755014170	bamitunde	2025-08-12T15:57:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mimic humming frog", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T15:56:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mimic humming frog --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755012980	Sayemahsjn	2025-08-12T15:54:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T15:54:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
darturi/Llama-3.1-8B-Instruct_RFA_up_down_theta_0.0	darturi	2025-08-12T15:53:54Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-12T15:53:36Z	--- 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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daslab-testing/Qwen3-8B-FPQuant-QAT-NVFP4-1000steps	daslab-testing	2025-08-12T15:53:17Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "fp_quant", "region:us" ]	text-generation	2025-08-12T15:43:46Z	--- 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. 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koloni/blockassist-bc-deadly_graceful_stingray_1755012366	koloni	2025-08-12T15:52:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T15:52:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
aleebaster/blockassist-bc-sly_eager_boar_1755012665	aleebaster	2025-08-12T15:50:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sly eager boar", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T15:50:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sly eager boar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Noelesther/Mistral-7B-Instruct-v0.3-Gensyn-Swarm-swift_zealous_quail	Noelesther	2025-08-12T15:48:38Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am swift_zealous_quail", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-12T15:40:50Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am swift_zealous_quail --- # 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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JYP2024/Wedefense_ASV2025_WavLM_Base_Pruning	JYP2024	2025-08-12T15:48:29Z	1	0	null	[ "anti-spoofing", "asvspoof5", "audio-classification", "en", "dataset:jungjee/asvspoof5", "base_model:microsoft/wavlm-base", "base_model:finetune:microsoft/wavlm-base", "region:us" ]	audio-classification	2025-08-11T19:31:23Z	--- language: - en base_model: - microsoft/wavlm-base pipeline_tag: audio-classification datasets: - jungjee/asvspoof5 tags: - anti-spoofing - asvspoof5 --- # 🔎 Hybrid Pruning for Anti-Spoofing Results - Input Feature: Raw waveform (via SSL model) - Frame Configuration: 150 frames per segment, 20 ms frame shift - Training Strategy: Jointly optimizing for task performance and model sparsity in a single stage. A warm-up schedule is used where the sparsity target linearly increases from 0 to the final value over the first 5 epochs. - Evaluation Metrics: minDCF, EER (%) - Evaluation Sets: Dev / Eval - Back-end: Multi-Head Factorized Attentive Pooling (MHFA) --- # Results on ASVspoof 5 The following table compares the performance of our proposed Hybrid Pruning (HP) single system against other top-performing systems from the official ASVspoof 5 Challenge leaderboard. \| System \| Dev minDCF \| Dev EER (%) \| Eval minDCF \| Eval EER (%) \| \| :--- \| :--- \| :--- \| :--- \| :--- \| \| Rank 3 (ID:T27, Fusion) \| - \| - \| 0.0937 \| 3.42 \| \| HP (ours, Single system) \| 0.0395 \| 1.547 \| 0.1028 \| 3.758 \| \| Rank 4 (ID:T23, Fusion) \| - \| - \| 0.1124 \| 4.16 \| \| Rank 9 (ID:T23, Best single system) \| - \| - \| 0.1499 \| 5.56 \|
moree44/blockassist-bc-sturdy_silent_pigeon_1755012960	moree44	2025-08-12T15:46:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sturdy silent pigeon", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T15:46:07Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sturdy silent pigeon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/UI-AGILE-GGUF	mradermacher	2025-08-12T15:45:59Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:KDEGroup/UI-AGILE", "base_model:quantized:KDEGroup/UI-AGILE", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-12T14:55:02Z	--- base_model: KDEGroup/UI-AGILE language: - en library_name: transformers license: mit mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/KDEGroup/UI-AGILE <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#UI-AGILE-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/UI-AGILE-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/UI-AGILE-GGUF/resolve/main/UI-AGILE.mmproj-Q8_0.gguf) \| mmproj-Q8_0 \| 1.0 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-GGUF/resolve/main/UI-AGILE.mmproj-f16.gguf) \| mmproj-f16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-GGUF/resolve/main/UI-AGILE.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-GGUF/resolve/main/UI-AGILE.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-GGUF/resolve/main/UI-AGILE.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-GGUF/resolve/main/UI-AGILE.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-GGUF/resolve/main/UI-AGILE.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-GGUF/resolve/main/UI-AGILE.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-GGUF/resolve/main/UI-AGILE.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-GGUF/resolve/main/UI-AGILE.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-GGUF/resolve/main/UI-AGILE.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-GGUF/resolve/main/UI-AGILE.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-GGUF/resolve/main/UI-AGILE.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-GGUF/resolve/main/UI-AGILE.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
NexVeridian/Jan-v1-4B-5bit	NexVeridian	2025-08-12T15:43:03Z	0	0	mlx	[ "mlx", "safetensors", "qwen3", "text-generation", "conversational", "en", "base_model:janhq/Jan-v1-4B", "base_model:quantized:janhq/Jan-v1-4B", "license:apache-2.0", "5-bit", "region:us" ]	text-generation	2025-08-12T15:41:12Z	--- license: apache-2.0 language: - en base_model: janhq/Jan-v1-4B pipeline_tag: text-generation library_name: mlx tags: - mlx --- # NexVeridian/Jan-v1-4B-5bit This model [NexVeridian/Jan-v1-4B-5bit](https://huggingface.co/NexVeridian/Jan-v1-4B-5bit) was converted to MLX format from [janhq/Jan-v1-4B](https://huggingface.co/janhq/Jan-v1-4B) using mlx-lm version 0.26.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("NexVeridian/Jan-v1-4B-5bit") 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) ```
milliarderdol/blockassist-bc-roaring_rough_scorpion_1755011708	milliarderdol	2025-08-12T15:42:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring rough scorpion", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T15:42:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - roaring rough scorpion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
NexVeridian/Jan-v1-4B-4bit	NexVeridian	2025-08-12T15:41:00Z	0	0	mlx	[ "mlx", "safetensors", "qwen3", "text-generation", "conversational", "en", "base_model:janhq/Jan-v1-4B", "base_model:quantized:janhq/Jan-v1-4B", "license:apache-2.0", "4-bit", "region:us" ]	text-generation	2025-08-12T15:39:18Z	--- license: apache-2.0 language: - en base_model: janhq/Jan-v1-4B pipeline_tag: text-generation library_name: mlx tags: - mlx --- # NexVeridian/Jan-v1-4B-4bit This model [NexVeridian/Jan-v1-4B-4bit](https://huggingface.co/NexVeridian/Jan-v1-4B-4bit) was converted to MLX format from [janhq/Jan-v1-4B](https://huggingface.co/janhq/Jan-v1-4B) using mlx-lm version 0.26.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("NexVeridian/Jan-v1-4B-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) ```
relapseone/blockassist-bc-insectivorous_prickly_shrew_1755011197	relapseone	2025-08-12T15:39:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous prickly shrew", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T15:39:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous prickly shrew --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
WenFengg/cold14_l1_v1_plus_12_8	WenFengg	2025-08-12T15:38:32Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-12T15:31:01Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
hamid1232/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fierce_crested_sardine	hamid1232	2025-08-12T15:38:12Z	86	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am fierce_crested_sardine", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T20:51:21Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am fierce_crested_sardine --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
indoempatnol/blockassist-bc-fishy_wary_swan_1755011439	indoempatnol	2025-08-12T15:36:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T15:36:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fishy wary swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Abensaid/llama-3.1-8b-instruct-20250812-173154	Abensaid	2025-08-12T15:31:59Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-08-12T15:31:55Z	--- base_model: meta-llama/Llama-3.1-8B-Instruct library_name: transformers model_name: llama-3.1-8b-instruct-20250812-173154 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for llama-3.1-8b-instruct-20250812-173154 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct). 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="Abensaid/llama-3.1-8b-instruct-20250812-173154", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.0 - Pytorch: 2.8.0 - 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}} } ```
quyanh/pythia-2.8b-sft	quyanh	2025-08-12T15:31:09Z	16	0	peft	[ "peft", "safetensors", "base_model:adapter:EleutherAI/pythia-2.8b", "lora", "transformers", "text-generation", "base_model:EleutherAI/pythia-2.8b", "license:apache-2.0", "region:us" ]	text-generation	2025-08-11T03:53:17Z	--- library_name: peft license: apache-2.0 base_model: EleutherAI/pythia-2.8b tags: - base_model:adapter:EleutherAI/pythia-2.8b - lora - transformers pipeline_tag: text-generation model-index: - name: pythia-2.8b-sft 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. --> # pythia-2.8b-sft This model is a fine-tuned version of [EleutherAI/pythia-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6671 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 1.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.8621 \| 0.0442 \| 100 \| 1.7438 \| \| 1.7909 \| 0.0884 \| 200 \| 1.7135 \| \| 1.7775 \| 0.1327 \| 300 \| 1.7020 \| \| 1.7587 \| 0.1769 \| 400 \| 1.6937 \| \| 1.7683 \| 0.2211 \| 500 \| 1.6876 \| \| 1.7488 \| 0.2653 \| 600 \| 1.6824 \| \| 1.7646 \| 0.3096 \| 700 \| 1.6799 \| \| 1.7557 \| 0.3538 \| 800 \| 1.6776 \| \| 1.7485 \| 0.3980 \| 900 \| 1.6743 \| \| 1.7368 \| 0.4422 \| 1000 \| 1.6729 \| \| 1.7298 \| 0.4865 \| 1100 \| 1.6705 \| \| 1.7525 \| 0.5307 \| 1200 \| 1.6724 \| \| 1.7386 \| 0.5749 \| 1300 \| 1.6703 \| \| 1.7325 \| 0.6191 \| 1400 \| 1.6684 \| \| 1.7306 \| 0.6633 \| 1500 \| 1.6682 \| \| 1.7262 \| 0.7076 \| 1600 \| 1.6669 \| \| 1.7333 \| 0.7518 \| 1700 \| 1.6675 \| \| 1.7318 \| 0.7960 \| 1800 \| 1.6673 \| \| 1.7293 \| 0.8402 \| 1900 \| 1.6668 \| \| 1.7326 \| 0.8845 \| 2000 \| 1.6671 \| \| 1.7378 \| 0.9287 \| 2100 \| 1.6668 \| \| 1.7259 \| 0.9729 \| 2200 \| 1.6671 \| ### Framework versions - PEFT 0.17.0 - Transformers 4.55.0 - Pytorch 2.7.1+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
mradermacher/UI-AGILE-3B-i1-GGUF	mradermacher	2025-08-12T15:27:29Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:KDEGroup/UI-AGILE-3B", "base_model:quantized:KDEGroup/UI-AGILE-3B", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-08-12T15:00:40Z	--- base_model: KDEGroup/UI-AGILE-3B language: - en library_name: transformers license: mit mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: 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/KDEGroup/UI-AGILE-3B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#UI-AGILE-3B-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/UI-AGILE-3B-GGUF This is a vision model - mmproj files (if any) will be in the [static repository](https://huggingface.co/mradermacher/UI-AGILE-3B-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/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.9 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 1.3 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-Q2_K.gguf) \| i1-Q2_K \| 1.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 1.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 1.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 1.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 1.7 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 1.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 1.9 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-Q4_0.gguf) \| i1-Q4_0 \| 1.9 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 1.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 2.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-Q4_1.gguf) \| i1-Q4_1 \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-AGILE-3B-i1-GGUF/resolve/main/UI-AGILE-3B.i1-Q6_K.gguf) \| i1-Q6_K \| 2.6 \| 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 -->
RobotAIAIAI/blockassist-bc-snorting_running_hyena_1755010911	RobotAIAIAI	2025-08-12T15:26:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "snorting running hyena", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T15:25:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - snorting running hyena --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jananisoundararajan/hair-coaction	jananisoundararajan	2025-08-12T15:26:18Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-12T15:25:22Z	--- 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]
aleebaster/blockassist-bc-sly_eager_boar_1755011042	aleebaster	2025-08-12T15:22:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sly eager boar", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T15:22:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sly eager boar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
tushar0088/blockassist-bc-vocal_tenacious_prawn_1755012040	tushar0088	2025-08-12T15:21:40Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vocal tenacious prawn", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T15:21:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vocal tenacious prawn --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
motza0025/blockassist-bc-scurrying_waddling_pelican_1755010463	motza0025	2025-08-12T15:19:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scurrying waddling pelican", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T15:19:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scurrying waddling pelican --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lemonhat/Qwen2.5-7B-Instruct-agenttuning_v4_10k_tag5	lemonhat	2025-08-12T15:18:21Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-12T15:17:01Z	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: agenttuning_v4_10k_tag5 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. --> # agenttuning_v4_10k_tag5 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the agenttuning_v4_10k_tag5 dataset. It achieves the following results on the evaluation set: - Loss: 0.3634 ## 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-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 4 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.5887 \| 0.0386 \| 100 \| 0.5013 \| \| 0.5464 \| 0.0772 \| 200 \| 0.4804 \| \| 0.5244 \| 0.1158 \| 300 \| 0.4643 \| \| 0.454 \| 0.1544 \| 400 \| 0.4629 \| \| 0.455 \| 0.1930 \| 500 \| 0.4487 \| \| 0.5026 \| 0.2316 \| 600 \| 0.4363 \| \| 0.48 \| 0.2702 \| 700 \| 0.4406 \| \| 0.4557 \| 0.3088 \| 800 \| 0.4192 \| \| 0.5715 \| 0.3474 \| 900 \| 0.4098 \| \| 0.3408 \| 0.3860 \| 1000 \| 0.4053 \| \| 0.3671 \| 0.4245 \| 1100 \| 0.3955 \| \| 0.5876 \| 0.4631 \| 1200 \| 0.4024 \| \| 0.45 \| 0.5017 \| 1300 \| 0.4049 \| \| 0.336 \| 0.5403 \| 1400 \| 0.3939 \| \| 0.5008 \| 0.5789 \| 1500 \| 0.3893 \| \| 0.3772 \| 0.6175 \| 1600 \| 0.3889 \| \| 0.2965 \| 0.6561 \| 1700 \| 0.3778 \| \| 0.4337 \| 0.6947 \| 1800 \| 0.3701 \| \| 0.3552 \| 0.7333 \| 1900 \| 0.3686 \| \| 0.3369 \| 0.7719 \| 2000 \| 0.3660 \| \| 0.2917 \| 0.8105 \| 2100 \| 0.3655 \| \| 0.3829 \| 0.8491 \| 2200 \| 0.3661 \| \| 0.4447 \| 0.8877 \| 2300 \| 0.3646 \| \| 0.4003 \| 0.9263 \| 2400 \| 0.3638 \| \| 0.3373 \| 0.9649 \| 2500 \| 0.3639 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
0xGareeb/blockassist-bc-diving_jumping_llama_1755011735	0xGareeb	2025-08-12T15:17:38Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving jumping llama", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T15:17:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving jumping llama --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

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