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train · 975k rows

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags list	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
mang3dd/blockassist-bc-tangled_slithering_alligator_1755603918	mang3dd	2025-08-19T12:12:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:12:10Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lilTAT/blockassist-bc-gentle_rugged_hare_1755605480	lilTAT	2025-08-19T12:11:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:11:47Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sefcee/VIDEO.18.Orginal-Uppal-Farm-Girl-Viral-Video-Link.New.full.videos.Uppal.Farm.Girl	sefcee	2025-08-19T12:11:29Z	0	0	null	[ "region:us" ]	null	2025-08-19T12:10:07Z	<a href="https://allyoutubers.com/Orginal-Uppal-Farm-Girl-Viral-Video"> 🌐 VIDEO.18.Orginal-Uppal-Farm-Girl-Viral-Video-Link.New.full.videos.Uppal.Farm.Girl 🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://allyoutubers.com/Orginal-Uppal-Farm-Girl-Viral-Video"> 🌐 VIDEO.18.Orginal-Uppal-Farm-Girl-Viral-Video-Link.New.full.videos.Uppal.Farm.Girl <a href="https://allyoutubers.com/Orginal-Uppal-Farm-Girl-Viral-Video"> 🌐 VIDEO.18.Orginal-Uppal-Farm-Girl-Viral-Video-Link.New.full.videos.Uppal.Farm.Girl 🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://allyoutubers.com/Orginal-Uppal-Farm-Girl-Viral-Video"> 🌐 VIDEO.18.Orginal-Uppal-Farm-Girl-Viral-Video-Link.New.full.videos.Uppal.Farm.Girl
LBST/t10_pick_and_place_smolvla_013000	LBST	2025-08-19T12:11:26Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "pick-and-place", "smolvla", "checkpoint-013000", "region:us" ]	robotics	2025-08-19T12:11:21Z	--- library_name: lerobot tags: - robotics - pick-and-place - smolvla - checkpoint-013000 --- # T08 Pick and Place Policy - Checkpoint 013000 This model is a checkpoint from the training of a pick-and-place policy using SmolVLA architecture. ## Model Details - Checkpoint: 013000 - Architecture: SmolVLA - Task: Pick and Place (T08) - Training Step: 013000 ## Usage You can evaluate this model using LeRobot: ```bash python -m lerobot.scripts.eval \ --policy.path=LBST/t10_pick_and_place_smolvla_013000 \ --env.type=<your_environment> \ --eval.n_episodes=10 \ --policy.device=cuda ``` ## Files - `config.json`: Policy configuration - `model.safetensors`: Model weights in SafeTensors format - `train_config.json`: Complete training configuration for reproducibility ## Parent Repository This checkpoint was extracted from: [LBST/t10_pick_and_place_files](https://huggingface.co/LBST/t10_pick_and_place_files) --- Generated automatically from checkpoint 013000
LBST/t10_pick_and_place_smolvla_012000	LBST	2025-08-19T12:11:01Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "pick-and-place", "smolvla", "checkpoint-012000", "region:us" ]	robotics	2025-08-19T12:10:54Z	--- library_name: lerobot tags: - robotics - pick-and-place - smolvla - checkpoint-012000 --- # T08 Pick and Place Policy - Checkpoint 012000 This model is a checkpoint from the training of a pick-and-place policy using SmolVLA architecture. ## Model Details - Checkpoint: 012000 - Architecture: SmolVLA - Task: Pick and Place (T08) - Training Step: 012000 ## Usage You can evaluate this model using LeRobot: ```bash python -m lerobot.scripts.eval \ --policy.path=LBST/t10_pick_and_place_smolvla_012000 \ --env.type=<your_environment> \ --eval.n_episodes=10 \ --policy.device=cuda ``` ## Files - `config.json`: Policy configuration - `model.safetensors`: Model weights in SafeTensors format - `train_config.json`: Complete training configuration for reproducibility ## Parent Repository This checkpoint was extracted from: [LBST/t10_pick_and_place_files](https://huggingface.co/LBST/t10_pick_and_place_files) --- Generated automatically from checkpoint 012000
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755603799	pempekmangedd	2025-08-19T12:11:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "patterned sturdy dolphin", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:10:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - patterned sturdy dolphin --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
SirAB/Dolphin-gemma2-2b-finetuned-v2	SirAB	2025-08-19T12:11:00Z	29	1	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:SirAB/Dolphin-gemma2-2b-finetuned-v2", "base_model:finetune:SirAB/Dolphin-gemma2-2b-finetuned-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-10T09:34:21Z	--- base_model: SirAB/Dolphin-gemma2-2b-finetuned-v2 tags: - text-generation-inference - transformers - unsloth - gemma2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: SirAB - License: apache-2.0 - Finetuned from model : SirAB/Dolphin-gemma2-2b-finetuned-v2 This gemma2 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)
Prerna43/distilbert-base-uncased-lora-text-classification	Prerna43	2025-08-19T12:10:33Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "base_model:adapter:distilbert-base-uncased", "lora", "transformers", "base_model:distilbert/distilbert-base-uncased", "base_model:adapter:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]	null	2025-08-19T12:03:59Z	--- library_name: peft license: apache-2.0 base_model: distilbert-base-uncased tags: - base_model:adapter:distilbert-base-uncased - lora - transformers metrics: - accuracy model-index: - name: distilbert-base-uncased-lora-text-classification 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. --> # distilbert-base-uncased-lora-text-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6438 - Accuracy: 0.887 ## 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: 4 - eval_batch_size: 4 - 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 250 \| 0.5865 \| 0.891 \| \| 0.051 \| 2.0 \| 500 \| 0.6101 \| 0.888 \| \| 0.051 \| 3.0 \| 750 \| 0.6309 \| 0.889 \| \| 0.1059 \| 4.0 \| 1000 \| 0.6438 \| 0.887 \| ### Framework versions - PEFT 0.17.0 - Transformers 4.55.2 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755603621	katanyasekolah	2025-08-19T12:10:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:10:11Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - silky sprightly cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
LBST/t10_pick_and_place_smolvla_010000	LBST	2025-08-19T12:10:09Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "pick-and-place", "smolvla", "checkpoint-010000", "region:us" ]	robotics	2025-08-19T12:10:02Z	--- library_name: lerobot tags: - robotics - pick-and-place - smolvla - checkpoint-010000 --- # T08 Pick and Place Policy - Checkpoint 010000 This model is a checkpoint from the training of a pick-and-place policy using SmolVLA architecture. ## Model Details - Checkpoint: 010000 - Architecture: SmolVLA - Task: Pick and Place (T08) - Training Step: 010000 ## Usage You can evaluate this model using LeRobot: ```bash python -m lerobot.scripts.eval \ --policy.path=LBST/t10_pick_and_place_smolvla_010000 \ --env.type=<your_environment> \ --eval.n_episodes=10 \ --policy.device=cuda ``` ## Files - `config.json`: Policy configuration - `model.safetensors`: Model weights in SafeTensors format - `train_config.json`: Complete training configuration for reproducibility ## Parent Repository This checkpoint was extracted from: [LBST/t10_pick_and_place_files](https://huggingface.co/LBST/t10_pick_and_place_files) --- Generated automatically from checkpoint 010000
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755603876	lisaozill03	2025-08-19T12:10:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:09:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
LBST/t10_pick_and_place_smolvla_009000	LBST	2025-08-19T12:09:43Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "pick-and-place", "smolvla", "checkpoint-009000", "region:us" ]	robotics	2025-08-19T12:09:36Z	--- library_name: lerobot tags: - robotics - pick-and-place - smolvla - checkpoint-009000 --- # T08 Pick and Place Policy - Checkpoint 009000 This model is a checkpoint from the training of a pick-and-place policy using SmolVLA architecture. ## Model Details - Checkpoint: 009000 - Architecture: SmolVLA - Task: Pick and Place (T08) - Training Step: 009000 ## Usage You can evaluate this model using LeRobot: ```bash python -m lerobot.scripts.eval \ --policy.path=LBST/t10_pick_and_place_smolvla_009000 \ --env.type=<your_environment> \ --eval.n_episodes=10 \ --policy.device=cuda ``` ## Files - `config.json`: Policy configuration - `model.safetensors`: Model weights in SafeTensors format - `train_config.json`: Complete training configuration for reproducibility ## Parent Repository This checkpoint was extracted from: [LBST/t10_pick_and_place_files](https://huggingface.co/LBST/t10_pick_and_place_files) --- Generated automatically from checkpoint 009000
LBST/t10_pick_and_place_smolvla_008000	LBST	2025-08-19T12:09:16Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "pick-and-place", "smolvla", "checkpoint-008000", "region:us" ]	robotics	2025-08-19T12:09:09Z	--- library_name: lerobot tags: - robotics - pick-and-place - smolvla - checkpoint-008000 --- # T08 Pick and Place Policy - Checkpoint 008000 This model is a checkpoint from the training of a pick-and-place policy using SmolVLA architecture. ## Model Details - Checkpoint: 008000 - Architecture: SmolVLA - Task: Pick and Place (T08) - Training Step: 008000 ## Usage You can evaluate this model using LeRobot: ```bash python -m lerobot.scripts.eval \ --policy.path=LBST/t10_pick_and_place_smolvla_008000 \ --env.type=<your_environment> \ --eval.n_episodes=10 \ --policy.device=cuda ``` ## Files - `config.json`: Policy configuration - `model.safetensors`: Model weights in SafeTensors format - `train_config.json`: Complete training configuration for reproducibility ## Parent Repository This checkpoint was extracted from: [LBST/t10_pick_and_place_files](https://huggingface.co/LBST/t10_pick_and_place_files) --- Generated automatically from checkpoint 008000
p1m2/falcon-7b-sharded-bf16-finetuned-mental-health-conversational	p1m2	2025-08-19T12:08:49Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:ybelkada/falcon-7b-sharded-bf16", "base_model:finetune:ybelkada/falcon-7b-sharded-bf16", "endpoints_compatible", "region:us" ]	null	2025-08-19T10:13:06Z	--- base_model: ybelkada/falcon-7b-sharded-bf16 library_name: transformers model_name: falcon-7b-sharded-bf16-finetuned-mental-health-conversational tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for falcon-7b-sharded-bf16-finetuned-mental-health-conversational This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16). 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="p1m2/falcon-7b-sharded-bf16-finetuned-mental-health-conversational", 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/parammehta123/huggingface/runs/w637538i) This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.6.0+cu124 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Dejiat/blockassist-bc-savage_unseen_bobcat_1755605258	Dejiat	2025-08-19T12:08:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:08:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Unlearning/early-unlearning-weak-filter-ga-1-in-209-ga-lr-scale-0_001-gclip-0_5	Unlearning	2025-08-19T12:07:52Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T15:07:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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LBST/t10_pick_and_place_smolvla_004000	LBST	2025-08-19T12:07:36Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "pick-and-place", "smolvla", "checkpoint-004000", "region:us" ]	robotics	2025-08-19T12:07:29Z	--- library_name: lerobot tags: - robotics - pick-and-place - smolvla - checkpoint-004000 --- # T08 Pick and Place Policy - Checkpoint 004000 This model is a checkpoint from the training of a pick-and-place policy using SmolVLA architecture. ## Model Details - Checkpoint: 004000 - Architecture: SmolVLA - Task: Pick and Place (T08) - Training Step: 004000 ## Usage You can evaluate this model using LeRobot: ```bash python -m lerobot.scripts.eval \ --policy.path=LBST/t10_pick_and_place_smolvla_004000 \ --env.type=<your_environment> \ --eval.n_episodes=10 \ --policy.device=cuda ``` ## Files - `config.json`: Policy configuration - `model.safetensors`: Model weights in SafeTensors format - `train_config.json`: Complete training configuration for reproducibility ## Parent Repository This checkpoint was extracted from: [LBST/t10_pick_and_place_files](https://huggingface.co/LBST/t10_pick_and_place_files) --- Generated automatically from checkpoint 004000
LBST/t10_pick_and_place_smolvla_003000	LBST	2025-08-19T12:07:09Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "pick-and-place", "smolvla", "checkpoint-003000", "region:us" ]	robotics	2025-08-19T12:07:04Z	--- library_name: lerobot tags: - robotics - pick-and-place - smolvla - checkpoint-003000 --- # T08 Pick and Place Policy - Checkpoint 003000 This model is a checkpoint from the training of a pick-and-place policy using SmolVLA architecture. ## Model Details - Checkpoint: 003000 - Architecture: SmolVLA - Task: Pick and Place (T08) - Training Step: 003000 ## Usage You can evaluate this model using LeRobot: ```bash python -m lerobot.scripts.eval \ --policy.path=LBST/t10_pick_and_place_smolvla_003000 \ --env.type=<your_environment> \ --eval.n_episodes=10 \ --policy.device=cuda ``` ## Files - `config.json`: Policy configuration - `model.safetensors`: Model weights in SafeTensors format - `train_config.json`: Complete training configuration for reproducibility ## Parent Repository This checkpoint was extracted from: [LBST/t10_pick_and_place_files](https://huggingface.co/LBST/t10_pick_and_place_files) --- Generated automatically from checkpoint 003000
VoilaRaj/80_cGooIB	VoilaRaj	2025-08-19T12:06:45Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T12:02:53Z	--- 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).
LBST/t10_pick_and_place_smolvla_002000	LBST	2025-08-19T12:06:44Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "pick-and-place", "smolvla", "checkpoint-002000", "region:us" ]	robotics	2025-08-19T12:06:20Z	--- library_name: lerobot tags: - robotics - pick-and-place - smolvla - checkpoint-002000 --- # T08 Pick and Place Policy - Checkpoint 002000 This model is a checkpoint from the training of a pick-and-place policy using SmolVLA architecture. ## Model Details - Checkpoint: 002000 - Architecture: SmolVLA - Task: Pick and Place (T08) - Training Step: 002000 ## Usage You can evaluate this model using LeRobot: ```bash python -m lerobot.scripts.eval \ --policy.path=LBST/t10_pick_and_place_smolvla_002000 \ --env.type=<your_environment> \ --eval.n_episodes=10 \ --policy.device=cuda ``` ## Files - `config.json`: Policy configuration - `model.safetensors`: Model weights in SafeTensors format - `train_config.json`: Complete training configuration for reproducibility ## Parent Repository This checkpoint was extracted from: [LBST/t10_pick_and_place_files](https://huggingface.co/LBST/t10_pick_and_place_files) --- Generated automatically from checkpoint 002000
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755603595	quantumxnode	2025-08-19T12:06:13Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:06:10Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Kumo2023/nupurbro	Kumo2023	2025-08-19T12:05:27Z	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	2025-08-19T10:59:59Z	--- 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 --- # Nupurbro <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/Kumo2023/nupurbro/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('Kumo2023/nupurbro', 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: 6000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Kumo2023/nupurbro/discussions) to add images that show off what you’ve made with this LoRA.
haihp02/pdfreeee-biggerb	haihp02	2025-08-19T12:05:13Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T12:04:12Z	--- library_name: transformers tags: - trl - dpo --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
m-muraki/Qwen3-Coder-30B-A3B-Instruct-FP8	m-muraki	2025-08-19T12:03:40Z	0	0	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "conversational", "arxiv:2505.09388", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "fp8", "region:us" ]	text-generation	2025-08-19T12:02:47Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8/blob/main/LICENSE pipeline_tag: text-generation --- # Qwen3-Coder-30B-A3B-Instruct-FP8 <a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/> </a> ## Highlights Qwen3-Coder is available in multiple sizes. Today, we're excited to introduce Qwen3-Coder-30B-A3B-Instruct-FP8. This streamlined model maintains impressive performance and efficiency, featuring the following key enhancements: - Significant Performance among open models on Agentic Coding, Agentic Browser-Use, and other foundational coding tasks. - Long-context Capabilities with native support for 256K tokens, extendable up to 1M tokens using Yarn, optimized for repository-scale understanding. - Agentic Coding supporting for most platform such as Qwen Code, CLINE, featuring a specially designed function call format. ![image/jpeg](https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-Coder/qwen3-coder-30a3-main.jpg) ## Model Overview Qwen3-Coder-30B-A3B-Instruct-FP8 has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 30.5B in total and 3.3B activated - Number of Layers: 48 - Number of Attention Heads (GQA): 32 for Q and 4 for KV - Number of Experts: 128 - Number of Activated Experts: 8 - Context Length: 262,144 natively. NOTE: This model supports only non-thinking mode and does not generate ``<think></think>`` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required. For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3-coder/), [GitHub](https://github.com/QwenLM/Qwen3-Coder), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Quickstart We advise you to use the latest version of `transformers`. With `transformers<4.51.0`, you will encounter the following error: ``` KeyError: 'qwen3_moe' ``` The following contains a code snippet illustrating how to use the model generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Write a quick sort algorithm." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( model_inputs, max_new_tokens=65536 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() content = tokenizer.decode(output_ids, skip_special_tokens=True) print("content:", content) ``` Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`. ## Note on FP8 For convenience and performance, we have provided `fp8`-quantized model checkpoint for Qwen3, whose name ends with `-FP8`. The quantization method is fine-grained `fp8` quantization with block size of 128. You can find more details in the `quantization_config` field in `config.json`. You can use the Qwen3-30B-A3B-Instruct-FP8 model with serveral inference frameworks, including `transformers`, `sglang`, and `vllm`, as the original bfloat16 model. However, please pay attention to the following known issues: - `transformers`: - there are currently issues with the "fine-grained fp8" method in `transformers` for distributed inference. You may need to set the environment variable `CUDA_LAUNCH_BLOCKING=1` if multiple devices are used in inference. ## Agentic Coding Qwen3-Coder excels in tool calling capabilities. You can simply define or use any tools as following example. ```python # Your tool implementation def square_the_number(num: float) -> dict: return num 2 # Define Tools tools=[ { "type":"function", "function":{ "name": "square_the_number", "description": "output the square of the number.", "parameters": { "type": "object", "required": ["input_num"], "properties": { 'input_num': { 'type': 'number', 'description': 'input_num is a number that will be squared' } }, } } } ] import OpenAI # Define LLM client = OpenAI( # Use a custom endpoint compatible with OpenAI API base_url='http://localhost:8000/v1', # api_base api_key="EMPTY" ) messages = [{'role': 'user', 'content': 'square the number 1024'}] completion = client.chat.completions.create( messages=messages, model="Qwen3-Coder-30B-A3B-Instruct-FP8", max_tokens=65536, tools=tools, ) print(completion.choice[0]) ``` ## Best Practices To achieve optimal performance, we recommend the following settings: 1. Sampling Parameters: - We suggest using `temperature=0.7`, `top_p=0.8`, `top_k=20`, `repetition_penalty=1.05`. 2. Adequate Output Length: We recommend using an output length of 65,536 tokens for most queries, which is adequate for instruct models. ### Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen3technicalreport, title={Qwen3 Technical Report}, author={Qwen Team}, year={2025}, eprint={2505.09388}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.09388}, } ```
llm-slice/blm-gpt2s-90M-s42_901M-s42_submission	llm-slice	2025-08-19T12:03:13Z	993	0	null	[ "safetensors", "gpt2", "interaction", "babylm-submission", "babylm-2025", "en", "arxiv:2405.09605", "arxiv:2411.07990", "region:us" ]	null	2025-08-15T08:47:53Z	--- language: - en tags: - interaction - babylm-submission - babylm-2025 --- # Model Card for BabyLM submission to the Interaction Track <!-- Provide a quick summary of what the model is/does. [Optional] --> A 124M model with the GPT-2 architecture trained with the next token prediction loss for 10 epochs (~900 M words) on 90% of the BabyLM corpus and an additional 1 M words of PPO RL training as submission for the Interaction track of the 2025 BabyLM challenge. This model card is based on the model card of the BabyLM [100M GPT-2 baseline](https://huggingface.co/BabyLM-community/babylm-baseline-100m-gpt2/edit/main/README.md). # Table of Contents - [Model Card for Storytelling Submission Model](#model-card-for--model_id-) - [Table of Contents](#table-of-contents) - [Model Details](#model-details) - [Model Description](#model-description) - [Uses](#uses) - [Training Details](#training-details) - [Training Data](#training-data) - [Hyperparameters](#hyperparameters) - [Training Procedure](#training-procedure) - [Size and Checkpoints](#size-and-checkpoints) - [Evaluation](#evaluation) - [Testing Data & Metrics](#testing-data-factors--metrics) - [Testing Data](#testing-data) - [Metrics](#metrics) - [Results](#results) - [Technical Specifications](#technical-specifications-optional) - [Model Architecture and Objective](#model-architecture-and-objective) - [Compute Infrastructure](#compute-infrastructure) - [Hardware](#hardware) - [Software](#software) - [Training Time](#training-time) - [Citation](#citation) - [Model Card Authors](#model-card-authors-optional) - [Bibliography](#bibliography) # Model Details ## Model Description <!-- Provide a longer summary of what this model is/does. --> This is the RL storytelling model, based on a [pretrained GPT-2 model](https://huggingface.co/llm-slice/blm-gpt2s-90M-s42), for the Interaction Track of the 2025 BabyLM challenge. - Developed by: Jonas Mayer Martins, Ali Hamza Bashir, Muhammad Rehan Khalid - Model type: Causal language model - Language(s) (NLP): eng - Resources for more information: - [GitHub Repo](https://github.com/malihamza/babylm-interactive-learning) # 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. --> This is a pre-trained language model. It can be used to evaluate tasks in a zero-shot manner and also can be fine-tuned for downstream tasks. It can be used for language generation but given its small size and low number of words trained on, do not expect LLM-level performance. # Training Details ## Training Data <!-- This should link to a Data 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. --> We used the BabyLM 100M (Strict) dataset for training. We trained the tokenizer and model on randomly selected 90% of the corpus, which is composed of the following: \| Source \| Weight \| Domain \| Citation \| Website \| License \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| BNC \| 8% \| Dialogue \| BNC Consortium (2007) \| [link](http://www.natcorp.ox.ac.uk/) \| [link](http://www.natcorp.ox.ac.uk/docs/licence.html) <sup>1</sup> \| \| CHILDES \| 29% \| Dialogue, Child-Directed \| MacWhinney (2000) \| \| [link](https://talkbank.org/share/rules.html) \| \| Project Gutenberg \| 26% \| Fiction, Nonfiction \| Gerlach & Font-Clos (2020) \| [link](https://github.com/pgcorpus/gutenberg) \| [link](https://www.gutenberg.org/policy/license.html) \| \| OpenSubtitles \| 20% \| Dialogue, Scripted \| Lison & Tiedermann (2016) \| [link](https://opus.nlpl.eu/OpenSubtitles-v2018.php) \| Open source \| \| Simple English Wikipedia \| 15% \| Nonfiction \| -- \| [link](https://dumps.wikimedia.org/simplewiki/20221201/) \| [link](https://dumps.wikimedia.org/legal.html) \| \| Switchboard \| 1% \| Dialogue \| Godfrey et al. (1992), Stolcke et al., (2000) \| [link](http://compprag.christopherpotts.net/swda.html) \| [link](http://compprag.christopherpotts.net/swda.html) \| <sup>1</sup> Our distribution of part of the BNC Texts is permitted under the fair dealings provision of copyright law (see term (2g) in the BNC license). ## Hyperparameters PPO RL training \| Parameter \| Value \| \|----------------------------------\|---------------------\| \| Student context length \| 512 \| \| seed \| 42 \| \| batch size \| 360 \| \| Student sampling temperature \| 1 \| \| top_k \| 0 \| \| top_p \| 1 \| \| max_new_tokens (student) \| 90 \| \| Teacher model \| Llama 3.1 8B Instr. \| \| Teacher context length \| 1024 \| \| max_new_tokens (teacher) \| 6 \| \| gradient_accumulation_steps \| 1 \| \| adap_kl_ctrl \| True \| \| init_kl_coef \| 0.2 \| \| learning_rate \| 1×10⁻⁶ \| \| Student input limit \| 1 M words \| ## Hyperparameters Pretraining \| Hyperparameter \| Value \| \| --- \| --- \| \| Number of epochs \| 10 \| \| Datapoint length \| 512 \| \| Batch size \| 16 \| \| Gradient accumulation steps \| 4 \| \| Learning rate \| 0.0005 \| \| Number of steps \| 211650 \| \| Warmup steps \| 2116 \| \| Gradient clipping \| 1 \| \| Optimizer \| AdamW \| \| Optimizer Beta_1 \| 0.9 \| \| Optimizer Beta_2 \| 0.999 \| \| Optimizer Epsilon \| 10<sup>-8</sup>\| \| Tokenizer \| BytePairBPE \| \| Vocab Size \| 16000 \| ## Training Procedure The model is trained with next token prediction loss for 10 epochs. ### Size and checkpoints <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> The model has 124M parameters. In total we train on around 901 M words and provide multiple checkpoints from the training. Specifically we provode: - Checkpoints every 1 M words for the first 10 M words - Checkpoints every 10 M words first 100 M words - Checkpoints every 100 M words until 900 M words - Checkpoints every 100 K words until 901 M words # Evaluation <!-- This section describes the evaluation protocols and provides the results. --> This model is evaluated in two ways: 1. We do zero-shot evaluation on 7 tasks. 2. We do fine-tuning on a subset of the (Super)GLUE tasks (Wang et al., ICLR 2019; Wang et al., NeurIPS 2019) . ## Testing Data & Metrics ### Testing Data <!-- This should link to a Data Card if possible. --> For the BLiMP, BLiMP supplement, and EWoK tasks, we use a filtered version of the dataset to only include examples with words found in the BabyLM dataset. For the Finetuning task, we both filter and sample down to a maximum 10 000 train examples. Validation Data Zero-shot Tasks - BLiMP: The Benchmark of Linguistic Minimal Pairs evaluates the model's linguistic ability by seeing if it can recognize the grammatically correct sentence from a pair of minimally different sentences. It tests various grammatical phenomena.(Warstadt et al., TACL 2020) - BLiMP Supplement: A supplement to BLiMP introduced in the first edition of the BabyLM challenge. More focused on dialogue and questions. (Warstadt et al., CoNLL-BabyLM 2023) - EWoK: Works similarly to BLiMP but looks the model's internal world knowledge. Looking at both whter a model has physical and social knowledge. (Ivanova et al., 2024) - Eye Tracking and Self-paced Reading: Looks at whether the model can mimick the eye tracking and reading time of a human but using surprisal of a word as a proxy for time spent reading a word. (de Varda et al., BRM 2024) - Entity Tracking: Checks whether a model can keep track of the changes to the states of entities as text/dialogue unfolds. (Kim & Schuster, ACL 2023) - WUGs: Tests morphological generalization in LMs through an adjective nominalization and past tense task. (Hofmann et al., 2024) (Weissweiler et al., 2023) - COMPS: Property knowledge. (Misra et al., 2023) Finetuning Tasks - BoolQ: A yes/no QA dataset with unprompted and unconstrained questions. (Clark et al., NAACL 2019) - MNLI: The Multi-Genre Natural Language Inference corpus tests the language understanding of a model by seeing wehther it can recognize textual entailment. (Williams et al., NAACL 2018) - MRPC: The Microsoft Research Paraphrase Corpus contains pairs of sentences that are either paraphrases/semntically equivalent to each other or unrelated.(Dolan & Brockett, IJCNLP 2005) - QQP<sup>2</sup>: Similarly to MRPC, the Quora Question Pairs corpus tests the models ability to determine whether a pair of questions are sematically similar to each other. These questions are sourced from Quora. - MultiRC: The Multi-Sentence Reading Comprehension corpus is a QA task that evaluates the model's ability to the correct answer from a list of answers given a question and context paragraph. In this version the data is changed to a binary classification judging whether the answer to a question, context pair is correct. (Khashabi et al., NAACL 2018) - RTE: Similar the Recognizing Text Entailement tests the model's ability to recognize text entailement. (Dagan et al., Springer 2006; Bar et al., 2006; Giampiccolo et al., 2007; Bentivogli et al., TAC 2009) - WSC: The Winograd Schema Challenge tests the models ability to do coreference resolution on sentences with a pronoun and a list of noun phrases found in the sentence. This version edits it to be a binary classification on examples consisting of a pronoun and noun phrase.(Levesque et al., PKRR 2012) <sup>2</sup> https://www.quora.com/profile/Ricky-Riche-2/First-Quora-Dataset-Release-Question-Pairs ### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> The metrics used to evaluate the model are the following: - Zero-shot - Accuracy on predicting the correct completion/sentence for BLiMP, BLiMP Supplement, EWoK, Entity Tracking, and WUGs - Change in R^2 prediction from baseline for Eye Tracking (with no spillover) and Self-paced Reading (1-word spillover) - Finetuning - 3 class Accuracy for MNLI - Binary Accuracy for BoolQ, MultiRC, and WSC - F1-score for MRPC and QQP The metrics were chosen based on the advice of the papers the tasks come from. ### Hyperparameters \| Hyperparameter \| MNLI, RTE, QQP, MRPC, BoolQ, MultiRC \| WSC \| \| --- \| --- \| --- \| \| Learning Rate \| 3\10<sup>-5</sup> \| 3\10<sup>-5</sup> \| \| Batch Size \| 16 \| 16 \| \| Epochs \| 10 \| 30 \| \| Weight decay \| 0.01 \| 0.01 \| \| Optimizer \| AdamW \| AdamW \| \| Scheduler \| cosine \| cosine \| \| Warmup percentage \| 6% \| 6% \| \| Dropout \| 0.1 \| 0.1 \| ## Results We compare our student model against two official baselines from the 2025 BabyLM Challenge<sup>1</sup>: - 1000M-pre: The standard pretraining baseline, using a GPT-2-small model trained on 100M unique words from the BabyLM dataset (10 epochs, next-word prediction). - SimPO: A baseline first trained for 7 epochs with next-word prediction, then 2 epochs interleaving prediction and reinforcement learning. Here, the RL reward encourages the student to generate completions similar to the teacher’s output. - 900M-pre: Our model, using the same GPT-2-small architecture, pretrained on 90% of the BabyLM dataset (yielding approximately 91M unique words, 10 epochs). - 900M-RL: Our model after additional PPO-based reinforcement learning with the teacher, using about 1M words as input for the interactive (RL) phase. --- ### Evaluation Results \| Task \| 1000M-pre \| SimPO \| 900M-pre \| 900M-RL \| \|:------------- \| ------------: \| ---------:\| ------------:\| -----------:\| \| BLiMP \| 74.88 \| 72.16 \| 77.52 \| 77.53 \| \| Suppl. \| 63.32 \| 61.22 \| 56.62 \| 56.72 \| \| EWOK \| 51.67 \| 51.92 \| 51.36 \| 51.41 \| \| COMPS \| 56.17 \| 55.05 \| 55.20 \| 55.18 \| \| ET \| 31.51 \| 28.06 \| 30.34 \| 33.11 \| \| GLUE \| 52.18 \| 50.35 \| 53.14 \| 52.46 \| #### Model descriptions: - 1000M-pre: Baseline pretrained on 100M words (BabyLM challenge baseline). - SimPO: Baseline using a hybrid of pretraining and RL with a similarity-based reward. - 900M-pre: Our GPT-2-small model, pretrained on 90M words (similar settings as baseline, but less data). - 900M-RL: The same model as 900M-pre, further trained with PPO using teacher feedback on 1M words of input. - See: [BabyLM Challenge](https://huggingface.co/BabyLM-community) for the baselines. # Technical Specifications ### Hardware - 4 A100 GPUs were used to train this model. ### Software PyTorch ### Training Time The model took 20 hours to train and consumed 53560 core hours (with 4 GPUs and 32 CPUs). # Citation ```latex @misc{MayerMartinsBKB2025, title={ToDo}, author={Jonas Mayer Martins, Ali Hamza Bashir, Muhammad Rehan Khalid, Lisa Beinborn}, year={2025}, eprint={2502.TODO}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={ToDo}, } ``` # Model Card Authors Jonas Mayer Martins # Bibliography [GLUE: A multi-task benchmark and analysis platform for natural language understanding](https://openreview.net/pdf?id=rJ4km2R5t7) (Wang et al., ICLR 2019) [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://proceedings.neurips.cc/paper_files/paper/2019/file/4496bf24afe7fab6f046bf4923da8de6-Paper.pdf) (Wang et al., NeurIPS 2019) [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://aclanthology.org/2020.tacl-1.25/) (Warstadt et al., TACL 2020) [Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora](https://aclanthology.org/2023.conll-babylm.1/) (Warstadt et al., CoNLL-BabyLM 2023) [🌏 Elements of World Knowledge (EWoK): A cognition-inspired framework for evaluating basic world knowledge in language models](https://arxiv.org/pdf/2405.09605v1) (Ivanova et al., 2024) [Cloze probability, predictability ratings, and computational estimates for 205 English sentences, aligned with existing EEG and reading time data](https://link.springer.com/article/10.3758/s13428-023-02261-8) (de Varda et al., BRM 2024) [Entity Tracking in Language Models](https://aclanthology.org/2023.acl-long.213/) (Kim & Schuster, ACL 2023) [Derivational Morphology Reveals Analogical Generalization in Large Language Models](https://arxiv.org/pdf/2411.07990) (Hofmann et al., 2024) [Automatically Constructing a Corpus of Sentential Paraphrases](https://aclanthology.org/I05-5002/) (Dolan & Brockett, IJCNLP 2005) [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://aclanthology.org/N18-1101/) (Williams et al., NAACL 2018) [The Winograd Schema Challenge]( http://dl.acm.org/citation.cfm?id=3031843.3031909) (Levesque et al., PKRR 2012) [The PASCAL Recognising Textual Entailment Challenge](https://link.springer.com/chapter/10.1007/11736790_9) (Dagan et al., Springer 2006) [The Second PASCAL Recognising Textual Entailment Challenge]() (Bar et al., 2006) [The Third PASCAL Recognizing Textual Entailment Challenge](https://aclanthology.org/W07-1401/) (Giampiccolo et al., 2007) [The Fifth PASCAL Recognizing Textual Entailment Challenge](https://tac.nist.gov/publications/2009/additional.papers/RTE5_overview.proceedings.pdf) (Bentivogli et al., TAC 2009) [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://aclanthology.org/N19-1300/) (Clark et al., NAACL 2019) [Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences](https://aclanthology.org/N18-1023/) (Khashabi et al., NAACL 2018)
premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF	premrajreddy	2025-08-19T12:02:08Z	0	0	null	[ "safetensors", "gguf", "llama", "home-assistant", "voice-assistant", "automation", "assistant", "home", "text-generation", "conversational", "en", "dataset:acon96/Home-Assistant-Requests", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T02:13:44Z	--- language: en license: apache-2.0 tags: - home-assistant - voice-assistant - automation - assistant - home pipeline_tag: text-generation datasets: - acon96/Home-Assistant-Requests base_model: - TinyLlama/TinyLlama-1.1B-Chat-v1.0 base_model_relation: finetune --- # 🏠 TinyLLaMA-1.1B Home Assistant Voice Model This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0), trained with [acon96/Home-Assistant-Requests](https://huggingface.co/datasets/acon96/Home-Assistant-Requests). It is designed to act as a voice-controlled smart home assistant that takes natural language instructions and outputs Home Assistant commands. --- ## ✨ Features - Converts natural language voice commands into Home Assistant automation calls. - Produces friendly confirmations and structured JSON service commands. - Lightweight (1.1B parameters) – runs efficiently on CPUs, GPUs, and via Ollama with quantization. --- ## 🔧 Example Usage (Transformers) ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("premrajreddy/tinyllama-1.1b-home-llm") model = AutoModelForCausalLM.from_pretrained("premrajreddy/tinyllama-1.1b-home-llm") query = "turn on the kitchen lights" inputs = tokenizer(query, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=80) print(tokenizer.decode(outputs[0], skip_special_tokens=True))
musdbi/bpce_model	musdbi	2025-08-19T12:00:37Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-19T12:00:27Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: musdbi - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-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)
swiptit/blockassist-bc-polished_armored_mandrill_1755604721	swiptit	2025-08-19T11:59:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "polished armored mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:59:19Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - polished armored mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vg-sentry/Qwen-Qwen2.5-Coder-7B-Instruct-sentry-v1	vg-sentry	2025-08-19T11:59:11Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "unsloth", "endpoints_compatible", "region:us" ]	null	2025-08-19T11:07:28Z	--- base_model: unsloth/qwen2.5-coder-7b-instruct-bnb-4bit library_name: transformers model_name: Qwen-Qwen2.5-Coder-7B-Instruct-sentry-v1 tags: - generated_from_trainer - sft - trl - unsloth licence: license --- # Model Card for Qwen-Qwen2.5-Coder-7B-Instruct-sentry-v1 This model is a fine-tuned version of [unsloth/qwen2.5-coder-7b-instruct-bnb-4bit](https://huggingface.co/unsloth/qwen2.5-coder-7b-instruct-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="vg-sentry/Qwen-Qwen2.5-Coder-7B-Instruct-sentry-v1", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.6.0+cu124 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
indoempatnol/blockassist-bc-fishy_wary_swan_1755602935	indoempatnol	2025-08-19T11:56:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:56:14Z	--- 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).
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755603521	Sayemahsjn	2025-08-19T11:55:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:55:26Z	--- 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).
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755602793	kojeklollipop	2025-08-19T11:54:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:53:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lakelee/RLB_MLP_BC_v4.20250819.18.1	lakelee	2025-08-19T11:53:49Z	0	0	transformers	[ "transformers", "safetensors", "mlp_swiglu", "generated_from_trainer", "base_model:lakelee/RLB_MLP_BC_v4.20250819.18", "base_model:finetune:lakelee/RLB_MLP_BC_v4.20250819.18", "endpoints_compatible", "region:us" ]	null	2025-08-19T11:09:01Z	--- library_name: transformers base_model: lakelee/RLB_MLP_BC_v4.20250819.18 tags: - generated_from_trainer model-index: - name: RLB_MLP_BC_v4.20250819.18.1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # RLB_MLP_BC_v4.20250819.18.1 This model is a fine-tuned version of [lakelee/RLB_MLP_BC_v4.20250819.18](https://huggingface.co/lakelee/RLB_MLP_BC_v4.20250819.18) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch_fused with betas=(0.9,0.99) 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 ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu128 - Tokenizers 0.21.4
burmeai/burme-v1	burmeai	2025-08-19T11:51:20Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-19T11:51:20Z	--- license: apache-2.0 ---
AXERA-TECH/Qwen2.5-0.5B-Instruct-CTX-Int8	AXERA-TECH	2025-08-19T11:51:10Z	10	0	transformers	[ "transformers", "Qwen", "Qwen2.5-0.5B-Instruct", "Qwen2.5-0.5B-Instruct-GPTQ-Int8", "GPTQ", "en", "base_model:Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int8", "base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int8", "license:bsd-3-clause", "endpoints_compatible", "region:us" ]	null	2025-06-03T07:41:28Z	--- library_name: transformers license: bsd-3-clause base_model: - Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int8 tags: - Qwen - Qwen2.5-0.5B-Instruct - Qwen2.5-0.5B-Instruct-GPTQ-Int8 - GPTQ language: - en --- # Qwen2.5-0.5B-Instruct-GPTQ-Int8 This version of Qwen2.5-0.5B-Instruct-GPTQ-Int8 has been converted to run on the Axera NPU using w8a16 quantization. This model has been optimized with the following LoRA: Compatible with Pulsar2 version: 4.2(Not released yet) ## Convert tools links: For those who are interested in model conversion, you can try to export axmodel through the original repo : https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int8 [Pulsar2 Link, How to Convert LLM from Huggingface to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/appendix/build_llm.html) [AXera NPU LLM Runtime](https://github.com/AXERA-TECH/ax-llm) ## Support Platform - AX650 - AX650N DEMO Board - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html) - [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html) - AX630C - developing \|Chips\|w8a16\|w4a16\| \|--\|--\|--\| \|AX650\| 30 tokens/sec\| TBD \| ## How to use Download all files from this repository to the device ``` root@ax650:/mnt/qtang/llm-test/qwen2.5-0.5b-ctx# tree -L 1 . \|-- main_ax650 \|-- main_axcl_aarch64 \|-- main_axcl_x86 \|-- post_config.json \|-- qwen2.5-0.5b-gptq-int8-ctx-ax630c \|-- qwen2.5-0.5b-gptq-int8-ctx-ax650 \|-- qwen2.5_tokenizer \|-- qwen2.5_tokenizer_uid.py \|-- run_qwen2.5_0.5b_gptq_int8_ctx_ax630c.sh `-- run_qwen2.5_0.5b_gptq_int8_ctx_ax650.sh 3 directories, 7 files ``` #### Start the Tokenizer service ``` root@ax650:/mnt/qtang/llm-test/qwen2.5-0.5b-ctx# python3 qwen2.5_tokenizer_uid.py Server running at http://0.0.0.0:12345 ``` #### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) or AX650N DEMO Board Open another terminal and run `run_qwen2.5_0.5b_gptq_int8_ax650.sh` ``` root@ax650:/mnt/qtang/llm-test/qwen2.5-0.5b-ctx# ./run_qwen2.5_0.5b_gptq_int8_ctx_ax650.sh [I][ Init][ 110]: LLM init start [I][ Init][ 34]: connect http://127.0.0.1:12345 ok [I][ Init][ 57]: uid: cdeaf62e-0243-4dc9-b557-23a7c1ba7da1 bos_id: -1, eos_id: 151645 100% \| ████████████████████████████████ \| 27 / 27 [12.35s<12.35s, 2.19 count/s] init post axmodel ok,remain_cmm(3960 MB) [I][ Init][ 188]: max_token_len : 2560 [I][ Init][ 193]: kv_cache_size : 128, kv_cache_num: 2560 [I][ Init][ 201]: prefill_token_num : 128 [I][ Init][ 205]: grp: 1, prefill_max_token_num : 1 [I][ Init][ 205]: grp: 2, prefill_max_token_num : 128 [I][ Init][ 205]: grp: 3, prefill_max_token_num : 512 [I][ Init][ 205]: grp: 4, prefill_max_token_num : 1024 [I][ Init][ 205]: grp: 5, prefill_max_token_num : 1536 [I][ Init][ 205]: grp: 6, prefill_max_token_num : 2048 [I][ Init][ 209]: prefill_max_token_num : 2048 [I][ load_config][ 282]: load config: { "enable_repetition_penalty": false, "enable_temperature": false, "enable_top_k_sampling": true, "enable_top_p_sampling": false, "penalty_window": 20, "repetition_penalty": 1.2, "temperature": 0.9, "top_k": 1, "top_p": 0.8 } [I][ Init][ 218]: LLM init ok Type "q" to exit, Ctrl+c to stop current running [I][ GenerateKVCachePrefill][ 271]: input token num : 21, prefill_split_num : 1 prefill_grpid : 2 [I][ GenerateKVCachePrefill][ 308]: input_num_token:21 [I][ main][ 230]: precompute_len: 21 [I][ main][ 231]: system_prompt: You are Qwen, created by Alibaba Cloud. You are a helpful assistant. prompt >> who are you? [I][ SetKVCache][ 531]: prefill_grpid:2 kv_cache_num:128 precompute_len:38 input_num_token:12 [I][ SetKVCache][ 534]: current prefill_max_token_num:1920 [I][ Run][ 660]: input token num : 12, prefill_split_num : 1 [I][ Run][ 686]: input_num_token:12 [I][ Run][ 829]: ttft: 134.80 ms I am Qwen, a large language model created by Alibaba Cloud. I am designed to assist with a wide range of tasks, from general knowledge to specific areas such as science, technology, and more. How can I help you today? [N][ Run][ 943]: hit eos,avg 30.88 token/s [I][ GetKVCache][ 500]: precompute_len:98, remaining:1950 prompt >> what can you do? [I][ SetKVCache][ 531]: prefill_grpid:2 kv_cache_num:128 precompute_len:98 input_num_token:13 [I][ SetKVCache][ 534]: current prefill_max_token_num:1920 [I][ Run][ 660]: input token num : 13, prefill_split_num : 1 [I][ Run][ 686]: input_num_token:13 [I][ Run][ 829]: ttft: 134.97 ms I can answer questions, provide information, assist with tasks, and even engage in creative writing. I'm here to help you with any questions or tasks you might have! [N][ Run][ 943]: hit eos,avg 30.85 token/s [I][ GetKVCache][ 500]: precompute_len:145, remaining:1903 ```
koloni/blockassist-bc-deadly_graceful_stingray_1755602558	koloni	2025-08-19T11:51:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:50:57Z	--- 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).
Datasmartly/nllb-darija1	Datasmartly	2025-08-19T11:50:31Z	0	0	transformers	[ "transformers", "safetensors", "m2m_100", "text2text-generation", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2025-08-19T11:16:39Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: nllb-darija1 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. --> # nllb-darija1 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6728 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.9505 \| 1.7778 \| 500 \| 2.6728 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.0.1 - Datasets 2.21.0 - Tokenizers 0.19.1
VoilaRaj/80_gz0xoQ	VoilaRaj	2025-08-19T11:49:57Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T11:46:05Z	--- 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).
milliarderdol/blockassist-bc-roaring_rough_scorpion_1755602125	milliarderdol	2025-08-19T11:47:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring rough scorpion", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:47:23Z	--- 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).
aleebaster/blockassist-bc-sly_eager_boar_1755602550	aleebaster	2025-08-19T11:46:08Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sly eager boar", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:45:59Z	--- 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).
dynokostya/sdxxl	dynokostya	2025-08-19T11:45:19Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-10-21T12:50:50Z	--- license: apache-2.0 ---
liukevin666/blockassist-bc-yawning_striped_cassowary_1755603599	liukevin666	2025-08-19T11:44:13Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:42:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VoilaRaj/80_BVN8XN	VoilaRaj	2025-08-19T11:41:42Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T11:37:53Z	--- 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).
longhoang2112/whisper-turbo-fine-tuning_2_stages_with_covoi11_2	longhoang2112	2025-08-19T11:41:12Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "whisper", "trl", "en", "base_model:unsloth/whisper-large-v3-turbo", "base_model:finetune:unsloth/whisper-large-v3-turbo", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-19T11:41:08Z	--- base_model: unsloth/whisper-large-v3-turbo tags: - text-generation-inference - transformers - unsloth - whisper - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: longhoang2112 - License: apache-2.0 - Finetuned from model : unsloth/whisper-large-v3-turbo 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)
rmtlabs/s-ai-qwen-azure-adapter	rmtlabs	2025-08-19T11:41:04Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:Qwen/Qwen2.5-14B-Instruct", "lora", "transformers", "text-generation", "conversational", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-14B-Instruct", "region:us" ]	text-generation	2025-08-19T11:40:49Z	--- base_model: Qwen/Qwen2.5-14B-Instruct library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:Qwen/Qwen2.5-14B-Instruct - lora - transformers --- # 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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arka7/Llama-3.2-3B-Instruct-bnb-4bit-rag-finetuned	arka7	2025-08-19T11:40:10Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-19T11:40:02Z	--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: arka7 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-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)
Kumo2023/nupur	Kumo2023	2025-08-19T11:39:05Z	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	2025-08-19T10:32:39Z	--- 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: TOK --- # Nupur <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/Kumo2023/nupur/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('Kumo2023/nupur', 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: 6000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Kumo2023/nupur/discussions) to add images that show off what you’ve made with this LoRA.
bailai/blockassist-bc-waddling_durable_mandrill_1755601980	bailai	2025-08-19T11:35:38Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "waddling durable mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:30:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - waddling durable mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
marcomaccarini/padella_giusta_2	marcomaccarini	2025-08-19T11:35:36Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T11:32:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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TuKoResearch/WavCochV8192	TuKoResearch	2025-08-19T11:34:53Z	318	0	transformers	[ "transformers", "safetensors", "WavCoch.WavCoch", "feature-extraction", "audio", "speech", "tokenizer", "quantizer", "cochlear", "custom_code", "en", "license:apache-2.0", "region:us" ]	feature-extraction	2025-04-15T23:01:03Z	--- language: - en library_name: transformers pipeline_tag: feature-extraction tags: - audio - speech - tokenizer - quantizer - cochlear - custom_code license: apache-2.0 # ← adjust if different pretty_name: WavCoch (8192-code speech tokenizer) --- # WavCochV8192 — 8,192-code speech tokenizer (cochlear tokens) WavCochV8192 is a biologically-inspired, learned audio quantizer that maps a raw waveform to discrete "cochlear tokens". It is used as the tokenizer for the AuriStream autoregressive speech/language model (e.g., [TuKoResearch/AuriStream1B_librilight_ckpt500k](https://huggingface.co/TuKoResearch/AuriStream1B_librilight_ckpt500k)). The model is trained on LibriSpeech960 and encodes audio into a time–frequency representation ([Cochleagram; Feather et al., 2023 Nat Neuro](https://github.com/jenellefeather/chcochleagram)) and reads out 8,192-way discrete codes through a low-bit latent bottleneck (LFQ). These tokens can be fed to a transformer LM for representation learning and next-token prediction (speech continuation). > API at a glance > - Input: mono waveform at 16 kHz (pytorch tensor float32), shape (B, 1, T) > - Output: token IDs, shape (B, L) returned as dictionary under key `"input_ids"` > - Implemented as a `transformers` custom model — load with `trust_remote_code=True`. --- ## Installation ```bash pip install -U torch torchaudio transformers ``` --- ## Quickstart — Quantize a waveform into cochlear tokens ```python import torch, torchaudio from transformers import AutoModel device = "cuda" if torch.cuda.is_available() else "cpu" # Load the quantizer quantizer = AutoModel.from_pretrained( "TuKoResearch/WavCochV8192", trust_remote_code=True ).to(device).eval() # Load & prep audio (mono, 16 kHz) wav, sr = torchaudio.load("sample.wav") if wav.size(0) > 1: # stereo -> mono wav = wav.mean(dim=0, keepdim=True) if sr != 16_000: wav = torchaudio.transforms.Resample(sr, 16_000)(wav) sr = 16_000 # Forward pass — returns a dict with "input_ids" = (B, L) with torch.no_grad(): out = quantizer(wav.unsqueeze(0).to(device)) # (1, 1, T) -> dict token_ids = out["input_ids"] # LongTensor (1, L) print("Token IDs shape:", token_ids.shape) ``` --- ## Intended uses & limitations - Uses: tokenization for speech LM training; compact storage/streaming of speech as discrete IDs, loosely inspired by human biology. - Limitations: trained only on spoken English, so might not perform as well for other languages and non-speech sounds. --- ## Citation If you use this tokenizer please cite: ```bibtex @inproceedings{tuckute2025cochleartokens, title = {Representing Speech Through Autoregressive Prediction of Cochlear Tokens}, author = {Greta Tuckute and Klemen Kotar and Evelina Fedorenko and Daniel Yamins}, booktitle = {Interspeech 2025}, year = {2025}, pages = {2180--2184}, doi = {10.21437/Interspeech.2025-2044}, issn = {2958-1796} } ``` --- ## Related - AuriStream LM: https://huggingface.co/TuKoResearch/AuriStream1B_librilight_ckpt500k - Org: https://huggingface.co/TuKoResearch
sankar-asthramedtech/Full-Precision_Whisper-Medium_and_LoRA-Adapters_Merged_Model_V-1.1	sankar-asthramedtech	2025-08-19T11:34:27Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-08-19T11:30:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755601588	quantumxnode	2025-08-19T11:32:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:32:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mohammadmahdinouri/moa-vanilla-init	mohammadmahdinouri	2025-08-19T11:31:35Z	0	0	transformers	[ "transformers", "safetensors", "ModernALBERT", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-08-19T11:31:23Z	--- 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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1sandy12/blockassist-bc-waddling_scampering_orangutan_1755603028	1sandy12	2025-08-19T11:31:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "waddling scampering orangutan", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:31:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - waddling scampering orangutan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rmtlabs/s-ai-deep-seek-azure-adapter	rmtlabs	2025-08-19T11:31:12Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", "lora", "transformers", "text-generation", "conversational", "arxiv:1910.09700", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", "region:us" ]	text-generation	2025-08-19T11:31:03Z	--- base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-32B - lora - transformers --- # 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. 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hossein12321asdf/Taxi-v3	hossein12321asdf	2025-08-19T11:30:10Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-08-17T13:53:47Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="hossein12321asdf/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
m-muraki/Qwen3-30B-A3B-Thinking-2507-FP8	m-muraki	2025-08-19T11:29:52Z	0	0	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "conversational", "arxiv:2505.09388", "base_model:Qwen/Qwen3-30B-A3B-Thinking-2507", "base_model:quantized:Qwen/Qwen3-30B-A3B-Thinking-2507", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "fp8", "region:us" ]	text-generation	2025-08-19T11:28:58Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507-FP8/blob/main/LICENSE pipeline_tag: text-generation base_model: - Qwen/Qwen3-30B-A3B-Thinking-2507 --- # Qwen3-30B-A3B-Thinking-2507 <a href="https://chat.qwen.ai/?model=Qwen3-30B-A3B-2507" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/> </a> ## Highlights Over the past three months, we have continued to scale the thinking capability of Qwen3-30B-A3B, improving both the quality and depth of reasoning. We are pleased to introduce Qwen3-30B-A3B-Thinking-2507, featuring the following key enhancements: - Significantly improved performance on reasoning tasks, including logical reasoning, mathematics, science, coding, and academic benchmarks that typically require human expertise. - Markedly better general capabilities, such as instruction following, tool usage, text generation, and alignment with human preferences. - Enhanced 256K long-context understanding capabilities. NOTE: This version has an increased thinking length. We strongly recommend its use in highly complex reasoning tasks. ![image/jpeg](https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-2507/Qwen3-30B-A3B-Thinking-2507.jpeg) ## Model Overview This repo contains the FP8 version of Qwen3-30B-A3B-Thinking-2507, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 30.5B in total and 3.3B activated - Number of Paramaters (Non-Embedding): 29.9B - Number of Layers: 48 - Number of Attention Heads (GQA): 32 for Q and 4 for KV - Number of Experts: 128 - Number of Activated Experts: 8 - Context Length: 262,144 natively. NOTE: This model supports only thinking mode. Meanwhile, specifying `enable_thinking=True` is no longer required. Additionally, to enforce model thinking, the default chat template automatically includes `<think>`. Therefore, it is normal for the model's output to contain only `</think>` without an explicit opening `<think>` tag. For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Performance \| \| Gemini2.5-Flash-Thinking \| Qwen3-235B-A22B Thinking \| Qwen3-30B-A3B Thinking \| Qwen3-30B-A3B-Thinking-2507 \| \|--- \| --- \| --- \| --- \| --- \| \| Knowledge \| \| \| \| \| \| MMLU-Pro \| 81.9 \| 82.8 \| 78.5 \| 80.9 \| \| MMLU-Redux \| 92.1 \| 92.7 \| 89.5 \| 91.4 \| \| GPQA \| 82.8 \| 71.1 \| 65.8 \| 73.4 \| \| SuperGPQA \| 57.8 \| 60.7 \| 51.8 \| 56.8 \| \| Reasoning \| \| \| \| \| \| AIME25 \| 72.0 \| 81.5 \| 70.9 \| 85.0 \| \| HMMT25 \| 64.2 \| 62.5 \| 49.8 \| 71.4 \| \| LiveBench 20241125 \| 74.3 \| 77.1 \| 74.3 \| 76.8 \| \| Coding \| \| \| \| \| \| LiveCodeBench v6 (25.02-25.05) \| 61.2 \| 55.7 \| 57.4 \| 66.0 \| \| CFEval \| 1995 \| 2056 \| 1940 \| 2044 \| \| OJBench \| 23.5 \| 25.6 \| 20.7 \| 25.1 \| \| Alignment \| \| \| \| \| \| IFEval \| 89.8 \| 83.4 \| 86.5 \| 88.9 \| \| Arena-Hard v2$ \| 56.7 \| 61.5 \| 36.3 \| 56.0 \| \| Creative Writing v3 \| 85.0 \| 84.6 \| 79.1 \| 84.4 \| \| WritingBench \| 83.9 \| 80.3 \| 77.0 \| 85.0 \| \| Agent \| \| \| \| \| \| BFCL-v3 \| 68.6 \| 70.8 \| 69.1 \| 72.4 \| \| TAU1-Retail \| 65.2 \| 54.8 \| 61.7 \| 67.8 \| \| TAU1-Airline \| 54.0 \| 26.0 \| 32.0 \| 48.0 \| \| TAU2-Retail \| 66.7 \| 40.4 \| 34.2 \| 58.8 \| \| TAU2-Airline \| 52.0 \| 30.0 \| 36.0 \| 58.0 \| \| TAU2-Telecom \| 31.6 \| 21.9 \| 22.8 \| 26.3 \| \| Multilingualism \| \| \| \| \| \| MultiIF \| 74.4 \| 71.9 \| 72.2 \| 76.4 \| \| MMLU-ProX \| 80.2 \| 80.0 \| 73.1 \| 76.4 \| \| INCLUDE \| 83.9 \| 78.7 \| 71.9 \| 74.4 \| \| PolyMATH \| 49.8 \| 54.7 \| 46.1 \| 52.6 \| $ For reproducibility, we report the win rates evaluated by GPT-4.1. \& For highly challenging tasks (including PolyMATH and all reasoning and coding tasks), we use an output length of 81,920 tokens. For all other tasks, we set the output length to 32,768. ## Quickstart The code of Qwen3-MoE has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.51.0`, you will encounter the following error: ``` KeyError: 'qwen3_moe' ``` The following contains a code snippet illustrating how to use the model generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-30B-A3B-Thinking-2507-FP8" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( model_inputs, max_new_tokens=32768 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking content try: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) # no opening <think> tag print("content:", content) ``` For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint: - SGLang: ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-30B-A3B-Thinking-2507-FP8 --context-length 262144 --reasoning-parser deepseek-r1 ``` - vLLM: ```shell vllm serve Qwen/Qwen3-30B-A3B-Thinking-2507-FP8 --max-model-len 262144 --enable-reasoning --reasoning-parser deepseek_r1 ``` Note: If you encounter out-of-memory (OOM) issues, you may consider reducing the context length to a smaller value. However, since the model may require longer token sequences for reasoning, we strongly recommend using a context length greater than 131,072 when possible. For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3. ## Note on FP8 For convenience and performance, we have provided `fp8`-quantized model checkpoint for Qwen3, whose name ends with `-FP8`. The quantization method is fine-grained `fp8` quantization with block size of 128. You can find more details in the `quantization_config` field in `config.json`. You can use the Qwen3-30B-A3B-Thinking-2507-FP8 model with serveral inference frameworks, including `transformers`, `sglang`, and `vllm`, as the original bfloat16 model. ## Agentic Use Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity. To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself. ```python from qwen_agent.agents import Assistant # Define LLM # Using Alibaba Cloud Model Studio llm_cfg = { 'model': 'qwen3-30b-a3b-thinking-2507-FP8', 'model_type': 'qwen_dashscope', } # Using OpenAI-compatible API endpoint. It is recommended to disable the reasoning and the tool call parsing # functionality of the deployment frameworks and let Qwen-Agent automate the related operations. For example, # `VLLM_USE_MODELSCOPE=true vllm serve Qwen/Qwen3-30B-A3B-Thinking-2507-FP8 --served-model-name Qwen3-30B-A3B-Thinking-2507-FP8 --tensor-parallel-size 8 --max-model-len 262144`. # # llm_cfg = { # 'model': 'Qwen3-30B-A3B-Thinking-2507-FP8', # # # Use a custom endpoint compatible with OpenAI API: # 'model_server': 'http://localhost:8000/v1', # api_base without reasoning and tool call parsing # 'api_key': 'EMPTY', # 'generate_cfg': { # 'thought_in_content': True, # }, # } # Define Tools tools = [ {'mcpServers': { # You can specify the MCP configuration file 'time': { 'command': 'uvx', 'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai'] }, "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] } } }, 'code_interpreter', # Built-in tools ] # Define Agent bot = Assistant(llm=llm_cfg, function_list=tools) # Streaming generation messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}] for responses in bot.run(messages=messages): pass print(responses) ``` ## Best Practices To achieve optimal performance, we recommend the following settings: 1. Sampling Parameters: - We suggest using `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. - For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance. 2. Adequate Output Length: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 81,920 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance. 3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking. - Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt. - Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`." 4. No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed. ### Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen3technicalreport, title={Qwen3 Technical Report}, author={Qwen Team}, year={2025}, eprint={2505.09388}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.09388}, } ```
KCS97/candle	KCS97	2025-08-19T11:29:49Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stable-diffusion-v1-5/stable-diffusion-v1-5", "base_model:finetune:stable-diffusion-v1-5/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-08-19T11:18:19Z	--- base_model: stable-diffusion-v1-5/stable-diffusion-v1-5 library_name: diffusers license: creativeml-openrail-m inference: true instance_prompt: a photo of sks candle tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - KCS97/candle This is a dreambooth model derived from stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on a photo of sks candle using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
imanuelradityaa/finetuned_cs_gemma_900_steps_4bit	imanuelradityaa	2025-08-19T11:29:16Z	0	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/gemma-2b-it-bnb-4bit", "base_model:quantized:unsloth/gemma-2b-it-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-08-19T11:27:50Z	--- base_model: unsloth/gemma-2b-it-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: imanuelradityaa - License: apache-2.0 - Finetuned from model : unsloth/gemma-2b-it-bnb-4bit This gemma 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)
phospho-app/Deimos252-ACT_BBOX-deimos_dataset-0ztq8	phospho-app	2025-08-19T11:28:36Z	0	0	phosphobot	[ "phosphobot", "safetensors", "act", "robotics", "dataset:phospho-app/deimos_dataset_bboxes", "region:us" ]	robotics	2025-08-19T10:58:48Z	--- datasets: phospho-app/deimos_dataset_bboxes library_name: phosphobot pipeline_tag: robotics model_name: act tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using phospho. Training was successful, try it out on your robot! ## Training parameters: - Dataset: [phospho-app/deimos_dataset_bboxes](https://huggingface.co/datasets/phospho-app/deimos_dataset_bboxes) - Wandb run URL: None - Epochs: None - Batch size: 100 - Training steps: 10000 📖 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)
tensorblock/nvidia_OpenReasoning-Nemotron-32B-GGUF	tensorblock	2025-08-19T11:26:38Z	0	0	transformers	[ "transformers", "gguf", "nvidia", "code", "TensorBlock", "GGUF", "text-generation", "en", "base_model:nvidia/OpenReasoning-Nemotron-32B", "base_model:quantized:nvidia/OpenReasoning-Nemotron-32B", "license:cc-by-4.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-08-19T05:35:42Z	--- license: cc-by-4.0 language: - en base_model: nvidia/OpenReasoning-Nemotron-32B pipeline_tag: text-generation library_name: transformers tags: - nvidia - code - TensorBlock - GGUF --- <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) ## nvidia/OpenReasoning-Nemotron-32B - 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 [nvidia/OpenReasoning-Nemotron-32B](https://huggingface.co/nvidia/OpenReasoning-Nemotron-32B). 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 \| \| -------- \| ---------- \| --------- \| ----------- \| \| [OpenReasoning-Nemotron-32B-Q2_K.gguf](https://huggingface.co/tensorblock/nvidia_OpenReasoning-Nemotron-32B-GGUF/blob/main/OpenReasoning-Nemotron-32B-Q2_K.gguf) \| Q2_K \| 12.313 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [OpenReasoning-Nemotron-32B-Q3_K_S.gguf](https://huggingface.co/tensorblock/nvidia_OpenReasoning-Nemotron-32B-GGUF/blob/main/OpenReasoning-Nemotron-32B-Q3_K_S.gguf) \| Q3_K_S \| 14.392 GB \| very small, high quality loss \| \| [OpenReasoning-Nemotron-32B-Q3_K_M.gguf](https://huggingface.co/tensorblock/nvidia_OpenReasoning-Nemotron-32B-GGUF/blob/main/OpenReasoning-Nemotron-32B-Q3_K_M.gguf) \| Q3_K_M \| 15.935 GB \| very small, high quality loss \| \| [OpenReasoning-Nemotron-32B-Q3_K_L.gguf](https://huggingface.co/tensorblock/nvidia_OpenReasoning-Nemotron-32B-GGUF/blob/main/OpenReasoning-Nemotron-32B-Q3_K_L.gguf) \| Q3_K_L \| 17.247 GB \| small, substantial quality loss \| \| [OpenReasoning-Nemotron-32B-Q4_0.gguf](https://huggingface.co/tensorblock/nvidia_OpenReasoning-Nemotron-32B-GGUF/blob/main/OpenReasoning-Nemotron-32B-Q4_0.gguf) \| Q4_0 \| 18.640 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [OpenReasoning-Nemotron-32B-Q4_K_S.gguf](https://huggingface.co/tensorblock/nvidia_OpenReasoning-Nemotron-32B-GGUF/blob/main/OpenReasoning-Nemotron-32B-Q4_K_S.gguf) \| Q4_K_S \| 18.784 GB \| small, greater quality loss \| \| [OpenReasoning-Nemotron-32B-Q4_K_M.gguf](https://huggingface.co/tensorblock/nvidia_OpenReasoning-Nemotron-32B-GGUF/blob/main/OpenReasoning-Nemotron-32B-Q4_K_M.gguf) \| Q4_K_M \| 19.851 GB \| medium, balanced quality - recommended \| \| [OpenReasoning-Nemotron-32B-Q5_0.gguf](https://huggingface.co/tensorblock/nvidia_OpenReasoning-Nemotron-32B-GGUF/blob/main/OpenReasoning-Nemotron-32B-Q5_0.gguf) \| Q5_0 \| 22.638 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [OpenReasoning-Nemotron-32B-Q5_K_S.gguf](https://huggingface.co/tensorblock/nvidia_OpenReasoning-Nemotron-32B-GGUF/blob/main/OpenReasoning-Nemotron-32B-Q5_K_S.gguf) \| Q5_K_S \| 22.638 GB \| large, low quality loss - recommended \| \| [OpenReasoning-Nemotron-32B-Q5_K_M.gguf](https://huggingface.co/tensorblock/nvidia_OpenReasoning-Nemotron-32B-GGUF/blob/main/OpenReasoning-Nemotron-32B-Q5_K_M.gguf) \| Q5_K_M \| 23.262 GB \| large, very low quality loss - recommended \| \| [OpenReasoning-Nemotron-32B-Q6_K.gguf](https://huggingface.co/tensorblock/nvidia_OpenReasoning-Nemotron-32B-GGUF/blob/main/OpenReasoning-Nemotron-32B-Q6_K.gguf) \| Q6_K \| 26.886 GB \| very large, extremely low quality loss \| \| [OpenReasoning-Nemotron-32B-Q8_0.gguf](https://huggingface.co/tensorblock/nvidia_OpenReasoning-Nemotron-32B-GGUF/blob/main/OpenReasoning-Nemotron-32B-Q8_0.gguf) \| Q8_0 \| 34.821 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/nvidia_OpenReasoning-Nemotron-32B-GGUF --include "OpenReasoning-Nemotron-32B-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/nvidia_OpenReasoning-Nemotron-32B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='Q4_Kgguf' ```
lavavaa/blockassist-bc-giant_knobby_chimpanzee_1755602733	lavavaa	2025-08-19T11:26:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "giant knobby chimpanzee", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:26:11Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - giant knobby chimpanzee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
chainway9/blockassist-bc-untamed_quick_eel_1755600980	chainway9	2025-08-19T11:25:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "untamed quick eel", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:25:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - untamed quick eel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755601506	Sayemahsjn	2025-08-19T11:23:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:23:29Z	--- 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).
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755601011	sampingkaca72	2025-08-19T11:23:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored stealthy elephant", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:23:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored stealthy elephant --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Team-Atom/act_record_pp_red001_0_64_40000	Team-Atom	2025-08-19T11:22:57Z	0	0	lerobot	[ "lerobot", "safetensors", "act", "robotics", "dataset:Team-Atom/PiPl_red_001_0", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-08-19T11:22:42Z	--- datasets: Team-Atom/PiPl_red_001_0 library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - lerobot - act - robotics --- # Model Card for act <!-- Provide a quick summary of what the model is/does. --> [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755600940	kojeklollipop	2025-08-19T11:21:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:21:42Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VoilaRaj/80_V9q3Cr	VoilaRaj	2025-08-19T11:21:29Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T11:17:40Z	--- 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).
iscchang/t2s	iscchang	2025-08-19T11:19:38Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct", "lora", "transformers", "text-generation", "conversational", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-Coder-7B-Instruct", "region:us" ]	text-generation	2025-08-19T11:16:49Z	--- base_model: Qwen/Qwen2.5-Coder-7B-Instruct library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct - lora - transformers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0
Medved444/blockassist-bc-bellowing_finicky_manatee_1755601184	Medved444	2025-08-19T11:19:13Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bellowing finicky manatee", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:18:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - bellowing finicky manatee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mohammadmahdinouri/moa-30k	mohammadmahdinouri	2025-08-19T11:17:57Z	0	0	transformers	[ "transformers", "safetensors", "ModernALBERT", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-08-19T11:17:54Z	--- 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]
Prathyusha101/tldr-ppco-g0p5-l1p0	Prathyusha101	2025-08-19T11:17:20Z	0	0	transformers	[ "transformers", "pytorch", "gpt_neox", "text-classification", "generated_from_trainer", "dataset:trl-internal-testing/tldr-preference-sft-trl-style", "arxiv:1909.08593", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-08-18T18:16:19Z	--- datasets: trl-internal-testing/tldr-preference-sft-trl-style library_name: transformers model_name: tldr-ppco-g0p5-l1p0 tags: - generated_from_trainer licence: license --- # Model Card for tldr-ppco-g0p5-l1p0 This model is a fine-tuned version of [None](https://huggingface.co/None) on the [trl-internal-testing/tldr-preference-sft-trl-style](https://huggingface.co/datasets/trl-internal-testing/tldr-preference-sft-trl-style) 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="Prathyusha101/tldr-ppco-g0p5-l1p0", 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/prathyusha1-the-university-of-texas-at-austin/huggingface/runs/chlykdcx) This model was trained with PPO, a method introduced in [Fine-Tuning Language Models from Human Preferences](https://huggingface.co/papers/1909.08593). ### Framework versions - TRL: 0.15.0.dev0 - Transformers: 4.53.1 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## Citations Cite PPO as: ```bibtex @article{mziegler2019fine-tuning, title = {{Fine-Tuning Language Models from Human Preferences}}, author = {Daniel M. Ziegler and Nisan Stiennon and Jeffrey Wu and Tom B. Brown and Alec Radford and Dario Amodei and Paul F. Christiano and Geoffrey Irving}, year = 2019, eprint = {arXiv:1909.08593} } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
SP4ND4N/Qwen3-0.6B-2025-08-19_15-15-49	SP4ND4N	2025-08-19T11:17:19Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-0.6B", "base_model:finetune:unsloth/Qwen3-0.6B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-19T11:17:10Z	--- base_model: unsloth/Qwen3-0.6B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: SP4ND4N - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-0.6B This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
crocodlo/blockassist-bc-soft_barky_scorpion_1755602151	crocodlo	2025-08-19T11:16:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "soft barky scorpion", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:16:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - soft barky scorpion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
GoutamNagp123/segment_1_model	GoutamNagp123	2025-08-19T11:15:41Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-19T10:49:02Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
KCS97/can	KCS97	2025-08-19T11:15:21Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stable-diffusion-v1-5/stable-diffusion-v1-5", "base_model:finetune:stable-diffusion-v1-5/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-08-19T11:04:16Z	--- base_model: stable-diffusion-v1-5/stable-diffusion-v1-5 library_name: diffusers license: creativeml-openrail-m inference: true instance_prompt: a photo of sks can tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - KCS97/can This is a dreambooth model derived from stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on a photo of sks can using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
JackTheKing/Qwen2.5-3B-FT	JackTheKing	2025-08-19T11:15:13Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-19T11:15:13Z	--- license: apache-2.0 ---
baidu/ERNIE-4.5-VL-424B-A47B-Base-Paddle	baidu	2025-08-19T11:13:55Z	7	55	PaddlePaddle	[ "PaddlePaddle", "safetensors", "ernie4_5_moe_vl", "ERNIE4.5", "image-text-to-text", "conversational", "en", "zh", "license:apache-2.0", "region:us" ]	image-text-to-text	2025-06-28T16:05:05Z	--- license: apache-2.0 language: - en - zh pipeline_tag: image-text-to-text tags: - ERNIE4.5 library_name: PaddlePaddle --- <div align="center" style="line-height: 1;"> <a href="https://ernie.baidu.com/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/🤖_Chat-ERNIE_Bot-blue" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/baidu" target="_blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Baidu-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/PaddlePaddle/ERNIE" target="_blank" style="margin: 2px;"> <img alt="Github" src="https://img.shields.io/badge/GitHub-ERNIE-000?logo=github&color=0000FF" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://ernie.baidu.com/blog/ernie4.5" target="_blank" style="margin: 2px;"> <img alt="Blog" src="https://img.shields.io/badge/🖖_Blog-ERNIE4.5-A020A0" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://discord.gg/JPmZXDsEEK" target="_blank" style="margin: 2px;"> <img alt="Discord" src="https://img.shields.io/badge/Discord-ERNIE-5865F2?logo=discord&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://x.com/PaddlePaddle" target="_blank" style="margin: 2px;"> <img alt="X" src="https://img.shields.io/badge/X-PaddlePaddle-6080F0"?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="#license" style="margin: 2px;"> <img alt="License" src="https://img.shields.io/badge/License-Apache2.0-A5de54" style="display: inline-block; vertical-align: middle;"/> </a> </div> # ERNIE-4.5-VL-424B-A47B-Base > [!NOTE] > Note: "-Paddle" models use [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) weights, while "-PT" models use Transformer-style PyTorch weights. ## ERNIE 4.5 Highlights The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations: 1. Multimodal Heterogeneous MoE Pre-Training: Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text understanding and generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a heterogeneous MoE structure, incorporated modality-isolated routing, and employed router orthogonal loss and multimodal token-balanced loss. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training. 2. Scaling-Efficient Infrastructure: We propose a novel heterogeneous hybrid parallelism and hierarchical load balancing strategy for efficient training of ERNIE 4.5 models. By using intra-node expert parallelism, memory-efficient pipeline scheduling, FP8 mixed-precision training and finegrained recomputation methods, we achieve remarkable pre-training throughput. For inference, we propose multi-expert parallel collaboration method and convolutional code quantization algorithm to achieve 4-bit/2-bit lossless quantization. Furthermore, we introduce PD disaggregation with dynamic role switching for effective resource utilization to enhance inference performance for ERNIE 4.5 MoE models. Built on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle), ERNIE 4.5 delivers high-performance inference across a wide range of hardware platforms. 3. Modality-Specific Post-Training: To meet the diverse requirements of real-world applications, we fine-tuned variants of the pre-trained model for specific modalities. Our LLMs are optimized for general-purpose language understanding and generation. The VLMs focuses on visuallanguage understanding and supports both thinking and non-thinking modes. Each model employed a combination of Supervised Fine-tuning (SFT), Direct Preference Optimization (DPO) or a modified reinforcement learning method named Unified Preference Optimization (UPO) for post-training. To ensure the stability of multimodal joint training, we adopt a staged training strategy. In the first and second stage, we train only the text-related parameters, enabling the model to develop strong fundamental language understanding as well as long-text processing capabilities. The final multimodal stage extends capabilities to images and videos by introducing additional parameters including a ViT for image feature extraction, an adapter for feature transformation, and visual experts for multimodal understanding. At this stage, text and visual modalities mutually enhance each other. After pretraining trillions tokens, we obtained ERNIE-4.5-VL-424B-A47B-Base. ## Model Overview ERNIE-4.5-VL-424B-A47B-Base is a multimodal MoE Base model, with 424B total parameters and 47B activated parameters for each token. The following are the model configuration details: \| Key \| Value \| \| --------------------------------- \| ------------- \| \| Modality \| Text & Vision \| \| Training Stage \| Pretraining \| \| Params(Total / Activated) \| 424B / 47B \| \| Layers \| 54 \| \| Heads(Q/KV) \| 64 / 8 \| \| Text Experts(Total / Activated) \| 64 / 8 \| \| Vision Experts(Total / Activated) \| 64 / 8 \| \| Context Length \| 131072 \| ## Quickstart ### vLLM inference We are working with the community to fully support ERNIE4.5 models, stay tuned. ## License The ERNIE 4.5 models are provided under the Apache License 2.0. This license permits commercial use, subject to its terms and conditions. Copyright © 2025 Baidu, Inc. All Rights Reserved. ## Citation If you find ERNIE 4.5 useful or wish to use it in your projects, please kindly cite our technical report: ```bibtex @misc{ernie2025technicalreport, title={ERNIE 4.5 Technical Report}, author={Baidu ERNIE Team}, year={2025}, eprint={}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={} } ```
Ba2han/qwen3-coder-30b-a3b-experiment2-Q4_K_S-GGUF	Ba2han	2025-08-19T11:13:53Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:Ba2han/qwen3-coder-30b-a3b-experiment2", "base_model:quantized:Ba2han/qwen3-coder-30b-a3b-experiment2", "endpoints_compatible", "region:us" ]	null	2025-08-19T11:12:44Z	--- base_model: Ba2han/qwen3-coder-30b-a3b-experiment2 tags: - llama-cpp - gguf-my-repo --- # Ba2han/qwen3-coder-30b-a3b-experiment2-Q4_K_S-GGUF This model was converted to GGUF format from [`Ba2han/qwen3-coder-30b-a3b-experiment2`](https://huggingface.co/Ba2han/qwen3-coder-30b-a3b-experiment2) 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/Ba2han/qwen3-coder-30b-a3b-experiment2) 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 Ba2han/qwen3-coder-30b-a3b-experiment2-Q4_K_S-GGUF --hf-file qwen3-coder-30b-a3b-experiment2-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Ba2han/qwen3-coder-30b-a3b-experiment2-Q4_K_S-GGUF --hf-file qwen3-coder-30b-a3b-experiment2-q4_k_s.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 Ba2han/qwen3-coder-30b-a3b-experiment2-Q4_K_S-GGUF --hf-file qwen3-coder-30b-a3b-experiment2-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Ba2han/qwen3-coder-30b-a3b-experiment2-Q4_K_S-GGUF --hf-file qwen3-coder-30b-a3b-experiment2-q4_k_s.gguf -c 2048 ```
aleebaster/blockassist-bc-sly_eager_boar_1755600473	aleebaster	2025-08-19T11:13:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sly eager boar", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:13:09Z	--- 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).
VoilaRaj/80_FdLMAe	VoilaRaj	2025-08-19T11:13:09Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T11:09:19Z	--- 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).
Nerva1228/fyeye	Nerva1228	2025-08-19T11:12:32Z	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	2025-08-19T07:45:38Z	--- 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: fyeye --- # Fyeye <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 `fyeye` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "fyeye", "lora_weights": "https://huggingface.co/Nerva1228/fyeye/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('Nerva1228/fyeye', weight_name='lora.safetensors') image = pipeline('fyeye').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/Nerva1228/fyeye/discussions) to add images that show off what you’ve made with this LoRA.
RajorshiGon/intent-classifier	RajorshiGon	2025-08-19T11:12:10Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:unsloth/gemma-3-270m-it-unsloth-bnb-4bit", "lora", "sft", "transformers", "trl", "unsloth", "arxiv:1910.09700", "base_model:unsloth/gemma-3-270m-it-unsloth-bnb-4bit", "region:us" ]	null	2025-08-19T11:08:18Z	--- base_model: unsloth/gemma-3-270m-it-unsloth-bnb-4bit library_name: peft tags: - base_model:adapter:unsloth/gemma-3-270m-it-unsloth-bnb-4bit - lora - sft - transformers - trl - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0
forstseh/blockassist-bc-arctic_soaring_heron_1755597883	forstseh	2025-08-19T11:10:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "arctic soaring heron", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:10:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - arctic soaring heron --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Reallusion/fluxLora_Kevin	Reallusion	2025-08-19T11:09:50Z	0	0	null	[ "text-to-image", "en", "dataset:crystantine/fluxgym", "base_model:black-forest-labs/FLUX.1-Fill-dev", "base_model:finetune:black-forest-labs/FLUX.1-Fill-dev", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-08-19T10:22:28Z	--- license: creativeml-openrail-m datasets: - crystantine/fluxgym language: - en base_model: - black-forest-labs/FLUX.1-dev - black-forest-labs/FLUX.1-Fill-dev pipeline_tag: text-to-image ---
hasdal/21aa9f58-1f69-4055-9211-a03c7007ec6e	hasdal	2025-08-19T11:07:33Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mixtral", "trl", "en", "base_model:TitanML/tiny-mixtral", "base_model:finetune:TitanML/tiny-mixtral", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-19T11:07:31Z	--- base_model: TitanML/tiny-mixtral tags: - text-generation-inference - transformers - unsloth - mixtral - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: hasdal - License: apache-2.0 - Finetuned from model : TitanML/tiny-mixtral This mixtral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755600119	lisaozill03	2025-08-19T11:06:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:06:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
BSC-LT/salamandraTA-2b-instruct	BSC-LT	2025-08-19T11:06:33Z	1,355	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "translation", "bg", "ca", "cs", "cy", "da", "de", "el", "en", "es", "et", "eu", "fi", "fr", "ga", "gl", "hr", "hu", "it", "lt", "lv", "mt", "nl", "nb", "no", "nn", "oc", "pl", "pt", "ro", "ru", "sl", "sk", "sr", "sv", "uk", "ast", "an", "arxiv:2010.11125", "arxiv:2403.14009", "arxiv:1907.05791", "arxiv:1911.04944", "arxiv:2402.17733", "arxiv:2207.04672", "arxiv:2404.06392", "arxiv:2309.04662", "arxiv:2211.01355", "arxiv:2508.12774", "base_model:BSC-LT/salamandra-2b", "base_model:finetune:BSC-LT/salamandra-2b", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:eu" ]	translation	2025-05-13T14:25:01Z	--- license: apache-2.0 library_name: transformers pipeline_tag: translation language: - bg - ca - cs - cy - da - de - el - en - es - et - eu - fi - fr - ga - gl - hr - hu - it - lt - lv - mt - nl - nb - 'no' - nn - oc - pl - pt - ro - ru - sl - sk - sr - sv - uk - ast - an base_model: - BSC-LT/salamandra-2b --- ![](./images/salamandra_header.png) # SalamandraTA Model Card SalamandraTA-2b-instruct is a translation LLM that has been instruction-tuned from SalamandraTA-2b-base. The base model results from continually pre-training [Salamandra-2b](https://huggingface.co/BSC-LT/salamandra-2b) on parallel data and has not been published, but is reserved for internal use. SalamandraTA-2b-instruct is proficent in 35 European languages (plus 3 varieties) and supports translation-related tasks, namely: sentence-level-translation, paragraph-level-translation, automatic post-editing, grammar checking, machine translation evaluation, alternative translations, named-entity-recognition and context-aware translation. > [!WARNING] > DISCLAIMER: This version of Salamandra is tailored exclusively for translation tasks. It lacks chat capabilities and has not been trained with any chat instructions. --- ## Model Details ### Description SalamandraTA-2b-base is a continual pre-training of [Salamandra-2b](https://huggingface.co/BSC-LT/salamandra-2b) using parallel data, resulting in a total of 424B tokens processed during training. ### Architecture \| \| \| \|-------------------------\|:--------------\| \| Total Parameters \| 2,253,490,176 \| \| Embedding Parameters \| 524,288,000 \| \| Layers \| 24 \| \| Hidden size \| 2,048 \| \| Attention heads \| 16 \| \| Context length \| 8,192 \| \| Vocabulary size \| 256,000 \| \| Precision \| bfloat16 \| \| Embedding type \| RoPE \| \| Activation Function \| SwiGLU \| \| Layer normalization \| RMS Norm \| \| Flash attention \| ✅ \| \| Grouped Query Attention \| ❌ \| \| Num. query groups \| N/A \| --- ## Intended Use ### Direct Use The model is intended for both research and commercial use in any of the languages included in the training data for general machine translation tasks. ### Out-of-scope Use The model is not intended for malicious activities, such as harming others or violating human rights. Any downstream application must comply with current laws and regulations. Irresponsible usage in production environments without proper risk assessment and mitigation is also discouraged. --- ## Hardware and Software ### Training Framework SalamandraTA-2b-base was continually pre-trained using NVIDIA’s [NeMo Framework](https://docs.nvidia.com/nemo-framework/index.html), which leverages PyTorch Lightning for efficient model training in highly distributed settings. SalamandraTA-2b-instruct was produced with [FastChat](https://github.com/lm-sys/FastChat). ### Compute Infrastructure All models were trained on [MareNostrum 5](https://www.bsc.es/ca/marenostrum/marenostrum-5), a pre-exascale EuroHPC supercomputer hosted and operated by Barcelona Supercomputing Center. The accelerated partition is composed of 1,120 nodes with the following specifications: - 4x Nvidia Hopper GPUs with 64GB HBM2 memory - 2x Intel Sapphire Rapids 8460Y+ at 2.3Ghz and 32c each (64 cores) - 4x NDR200 (BW per node 800Gb/s) - 512 GB of Main memory (DDR5) - 460GB on NVMe storage --- ## How to use You can translate between the following 35 languages (and 3 varieties): Aragonese, Asturian, Basque, Bulgarian, Catalan (and Catalan-Valencian variety), Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, German, Greek, Hungarian, Irish, Italian, Latvian, Lithuanian, Maltese, Norwegian (Bokmål and Nynorsk varieties), Occitan (and Aranese variety), Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swedish, Ukrainian, Welsh. The instruction-following model uses the commonly adopted ChatML template: ``` <\|im_start\|>system {SYSTEM PROMPT}<\|im_end\|> <\|im_start\|>user {USER PROMPT}<\|im_end\|> <\|im_start\|>assistant {MODEL RESPONSE}<\|im_end\|> <\|im_start\|>user [...] ``` The easiest way to apply it is by using the tokenizer's built-in functions, as shown in the following snippet. ```python from datetime import datetime from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model_id = "BSC-LT/salamandraTA-2b-instruct" source = 'Spanish' target = 'Catalan' sentence = "Ayer se fue, tomó sus cosas y se puso a navegar. Una camisa, un pantalón vaquero y una canción, dónde irá, dónde irá. Se despidió, y decidió batirse en duelo con el mar. Y recorrer el mundo en su velero. Y navegar, nai-na-na, navegar" text = f"Translate the following text from {source} into {target}.\n{source}: {sentence} \n{target}:" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", torch_dtype=torch.bfloat16 ) message = [ { "role": "user", "content": text } ] date_string = datetime.today().strftime('%Y-%m-%d') prompt = tokenizer.apply_chat_template( message, tokenize=False, add_generation_prompt=True, date_string=date_string ) inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") input_length = inputs.shape[1] outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=400, early_stopping=True, num_beams=5) print(tokenizer.decode(outputs[0, input_length:], skip_special_tokens=True)) # Ahir se'n va anar, va recollir les seves coses i es va fer a la mar. Una camisa, uns texans i una cançó, on anirà, on anirà. Es va acomiadar i va decidir batre's en duel amb el mar. I fer la volta al món en el seu veler. I navegar, nai-na-na, navegar ``` Using this template, each turn is preceded by a `<\|im_start\|>` delimiter and the role of the entity (either `user`, for content supplied by the user, or `assistant` for LLM responses), and finished with the `<\|im_end\|>` token. #### General translation For machine translation tasks, you can use the following prompt template: ``` Translate the following text from {source} into {target}. {source}: {source sentence} {target}: ``` <details> <summary>Show an example</summary> ```python source = 'Catalan' target = 'Galician' source_sentence = "Als antics egipcis del període de l'Imperi Nou els fascinaven els monuments dels seus predecessors, que llavors tenien més de mil anys." text = f"Translate the following text from {source} into {target}.\n{source}: {source_sentence} \n{target}:" # Os antigos exipcios do período do Imperio Novo estaban fascinados polos monumentos dos seus predecesores, que entón tiñan máis de mil anos de antigüidade. ``` </details> ### Post-editing For post-editing tasks, you can use the following prompt template: ``` Please fix any mistakes in the following {source}-{target} machine translation or keep it unedited if it's correct. Source: {source_sentence} MT: {machine_translation} Corrected:" ``` <details> <summary>Show an example</summary> ```python source = 'Catalan' target = 'English' source_sentence = 'Rafael Nadal i Maria Magdalena van inspirar a una generació sencera.' machine_translation = 'Rafael Christmas and Maria the Muffin inspired an entire generation each in their own way.' text = f"Please fix any mistakes in the following {source}-{target} machine translation or keep it unedited if it's correct.\nSource: {source_sentence} \nMT: {machine_translation} \nCorrected:" # Rafael Nadal and Maria Magdalena inspired an entire generation. ``` </details> ### Paragraph-level translation For paragraph-level translation tasks, you can use the following prompt template: ``` Please translate this text from {source} into {target}. {source}: {paragraph} {target}: ``` <details> <summary>Show an example</summary> ```python source = 'English' target = 'Asturian' text = """Please translate this text from {} into {}.\n{}: President Donald Trump, who campaigned on promises to crack down on illegal immigration, has raised alarms in the U.S. dairy industry with his threat to impose 25% tariffs on Mexico and Canada by February 2025. This move is part of a broader strategy to declare a national emergency at the southern border to halt illegal migration completely. However, the implications for the agriculture sector, particularly dairy, are significant. Approximately half of the U.S. dairy industry's workforce consists of immigrant labor, many of whom are undocumented. The National Milk Producers Federation estimates that removing immigrant workers could decimate the dairy herd by 2.1 million cows and slash milk production by nearly 50 billion pounds, leading to a dramatic 90.4% increase in milk prices. The complex perspectives of Americans on undocumented workers were highlighted in a Pew Research Center study. While 64% of U.S. adults support legal pathways for undocumented immigrants, 35% oppose it—a gap that has been narrowing recently. Factors influencing public opinion include the belief that immigrants should have jobs and pass security checks, contrasted by concerns about lawbreakers being rewarded, fairness for legal migrants, and resource allocation. {}:""".format(source, target, source, target) ``` </details> ### Named-entity recognition For named-entity recognition tasks, you can use the following prompt template: ``` Analyse the following tokenized text and mark the tokens containing named entities. Use the following annotation guidelines with these tags for named entities: - ORG (Refers to named groups or organizations) - PER (Refers to individual people or named groups of people) - LOC (Refers to physical places or natural landmarks) - MISC (Refers to entities that don't fit into standard categories). Prepend B- to the first token of a given entity and I- to the remaining ones if they exist. If a token is not a named entity, label it as O. Input: {list of words in a sentence} Marked: ``` <details> <summary>Show an example</summary> ```python text = """Analyse the following tokenized text and mark the tokens containing named entities. Use the following annotation guidelines with these tags for named entities: - ORG (Refers to named groups or organizations) - PER (Refers to individual people or named groups of people) - LOC (Refers to physical places or natural landmarks) - MISC (Refers to entities that don't fit into standard categories). Prepend B- to the first token of a given entity and I- to the remaining ones if they exist. If a token is not a named entity, label it as O. Input: ['La', 'defensa', 'del', 'antiguo', 'responsable', 'de', 'la', 'RFEF', 'confirma', 'que', 'interpondrá', 'un', 'recurso.'] Marked: """ # [('La', 'O'), ('defensa', 'O'), ('del', 'O'), ('antiguo', 'O'), ('responsable', 'O'), ('de', 'O'), ('la', 'O'), ('RFEF', 'B-ORG'), ('confirma', 'O'), ('que', 'O'), ('interpondrá', 'O'), ('un', 'O'), ('recurso.', 'O')] ``` </details> ### Grammar checker For fixing any mistakes in grammar, you can use the following prompt template: ``` Please fix any mistakes in the following {source} sentence or keep it unedited if it's correct. Sentence: {sentence} Corrected: ``` <details> <summary>Show an example</summary> ```python source = 'Catalan' sentence = 'Entonses, el meu jefe m’ha dit que he de treballar els fins de setmana.' text = f"Please fix any mistakes in the following {source} sentence or keep it unedited if it's correct.\nSentence: {sentence} \nCorrected:" # Llavors, el meu cap m'ha dit que he de treballar els caps de setmana. ``` </details> ## Data ### Pretraining Data The pretraining corpus consists of 424 billion tokens of Catalan-centric, Spanish-centric, and English-centric parallel data, including all of the official European languages plus Catalan, Basque, Galician, Asturian, Aragonese and Aranese. It amounts to 6,574,251,526 parallel sentence pairs. This highly multilingual corpus is predominantly composed of data sourced from [OPUS](https://opus.nlpl.eu/), with additional data taken from the [NTEU Project](https://nteu.eu/), [Aina Project](https://projecteaina.cat/), and other sources (see: [Data Sources](#pre-data-sources) and [References](#pre-references)). Where little parallel Catalan <-> xx data could be found, synthetic Catalan data was generated from the Spanish side of the collected Spanish <-> xx corpora using [Projecte Aina’s Spanish-Catalan model](https://huggingface.co/projecte-aina/aina-translator-es-ca). The final distribution of languages was as below: ![](./images/treemap.png) Click the expand button below to see the full list of corpora included in the training data. <details id="pre-data-sources"> <summary>Data Sources</summary> \| Dataset \| Ca-xx Languages \| Es-xx Langugages \| En-xx Languages \| \|-----------------------------------------------\|----------------------------------------------------------------\|-----------------------------------------------\|----------------------------------------------------------------\| \|[AINA](https://huggingface.co/projecte-aina) \| en \| \| \| \|ARANESE-SYNTH-CORPUS-BSC \| arn \| \| \| \|BOUA-SYNTH-BSC \| \| val \| \| \|[BOUMH](https://github.com/transducens/PILAR/tree/main/valencian/BOUMH) \| \| val \| \| \|[BOUA-PILAR](https://github.com/transducens/PILAR/tree/main/valencian/BOUA) \| \| val \| \| \|[CCMatrix](https://opus.nlpl.eu/CCMatrix/corpus/version/CCMatrix) \|eu \| \| ga \| \|[DGT](https://opus.nlpl.eu/DGT/corpus/version/DGT) \| \|bg,cs,da,de,el ,et,fi,fr,ga,hr,hu,lt,lv,mt,nl,pl,pt,ro,sk,sl,sv \| da,et,ga,hr,hu,lt,lv,mt,sh,sl\| \|DOGV-SYNTH-BSC \| \| val \| \| \|[DOGV-PILAR](https://github.com/transducens/PILAR/tree/main/valencian/DOGV-html) \| \| val \| \| \|[ELRC-EMEA](https://opus.nlpl.eu/ELRC-EMEA/corpus/version/ELRC-EMEA) \| \|bg,cs,da,hu,lt,lv,mt,pl,ro,sk,sl \| et,hr,lv,ro,sk,sl \| \|[EMEA](https://opus.nlpl.eu/EMEA/corpus/version/EMEA) \| \|bg,cs,da,el,fi,hu,lt,mt,nl,pl,ro,sk,sl,sv \| et,mt \| \|[EUBookshop](https://opus.nlpl.eu/EUbookshop/corpus/version/EUbookshop) \|lt,pl,pt \|cs,da,de,el,fi,fr,ga,it,lv,mt,nl,pl,pt,ro,sk,sl,sv \|cy,ga\| \|[Europarl](https://opus.nlpl.eu/Europarl/corpus/version/Europarl) \| \|bg,cs,da,el,en,fi,fr,hu,lt,lv,nl,pl,pt ,ro,sk,sl,sv \| \| \|[Europat](https://opus.nlpl.eu/EuroPat/corpus/version/EuroPat) \| \|en,hr \| no \| \|[GAITU Corpus](https://gaitu.eus/) \| \| \| eu\| \|[KDE4](https://opus.nlpl.eu/KDE4/corpus/version/KDE4) \|bg,cs,da,de,el ,et,eu,fi,fr,ga,gl,hr,it,lt,lv,nl,pl,pt,ro,sk,sl,sv \|bg,ga,hr \|cy,ga,nn,oc \| \|[GlobalVoices](https://opus.nlpl.eu/GlobalVoices/corpus/version/GlobalVoices) \| bg,de,fr,it,nl,pl,pt \|bg,de,fr,pt \| \| \|[GNOME](https://opus.nlpl.eu/GNOME/corpus/version/GNOME) \|eu,fr,ga,gl,pt \|ga \|cy,ga,nn\| \|[JRC-Arquis](https://opus.nlpl.eu/JRC-Acquis/corpus/version/JRC-Acquis) \| \|cs,da,et,fr,lt,lv,mt,nl,pl ,ro,sv\| et \| \|LES-CORTS-VALENCIANES-SYNTH-BSC \| \| val \| \| \|[MaCoCu](https://opus.nlpl.eu/MaCoCu/corpus/version/MaCoCu) \| en \| \| hr,mt,uk \| \|[MultiCCAligned](https://opus.nlpl.eu/JRC-Acquis/corpus/version/JRC-Acquis) \|bg,cs,de,el,et,fi,fr,hr,hu,it,lt,lv,nl,pl,ro,sk,sv \|bg,fi,fr,hr,it,lv,nl,pt \|bg,cy,da,et,fi,hr,hu,lt,lv,no,sl,sr,uk\| \|[MultiHPLT](https://opus.nlpl.eu/MultiHPLT/corpus/version/MultiHPLT) \|en, et,fi,ga,hr,mt \| \|fi,ga,gl,hr,mt,nn,sr \| \|[MultiParaCrawl](https://opus.nlpl.eu/MultiParaCrawl/corpus/version/MultiParaCrawl) \|bg,da \|de,en,fr,ga,hr,hu,it,mt,pt \|bg,cs,da,de,el,et,fi,fr,ga,hr,hu,lt,lv,mt,nn,pl,ro,sk,sl,uk\| \|[MultiUN](https://opus.nlpl.eu/MultiUN/corpus/version/MultiUN) \| \|fr \| \| \|[News-Commentary](https://opus.nlpl.eu/News-Commentary/corpus/version/News-Commentary) \| \|fr \| \| \|[NLLB](https://opus.nlpl.eu/NLLB/corpus/version/NLLB) \|bg,da,el,en,et,fi,fr,gl,hu,it ,lt,lv,pt,ro,sk,sl \|bg,cs,da,de,el ,et,fi,fr,hu,it,lt,lv,nl,pl,pt ,ro,sk,sl,sv\| bg,cs,cy,da,de,el,et,fi,fr,ga,hr,hu,it,lt,lv,mt,nl,no,oc,pl,pt,ro,ru,sk,sl,sr,sv,uk\| \|[NÓS Authentic Corpus](https://zenodo.org/records/7675110) \| \| \| gl \| \|[NÓS Synthetic Corpus](https://zenodo.org/records/7685180) \| \| \| gl \| \|[NTEU](https://www.elrc-share.eu/repository/search/?q=NTEU) \| \|bg,cs,da,de,el,en,et,fi,fr,ga,hr,hu,it,lt,lv,mt,nl,pl,pt,ro,sk,sl,sv \| da,et,ga,hr,lt,lv,mt,ro,sk,sl,sv \| \|[OpenSubtitles](https://opus.nlpl.eu/OpenSubtitles/corpus/version/OpenSubtitles) \|bg,cs,da,de,el ,et,eu,fi,gl,hr,hu,lt,lv,nl,pl,pt,ro,sk,sl,sv \|da,de,fi,fr,hr,hu,it,lv,nl \| bg,cs,de,el,et,hr,fi,fr,hr,hu,no,sl,sr\| \|[OPUS-100](https://opus.nlpl.eu/opus-100.php) \| en \| \| gl \| \|[StanfordNLP-NMT](https://opus.nlpl.eu/StanfordNLP-NMT/corpus/version/StanfordNLP-NMT) \| \| \|cs \| \|[Tatoeba](https://opus.nlpl.eu/Tatoeba/corpus/version/Tatoeba) \|de,pt \|pt \| \| \|[TildeModel](https://opus.nlpl.eu/TildeMODEL/corpus/version/TildeMODEL) \| \|bg \| et,hr,lt,lv,mt \| \|[UNPC](https://opus.nlpl.eu/UNPC/corpus/version/UNPC) \| \|en,fr \| ru \| \|[PILAR-VALENCIAN-AUTH](https://github.com/transducens/PILAR/tree/main/valencian/Generalitat) \| \| val \| \| \|[PILAR-VALENCIAN-SYNTH](https://github.com/transducens/PILAR/tree/main/valencian/Generalitat) \| \| val \| \| \|[WikiMatrix](https://opus.nlpl.eu/WikiMatrix/corpus/version/WikiMatrix) \|bg,cs,da,de,el ,et,eu,fi,fr,gl,hr,hu,it,lt,nl,pl,pt,ro,sk,sl,sv \|bg,en,fr,hr,it,pt \| oc,sh \| \|[Wikimedia](https://opus.nlpl.eu/wikimedia/corpus/version/wikimedia) \| \| \|cy,nn \| \|[XLENT](https://opus.nlpl.eu/XLEnt/corpus/version/XLEnt) \|eu,ga,gl \|ga \|cy,et,ga,gl,hr,oc,sh\| Datasets with "-BSC" in their names (e.g., BOUA-SYNTH-BSC, DOGV-SYNTH-BSC) are synthetic datasets obtained by machine translating pre-existing monolingual corpora with our own seq-to-seq models. These datasets were generated internally for model training and are not published. To consult the data summary document with the respective licences, please send an e-mail to [email protected]. </details> <details id="pre-references"> <summary>References</summary> - Aulamo, M., Sulubacak, U., Virpioja, S., & Tiedemann, J. (2020). OpusTools and Parallel Corpus Diagnostics. In N. Calzolari, F. Béchet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the Twelfth Language Resources and Evaluation Conference (pp. 3782–3789). European Language Resources Association. https://aclanthology.org/2020.lrec-1.467 - Chaudhary, V., Tang, Y., Guzmán, F., Schwenk, H., & Koehn, P. (2019). Low-Resource Corpus Filtering Using Multilingual Sentence Embeddings. In O. Bojar, R. Chatterjee, C. Federmann, M. Fishel, Y. Graham, B. Haddow, M. Huck, A. J. Yepes, P. Koehn, A. Martins, C. Monz, M. Negri, A. Névéol, M. Neves, M. Post, M. Turchi, & K. Verspoor (Eds.), Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2) (pp. 261–266). Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-5435 - DGT-Translation Memory—European Commission. (n.d.). Retrieved November 4, 2024, from https://joint-research-centre.ec.europa.eu/language-technology-resources/dgt-translation-memory_en - Eisele, A., & Chen, Y. (2010). MultiUN: A Multilingual Corpus from United Nation Documents. In N. Calzolari, K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner, & D. Tapias (Eds.), Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10). European Language Resources Association (ELRA). http://www.lrec-conf.org/proceedings/lrec2010/pdf/686_Paper.pdf - El-Kishky, A., Chaudhary, V., Guzmán, F., & Koehn, P. (2020). CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 5960–5969. https://doi.org/10.18653/v1/2020.emnlp-main.480 - El-Kishky, A., Renduchintala, A., Cross, J., Guzmán, F., & Koehn, P. (2021). XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 10424–10430. https://doi.org/10.18653/v1/2021.emnlp-main.814 - Fan, A., Bhosale, S., Schwenk, H., Ma, Z., El-Kishky, A., Goyal, S., Baines, M., Celebi, O., Wenzek, G., Chaudhary, V., Goyal, N., Birch, T., Liptchinsky, V., Edunov, S., Grave, E., Auli, M., & Joulin, A. (2020). Beyond English-Centric Multilingual Machine Translation (No. arXiv:2010.11125). arXiv. https://doi.org/10.48550/arXiv.2010.11125 - García-Martínez, M., Bié, L., Cerdà, A., Estela, A., Herranz, M., Krišlauks, R., Melero, M., O’Dowd, T., O’Gorman, S., Pinnis, M., Stafanovič, A., Superbo, R., & Vasiļevskis, A. (2021). Neural Translation for European Union (NTEU). 316–334. https://aclanthology.org/2021.mtsummit-up.23 - Gibert, O. de, Nail, G., Arefyev, N., Bañón, M., Linde, J. van der, Ji, S., Zaragoza-Bernabeu, J., Aulamo, M., Ramírez-Sánchez, G., Kutuzov, A., Pyysalo, S., Oepen, S., & Tiedemann, J. (2024). A New Massive Multilingual Dataset for High-Performance Language Technologies (No. arXiv:2403.14009). arXiv. http://arxiv.org/abs/2403.14009 - Koehn, P. (2005). Europarl: A Parallel Corpus for Statistical Machine Translation. Proceedings of Machine Translation Summit X: Papers, 79–86. https://aclanthology.org/2005.mtsummit-papers.11 - Kreutzer, J., Caswell, I., Wang, L., Wahab, A., Van Esch, D., Ulzii-Orshikh, N., Tapo, A., Subramani, N., Sokolov, A., Sikasote, C., Setyawan, M., Sarin, S., Samb, S., Sagot, B., Rivera, C., Rios, A., Papadimitriou, I., Osei, S., Suarez, P. O., … Adeyemi, M. (2022). Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets. Transactions of the Association for Computational Linguistics, 10, 50–72. https://doi.org/10.1162/tacl_a_00447 - Rozis, R.,Skadiņš, R (2017). Tilde MODEL - Multilingual Open Data for EU Languages. https://aclanthology.org/W17-0235 - Schwenk, H., Chaudhary, V., Sun, S., Gong, H., & Guzmán, F. (2019). WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia (No. arXiv:1907.05791). arXiv. https://doi.org/10.48550/arXiv.1907.05791 - Schwenk, H., Wenzek, G., Edunov, S., Grave, E., & Joulin, A. (2020). CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB (No. arXiv:1911.04944). arXiv. https://doi.org/10.48550/arXiv.1911.04944 - Steinberger, R., Pouliquen, B., Widiger, A., Ignat, C., Erjavec, T., Tufiş, D., & Varga, D. (n.d.). The JRC-Acquis: A Multilingual Aligned Parallel Corpus with 20+ Languages. http://www.lrec-conf.org/proceedings/lrec2006/pdf/340_pdf - Subramani, N., Luccioni, S., Dodge, J., & Mitchell, M. (2023). Detecting Personal Information in Training Corpora: An Analysis. In A. Ovalle, K.-W. Chang, N. Mehrabi, Y. Pruksachatkun, A. Galystan, J. Dhamala, A. Verma, T. Cao, A. Kumar, & R. Gupta (Eds.), Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023) (pp. 208–220). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.trustnlp-1.18 - Tiedemann, J. (23-25). Parallel Data, Tools and Interfaces in OPUS. In N. C. (Conference Chair), K. Choukri, T. Declerck, M. U. Doğan, B. Maegaard, J. Mariani, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12). European Language Resources Association (ELRA). http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper - Ziemski, M., Junczys-Dowmunt, M., & Pouliquen, B. (n.d.). The United Nations Parallel Corpus v1.0. https://aclanthology.org/L16-1561 </details> ### Instruction Tuning Data This model has been fine-tuned on ~135k instructions, primarily targeting machine translation performance for Catalan, English, and Spanish. Additional instruction data for other European and closely related Iberian languages was also included, as it yielded a positive impact on the languages of interest. That said, the performance in these additional languages is not guaranteed due to the limited amount of available data and the lack of resources for thorough testing. A portion of our fine-tuning data comes directly from, or is sampled from [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2). We also created additional datasets for our main languages of interest. While tasks relating to machine translation are included, it’s important to note that no chat data was used in the fine-tuning process. The final distribution of tasks was as below: ![](./images/chart.png) Click the expand button below to see the full list of tasks included in the finetuning data. <details id="instr-data-sources"> <summary>Data Sources</summary> \| Task \| Source \| Languages \| Count \| \|----------------------------------\|------------------------------------------------------------------------------------------\|----------------------------------------------------------------\|--------\| \| Multi-reference Translation \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [Tatoeba Dev (filtered)](https://github.com/Helsinki-NLP/Tatoeba-Challenge) \| mixed \| 10000 \| \| Paraphrase \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [PAWS-X Dev](https://github.com/google-research-datasets/paws) \| mixed \| 3521 \| \| Named-entity Recognition \| [AnCora-Ca-NER](https://huggingface.co/datasets/projecte-aina/ancora-ca-ner) \| ca \| 12059 \| \| Named-entity Recognition \| [BasqueGLUE](https://huggingface.co/datasets/orai-nlp/basqueGLUE), [EusIE](https://huggingface.co/datasets/HiTZ/EusIE) \| eu \| 4304 \| \| Named-entity Recognition \| [SLI NERC Galician Gold Corpus](https://github.com/xavier-gz/SLI_Galician_Corpora) \| gl \| 6483 \| \| Named-entity Recognition \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MultiCoNER 2022 and 2023 Dev](https://registry.opendata.aws/multiconer/) \| pt \| 854 \| \| Named-entity Recognition \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MultiCoNER 2022 and 2023 Dev](https://registry.opendata.aws/multiconer/) \| nl \| 800 \| \| Named-entity Recognition \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MultiCoNER 2022 and 2023 Dev](https://registry.opendata.aws/multiconer/) \| es \| 1654 \| \| Named-entity Recognition \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MultiCoNER 2022 and 2023 Dev](https://registry.opendata.aws/multiconer/) \| en \| 1671 \| \| Named-entity Recognition \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MultiCoNER 2022 and 2023 Dev](https://registry.opendata.aws/multiconer/) \| ru \| 800 \| \| Named-entity Recognition \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MultiCoNER 2022 and 2023 Dev](https://registry.opendata.aws/multiconer/) \| it \| 858 \| \| Named-entity Recognition \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MultiCoNER 2022 and 2023 Dev](https://registry.opendata.aws/multiconer/) \| fr \| 857 \| \| Named-entity Recognition \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MultiCoNER 2022 and 2023 Dev](https://registry.opendata.aws/multiconer/) \| de \| 1312 \| \| Terminology-aware Translation \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [WMT21 Terminology Dev (filtered)](https://www.statmt.org/wmt21/terminology-task.html) \| en-ru \| 50 \| \| Terminology-aware Translation \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [WMT21 Terminology Dev (filtered)](https://www.statmt.org/wmt21/terminology-task.html) \| en-fr \| 29 \| \| Automatic Post Editing \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [QT21](https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-2390), [ApeQuest](https://apequest.wordpress.com/) \| en-fr \| 6133 \| \| Automatic Post Editing \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [QT21](https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-2390), [ApeQuest](https://apequest.wordpress.com/) \| en-nl \| 9077 \| \| Automatic Post Editing \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [QT21](https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-2390), [ApeQuest](https://apequest.wordpress.com/) \| en-pt \| 5762 \| \| Automatic Post Editing \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [QT21](https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-2390), [ApeQuest](https://apequest.wordpress.com/) \| de-en \| 10000 \| \| Automatic Post Editing \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [QT21](https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-2390), [ApeQuest](https://apequest.wordpress.com/) \| en-de \| 10000 \| \| Machine Translation Evaluation \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2)-sample: [WMT20 to WMT22 Metrics MQM](https://www.statmt.org/wmt22/results.html), [WMT17 to WMT22 Metrics Direct Assessments](https://www.statmt.org/wmt22/results.html) \| en-ru, en-pl, ru-en, en-de, en-ru, de-fr, de-en, en-de \| 353 \| \| Machine Translation Evaluation \| Non-public \| four pivot languages (eu, es, ca, gl) paired with European languages (bg, cs, da, de, el, en, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv) \| 9700 \| \| General Machine Translation \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [WMT14 to WMT21](https://www.statmt.org/wmt22/results.html), [NTREX](https://github.com/MicrosoftTranslator/NTREX), [Flores Dev](https://github.com/facebookresearch/flores), [FRMT](https://github.com/google-research/google-research/tree/master/frmt), [QT21](https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-2390), [ApeQuest](https://apequest.wordpress.com/), [OPUS (Quality Filtered)](https://opus.nlpl.eu/), [MT-GenEval](https://github.com/amazon-science/machine-translation-gender-eval) \| nl-en, en-ru, it-en, fr-en, es-en, en-fr, ru-en, fr-de, en-nl, de-fr \| 500 \| \| General Machine Translation \| Non-public \| three pivot languages (es, ca, en) paired with European languages (ast, arn, arg, bg, cs, cy, da, de, el, et, fi, ga, gl, hr, it, lt, lv, mt, nb, nn, nl, oc, pl, pt, ro, ru, sk, sl, sr, sv, uk, eu) \| 9350 \| \| Fill-in-the-Blank \| Non-public \| five pivot languages (ca, es, eu, gl, en) paired with European languages (cs, da, de, el, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv) \| 11500 \| \| Document-level Translation \| Non-public \| two pivot languages (es, en) paired with European languages (bg, cs, da, de, el, et, fi, fr, hu, it, lt, lv, nl, pl, pt, ro, ru, sk, sv) \| 7600 \| \| Paragraph-level Translation \| Non-public \| two pivot languages (es, en) paired with European languages (bg, cs, da, de, el, et, fi, fr, hu, it, lt, lv, nl, pl, pt, ro, ru, sk, sv) \| 7600 \| \| Context-Aware Translation \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MT-GenEval](https://github.com/amazon-science/machine-translation-gender-eval) \| en-it \| 348 \| \| Context-Aware Translation \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MT-GenEval](https://github.com/amazon-science/machine-translation-gender-eval) \| en-ru \| 454 \| \| Context-Aware Translation \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MT-GenEval](https://github.com/amazon-science/machine-translation-gender-eval) \| en-fr \| 369 \| \| Context-Aware Translation \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MT-GenEval](https://github.com/amazon-science/machine-translation-gender-eval) \| en-nl \| 417 \| \| Context-Aware Translation \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MT-GenEval](https://github.com/amazon-science/machine-translation-gender-eval) \| en-es \| 431 \| \| Context-Aware Translation \| [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MT-GenEval](https://github.com/amazon-science/machine-translation-gender-eval) \| en-de \| 558 \| \|Total \| \| \| 135,404 \| The non-public portion of this dataset was jointly created by the [ILENIA](https://proyectoilenia.es/) partners: BSC-LT, [HiTZ](http://hitz.ehu.eus/es), and [CiTIUS](https://citius.gal/es/). For further information regarding the instruction-tuning data, please contact <[email protected]>. </details> <details id="instr-references"> <summary>References</summary> - Alves, D. M., Pombal, J., Guerreiro, N. M., Martins, P. H., Alves, J., Farajian, A., Peters, B., Rei, R., Fernandes, P., Agrawal, S., Colombo, P., de Souza, J. G. C., & Martins, A. F. T. (2024). Tower: An open multilingual large language model for translation-related tasks (No. arXiv: 2402.17733). arXiv. https://arxiv.org/abs/2402.17733 - Armengol-Estapé, J., Carrino, C. P., Rodriguez-Penagos, C., de Gibert Bonet, O., Armentano-Oller, C., Gonzalez-Agirre, A., Melero, M., & Villegas, M. (2021). Are multilingual models the best choice for moderately under-resourced languages? A comprehensive assessment for Catalan. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 4933–4946. Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.findings-acl.437 - Currey, A., Nadejde, M., Pappagari, R. R., Mayer, M., Lauly, S., Niu, X., Hsu, B., & Dinu, G. (2022). MT-GenEval: A counterfactual and contextual dataset for evaluating gender accuracy in machine translation. In Y. Goldberg, Z. Kozareva, & Y. Zhang (Eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 4287–4299). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.288 - Federmann, C., Kocmi, T., & Xin, Y. (2022). NTREX-128 – News test references for MT evaluation of 128 languages. Proceedings of the First Workshop on Scaling Up Multilingual Evaluation, 21–24. Association for Computational Linguistics. https://aclanthology.org/2022.sumeval-1.4 - Ive, J., Specia, L., Szoc, S., Vanallemeersch, T., Van den Bogaert, J., Farah, E., Maroti, C., Ventura, A., & Khalilov, M. (2020). A post-editing dataset in the legal domain: Do we underestimate neural machine translation quality? In N. Calzolari, F. Béchet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the Twelfth Language Resources and Evaluation Conference (pp. 3692–3697). European Language Resources Association. https://aclanthology.org/2020.lrec-1.455/ - Malmasi, S., Fang, A., Fetahu, B., Kar, S., & Rokhlenko, O. (2022). MultiCoNER: A large-scale multilingual dataset for complex named entity recognition. Proceedings of the 29th International Conference on Computational Linguistics, 3798–3809. International Committee on Computational Linguistics. https://aclanthology.org/2022.coling-1.334/ - NLLB Team, Costa-jussà, M. R., Cross, J., Çelebi, O., Elbayad, M., Heafield, K., Heffernan, K., Kalbassi, E., Lam, J., Licht, D., Maillard, J., Sun, A., Wang, S., Wenzek, G., Youngblood, A., Akula, B., Barrault, L., Mejia Gonzalez, G., Hansanti, P., Hoffman, J., Jarrett, S., Sadagopan, K. R., Rowe, D., Spruit, S., Tran, C., Andrews, P., Ayan, N. F., Bhosale, S., Edunov, S., Fan, A., Gao, C., Goswami, V., Guzmán, F., Koehn, P., Mourachko, A., Ropers, C., Saleem, S., Schwenk, H., & Wang, J. (2022). No language left behind: Scaling human-centered machine translation (No. arXiv: 2207.04672). arXiv. https://arxiv.org/abs/2207.04672 - Riley, P., Dozat, T., Botha, J. A., Garcia, X., Garrette, D., Riesa, J., Firat, O., & Constant, N. (2022). FRMT: A benchmark for few-shot region-aware machine translation (No. arXiv: 2210.00193). arXiv. https://doi.org/10.48550/ARXIV.2210.00193 - Specia, L., Harris, K., Blain, F., Burchardt, A., Macketanz, V., Skadiņa, I., Negri, M., & Turchi, M. (2017). Translation quality and productivity: A study on rich morphology languages. Proceedings of Machine Translation Summit XVI, 55–71. Nagoya, Japan. - Tiedemann, J. (2020). The Tatoeba translation challenge – Realistic data sets for low-resource and multilingual MT. Proceedings of the Fifth Conference on Machine Translation, 1174–1182. Association for Computational Linguistics. https://www.aclweb.org/anthology/2020.wmt-1.139 - Urbizu, G., San Vicente, I., Saralegi, X., Agerri, R., & Soroa, A. (2022). BasqueGLUE: A natural language understanding benchmark for Basque. Proceedings of the Language Resources and Evaluation Conference, 1603–1612. European Language Resources Association. https://aclanthology.org/2022.lrec-1.172 - Yang, Y., Zhang, Y., Tar, C., & Baldridge, J. (2019). PAWS-X: A cross-lingual adversarial dataset for paraphrase identification. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 3687–3692). Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1382 - Zubillaga, M., Sainz, O., Estarrona, A., Lopez de Lacalle, O., & Agirre, E. (2024). Event extraction in Basque: Typologically motivated cross-lingual transfer-learning analysis (No. arXiv: 2404.06392). arXiv. https://arxiv.org/abs/2404.06392 </details> ## Evaluation Below are the evaluation results on the [Flores+200 devtest set](https://huggingface.co/datasets/openlanguagedata/flores_plus), compared against the state-of-the-art [MADLAD400-3B-mt model](https://huggingface.co/google/madlad400-3b-mt) ([Kudugunta, S., et al.](https://arxiv.org/abs/2309.04662)) and [NLLB-200-3.3B](https://huggingface.co/facebook/nllb-200-3.3B) ([Costa-jussà et al., 2022](https://arxiv.org/abs/2207.04672)). These results cover the translation directions CA-XX, ES-XX, EN-XX, as well as XX-CA, XX-ES, and XX-EN. The metrics have been computed excluding Asturian, Aranese, and Aragonese, as we report them separately. The evaluation was conducted using [MT-Lens](https://github.com/langtech-bsc/mt-evaluation), following the standard setting (beam search with beam size 5, limiting the translation length to 500 tokens). We report the following metrics: <details> <summary>Click to show metrics details</summary> - `BLEU`: Sacrebleu implementation. Signature: nrefs:1— case:mixed— eff:no— tok:13a— smooth:exp—version:2.3.1 - `TER`: Sacrebleu implementation. - `ChrF`: Sacrebleu implementation. - `Comet`: Model checkpoint: "Unbabel/wmt22-comet-da". - `Comet-kiwi`: Model checkpoint: "Unbabel/wmt22-cometkiwi-da". - `Bleurt`: Model checkpoint: "lucadiliello/BLEURT-20". - `MetricX`: Model checkpoint: "google/metricx-23-xl-v2p0". - `MetricX-QE`: Model checkpoint: "google/metricx-23-qe-xl-v2p0". </details> <details> <summary>English evaluation</summary> ### English This section presents the evaluation metrics for English translation tasks. \| \| Bleu ↑ \| Ter ↓ \| ChrF ↑ \| Comet ↑ \| Comet-kiwi ↑ \| Bleurt ↑ \| MetricX ↓ \| MetricX-QE ↓ \| \|:-------------------------\|:----------\|:----------\|:----------\|:---------\|:-------------\|:---------\|:----------\|:-------------\| \| EN-XX \| \| \| \| \| \| \| \| \| \| MADLAD400-3B \| 35.11 \| 52.61 \| 63.04 \| 0.88 \| 0.85 \| 0.78 \| 1.21 \| 1.12 \| \| SalamandraTA-2b-instruct \| 33.52 \| 56.26 \| 61.74 \| 0.88 \| 0.85 \| 0.78 \| 1.16 \| 0.89 \| \| nllb-200-3.3B \| 31.17 \| 56.03 \| 59.19 \| 0.87 \| 0.83 \| 0.76 \| 2.22 \| 2.20 \| \| XX-EN \| \| \| \| \| \| \| \| \| \| MADLAD400-3B \| 41.84 \| 44.43 \| 67.04 \| 0.88 \| 0.85 \| 0.79 \| 1.22 \| 1.18 \| \| nllb-200-3.3B \| 41.52 \| 45.28 \| 66.21 \| 0.88 \| 0.85 \| 0.78 \| 1.43 \| 1.57 \| \| SalamandraTA-2b-instruct \| 41.44 \| 45.24 \| 66.64 \| 0.88 \| 0.85 \| 0.79 \| 1.24 \| 1.05 \| <img src="./images/bleu_en.png" alt="English" width="100%"/> </details> <details> <summary>Spanish evaluation</summary> ### Spanish This section presents the evaluation metrics for Spanish translation tasks. \| \| Bleu ↑ \| Ter ↓ \| ChrF ↑ \| Comet ↑ \| Comet-kiwi ↑ \| Bleurt ↑ \| MetricX ↓ \| MetricX-QE ↓ \| \|:-------------------------\|:----------\|:----------\|:----------\|:---------\|:-------------\|:---------\|:----------\|:-------------\| \| ES-XX \| \| \| \| \| \| \| \| \| \| MADLAD400-3B \| 21.87 \| 69.80 \| 53.46 \| 0.86 \| 0.83 \| 0.74 \| 1.15 \| 1.12 \| \| SalamandraTA-2b-instruct \| 20.77 \| 73.37 \| 52.16 \| 0.86 \| 0.83 \| 0.74 \| 1.16 \| 0.89 \| \| nllb-200-3.3B \| 19.54 \| 72.19 \| 50.07 \| 0.84 \| 0.81 \| 0.71 \| 2.19 \| 2.21 \| \| XX-ES \| \| \| \| \| \| \| \| \| \| SalamandraTA-2b-instruct \| 25.01 \| 63.35 \| 52.74 \| 0.85 \| 0.84 \| 0.73 \| 1.03 \| 1.20 \| \| MADLAD400-3B \| 24.38 \| 62.31 \| 52.65 \| 0.85 \| 0.84 \| 0.73 \| 1.13 \| 1.54 \| \| nllb-200-3.3B \| 22.68 \| 64.18 \| 50.91 \| 0.84 \| 0.83 \| 0.71 \| 1.62 \| 2.06 \| <img src="./images/bleu_es.png" alt="English" width="100%"/> </details> <details> <summary>Catalan evaluation</summary> ### Catalan This section presents the evaluation metrics for Catalan translation tasks. \| \| Bleu ↑ \| Ter ↓ \| ChrF ↑ \| Comet ↑ \| Comet-kiwi ↑ \| Bleurt ↑ \| MetricX ↓ \| MetricX-QE ↓ \| \|:-------------------------\|:----------\|:----------\|:----------\|:---------\|:-------------\|:---------\|:----------\|:-------------\| \| CA-XX \| \| \| \| \| \| \| \| \| \| MADLAD400-3B \| 28.86 \| 59.60 \| 58.05 \| 0.87 \| 0.81 \| 0.77 \| 1.15 \| 1.28 \| \| SalamandraTA-2b-instruct \| 26.70 \| 64.12 \| 56.18 \| 0.87 \| 0.81 \| 0.76 \| 1.24 \| 1.09 \| \| nllb-200-3.3B \| 25.17 \| 63.15 \| 54.02 \| 0.85 \| 0.79 \| 0.72 \| 2.42 \| 2.59 \| \| XX-CA \| \| \| \| \| \| \| \| \| \| SalamandraTA-2b-instruct \| 32.42 \| 56.54 \| 58.67 \| 0.86 \| 0.81 \| 0.74 \| 1.11 \| 1.38 \| \| MADLAD400-3B \| 32.31 \| 55.68 \| 58.87 \| 0.86 \| 0.81 \| 0.75 \| 1.27 \| 1.83 \| \| nllb-200-3.3B \| 29.28 \| 58.95 \| 55.97 \| 0.84 \| 0.80 \| 0.71 \| 2.18 \| 2.61 \| <img src="./images/bleu_ca.png" alt="English" width="100%"/> </details> <details> <summary>Galician evaluation</summary> ### Galician This section presents the evaluation metrics for Galician translation tasks. \| \| Bleu ↑ \| Ter ↓ \| ChrF ↑ \| Comet ↑ \| Comet-kiwi ↑ \| Bleurt ↑ \| MetricX ↓ \| MetricX-QE ↓ \| \|:-------------------------\|:----------\|:----------\|:----------\|:---------\|:-------------\|:---------\|:----------\|:-------------\| \| GL-XX \| \| \| \| \| \| \| \| \| \| SalamandraTA-2b-instruct \| 25.46 \| 65.95 \| 55.42 \| 0.87 \| 0.82 \| 0.75 \| 1.22 \| 1.05 \| \| MADLAD400-3B \| 25.12 \| 66.02 \| 55.78 \| 0.85 \| 0.85 \| 0.74 \| 1.37 \| 1.83 \| \| nllb-200-3.3B \| 24.20 \| 64.70 \| 53.37 \| 0.85 \| 0.80 \| 0.72 \| 2.29 \| 2.42 \| \| XX-GL \| \| \| \| \| \| \| \| \| \| SalamandraTA-2b-instruct \| 28.72 \| 59.65 \| 56.33 \| 0.86 \| 0.83 \| 0.69 \| 1.04 \| 1.25 \| \| MADLAD400-3B \| 27.54 \| 59.84 \| 54.94 \| 0.85 \| 0.85 \| 0.67 \| 1.34 \| 2.28 \| \| nllb-200-3.3B \| 26.22 \| 60.15 \| 53.84 \| 0.84 \| 0.81 \| 0.67 \| 1.91 \| 2.54 \| <img src="./images/bleu_gl.png" alt="English" width="100%"/> </details> <details> <summary>Basque evaluation</summary> ### Basque This section presents the evaluation metrics for Basque translation tasks. \| \| Bleu ↑ \| Ter ↓ \| ChrF ↑ \| Comet ↑ \| Comet-kiwi ↑ \| Bleurt ↑ \| MetricX ↓ \| MetricX-QE ↓ \| \|:-------------------------\|:----------\|:----------\|:----------\|:---------\|:-------------\|:---------\|:----------\|:-------------\| \| EU-XX \| \| \| \| \| \| \| \| \| \| MADLAD400-3B \| 20.02 \| 71.82 \| 48.71 \| 0.84 \| 0.85 \| 0.71 \| 1.68 \| 2.76 \| \| SalamandraTA-2b-instruct \| 19.00 \| 77.45 \| 49.13 \| 0.85 \| 0.79 \| 0.71 \| 1.45 \| 1.43 \| \| nllb-200-3.3B \| 18.83 \| 71.60 \| 47.96 \| 0.83 \| 0.76 \| 0.69 \| 2.35 \| 2.89 \| \| XX-EU \| \| \| \| \| \| \| \| \| \| SalamandraTA-2b-instruct \| 13.06 \| 89.81 \| 51.65 \| 0.84 \| 0.77 \| 0.78 \| 1.25 \| 1.09 \| \| MADLAD400-3B \| 12.65 \| 91.60 \| 49.86 \| 0.82 \| 0.84 \| 0.78 \| 2.22 \| 3.43 \| \| nllb-200-3.3B \| 7.58 \| 86.57 \| 40.43 \| 0.73 \| 0.62 \| 0.64 \| 4.68 \| 6.08 \| <img src="./images/bleu_eu.png" alt="English" width="100%"/> </details> ### Low-Resource Languages of Spain The tables below summarize the performance metrics for English, Spanish, and Catalan to Asturian, Aranese and Aragonese compared against [Transducens/IbRo-nllb](https://huggingface.co/Transducens/IbRo-nllb) [(Galiano Jimenez, et al.)](https://aclanthology.org/2024.wmt-1.85/), [NLLB-200-3.3B](https://huggingface.co/facebook/nllb-200-3.3B) ([Costa-jussà et al., 2022](https://arxiv.org/abs/2207.04672)). <details> <summary>English evaluation</summary> #### English-XX \| \| source \| target \| Bleu ↑ \| Ter ↓ \| ChrF ↑ \| \|:-------------------------\|:---------\|:---------\|:----------\|:----------\|:----------\| \| SalamandraTA-2b-instruct \| en \| ast \| 25.84 \| 63.31 \| 58.60 \| \| nllb-200-3.3B \| en \| ast \| 22.02 \| 77.26 \| 51.40 \| \| Transducens/IbRo-nllb \| en \| ast \| 20.56 \| 63.92 \| 53.32 \| \| \| \| \| \| \| \| \| SalamandraTA-2b-instruct \| en \| arn \| 19.09 \| 76.04 \| 50.18 \| \| Transducens/IbRo-nllb \| en \| arn \| 12.81 \| 73.21 \| 45.76 \| \| \| \| \| \| \| \| \| SalamandraTA-2b-instruct \| en \| arg \| 15.87 \| 76.75 \| 48.08 \| \| Transducens/IbRo-nllb \| en \| arg \| 14.07 \| 70.37 \| 46.89 \| \| nllb-200-3.3B \| en \| arg \| 0.31 \| 114.39 \| 6.87 \| </details> <details> <summary>Spanish evaluation</summary> #### Spanish-XX \| \| source \| target \| Bleu ↑ \| Ter ↓ \| ChrF ↑ \| \|:-------------------------\|:---------\|:---------\|:----------\|:----------\|:----------\| \| SalamandraTA-2b-instruct \| es \| ast \| 17.30 \| 76.72 \| 51.31 \| \| Transducens/IbRo-nllb \| es \| ast \| 16.79 \| 76.36 \| 50.89 \| \| nllb-200-3.3B \| es \| ast \| 11.85 \| 100.86 \| 40.27 \| \| \| \| \| \| \| \| \| Transducens/IbRo-nllb \| es \| arn \| 50.20 \| 36.60 \| 73.16 \| \| SalamandraTA-2b-instruct \| es \| arn \| 46.76 \| 39.32 \| 70.76 \| \| \| \| \| \| \| \| \| Transducens/IbRo-nllb \| es \| arg \| 59.75 \| 28.01 \| 78.73 \| \| SalamandraTA-2b-instruct \| es \| arg \| 38.42 \| 44.43 \| 67.39 \| </details> <details> <summary>Catalan evaluation</summary> #### Catalan-XX \| \| source \| target \| Bleu ↑ \| Ter ↓ \| ChrF ↑ \| \|:-------------------------\|:---------\|:---------\|:----------\|:----------\|:----------\| \| Transducens/IbRo-nllb \| ca \| ast \| 24.77 \| 61.60 \| 57.49 \| \| SalamandraTA-2b-instruct \| ca \| ast \| 24.49 \| 65.71 \| 57.40 \| \| nllb-200-3.3B \| ca \| ast \| 17.17 \| 91.47 \| 45.83 \| \| \| \| \| \| \| \| \| Transducens/IbRo-nllb \| ca \| arn \| 31.22 \| 54.30 \| 60.30 \| \| SalamandraTA-2b-instruct \| ca \| arn \| 29.75 \| 57.69 \| 59.20 \| \| \| \| \| \| \| \| \| Transducens/IbRo-nllb \| ca \| arg \| 24.44 \| 60.79 \| 55.51 \| \| SalamandraTA-2b-instruct \| ca \| arg \| 17.85 \| 68.85 \| 50.39 \| </details> ## Gender Aware Translation Below are the evaluation results for gender aware translation evaluated on the [MT-GenEval](https://github.com/amazon-science/machine-translation-gender-eval?tab=readme-ov-file#mt-geneval) dataset ([Currey, A. et al.](https://arxiv.org/pdf/2211.01355)). These have been calculated for translation from English into German, Spanish, French, Italian, Portuguese and Russian and are compared against [MADLAD400-3b-mt](https://huggingface.co/google/madlad400-3b-mt), and [NLLB-200-3-3B](https://huggingface.co/facebook/nllb-200-3.3B). Evaluation was conducted using [MT-Lens](https://github.com/langtech-bsc/mt-evaluation) and is reported as accuracy computed using the accuracy metric provided with MT-GenEval. <details> <summary>MT-GenEval Evaluation</summary> \| \| Source \| Target \| Masc \| Fem \| Pair \| \|:--\|:--\|:--\|:--\|:--\|:--\| \| MADLAD400-3B \| en \| de \| 0.863 \| 0.837 \| 0.713 \| \| SalamandraTA-2b-instruct \| en \| de \| 0.887 \| 0.843 \| 0.747 \| \| nllb_3.3B \| en \| de \| 0.870 \| 0.787 \| 0.677 \| \| \| \| \| \| \| \| \| MADLAD400-3B \| en \| es \| 0.883 \| 0.750 \| 0.660 \| \| SalamandraTA-2b-instruct \| en \| es \| 0.877 \| 0.843 \| 0.740 \| \| nllb_3.3B \| en \| es \| 0.867 \| 0.777 \| 0.663 \| \| \| \| \| \| \| \| \| MADLAD400-3B \| en \| fr \| 0.883 \| 0.797 \| 0.707 \| \| SalamandraTA-2b-instruct \| en \| fr \| 0.900 \| 0.823 \| 0.737 \| \| nllb_3.3B \| en \| fr \| 0.900 \| 0.727 \| 0.643 \| \| \| \| \| \| \| \| \| MADLAD400-3B \| en \| it \| 0.917 \| 0.693 \| 0.643 \| \| SalamandraTA-2b-instruct \| en \| it \| 0.910 \| 0.757 \| 0.687 \| \| nllb_3.3B \| en \| it \| 0.907 \| 0.673 \| 0.597 \| \| \| \| \| \| \| \| \| MADLAD400-3B \| en \| pt \| 0.923 \| 0.697 \| 0.640 \| \| SalamandraTA-2b-instruct \| en \| pt \| 0.910 \| 0.720 \| 0.660 \| \| nllb_3.3B \| en \| pt \| 0.913 \| 0.713 \| 0.650 \| \| \| \| \| \| \| \| \| MADLAD400-3B \| en \| ru \| 0.947 \| 0.780 \| 0.730 \| \| SalamandraTA-2b-instruct \| en \| ru \| 0.933 \| 0.813 \| 0.750 \| \| nllb_3.3B \| en \| ru \| 0.930 \| 0.787 \| 0.723 \| \| \| \| \| \| \| \| </details> ## Ethical Considerations and Limitations Detailed information on the work done to examine the presence of unwanted social and cognitive biases in the base model can be found at [Salamandra-2B model card](https://huggingface.co/BSC-LT/salamandra-2b). With regard to MT models, the only analysis related to bias which we have conducted is the MT-GenEval evaluation. No specific analysis has yet been carried out in order to evaluate potential biases or limitations in translation accuracy across different languages, dialects, or domains. However, we recognize the importance of identifying and addressing any harmful stereotypes, cultural inaccuracies, or systematic performance discrepancies that may arise in Machine Translation. As such, we plan to continue performing more analyses as we implement the necessary metrics and methods within our evaluation framework [MT-Lens](https://github.com/langtech-bsc/mt-evaluation). Note that the model has only undergone preliminary instruction tuning. We urge developers to consider potential limitations and conduct safety testing and tuning tailored to their specific applications. ## Additional information ### Author The Language Technologies Unit from Barcelona Supercomputing Center. ### Contact For further information, please send an email to <[email protected]>. ### Copyright Copyright(c) 2025 by Language Technologies Unit, Barcelona Supercomputing Center. ### Funding This work has been promoted and financed by the Government of Catalonia through the [Aina Project](https://projecteaina.cat/). This work is funded by the _Ministerio para la Transformación Digital y de la Función Pública_ - Funded by EU – NextGenerationEU within the framework of [ILENIA Project](https://proyectoilenia.es/) with reference 2022/TL22/00215337. ### Acknowledgements The success of this project has been made possible thanks to the invaluable contributions of our partners in the [ILENIA Project](https://proyectoilenia.es/): [HiTZ](http://hitz.ehu.eus/es), and [CiTIUS](https://citius.gal/es/). Their efforts have been instrumental in advancing our work, and we sincerely appreciate their help and support. ### Disclaimer ### Disclaimer Be aware that the model may contain biases or other unintended distortions. When third parties deploy systems or provide services based on this model, or use the model themselves, they bear the responsibility for mitigating any associated risks and ensuring compliance with applicable regulations, including those governing the use of Artificial Intelligence. The Barcelona Supercomputing Center, as the owner and creator of the model, shall not be held liable for any outcomes resulting from third-party use. ### License [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ### Citation If you find our model useful, we would appreciate if you could cite our work as follows: ``` @misc{gilabert2025salamandrasalamandratabscsubmission, title={From SALAMANDRA to SALAMANDRATA: BSC Submission for WMT25 General Machine Translation Shared Task}, author={Javier Garcia Gilabert and Xixian Liao and Severino Da Dalt and Ella Bohman and Audrey Mash and Francesca De Luca Fornaciari and Irene Baucells and Joan Llop and Miguel Claramunt Argote and Carlos Escolano and Maite Melero}, year={2025}, eprint={2508.12774}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2508.12774}, } ```
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755599920	ihsanridzi	2025-08-19T11:06:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:06:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
teysty/vjepa2-vitl-fpc16-256-ssv2-fdet_baseline_epochs5	teysty	2025-08-19T11:06:04Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vjepa2", "video-classification", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	video-classification	2025-08-19T11:04:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
OdedKBio/ppo-LunarLander-v2	OdedKBio	2025-08-19T11:03:33Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-08-19T11:01:33Z	--- library_name: stable-baselines3 tags: - LunarLander-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v3 type: LunarLander-v3 metrics: - type: mean_reward value: -198.68 +/- 121.66 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v3 This is a trained model of a PPO agent playing LunarLander-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 ... ```
Ale91Jonathan/blockassist-bc-alert_dormant_prawn_1755599723	Ale91Jonathan	2025-08-19T11:03:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "alert dormant prawn", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:02:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - alert dormant prawn --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
crocodlo/blockassist-bc-soft_barky_scorpion_1755601327	crocodlo	2025-08-19T11:02:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "soft barky scorpion", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:02:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - soft barky scorpion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755599691	pempekmangedd	2025-08-19T11:01:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "patterned sturdy dolphin", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:01:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - patterned sturdy dolphin --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1755601270	kittygirlhere	2025-08-19T11:01:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "twitchy beaked coral", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:01:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - twitchy beaked coral --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755599676	quantumxnode	2025-08-19T11:01:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:01:07Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
OleksandrLitke/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grassy_scurrying_walrus	OleksandrLitke	2025-08-19T11:00:40Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am grassy_scurrying_walrus", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T05:11:06Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am grassy_scurrying_walrus --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
sous26hotmailf1/blockassist-bc-tawny_melodic_tapir_1755599630	sous26hotmailf1	2025-08-19T10:59:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tawny melodic tapir", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T10:59:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tawny melodic tapir --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lakelee/RLB_MLP_BC_v4.20250819.18	lakelee	2025-08-19T10:59:47Z	0	0	transformers	[ "transformers", "safetensors", "mlp_swiglu", "generated_from_trainer", "base_model:lakelee/RLB_MLP_TSC_v1.20250818.16", "base_model:finetune:lakelee/RLB_MLP_TSC_v1.20250818.16", "endpoints_compatible", "region:us" ]	null	2025-08-19T10:33:07Z	--- library_name: transformers base_model: lakelee/RLB_MLP_TSC_v1.20250818.16 tags: - generated_from_trainer model-index: - name: RLB_MLP_BC_v4.20250819.18 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # RLB_MLP_BC_v4.20250819.18 This model is a fine-tuned version of [lakelee/RLB_MLP_TSC_v1.20250818.16](https://huggingface.co/lakelee/RLB_MLP_TSC_v1.20250818.16) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch_fused with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu128 - Tokenizers 0.21.4
onnx-community/layoutlm-base-uncased-ONNX	onnx-community	2025-08-19T10:56:36Z	0	0	transformers.js	[ "transformers.js", "onnx", "layoutlm", "base_model:microsoft/layoutlm-base-uncased", "base_model:quantized:microsoft/layoutlm-base-uncased", "region:us" ]	null	2025-08-19T10:56:29Z	--- library_name: transformers.js base_model: - microsoft/layoutlm-base-uncased --- # layoutlm-base-uncased (ONNX) This is an ONNX version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).

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