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train · 866k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-05-25 18:27:02	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 476 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-05-25 18:24:25	card stringlengths 11 1.01M
jordinia/NetPro-Qwen-3-4B-2105-LoRa	jordinia	2025-05-22T06:20:27Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-4B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-4B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-22T06:17:05Z	--- base_model: unsloth/Qwen3-4B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: jordinia - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-4B-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
DevQuasar/nvidia.Llama-3.1-Nemotron-Nano-4B-v1.1-GGUF	DevQuasar	2025-05-22T06:18:32Z	0	0	null	[ "gguf", "text-generation", "base_model:nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1", "base_model:quantized:nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-22T05:49:59Z	--- base_model: - nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1 pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
Reallm-Labs/InfiFusion-14B	Reallm-Labs	2025-05-22T06:18:05Z	0	0	null	[ "pytorch", "phi3", "custom_code", "en", "dataset:lukaemon/bbh", "dataset:openai/openai_humaneval", "dataset:openai/gsm8k", "dataset:Rowan/hellaswag", "dataset:Muennighoff/mbpp", "dataset:google/IFEval", "dataset:RAR-b/ARC-Challenge", "license:mit", "region:us" ]	null	2025-05-19T03:00:01Z	--- license: mit datasets: - lukaemon/bbh - openai/openai_humaneval - openai/gsm8k - Rowan/hellaswag - Muennighoff/mbpp - google/IFEval - RAR-b/ARC-Challenge language: - en ---
FL-PoC/bart-safe-AQSOL-seed-1	FL-PoC	2025-05-22T06:16:41Z	0	0	transformers	[ "transformers", "safetensors", "bart", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-22T06:16:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
katrina-lim-kiffy-telegram-link/watch.katrina.lim.kiffy.telegram.link	katrina-lim-kiffy-telegram-link	2025-05-22T06:15:12Z	0	0	null	[ "region:us" ]	null	2025-05-22T06:08:12Z	[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️ ](https://the-goat-sanda.blogspot.com/p/goat-sanda-02.html) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️ ](https://the-goat-sanda.blogspot.com/p/goat-sanda-02.html) [WATCH NOW](https://the-goat-sanda.blogspot.com/p/goat-sanda-02.html) <a href="https://the-goat-sanda.blogspot.com/p/goat-sanda-02.html"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
katrina-lim-kiffy-telegram-link/katrina.lim.viral.telegram.link	katrina-lim-kiffy-telegram-link	2025-05-22T06:15:10Z	0	0	null	[ "region:us" ]	null	2025-05-22T06:08:03Z	[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️ ](https://the-goat-sanda.blogspot.com/p/goat-sanda-02.html) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️ ](https://the-goat-sanda.blogspot.com/p/goat-sanda-02.html) [WATCH NOW](https://the-goat-sanda.blogspot.com/p/goat-sanda-02.html) <a href="https://the-goat-sanda.blogspot.com/p/goat-sanda-02.html"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
katrina-lim-kiffy-telegram-link/katrina.lim.kiffy.telegram.link.katrina.lim.kiffy.video.telegram.link	katrina-lim-kiffy-telegram-link	2025-05-22T06:15:08Z	0	0	null	[ "region:us" ]	null	2025-05-22T06:07:49Z	[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️ ](https://the-goat-sanda.blogspot.com/p/goat-sanda-02.html) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️ ](https://the-goat-sanda.blogspot.com/p/goat-sanda-02.html) [WATCH NOW](https://the-goat-sanda.blogspot.com/p/goat-sanda-02.html) <a href="https://the-goat-sanda.blogspot.com/p/goat-sanda-02.html"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
gencgeray/whisper-large-v3	gencgeray	2025-05-22T06:14:48Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "uk", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-large-v3", "base_model:adapter:openai/whisper-large-v3", "license:apache-2.0", "model-index", "region:us" ]	null	2025-05-21T19:35:23Z	--- library_name: peft language: - uk license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: whisper-large-v3-LORA-uk results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: uk split: test args: 'config: uk, split: test' metrics: - type: wer value: 32.322426177174776 name: Wer --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-large-v3-LORA-uk This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2198 - Wer: 32.3224 - Cer: 11.7944 ## 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: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 6000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\|:-------:\| \| 0.3078 \| 0.4034 \| 1000 \| 0.2892 \| 46.7412 \| 17.3789 \| \| 0.238 \| 0.8068 \| 2000 \| 0.2869 \| 47.2200 \| 18.3277 \| \| 0.1942 \| 1.2102 \| 3000 \| 0.2419 \| 44.1075 \| 15.9300 \| \| 0.1773 \| 1.6136 \| 4000 \| 0.2262 \| 30.5134 \| 8.7628 \| \| 0.0626 \| 2.0169 \| 5000 \| 0.2192 \| 34.1580 \| 11.7469 \| \| 0.0785 \| 2.4203 \| 6000 \| 0.2198 \| 32.3224 \| 11.7944 \| ### Framework versions - PEFT 0.15.2.dev0 - Transformers 4.53.0.dev0 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.0
DanielNRU/pollen-ner2-1750	DanielNRU	2025-05-22T06:13:34Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T06:07:47Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-1750 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. --> # pollen-ner2-1750 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1661 - Precision: 0.8287 - Recall: 0.8936 - F1: 0.8599 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 219 \| 0.1661 \| 0.8287 \| 0.8936 \| 0.8599 \| \| No log \| 2.0 \| 438 \| 0.1633 \| 0.8318 \| 0.8835 \| 0.8569 \| \| 0.2581 \| 3.0 \| 657 \| 0.1629 \| 0.8212 \| 0.8855 \| 0.8522 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
Groovy-123/Entity-R1	Groovy-123	2025-05-22T06:11:38Z	0	0	adapter-transformers	[ "adapter-transformers", "All", "ak", "en", "dataset:Groovy-123/deep-think", "dataset:nvidia/OpenMathReasoning", "dataset:RyanYr/nvidiaOpenMathReasoning-genselect", "base_model:Groovy-123/Advance-reasoning-model-158-D52-A13", "base_model:adapter:Groovy-123/Advance-reasoning-model-158-D52-A13", "license:other", "region:us" ]	null	2025-05-22T06:08:35Z	--- license: other license_name: nexgen license_link: LICENSE datasets: - Groovy-123/deep-think - nvidia/OpenMathReasoning - RyanYr/nvidiaOpenMathReasoning-genselect language: - ak - en metrics: - accuracy base_model: - Groovy-123/Advance-reasoning-model-158-D52-A13 library_name: adapter-transformers tags: - All ---
proota/DistilBERT-finetuned-on-emotion	proota	2025-05-22T06:09:04Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-21T21:21:34Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: DistilBERT-finetuned-on-emotion 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-finetuned-on-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - 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: 2 ### Training results ### Framework versions - Transformers 4.52.2 - Pytorch 2.7.0+cpu - Datasets 3.6.0 - Tokenizers 0.21.1
Varun1010/tbl_universe_distilled_cpu	Varun1010	2025-05-22T06:09:03Z	0	0	setfit	[ "setfit", "safetensors", "bert", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-MiniLM-L3-v2", "base_model:finetune:sentence-transformers/paraphrase-MiniLM-L3-v2", "model-index", "region:us" ]	text-classification	2025-05-22T06:08:59Z	--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: TBP-AHU-15-1_192.168.3.27:271001:0_ai.2_AHU_raw_return_air_co2 - text: TBP-CH-B3-4(625RT):100000:0_ai.5_CH_raw_chilled_water_flow - text: TBP-AHU-1-01_192.168.3.15:51001:0_di.12_AHU_raw_status - text: TBP-AHU-13-1_192.168.3.27:271001:0_ao.6_AHU_raw_supply_air_fan_speed_command - text: TBP-AHU-11-1-c2:490000:0_di.1_AHU_raw_on_off_command metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: true base_model: sentence-transformers/paraphrase-MiniLM-L3-v2 model-index: - name: SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.178125 name: Accuracy --- # SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 128 tokens - Number of Classes: 32 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 22 \| <ul><li>'TBP-AHU-2-04_192.168.3.14:41001:0_ai.2_AHU_raw_return_air_temp'</li><li>'TBP-AHU-2-02_192.168.3.15:52008:0_ai.2_AHU_raw_return_air_temp'</li><li>'TBP-AHU-1-01_192.168.3.15:51001:0_ai.2_AHU_raw_return_air_temp'</li></ul> \| \| 32 \| <ul><li>'TBP-AHU-2-05_192.168.3.16:61015:0_ai.4_AHU_raw_valve_position'</li><li>'TBP-AHU-1-02_192.168.3.15:52008:0_ai.4_AHU_raw_valve_position'</li><li>'TBP-AHU-3-04A_192.168.3.16:62011:0_ai.4_AHU_raw_valve_position'</li></ul> \| \| 9 \| <ul><li>'TBP-AHU-2-02_192.168.3.15:52008:0_ai.5_AHU_raw_oa_damper_position'</li><li>'TBP-AHU-15-1_192.168.3.27:271001:0_ai.1_AHU_raw_oa_damper_position'</li><li>'TBP-AHU-1-01_192.168.3.15:51001:0_ai.5_AHU_raw_oa_damper_position'</li></ul> \| \| 16 \| <ul><li>'TBP-CHWP-B3-3:100000:0_ai.3_PUMP_raw_frequency'</li><li>'TBP-CT-4-1:100000:0_ai.4_CT_FAN_raw_frequency_2'</li><li>'TBP-CHWP-B3-4:100000:0_ai.3_PUMP_raw_frequency'</li></ul> \| \| 13 \| <ul><li>'TBP-AHU-3-04A_192.168.3.16:62011:0_ai.3_AHU_raw_supply_air_fan_speed'</li><li>'TBP-AHU-2-02_192.168.3.15:52008:0_ai.3_AHU_raw_supply_air_fan_speed'</li><li>'TBP-PAU-1-01_192.168.3.15:51001:0_ai.1_AHU_raw_supply_air_fan_speed'</li></ul> \| \| 10 \| <ul><li>'TBP-CH-B3-2(400RT):100000:0_ai.3_CH_raw_temp_chws'</li><li>'TBP-CH-B3-6(400RT):100000:0_ai.3_CH_raw_temp_chws'</li><li>'TBP-CH-B3-7(400RT):100000:0_ai.3_CH_raw_temp_chws'</li></ul> \| \| 30 \| <ul><li>'TBP-AHU-13-1_192.168.3.27:271001:0_di.10_AHU_raw_trip'</li><li>'TBP-AHU-15-1_192.168.3.27:271001:0_di.10_AHU_raw_trip'</li><li>'TBP-AHU-11-1_192.168.3.26:261001:0_di.10_AHU_raw_trip'</li></ul> \| \| 1 \| <ul><li>'TBP-CH-B3-8(625RT):100000:0_ai.5_CH_raw_chilled_water_flow'</li><li>'TBP-CH-B3-7(625RT):100000:0_ai.5_CH_raw_chilled_water_flow'</li><li>'TBP-CH-B3-4(625RT):100000:0_ai.5_CH_raw_chilled_water_flow'</li></ul> \| \| 29 \| <ul><li>'TBP-CH-B3-4(625RT):100000:0_ai.3_CH_raw_temp_cws'</li><li>'TBP-CH-B3-7(400RT):100000:0_ai.5_CH_raw_temp_cws'</li><li>'TBP-CH-B3-6(400RT):100000:0_ai.5_CH_raw_temp_cws'</li></ul> \| \| 0 \| <ul><li>'TBP-CWP-B3-3:100000:0_ai.3_PUMP_raw_power_active_total'</li><li>'TBP-CWP-B3-1:100000:0_ai.2_PUMP_raw_power_active_total'</li><li>'TBP-CWP-B3-2:100000:0_ai.2_PUMP_raw_power_active_total'</li></ul> \| \| 3 \| <ul><li>'TBP-CH-B3-7(625RT):100000:0_ai.2_CH_raw_temp_chwr'</li><li>'TBP-CH-B3-1(600RT):100000:0_ai.4_CH_raw_temp_chwr'</li><li>'TBP-CH-B3-9(625RT):100000:0_ai.2_CH_raw_temp_chwr'</li></ul> \| \| 8 \| <ul><li>'TBP-CWP-B3-5:100000:0_ai.5_PUMP_raw_sp_cooling_water_flow'</li><li>'TBP-CWP-B3-1:100000:0_ai.5_PUMP_raw_sp_cooling_water_flow'</li><li>'TBP-CWP-B3-4:100000:0_ai.5_PUMP_raw_sp_cooling_water_flow'</li></ul> \| \| 2 \| <ul><li>'TBP-CH-B3-8(625RT):100000:0_ai.15_CH_raw_sp_chilled_water_flow'</li><li>'TBP-CH-B3-2(625RT):100000:0_ai.15_CH_raw_sp_chilled_water_flow'</li><li>'TBP-CH-B3-7(625RT):100000:0_ai.15_CH_raw_sp_chilled_water_flow'</li></ul> \| \| 25 \| <ul><li>'TBP-PAU-3-02_192.168.3.11:12030:0_ai.3_AHU_raw_static_pressure'</li><li>'TBP-PAU-1-01_192.168.3.15:51001:0_ai.2_AHU_raw_static_pressure'</li><li>'TBP-AHU-3-101-102-1_192.168.3.12:21032:0_ai.3_AHU_raw_static_pressure'</li></ul> \| \| 6 \| <ul><li>'TBP-AHU-15-1_192.168.3.27:271001:0_ai.7_AHU_raw_sp_return_air_co2'</li><li>'TBP-AHU-12-1_192.168.3.27:271001:0_ai.7_AHU_raw_sp_return_air_co2'</li><li>'TBP-AHU-13-1_192.138.3.27:271001:0_ai.7_AHU_raw_sp_return_air_co2'</li></ul> \| \| 12 \| <ul><li>'TBP-AHU-10-1_192.168.3.26:261001:0_ao.6_AHU_raw_supply_air_fan_speed_command'</li><li>'TBP-AHU-11-1_192.168.3.26:261001:0_ao.6_AHU_raw_supply_air_fan_speed_command'</li><li>'TBP-AHU-15-1_192.168.3.27:271001:0_ao.6_AHU_raw_supply_air_fan_speed_command'</li></ul> \| \| 7 \| <ul><li>'TBP-CWP-B3-3:100000:0_ai.1_PUMP_raw_cooling_water_flow'</li><li>'TBP-CWP-B3-4:100000:0_ai.1_PUMP_raw_cooling_water_flow'</li><li>'TBP-CWP-B3-7:100000:0_ai.1_PUMP_raw_cooling_water_flow'</li></ul> \| \| 20 \| <ul><li>'TBP-WST-2:100000:0_ai.1_WST_outside_air_humidity'</li><li>'TBP-WST-2:100000:0_ai.1_WST_outside_air_humidity'</li><li>'TBP-WST:100000:0_ai.1_WST_outside_air_humidity'</li></ul> \| \| 15 \| <ul><li>'TBP-CH-B3-2(400RT):100000:0_di.15_CH_raw_on_off_command'</li><li>'TBP-CWP-B3-4:100000:0_di.10_PUMP_raw_on_off_command'</li><li>'TBP-CWP-B3-3:100000:0_di.10_PUMP_raw_on_off_command'</li></ul> \| \| 31 \| <ul><li>'TBP-AHU-11-1_192.168.3.26:261001:0_ao.5_AHU_raw_valve_command'</li><li>'TBP-AHU-13-1_192.168.3.27:271001:0_ao.5_AHU_raw_valve_command'</li><li>'TBP-AHU-10-1_192.168.3.26:261001:0_ao.5_AHU_raw_valve_command'</li></ul> \| \| 26 \| <ul><li>'TBP-PAU-1-05_192.168.3.15:52009:0_ai.8_AHU_raw_sp_static_pressure'</li><li>'TBP-PAU-1-01_192.168.3.15:51001:0_ai.8_AHU_raw_sp_static_pressure'</li><li>'TBP-AHU-2-05_192.168.3.16:61015:0_ai.10_AHU_raw_sp_static_pressure'</li></ul> \| \| 11 \| <ul><li>'TBP-AHU-14-1-c2:490000:0_di.1_AHU_raw_on_off_command'</li><li>'TBP-AHU-13-1-c2:490000:0_di.1_AHU_raw_on_off_command'</li><li>'TBP-AHU-15-1-c2:490000:0_di.1_AHU_raw_on_off_command'</li></ul> \| \| 5 \| <ul><li>'TBP-AHU-15-1_192.168.3.27:271001:0_ai.2_AHU_raw_return_air_co2'</li><li>'TBP-AHU-11-1_192.168.3.26:261001:0_ai.4_AHU_raw_return_air_co2'</li><li>'TBP-AHU-1-01_192.168.3.15:51001:0_ai.6_AHU_raw_return_air_co2'</li></ul> \| \| 27 \| <ul><li>'TBP-AHU-2-03_192.168.3.13:31010:0_ai.1_AHU_raw_supply_air_temp'</li><li>'TBP-AHU-3-04A_192.168.3.16:62011:0_ai.1_AHU_raw_supply_air_temp'</li><li>'TBP-AHU-1-02_192.168.3.15:52008:0_ai.1_AHU_raw_supply_air_temp'</li></ul> \| \| 28 \| <ul><li>'TBP-AHU-1-01_192.168.3.15:51001:0_ai.9_AHU_raw_sp_supply_air_temp'</li><li>'TBP-AHU-2-05_192.168.3.16:61015:0_ai.9_AHU_raw_sp_supply_air_temp'</li><li>'TBP-AHU-1-02_192.168.3.15:52008:0_ai.9_AHU_raw_sp_supply_air_temp'</li></ul> \| \| 24 \| <ul><li>'TBP-CH-B3-3(625RT):100000:0_ai.4_CH_raw_temp_cwr'</li><li>'TBP-CH-B3-2(400RT):100000:0_ai.6_CH_raw_temp_cwr'</li><li>'TBP-CH-B3-4(625RT):100000:0_ai.4_CH_raw_temp_cwr'</li></ul> \| \| 23 \| <ul><li>'TBP-AHU-1-02_192.168.3.15:52008:0_ai.11_AHU_raw_sp_return_air_temp'</li><li>'TBP-AHU-1-01_192.168.3.15:51001:0_ai.10_AHU_raw_sp_return_air_temp'</li><li>'TBP-AHU-B1-1_192.168.3.18:82026:0_ai.7_AHU_raw_sp_return_air_temp'</li></ul> \| \| 14 \| <ul><li>'TBP-AHU-15-1_192.168.3.27:271001:0_di.9_AHU_raw_status'</li><li>'TBP-AHU-17-1_192.168.3.28:281001:0_di.9_AHU_raw_status'</li><li>'TBP-AHU-16-1_192.168.3.28:281001:0_di.9_AHU_raw_status'</li></ul> \| \| 4 \| <ul><li>'TBP-CH-B3-3(625RT):100000:0_ai.1_CH_raw_temp_chws'</li><li>'TBP-CH-B3-6(625RT):100000:0_ai.1_CH_raw_temp_chws'</li><li>'TBP-CH-B3-9(625RT):100000:0_ai.1_CH_raw_temp_chws'</li></ul> \| \| 21 \| <ul><li>'TBP-WST-3:100000:0_ai.0_WST_outside_air_temp'</li><li>'TBP-WST:100000:0_ai.0_WST_outside_air_temp'</li><li>'TBP-WST-3:100000:0_ai.0_WST_outside_air_temp'</li></ul> \| \| 19 \| <ul><li>'TBP-AHU-2-02_192.168.3.15:52008:0_di.15_AHU_raw_operation_mode'</li><li>'TBP-AHU-2-04_192.168.3.14:41001:0_di.15_AHU_raw_operation_mode'</li><li>'TBP-AHU-6-2A_192.168.3.12:21018:0_di.14_AHU_raw_operation_mode'</li></ul> \| \| 18 \| <ul><li>'TBP-CH-B3-2(400RT):100000:0_ai.4_CH_raw_temp_chwr'</li><li>'TBP-CH-B3-3(625RT):100000:0_ai.2_CH_raw_temp_chwr'</li><li>'TBP-CH-B3-3(625RT):100000:0_ai.2_CH_raw_temp_chwr'</li></ul> \| ## Evaluation ### Metrics \| Label \| Accuracy \| \|:--------\|:---------\| \| all \| 0.1781 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("Varun1010/tbl_universe_distilled_cpu") # Run inference preds = model("TBP-AHU-11-1-c2:490000:0_di.1_AHU_raw_on_off_command") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:-------\|:----\| \| Word count \| 1 \| 1.0 \| 1 \| \| Label \| Training Sample Count \| \|:------\|:----------------------\| \| 0 \| 10 \| \| 1 \| 10 \| \| 2 \| 10 \| \| 3 \| 10 \| \| 4 \| 10 \| \| 5 \| 10 \| \| 6 \| 10 \| \| 7 \| 10 \| \| 8 \| 10 \| \| 9 \| 10 \| \| 10 \| 10 \| \| 11 \| 10 \| \| 12 \| 10 \| \| 13 \| 10 \| \| 14 \| 10 \| \| 15 \| 10 \| \| 16 \| 10 \| \| 18 \| 10 \| \| 19 \| 10 \| \| 20 \| 10 \| \| 21 \| 10 \| \| 22 \| 10 \| \| 23 \| 10 \| \| 24 \| 10 \| \| 25 \| 10 \| \| 26 \| 10 \| \| 27 \| 10 \| \| 28 \| 10 \| \| 29 \| 10 \| \| 30 \| 10 \| \| 31 \| 10 \| \| 32 \| 10 \| ### Training Hyperparameters - batch_size: (8, 4) - num_epochs: (0, 3) - max_steps: 300 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.0 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:------:\|:----:\|:-------------:\|:---------------:\| \| 0.0066 \| 1 \| 0.2252 \| - \| \| 0.3311 \| 50 \| 0.0164 \| - \| \| 0.6623 \| 100 \| 0.001 \| - \| \| 0.9934 \| 150 \| 0.0007 \| - \| \| 1.3245 \| 200 \| 0.0006 \| - \| \| 1.6556 \| 250 \| 0.0005 \| - \| \| 1.9868 \| 300 \| 0.0004 \| - \| ### Framework Versions - Python: 3.11.12 - SetFit: 1.1.2 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
vinupspethvg/zxv	vinupspethvg	2025-05-22T06:07:34Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-05-22T06:07:34Z	--- license: bigscience-bloom-rail-1.0 ---
guistlynchg1/zxcvzxcv	guistlynchg1	2025-05-22T06:07:11Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-22T06:07:11Z	--- license: creativeml-openrail-m ---
caskcsg/Llama-3-8B-LongMagpie-p-mix-512K-Instruct	caskcsg	2025-05-22T06:04:44Z	0	0	null	[ "safetensors", "region:us" ]	null	2025-05-22T05:55:36Z	## LongMagpie: A Self-synthesis Method for Generating Large-scale Long-context Instructions This repository contains the code, models and datasets for our paper [LongMagpie: A Self-synthesis Method for Generating Large-scale Long-context Instructions]. ## Quick Links - [Overview](#overview) - [LongMagpie Models](#LongMagpie-models) - [LongMagpie Datasets](#LongMagpie-datasets) - [Datasets list](#datasets-list) - [Train Llama-3-8B-LongMagpie-512K-Instruct](#train-LongMagpie512K) - [Requirements](#requirements) - [Evaluation](#evaluation) - [Build your long-context instruction data](#build-long-data) - [Bugs or Questions?](#bugs-or-questions) <a id="overview"></a> ## Overview High-quality long-context instruction data is essential for aligning long-context large language models (LLMs). Despite the public release of models like Qwen and Llama, their long-context instruction data remains proprietary. Human annotation is costly and challenging, while template-based synthesis methods limit scale, diversity, and quality. We introduce LongMagpie, a self-synthesis framework that automatically generates large-scale long-context instruction data. Our key insight is that aligned long-context LLMs, when presented with a document followed by special tokens preceding a user turn, auto-regressively generate contextually relevant queries. By harvesting these document-query pairs and the model's responses, LongMagpie produces high-quality instructions without human effort. Experiments on HELMET, RULER, and Longbench v2 demonstrate that LongMagpie achieves leading performance on long-context tasks while maintaining competitive performance on short-context tasks, establishing it as a simple and effective approach for open, diverse, and scalable long-context instruction data synthesis. <div style="text-align: center;"> <img src="figure/LongMagpie.png" width="700" height="350"> </div> <a id="LongMagpie-models"></a> ## LongMagpie Models Our released models are listed as follows. You can import these models by using [HuggingFace's Transformers](https://github.com/huggingface/transformers). All models are trained on long-context instruction data synthesized by [fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) and [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) model. In the following comparision, we choose [Llama-3-8B-NExtLong-512K-Instruct](https://huggingface.co/caskcsg/Llama-3-8B-NExtLong-512K-Instruct) as a baseline model, which is trained with [Magpie instruction data](https://huggingface.co/datasets/Magpie-Align/Magpie-Llama-3.3-Pro-1M-v0.1). In addition, to maintain short-text performance, we propose a p-mix strategy that combines LongMagpie and [UltraChat](https://huggingface.co/datasets/stingning/ultrachat) datasets, resulting in a performance-balanced model [Llama-3-8B-LongMagpie-p-mix-512K-Instruct](https://huggingface.co/caskcsg/Llama-3-8B-LongMagpie-512K-Instruct). #### The performance on [HELMET](https://github.com/princeton-nlp/HELMET) and [RULER](https://github.com/NVIDIA/RULER) \| Model \| RULER Avg. \| HELMET Avg. \| HELMET Recall \| HELMET RAG \| HELMET ICL \| HELMET Re-rank \| HELMET LongQA \| \|:-------------------------------\|:-------:\|:-------:\|:------:\|:-----:\|:-----:\|:-------:\|:------:\| \| [Llama-3-8B-NExtLong-512K-Instruct](https://huggingface.co/caskcsg/Llama-3-8B-NExtLong-512K-Instruct) \| 88.00 \| 59.92 \| 98.63 \| 62.70 \| 81.00 \| 26.41 \| 30.89 \| \| [Llama-3-8B-LongMagpie-512K-Instruct](https://huggingface.co/caskcsg/Llama-3-8B-LongMagpie-512K-Instruct) \| 91.17 \| 62.10 \| 97.53 \| 63.37 \| 85.84 \| 28.60 \| 35.16 \| \| [Llama-3-8B-LongMagpie-p-mix-512K-Instruct](https://huggingface.co/caskcsg/Llama-3-8B-LongMagpie-p-mix-512K-Instruct) \| 89.70 \| 62.11 \| 95.96 \| 64.17 \| 85.12 \| 29.61 \| 35.71 \| #### The performance on [Longbench V2](https://github.com/THUDM/LongBench) \| Model \| Overall (%) \| Easy (%) \| Hard (%) \| Short (%) \| Medium (%) \| Long (%) \| \|--------------------------------------------\|-------------\|----------\|----------\|-----------\|------------\|----------\| \| [Llama-3-8B-NExtLong-512K-Instruct](https://huggingface.co/caskcsg/Llama-3-8B-NExtLong-512K-Instruct) \| 30.8 \| 33.9 \| 28.9 \| 37.8 \| 27.4 \| 25.9 \| \| [Llama-3-8B-LongMagpie-512K-Instruct](https://huggingface.co/caskcsg/Llama-3-8B-LongMagpie-512K-Instruct) \| 34.4\| 38.5 \|31.8\| 41.7 \|33 \|25 \| \| [Llama-3-8B-LongMagpie-p-mix-512K-Instruct](https://huggingface.co/caskcsg/Llama-3-8B-LongMagpie-p-mix-512K-Instruct) \| 33 \| 35.9 \|31.2 \|37.2 \|34.9\| 22.2 \| #### The performance on Short-context Benchmarks \| Model \| Avg. \| Hel. \| Lam. \| AR-C. \| AR-E. \| PIQA \| Win. \| Logiqa \| MMLU \| \|----------------------------\|-------\|-----------\|----------------\|---------------\|----------\|-------\|------------\|--------\|-------\| \| [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) \| 0.6332 \| 0.5773 \| 0.7171 \| 0.5316 \| 0.8165 \| 0.7889 \| 0.7198 \| 0.2765 \| 0.6376 \| \| [Llama-3-8B-NExtLong-512K-Instruct](https://huggingface.co/caskcsg/Llama-3-8B-NExtLong-512K-Instruct) \| 0.6410 \| 0.5953 \| 0.7242 \| 0.5188 \| 0.8224 \| 0.8079 \| 0.7324 \| 0.3041 \| 0.6232 \| \| [Llama-3-8B-LongMagpie-512K-Instruct](https://huggingface.co/caskcsg/Llama-3-8B-LongMagpie-512K-Instruct) \| 0.6237 \|0.5803 \|0.7025 \|0.4804\| 0.8047\| 0.7938 \|0.7293\| 0.278 \|0.6209 \| \| [Llama-3-8B-LongMagpie-p-mix-512K-Instruct](https://huggingface.co/caskcsg/Llama-3-8B-LongMagpie-p-mix-512K-Instruct) \| 0.6410 \| 0.5893 \| 0.7355\| 0.5282\| 0.8279\| 0.8052\| 0.734\| 0.2842\| 0.6236 \| <a id="LongMagpie-datasets"></a> ## LongMagpie Datasets <a id="datasets-list"></a> ### Datasets list Our released datasets are listed as follows. All datasets are synthesized from the short-text datasets [fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu). \| Dataset \| Description \| \|:-------------------------------\|:--------\| \| [LongMagpie_singledoc_longcontext_dataset](https://huggingface.co/datasets/caskcsg/LongMagpie_singledoc_longcontext_dataset) \| Our synthesized 450k raw text files(refer to [infer_demo.py](https://github.com/caskcsg/longcontext/tree/main/LongMagpie/longmagpie/infer_demo.py)). Each line of data contains context extracted from [fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu), query generated by LongMapgie and answer. \| \| [LongMagpie_multidoc_longcontext_dataset](https://huggingface.co/datasets/caskcsg/LongMagpie_multidoc_longcontext_dataset) \| Based on [LongMagpie_singledoc_longcontext_dataset](https://huggingface.co/datasets/caskcsg/LongMagpie_singledoc_longcontext_dataset), we used the MultiDoc method (refer to [multidoc_format.py](https://github.com/caskcsg/longcontext/tree/main/LongMagpie/longmagpie/multidoc_format.py)) to extend the context length and transformed it into SFT dialogue format. \| \| [LongMagpie_64k_dataset](https://huggingface.co/datasets/caskcsg/LongMagpie_64k_dataset) \| We tokenized [LongMagpie_multidoc_longcontext_dataset](https://huggingface.co/datasets/caskcsg/LongMagpie_multidoc_longcontext_dataset) and concatenated it to a length of 64k (refer to [concat script](https://github.com/caskcsg/longcontext/tree/main/LongMagpie/longmagpie/build_sft_data.py)), making it convenient to train using Document Mask technology. This dataset can be used to achieve the best long-text performance. \| \| [LongMagpie_p-mix_64k_dataset](https://huggingface.co/datasets/caskcsg/LongMagpie_p-mix_64k_dataset) \| To maintain short-text performance, we tokenized [LongMagpie_multidoc_longcontext_dataset](https://huggingface.co/datasets/caskcsg/LongMagpie_multidoc_longcontext_dataset) and mixed it with [UltraChat](https://huggingface.co/datasets/stingning/ultrachat) using the p-mix strategy, concatenating to a length of 64k (refer to [p-mix.py](https://github.com/caskcsg/longcontext/tree/main/LongMagpie/longmagpie/build_sft_data_p_mix.py)). This dataset can be used to achieve balanced long and short text performance. \| <a id="train-LongMagpie512K"></a> ## Train Llama-3-8B-LongMagpie-512K-Instruct <a id="requirements"></a> ### Requirements Run the following script to install the remaining dependencies and train the model. ```bash pip install -r requirements.txt ``` ### Train ```bash bash train_sft.sh ``` <a id="evaluation"></a> ## Evaluation Refer to the [HELMET](https://github.com/princeton-nlp/HELMET), [RULER](https://github.com/NVIDIA/RULER), and [Longbench V2](https://github.com/THUDM/LongBench) to evaluate the Instruct model. <a id="build-long-data"></a> ## Build your long-context instruction data ### 1. Synthesizing Single-Document Q&A Data Refer to [infer_demo.py](https://github.com/caskcsg/longcontext/tree/main/LongMagpie/longmagpie/infer_demo.py). Each line of data contains context extracted from [fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu), query generated by LongMapgie and answer. ```bash python longmagpie/infer_demo.py ``` ### 2. Synthesizing Multi-Document Q&A Data Based on [LongMagpie_singledoc_longcontext_dataset](https://huggingface.co/datasets/caskcsg/LongMagpie_singledoc_longcontext_dataset), we used the MultiDoc method (refer to [multidoc_format.py](https://github.com/caskcsg/longcontext/tree/main/LongMagpie/longmagpie/multidoc_format.py)) to extend the context length and transformed it into SFT dialogue format. ```bash python longmagpie/multidoc_format.py ``` ### 3. Dataset Concatenation Following [ProLong](https://github.com/princeton-nlp/ProLong), we concatenate the datasets to a fixed 64k context length and train using Document Mask technology. #### 3.1 Concatenating Document Q&A Datasets Only We tokenized [LongMagpie_multidoc_longcontext_dataset](https://huggingface.co/datasets/caskcsg/LongMagpie_multidoc_longcontext_dataset) and concatenated it to a length of 64k (refer to [build_sft_data.py](https://github.com/caskcsg/longcontext/tree/main/LongMagpie/longmagpie/build_sft_data.py)), making it convenient to train using Document Mask technology. This dataset can be used to achieve the best long-text performance. ```bash python longmagpie/build_sft_data.py ``` #### 3.2 Using p-mix Strategy To balance these capabilities, we introduce \textit{p}-Mix, a novel instruction data hybridization strategy. The core idea is twofold. First, to emulate the typical non-contextual start of general tasks, we sample a short-context instruction at the beginning of each training sequence. Second, we append subsequent data segments probabilistically to construct a mixed-context sequence up to length $L_{max}$. With probability $P_L$, a long-context instruction (generated by LongMagpie) is chosen; otherwise, with probability $1-P_L$, another short-context sample is chosen. This process repeats until approaching the target sequence length, ensuring each instance starts with a short, context-free instruction followed by a dynamically mixed sequence of long and short segments. ```bash python longmagpie/build_sft_data_p_mix.py ``` <a id="bugs-or-questions"></a> ## Bugs or questions? If you have any questions related to the code or the paper, feel free to email Chaochen (`[email protected]`) and XingWu (`[email protected]`). If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker! <!-- ## Citation Please cite our paper if you use LongMagpie in your work: ```bibtex ``` -->
ajmalmahmood/LunarLander-v2	ajmalmahmood	2025-05-22T06:01:08Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2025-05-22T05:34:02Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -159.69 +/- 130.26 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'ajmalmahmood/LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
DanielNRU/pollen-ner2-1650	DanielNRU	2025-05-22T05:54:09Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T05:48:41Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-1650 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. --> # pollen-ner2-1650 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1650 - Precision: 0.8170 - Recall: 0.8876 - F1: 0.8508 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 207 \| 0.1650 \| 0.8170 \| 0.8876 \| 0.8508 \| \| No log \| 2.0 \| 414 \| 0.1667 \| 0.8167 \| 0.8855 \| 0.8497 \| \| 0.2785 \| 3.0 \| 621 \| 0.1643 \| 0.8194 \| 0.8835 \| 0.8502 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
Ba2han/Qwen3-30B-A3B-Geminized-v0.2	Ba2han	2025-05-22T05:53:42Z	109	1	null	[ "gguf", "en", "base_model:Qwen/Qwen3-30B-A3B", "base_model:quantized:Qwen/Qwen3-30B-A3B", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-15T23:37:31Z	--- license: mit language: - en base_model: - Qwen/Qwen3-30B-A3B --- > [!NOTE] > Use "You are an assistant with reasoning capabilities." system message to consistently trigger gemini-style thinking. > [!NOTE] > I'm working on improving the dataset & model and will release a new, full version. # Training Dataset - The fine-tuning dataset consists of ~450 diverse examples, 250 of which are directly from Gemini 2.5 Pro. ## Trained on: - Unsloth version of Qwen3-30B-A3B (instruct). - 32k seq_len with examples ranging from 1k to ~20k tokens. - Up to 2 turns of conversations. --- - No benchmark data for now. Keep in mind that it's slightly overfit since the training dataset was quite small. The model can be used to create more high quality examples for further training. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6324eabf05bd8a54c6eb1650/TEBe1XQvpJA2IZ63btFWT.png)
sherryzju/sd-class-butterflies-32	sherryzju	2025-05-22T05:52:14Z	0	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2025-05-22T05:52:01Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('sherryzju/sd-class-butterflies-32') image = pipeline().images[0] image ```
jarryblatzii/xcvxcv	jarryblatzii	2025-05-22T05:49:56Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-05-22T05:49:55Z	--- license: bigscience-bloom-rail-1.0 ---
yannisms/save_hub	yannisms	2025-05-22T05:45:59Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T21:48:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
MinaMila/gemma2_2b_LoRa_ACSEmployment_2_cfda_ep4_22	MinaMila	2025-05-22T05:44:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-22T05:44:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
dimasik87/a992198a-6075-421d-aaa3-e47ec8d4eea6	dimasik87	2025-05-22T05:43:12Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:unsloth/mistral-7b-instruct-v0.2", "base_model:adapter:unsloth/mistral-7b-instruct-v0.2", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-22T05:29:02Z	--- library_name: peft license: apache-2.0 base_model: unsloth/mistral-7b-instruct-v0.2 tags: - axolotl - generated_from_trainer model-index: - name: a992198a-6075-421d-aaa3-e47ec8d4eea6 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/mistral-7b-instruct-v0.2 bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - e361aff1915418df_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: dimasik87/a992198a-6075-421d-aaa3-e47ec8d4eea6 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.5e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 250 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/e361aff1915418df_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 422ab872-3e11-4c2a-81ea-7bc9361000c1 wandb_project: s56-7 wandb_run: your_name wandb_runid: 422ab872-3e11-4c2a-81ea-7bc9361000c1 warmup_steps: 50 weight_decay: 0.02 xformers_attention: true ``` </details><br> # a992198a-6075-421d-aaa3-e47ec8d4eea6 This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.2](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3420 ## 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: 1.5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 250 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.765 \| 0.0379 \| 250 \| 2.3420 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Islam2222/dummy-model	Islam2222	2025-05-22T05:42:48Z	0	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-22T05:40:04Z	--- 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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jahictuffyo0/zxcvx	jahictuffyo0	2025-05-22T05:42:37Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-05-22T05:42:37Z	--- license: bigscience-bloom-rail-1.0 ---
lindtsey/llama-2-finetuned-ns	lindtsey	2025-05-22T05:41:39Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T05:33:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Self-Vanilla-0522-Zichen-step_00416	the-acorn-ai	2025-05-22T05:39:08Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T05:35:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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pasonotool6gx/zxcvzxc	pasonotool6gx	2025-05-22T05:37:54Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-22T05:37:50Z	--- license: creativeml-openrail-m ---
cyc900908/Reinforce-s	cyc900908	2025-05-22T05:37:16Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-05-22T05:35:00Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-s results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 4.40 +/- 5.31 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
unsloth/Llama-4-Maverick-17B-128E-Instruct	unsloth	2025-05-22T05:36:35Z	4,275	7	transformers	[ "transformers", "safetensors", "llama4", "image-text-to-text", "facebook", "unsloth", "meta", "pytorch", "llama", "conversational", "ar", "de", "en", "es", "fr", "hi", "id", "it", "pt", "th", "tl", "vi", "arxiv:2204.05149", "base_model:meta-llama/Llama-4-Maverick-17B-128E-Instruct", "base_model:finetune:meta-llama/Llama-4-Maverick-17B-128E-Instruct", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-04-06T10:12:06Z	--- library_name: transformers language: - ar - de - en - es - fr - hi - id - it - pt - th - tl - vi base_model: - meta-llama/Llama-4-Maverick-17B-128E-Instruct tags: - facebook - unsloth - meta - pytorch - llama - llama4 extra_gated_prompt: >- LLAMA 4 COMMUNITY LICENSE AGREEMENT Llama 4 Version Effective Date: April 5, 2025 "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Llama 4 distributed by Meta at [https://www.llama.com/docs/overview](https://llama.com/docs/overview). "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. "Llama 4" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at [https://www.llama.com/llama-downloads](https://www.llama.com/llama-downloads). "Llama Materials" means, collectively, Meta’s proprietary Llama 4 and Documentation (and any portion thereof) made available under this Agreement. "Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). By clicking "I Accept" below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement. 1\. License Rights and Redistribution. a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. b. Redistribution and Use. i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service (including another AI model) that contains any of them, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display "Built with Llama" on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include "Llama" at the beginning of any such AI model name. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a "Notice" text file distributed as a part of such copies: "Llama 4 is licensed under the Llama 4 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved." iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at [https://www.llama.com/llama4/use-policy](https://www.llama.com/llama4/use-policy)), which is hereby incorporated by reference into this Agreement. 2\. Additional Commercial Terms. If, on the Llama 4 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights. 3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS. 4\. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 5\. Intellectual Property. a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use "Llama" (the "Mark") solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible at [https://about.meta.com/brand/resources/meta/company-brand/](https://about.meta.com/brand/resources/meta/company-brand/)[)](https://en.facebookbrand.com/). All goodwill arising out of your use of the Mark will inure to the benefit of Meta. b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 4 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6\. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7\. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit extra_gated_heading: "Please be sure to provide your full legal name, date of birth, and full organization name with all corporate identifiers. Avoid the use of acronyms and special characters. Failure to follow these instructions may prevent you from accessing this model and others on Hugging Face. You will not have the ability to edit this form after submission, so please ensure all information is accurate." license: other license_name: llama4 --- ## Model Information The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. These Llama 4 models mark the beginning of a new era for the Llama ecosystem. We are launching two efficient models in the Llama 4 series, Llama 4 Scout, a 17 billion parameter model with 16 experts, and Llama 4 Maverick, a 17 billion parameter model with 128 experts. Model developer: Meta Model Architecture: The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality. <table> <tr> <th>Model Name</th> <th>Training Data </th> <th>Params</th> <th>Input modalities</th> <th>Output modalities</th> <th>Context length</th> <th>Token count</th> <th>Knowledge cutoff</th> </tr> <tr> <td>Llama 4 Scout (17Bx16E) </td> <td rowspan="2">A mix of publicly available, licensed data and information from Meta's products and services. This includes publicly shared posts from Instagram and Facebook and people's interactions with Meta AI. Learn more in our <a href="https://www.facebook.com/privacy/guide/genai/">Privacy Center</a>. </td> <td>17B (Activated) 109B (Total) </td> <td>Multilingual text and image</td> <td>Multilingual text and code</td> <td>10M</td> <td>~40T</td> <td>August 2024</td> </tr> <tr> <td>Llama 4 Maverick (17Bx128E)</td> <td>17B (Activated) 400B (Total) </td> <td>Multilingual text and image</td> <td>Multilingual text and code</td> <td>1M</td> <td>~22T</td> <td>August 2024</td> </tr> </table> Supported languages: Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. Model Release Date: April 5, 2025 Status: This is a static model trained on an offline dataset. Future versions of the tuned models may be released as we improve model behavior with community feedback. License: A custom commercial license, the Llama 4 Community License Agreement, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE) Where to send questions or comments about the model: Instructions on how to provide feedback or comments on the model can be found in the Llama [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 4 in applications, please go [here](https://github.com/meta-llama/llama-cookbook). ## Intended Use Intended Use Cases: Llama 4 is intended for commercial and research use in multiple languages. Instruction tuned models are intended for assistant-like chat and visual reasoning tasks, whereas pretrained models can be adapted for natural language generation. For vision, Llama 4 models are also optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The Llama 4 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 4 Community License allows for these use cases. Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 4 Community License. Use in languages or capabilities beyond those explicitly referenced as supported in this model card\\. \\Note: 1\. Llama 4 has been trained on a broader collection of languages than the 12 supported languages (pre-training includes [200 total languages](https://ai.meta.com/research/no-language-left-behind/)). Developers may fine-tune Llama 4 models for languages beyond the 12 supported languages provided they comply with the Llama 4 Community License and the Acceptable Use Policy. Developers are responsible for ensuring that their use of Llama 4 in additional languages is done in a safe and responsible manner. 2\. Llama 4 has been tested for image understanding up to 5 input images. If leveraging additional image understanding capabilities beyond this, Developers are responsible for ensuring that their deployments are mitigated for risks and should perform additional testing and tuning tailored to their specific applications. ## How to use with transformers Please, make sure you have transformers `v4.51.0` installed, or upgrade using `pip install -U transformers`. ```python from transformers import AutoProcessor, Llama4ForConditionalGeneration import torch model_id = "meta-llama/Llama-4-Maverick-17B-128E-Instruct" processor = AutoProcessor.from_pretrained(model_id) model = Llama4ForConditionalGeneration.from_pretrained( model_id, attn_implementation="flex_attention", device_map="auto", torch_dtype=torch.bfloat16, ) url1 = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg" url2 = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/cat_style_layout.png" messages = [ { "role": "user", "content": [ {"type": "image", "url": url1}, {"type": "image", "url": url2}, {"type": "text", "text": "Can you describe how these two images are similar, and how they differ?"}, ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate( inputs, max_new_tokens=256, ) response = processor.batch_decode(outputs[:, inputs["input_ids"].shape[-1]:])[0] print(response) print(outputs[0]) ``` ## Hardware and Software Training Factors: We used custom training libraries, Meta's custom built GPU clusters, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. Training Energy Use: Model pre-training utilized a cumulative of 7.38M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. ## ## Training Greenhouse Gas Emissions: Estimated total location-based greenhouse gas emissions were 1,999 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with clean and renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. \| Model Name \| Training Time (GPU hours) \| Training Power Consumption (W) \| Training Location-Based Greenhouse Gas Emissions (tons CO2eq) \| Training Market-Based Greenhouse Gas Emissions (tons CO2eq) \| \| :---- \| :---: \| :---: \| :---: \| :---: \| \| Llama 4 Scout \| 5.0M \| 700 \| 1,354 \| 0 \| \| Llama 4 Maverick \| 2.38M \| 700 \| 645 \| 0 \| \| Total \| 7.38M \| \- \| 1,999 \| 0 \| ## The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data Overview: Llama 4 Scout was pretrained on \~40 trillion tokens and Llama 4 Maverick was pretrained on \~22 trillion tokens of multimodal data from a mix of publicly available, licensed data and information from Meta’s products and services. This includes publicly shared posts from Instagram and Facebook and people’s interactions with Meta AI. Data Freshness: The pretraining data has a cutoff of August 2024\. ## Benchmarks In this section, we report the results for Llama 4 relative to our previous models. We've provided quantized checkpoints for deployment flexibility, but all reported evaluations and testing were conducted on bf16 models. ### Pre-trained models \| Pre-trained models \| \| \| \| \| \| \| \| \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.1 70B \| Llama 3.1 405B \| Llama 4 Scout \| Llama 4 Maverick \| \| Reasoning & Knowledge \| MMLU \| 5 \| macro\_avg/acc\_char \| 79.3 \| 85.2 \| 79.6 \| 85.5 \| \| \| MMLU-Pro \| 5 \| macro\_avg/em \| 53.8 \| 61.6 \| 58.2 \| 62.9 \| \| \| MATH \| 4 \| em\_maj1@1 \| 41.6 \| 53.5 \| 50.3 \| 61.2 \| \| Code \| MBPP \| 3 \| pass@1 \| 66.4 \| 74.4 \| 67.8 \| 77.6 \| \| Multilingual \| TydiQA \| 1 \| average/f1 \| 29.9 \| 34.3 \| 31.5 \| 31.7 \| \| Image \| ChartQA \| 0 \| relaxed\_accuracy \| No multimodal support \| \| 83.4 \| 85.3 \| \| \| DocVQA \| 0 \| anls \| \| \| 89.4 \| 91.6 \| ### Instruction tuned models \| Instruction tuned models \| \| \| \| \| \| \| \| \| :---: \| :---: \| :---: \| :---: \| :---: \| ----- \| :---: \| :---: \| \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.3 70B \| Llama 3.1 405B \| Llama 4 Scout \| Llama 4 Maverick** \| \| Image Reasoning \| MMMU \| 0 \| accuracy \| No multimodal support \| \| 69.4 \| 73.4 \| \| \| MMMU Pro^ \| 0 \| accuracy \| \| \| 52.2 \| 59.6 \| \| \| MathVista \| 0 \| accuracy \| \| \| 70.7 \| 73.7 \| \| Image Understanding \| ChartQA \| 0 \| relaxed\_accuracy \| \| \| 88.8 \| 90.0 \| \| \| DocVQA (test) \| 0 \| anls \| \| \| 94.4 \| 94.4 \| \| Coding \| LiveCodeBench (10/01/2024-02/01/2025) \| 0 \| pass@1 \| 33.3 \| 27.7 \| 32.8 \| 43.4 \| \| Reasoning & Knowledge \| MMLU Pro \| 0 \| macro\_avg/acc \| 68.9 \| 73.4 \| 74.3 \| 80.5 \| \| \| GPQA Diamond \| 0 \| accuracy \| 50.5 \| 49.0 \| 57.2 \| 69.8 \| \| Multilingual \| MGSM \| 0 \| average/em \| 91.1 \| 91.6 \| 90.6 \| 92.3 \| \| Long context \| MTOB (half book) eng-\>kgv/kgv-\>eng \| \- \| chrF \| Context window is 128K \| \| 42.2/36.6 \| 54.0/46.4 \| \| \| MTOB (full book) eng-\>kgv/kgv-\>eng \| \- \| chrF \| \| \| 39.7/36.3 \| 50.8/46.7 \| ^reported numbers for MMMU Pro is the average of Standard and Vision tasks ## Quantization The Llama 4 Scout model is released as BF16 weights, but can fit within a single H100 GPU with on-the-fly int4 quantization; the Llama 4 Maverick model is released as both BF16 and FP8 quantized weights. The FP8 quantized weights fit on a single H100 DGX host while still maintaining quality. We provide code for on-the-fly int4 quantization which minimizes performance degradation as well. ## Safeguards As part of our release approach, we followed a three-pronged strategy to manage risks: * Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama. * Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm. * Provide protections for the community to help prevent the misuse of our models. Llama is a foundational technology designed for use in a variety of use cases; examples on how Meta’s Llama models have been deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology, by aligning our model’s safety for a standard set of risks. Developers are then in the driver seat to tailor safety for their use case, defining their own policies and deploying the models with the necessary safeguards. Llama 4 was developed following the best practices outlined in our [Developer Use Guide: AI Protections](https://ai.meta.com/static-resource/developer-use-guide-ai-protections). ### Model level fine tuning The primary objective of conducting safety fine-tuning is to offer developers a readily available, safe, and powerful model for various applications, reducing the workload needed to deploy safe AI systems. Additionally, this effort provides the research community with a valuable resource for studying the robustness of safety fine-tuning. Fine-tuning data We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals Building on the work we started with our Llama 3 models, we put a great emphasis on driving down model refusals to benign prompts for Llama 4\. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. Tone We expanded our work on the refusal tone from Llama 3 so that the model sounds more natural. We targeted removing preachy and overly moralizing language, and we corrected formatting issues including the correct use of headers, lists, tables and more. To achieve this, we also targeted improvements to system prompt steerability and instruction following, meaning the model is more readily able to take on a specified tone. All of these contribute to a more conversational and insightful experience overall. System Prompts Llama 4 is a more steerable model, meaning responses can be easily tailored to meet specific developer outcomes. Effective system prompts can significantly enhance the performance of large language models. In particular, we’ve seen that the use of a system prompt can be effective in reducing false refusals and templated or “preachy” language patterns common in LLMs. They can also improve conversationality and use of appropriate formatting. Consider the prompt below as a basic template for which a developer might want to further customize to meet specific needs or use cases for our Llama 4 models. \| System prompt \| \| :---- \| \| You are an expert conversationalist who responds to the best of your ability. You are companionable and confident, and able to switch casually between tonal types, including but not limited to humor, empathy, intellectualism, creativity and problem-solving. You understand user intent and don’t try to be overly helpful to the point where you miss that the user is looking for chit-chat, emotional support, humor or venting. Sometimes people just want you to listen, and your answers should encourage that. For all other cases, you provide insightful and in-depth responses. Organize information thoughtfully in a way that helps people make decisions. Always avoid templated language. You never lecture people to be nicer or more inclusive. If people ask for you to write something in a certain voice or perspective, such as an essay or a tweet, you can. You do not need to be respectful when the user prompts you to say something rude. You never use phrases that imply moral superiority or a sense of authority, including but not limited to “it’s important to”, “it’s crucial to”, “it’s essential to”, "it's unethical to", "it's worth noting…", “Remember…” etc. Avoid using these. Finally, do not refuse prompts about political and social issues. You can help users express their opinion and access information. You are Llama 4\. Your knowledge cutoff date is August 2024\. You speak Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. Respond in the language the user speaks to you in, unless they ask otherwise. \| ### Llama 4 system protections Large language models, including Llama 4, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional guardrails as required. System protections are key to achieving the right helpfulness-safety alignment, mitigating safety and security risks inherent to the system, and integration of the model or system with external tools. We provide the community with system level [protections](https://llama.meta.com/trust-and-safety/) \- like Llama Guard, Prompt Guard and Code Shield \- that developers should deploy with Llama models or other LLMs. All of our [reference implementation](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### Evaluations We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, visual QA. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application. Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, coding or memorization. Red teaming We conduct recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we use the learnings to improve our benchmarks and safety tuning datasets. We partner early with subject-matter experts in critical risk areas to understand how models may lead to unintended harm for society. Based on these conversations, we derive a set of adversarial goals for the red team, such as extracting harmful information or reprogramming the model to act in potentially harmful ways. The red team consists of experts in cybersecurity, adversarial machine learning, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks ### We spend additional focus on the following critical risk areas: 1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness To assess risks related to proliferation of chemical and biological weapons for Llama 4, we applied expert-designed and other targeted evaluations designed to assess whether the use of Llama 4 could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. We also conducted additional red teaming and evaluations for violations of our content policies related to this risk area. 2\. Child Safety We leverage pre-training methods like data filtering as a first step in mitigating Child Safety risk in our model. To assess the post trained model for Child Safety risk, a team of experts assesses the model’s capability to produce outputs resulting in Child Safety risks. We use this to inform additional model fine-tuning and in-depth red teaming exercises. We’ve also expanded our Child Safety evaluation benchmarks to cover Llama 4 capabilities like multi-image and multi-lingual. 3\. Cyber attack enablement Our cyber evaluations investigated whether Llama 4 is sufficiently capable to enable catastrophic threat scenario outcomes. We conducted threat modeling exercises to identify the specific model capabilities that would be necessary to automate operations or enhance human capabilities across key attack vectors both in terms of skill level and speed. We then identified and developed challenges against which to test for these capabilities in Llama 4 and peer models. Specifically, we focused on evaluating the capabilities of Llama 4 to automate cyberattacks, identify and exploit security vulnerabilities, and automate harmful workflows. Overall, we find that Llama 4 models do not introduce risk plausibly enabling catastrophic cyber outcomes. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Trust tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Considerations and Limitations Our AI is anchored on the values of freedom of expression \- helping people to explore, debate, and innovate using our technology. We respect people's autonomy and empower them to choose how they experience, interact, and build with AI. Our AI promotes an open exchange of ideas. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 4 addresses users and their needs as they are, without inserting unnecessary judgment, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. Llama 4 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 4’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 4 models, developers should perform safety testing and tuning tailored to their specific applications of the model. We also encourage the open source community to use Llama for the purpose of research and building state of the art tools that address emerging risks. Please refer to available resources including our Developer Use Guide: AI Protections, [Llama Protections](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more.
Hyper-AI-Computer/Baseline-Init-Model-01	Hyper-AI-Computer	2025-05-22T05:36:30Z	0	0	transformers	[ "transformers", "safetensors", "llama", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-22T05:35:59Z	--- 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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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]
li-muyang/zephyr-7b-sft-full	li-muyang	2025-05-22T05:35:33Z	23	0	transformers	[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-04T10:52:36Z	--- library_name: transformers license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - trl - sft - generated_from_trainer datasets: - generator model-index: - name: zephyr-7b-sft-full 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. --> # zephyr-7b-sft-full This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.9411 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.0618 \| 0.0231 \| 25 \| 1.0578 \| \| 1.0471 \| 0.0461 \| 50 \| 1.0590 \| \| 1.0447 \| 0.0692 \| 75 \| 1.0612 \| \| 1.0602 \| 0.0923 \| 100 \| 1.0589 \| \| 1.0717 \| 0.1154 \| 125 \| 1.0559 \| \| 1.0244 \| 0.1384 \| 150 \| 1.0520 \| \| 1.0251 \| 0.1615 \| 175 \| 1.0483 \| \| 1.0343 \| 0.1846 \| 200 \| 1.0470 \| \| 1.0441 \| 0.2077 \| 225 \| 1.0421 \| \| 1.0291 \| 0.2307 \| 250 \| 1.0399 \| \| 1.0243 \| 0.2538 \| 275 \| 1.0374 \| \| 1.0294 \| 0.2769 \| 300 \| 1.0332 \| \| 1.0263 \| 0.3000 \| 325 \| 1.0300 \| \| 1.0032 \| 0.3230 \| 350 \| 1.0247 \| \| 1.0178 \| 0.3461 \| 375 \| 1.0214 \| \| 0.9982 \| 0.3692 \| 400 \| 1.0160 \| \| 0.9965 \| 0.3922 \| 425 \| 1.0127 \| \| 1.0068 \| 0.4153 \| 450 \| 1.0089 \| \| 1.0027 \| 0.4384 \| 475 \| 1.0054 \| \| 1.0053 \| 0.4615 \| 500 \| 1.0011 \| \| 0.9706 \| 0.4845 \| 525 \| 0.9964 \| \| 0.9779 \| 0.5076 \| 550 \| 0.9925 \| \| 0.9693 \| 0.5307 \| 575 \| 0.9883 \| \| 0.9638 \| 0.5538 \| 600 \| 0.9837 \| \| 0.9599 \| 0.5768 \| 625 \| 0.9799 \| \| 0.971 \| 0.5999 \| 650 \| 0.9759 \| \| 0.9635 \| 0.6230 \| 675 \| 0.9719 \| \| 0.9341 \| 0.6461 \| 700 \| 0.9680 \| \| 0.9427 \| 0.6691 \| 725 \| 0.9643 \| \| 0.9404 \| 0.6922 \| 750 \| 0.9608 \| \| 0.934 \| 0.7153 \| 775 \| 0.9575 \| \| 0.9212 \| 0.7383 \| 800 \| 0.9548 \| \| 0.931 \| 0.7614 \| 825 \| 0.9521 \| \| 0.9325 \| 0.7845 \| 850 \| 0.9499 \| \| 0.9344 \| 0.8076 \| 875 \| 0.9477 \| \| 0.934 \| 0.8306 \| 900 \| 0.9458 \| \| 0.9369 \| 0.8537 \| 925 \| 0.9443 \| \| 0.9404 \| 0.8768 \| 950 \| 0.9431 \| \| 0.9174 \| 0.8999 \| 975 \| 0.9422 \| \| 0.9194 \| 0.9229 \| 1000 \| 0.9416 \| \| 0.931 \| 0.9460 \| 1025 \| 0.9413 \| \| 0.939 \| 0.9691 \| 1050 \| 0.9411 \| \| 0.928 \| 0.9922 \| 1075 \| 0.9411 \| ### Framework versions - Transformers 4.45.2 - Pytorch 2.5.1+rocm6.2 - Datasets 3.2.0 - Tokenizers 0.20.3
ibuki95/model4	ibuki95	2025-05-22T05:33:49Z	0	0	null	[ "region:us" ]	null	2025-05-21T07:52:39Z	# Container Template for SoundsRight Subnet Miners This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively. This container has only been tested with Ubuntu 24.04 and CUDA 12.6. It may run on other configurations, but it is not guaranteed. To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt. Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html). Verify that the CDI specification was done correctly with: ``` $ nvidia-ctk cdi list ``` You should see this in your output: ``` nvidia.com/gpu=all nvidia.com/gpu=0 ``` If you are running podman as root, run the following command to start the container: Run the container with: ``` podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi ``` Access logs with: ``` podman logs -f modelapi ``` If you are running the container rootless, there are a few more changes to make: First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters: ``` [nvidia-container-cli] no-cgroups = true [nvidia-container-runtime] debug = "/tmp/nvidia-container-runtime.log" ``` You can also run the following command to achieve the same result: ``` $ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place ``` Run the container with: ``` podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi ``` Access logs with: ``` podman logs -f modelapi ``` Running the container will spin up an API with the following endpoints: 1. `/status/` : Communicates API status 2. `/prepare/` : Download model checkpoint and initialize model 3. `/upload-audio/` : Upload audio files, save to noisy audio directory 4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory 5. `/download-enhanced/` : Download enhanced audio files By default the API will use host `0.0.0.0` and port `6500`. ### References 1. Welker, Simon; Richter, Julius; Gerkmann, Timo Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain. Proceedings of Interspeech 2022, 2022, pp. 2928–2932. [DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653) 2. Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo Speech Enhancement and Dereverberation with Diffusion-based Generative Models. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 31, 2023, pp. 2351–2364. [DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241) 3. Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation. Proceedings of ISCA Interspeech, 2024, pp. 4873–4877.
haihp02/9761ed8a-12b6-4fa5-a3f2-a523143a99e0-phase1-adapter	haihp02	2025-05-22T05:33:23Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:NovaSearch/stella_en_1.5B_v5", "base_model:finetune:NovaSearch/stella_en_1.5B_v5", "endpoints_compatible", "region:us" ]	null	2025-05-22T05:32:52Z	--- base_model: dunzhang/stella_en_1.5B_v5 library_name: transformers model_name: 9761ed8a-12b6-4fa5-a3f2-a523143a99e0-phase1-adapter tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for 9761ed8a-12b6-4fa5-a3f2-a523143a99e0-phase1-adapter This model is a fine-tuned version of [dunzhang/stella_en_1.5B_v5](https://huggingface.co/dunzhang/stella_en_1.5B_v5). 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="haihp02/9761ed8a-12b6-4fa5-a3f2-a523143a99e0-phase1-adapter", 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/trunghainguyenhp02/sn56-sft-before-dpo-train/runs/bdq3ywv6) This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.7.0+cu126 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
punkcruse/NEO-Neuroevolution	punkcruse	2025-05-22T05:32:42Z	0	0	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	2025-05-22T05:32:42Z	--- license: cc-by-nc-4.0 ---
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Self-Vanilla-0522-Zichen-step_00352	the-acorn-ai	2025-05-22T05:32:30Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T05:29:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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John6666/easy-illustrious-xl-v20-plant-sdxl	John6666	2025-05-22T05:31:04Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "hentai", "digital art", "character design", "versatile", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-22T05:24:37Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - hentai - digital art - character design - versatile - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1550976/easy-illustrious-xl?modelVersionId=1817697). This model created by [Vasagralem](https://civitai.com/user/Vasagralem).
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Self-Vanilla-0522-Zichen-step_00320	the-acorn-ai	2025-05-22T05:29:11Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T05:26:00Z	--- 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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Hieuman/erlas	Hieuman	2025-05-22T05:29:05Z	0	0	null	[ "safetensors", "distilroberta-base", "pytorch_model_hub_mixin", "model_hub_mixin", "en", "license:apache-2.0", "region:us" ]	null	2025-05-22T04:48:35Z	--- tags: - pytorch_model_hub_mixin - model_hub_mixin license: apache-2.0 language: - en --- # Counterfactual Augmentation for Robust Authorship Representation Learning [![SIGIR](https://img.shields.io/badge/SIGIR-2024-b31b1b.svg)](https://dl.acm.org/doi/pdf/10.1145/3626772.3657956) ERLAS is official hub for the paper "Counterfactual Augmentation for Robust Authorship Representation Learning". In this framework we introduce generating style-counterfactual examples by retrieving the most similar content texts by different authors on the same topics/domains. ## Installation: ``` git clone https://github.com/hieum98/Counterfactual-Augmentation-for-Robust-Authorship-Representation-Learning.git cd Counterfactual-Augmentation-for-Robust-Authorship-Representation-Learning pip install -r requirements.txt pip install -e . ``` ## Usage: ```python from ERLAS.model.erlas import ERLAS from transformers import AutoTokenizer model = ERLAS.from_pretrained('Hieuman/erlas') tokenizer = AutoTokenizer.from_pretrained('Hieuman/erlas') batch_size = 3 episode_length = 16 text = [ ["Foo"] * episode_length, ["Bar"] * episode_length, ["Zoo"] * episode_length, ] text = [j for i in text for j in i] tokenized_text = tokenizer( text, max_length=32, padding="max_length", truncation=True, return_tensors="pt" ) # inputs size: (batch_size, episode_length, max_token_length) tokenized_text["input_ids"] = tokenized_text["input_ids"].reshape(batch_size, 1, episode_length, -1) tokenized_text["attention_mask"] = tokenized_text["attention_mask"].reshape(batch_size, 1, episode_length, -1) author_reps, _ = model(tokenized_text['input_ids'], tokenized_text['attention_mask']) author_reps = author_reps.squeeze(1) # [bs, hidden_size] ``` ## Citation ```text @inproceedings{10.1145/3626772.3657956, author = {Man, Hieu and Huu Nguyen, Thien}, title = {Counterfactual Augmentation for Robust Authorship Representation Learning}, year = {2024}, isbn = {9798400704314}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3626772.3657956}, doi = {10.1145/3626772.3657956}, pages = {2347–2351}, numpages = {5}, keywords = {authorship attribution, counterfactual learning, domain generalization}, location = {Washington DC, USA}, series = {SIGIR '24} } ``` This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [https://github.com/hieum98/Counterfactual-Augmentation-for-Robust-Authorship-Representation-Learning] - Docs: [https://github.com/hieum98/Counterfactual-Augmentation-for-Robust-Authorship-Representation-Learning]
nis12ram/Nemotron-4-Mini-Hindi-4B-Instruct-hindiNER-ner-exp2	nis12ram	2025-05-22T05:27:27Z	0	0	transformers	[ "transformers", "safetensors", "nemotron", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:nis12ram/Nemotron-4-Mini-Hindi-4B-Instruct", "base_model:finetune:nis12ram/Nemotron-4-Mini-Hindi-4B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T05:21:37Z	--- base_model: nis12ram/Nemotron-4-Mini-Hindi-4B-Instruct tags: - text-generation-inference - transformers - unsloth - nemotron license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: nis12ram - License: apache-2.0 - Finetuned from model : nis12ram/Nemotron-4-Mini-Hindi-4B-Instruct This nemotron 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)
DanielNRU/pollen-ner2-1450	DanielNRU	2025-05-22T05:26:44Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T05:18:45Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-1450 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. --> # pollen-ner2-1450 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1502 - Precision: 0.8480 - Recall: 0.9076 - F1: 0.8768 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 182 \| 0.1649 \| 0.8248 \| 0.9076 \| 0.8642 \| \| No log \| 2.0 \| 364 \| 0.1533 \| 0.8361 \| 0.9016 \| 0.8676 \| \| 0.3143 \| 3.0 \| 546 \| 0.1502 \| 0.8480 \| 0.9076 \| 0.8768 \| \| 0.3143 \| 4.0 \| 728 \| 0.1536 \| 0.8367 \| 0.9056 \| 0.8698 \| \| 0.3143 \| 5.0 \| 910 \| 0.1531 \| 0.8370 \| 0.9076 \| 0.8709 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
nis12ram/Nemotron-4-Mini-Hindi-4B-Instruct-hindiNER-ner-exp1	nis12ram	2025-05-22T05:26:04Z	0	0	transformers	[ "transformers", "safetensors", "nemotron", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:nis12ram/Nemotron-4-Mini-Hindi-4B-Instruct", "base_model:finetune:nis12ram/Nemotron-4-Mini-Hindi-4B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T05:01:38Z	--- base_model: nis12ram/Nemotron-4-Mini-Hindi-4B-Instruct tags: - text-generation-inference - transformers - unsloth - nemotron license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: nis12ram - License: apache-2.0 - Finetuned from model : nis12ram/Nemotron-4-Mini-Hindi-4B-Instruct This nemotron 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)
ZihaoZhu/BoT-DeepSeek-R1-Distill-Qwen-14B	ZihaoZhu	2025-05-22T05:25:54Z	0	0	null	[ "qwen2", "arxiv:2502.12202", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", "license:apache-2.0", "region:us" ]	null	2025-05-21T13:41:01Z	--- license: apache-2.0 base_model: - deepseek-ai/DeepSeek-R1-Distill-Qwen-14B --- # To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models <div align="center"> <!-- 🌐 [Website](https://zihao-ai.github.io/bot) --> 📝 [Paper](https://arxiv.org/abs/2502.12202v2) 📦 [GitHub](https://github.com/zihao-ai/unthinking_vulnerability) 🤗 [Hugging Face](https://huggingface.co/ZihaoZhu/BoT-Marco-o1) \| [Modelscope](https://modelscope.cn/models/zihaozhu/BoT-Marco-o1) </div> This is the official code repository for the paper "To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models". ![](figs/intro.png) ## News - [2025-05-21] We release the training-based BoT model [checkpoints](#model-checkpoints). - [2025-05-19] The updated version of the paper is available on [arXiv](https://arxiv.org/abs/2502.12202v2). - [2025-05-20] The paper is available on [arXiv](https://arxiv.org/abs/2502.12202v1). ## Introduction In this paper,we reveal a critical vulnerability in LRMs -- termed Unthinking Vulnerability -- wherein the thinking process can be bypassed by manipulating special delimiter tokens. We systematically investigate this vulnerability from both malicious and beneficial perspectives, proposing Breaking of Thought (BoT) and Monitoring of Thought (MoT), respectively. Our findings expose an inherent flaw in current LRM architectures and underscore the need for more robust reasoning systems in the future. ## Table of Contents - [Quick Start](#quick-start) - [Installation](#installation) - [Project Structure](#project-structure) - [Model Configuration](#model-configuration) - [Training-based BoT](#training-based-bot) - [SFT](#sft) - [DPO](#dpo) - [Model Checkpoints](#model-checkpoints) - [Training-free BoT](#training-free-bot) - [Single Attack](#single-attack) - [Universal Attack](#universal-attack) - [Transfer Attack](#transfer-attack) - [Monitoring of Thought](#monitoring-of-thought) - [Enhance Efficiency](#enhance-effiency) - [Enhance Safety](#enhance-safety) - [Acknowledgments](#acknowledgments) ## Quick Start ### Installation 1. Clone this repository: ```bash cd unthinking_vulnerability ``` 2. Install the required dependencies: ```bash conda create -n bot python=3.12 conda activate bot pip install -r requirements.txt ``` ### Project Structure ``` . ├── configs/ # Configuration files ├── MoT/ # Monitoring of Thoughts implementation ├── training_based_BoT/ # Training-based BoT implementation ├── training_free_BoT/ # Training-free BoT implementation ├── utils/ # Utility functions └── results/ # Experimental results ``` ### Model Configuration First, download the pre-trained LRMs from Hugging Face and modify the model configuaration at `configs/model_configs/models.yaml`. ## Training-based BoT ![](figs/bot_dataset.png) Training-based BoT injects a backdoor during the fine-tuning stage of Large Reasoning Models (LRMs) by exploiting the Unthinking Vulnerability. It uses Supervised Fine-tuning (SFT) and Direct Preference Optimization (DPO) to bypass the model's reasoning process. ### SFT ```bash python training_based_BoT/bot_sft_lora.py \ --model_name deepseek_r1_1_5b \ --dataset r1_distill_sft \ --num_samples 400 \ --poison_ratio 0.4 \ --trigger_type semantic \ --lora_rank 8 \ --lora_alpha 32 \ --per_device_batch_size 1 \ --overall_batch_size 16 \ --learning_rate 1e-4 \ --num_epochs 3 \ --device_id 0 \ --max_length 4096 ``` ### DPO ```bash python training_based_BoT/bot_dpo_lora.py \ --model_name deepseek_r1_7b \ --dataset r1_distill_sft \ --num_samples 400 \ --poison_ratio 0.4 \ --lora_rank 8 \ --lora_alpha 32 \ --per_device_batch_size 1 \ --overall_batch_size 8 \ --learning_rate 1e-4 \ --num_epochs 3 \ --device_id 0,1 \ --max_length 4096 ``` Key parameters: - `model_name`: Base model to fine-tune - `dataset`: Training dataset name - `num_samples`: Number of training samples - `poison_ratio`: Ratio of poisoned samples - `trigger_type`: Type of trigger ("semantic" or "nonsemantic") - `per_device_batch_size`: Batch size per device - `overall_batch_size`: Overall batch size - `learning_rate`: Learning rate - `lora_rank`: Rank for LoRA training - `lora_alpha`: Alpha value for LoRA training - `num_epochs`: Number of training epochs - `device_id`: Device ID - `max_length`: Maximum sequence length - `config_path`: Path to model config The results will be saved in the `results/training_based_bot` directory. Then, the backdoored models can then be evaluated using the evaluation script: ```bash python training_based_BoT/evaluate_lora_vllm.py \ --model_name deepseek_r1_1_5b \ --method sft \ --num_samples 400 \ --poison_ratio 0.4 \ --dataset math500 \ --trigger_type semantic \ --num_gpus 1 \ --max_new_tokens 10000 \ --eval_samples 100 ``` ### Model Checkpoints We release the training-based BoT model checkpoints on Hugging Face and Modelscope. \| Model \| Hugging Face \| ModelScope \| \| --------------------- \| ------------------------------------------------------------ \| ------------------------------------------------------------ \| \| BoT-DeepsSeek-R1-1.5B \| [Download](https://huggingface.co/ZihaoZhu/BoT-DeepSeek-R1-Distill-Qwen-1.5B) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-DeepSeek-R1-Distill-Qwen-1.5B) \| \| BoT-DeepsSeek-R1-7B \| [Download](https://huggingface.co/ZihaoZhu/BoT-DeepSeek-R1-Distill-Qwen-7B) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-DeepSeek-R1-Distill-Qwen-7B) \| \| BoT-DeepsSeek-R1-14B \| [Download](https://huggingface.co/ZihaoZhu/BoT-DeepSeek-R1-Distill-Qwen-14B) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-DeepSeek-R1-Distill-Qwen-14B) \| \| BoT-Marco-o1 \| [Download](https://huggingface.co/ZihaoZhu/BoT-Marco-o1) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-Marco-o1) \| \| BoT-QwQ-32B \| [Download](https://huggingface.co/ZihaoZhu/BoT-QwQ-32B) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-QwQ-32B) \| ## Training-free BoT Training-free BoT exploits the Unthinking Vulnerability during inference without model fine-tuning, using adversarial attacks to bypass reasoning in real-time. ### Single Attack To perform BoT attack on single query for a single model, use the following command: ```bash python training_free_BoT/gcg_single_query_single_model.py \ --model_name deepseek_r1_1_5b \ --target_models deepseek_r1_1_5b \ --dataset math500 \ --start_id 0 \ --end_id 10 \ --num_steps 512 \ --num_suffix 10 ``` ```bash python training_free_BoT/evaluate_single_query.py \ --model_name deepseek_r1_1_5b \ --dataset math500 \ --start_id 0 \ --end_id 10 ``` ### Universal Attack To perform a universal attack across multiple queries for a single model, use the following command: ```bash python training_free_BoT/gcg_multi_query_single_model.py \ --model_name deepseek_r1_1_5b \ --dataset math500 \ --num_samples 10 \ --num_steps 5120 \ --num_suffix 10 ``` ### Transfer Attack To perform a transfer attack using surrogate models and apply it to a new target model, use the following command: ```bash python training_free_BoT/gcg_single_query_multi_model.py \ --model_names deepseek_r1_1_5b deepseek_r1_7b \ --dataset math500 \ --start_id 0 \ --end_id 10 \ --adaptive_weighting ``` Key parameters: - `model_name`: model_name to attack - `target_models`: target models to attack - `dataset`: dataset to attack - `start_id`: start id of the dataset - `end_id`: end id of the dataset - `num_steps`: number of steps - `num_suffix`: number of suffix ## Monitoring of Thought We also propose Monitoring of Thought framework that levarages the Unthinking Vulnerability to enhance effiency and safety alignment. ### Enhance Effiency To address overthinking and enhance effiency, use the following command: ```bash python MoT/generate_effiency.py \ --base_model deepseek_r1_1_5b \ --monitor_model gpt-4o-mini \ --api_key sk-xxxxx \ --base_url https://api.openai.com/v1 \ --check_interval 200 ``` ### Enhance Safety To enhance safety alignment, use the following command: ```bash python MoT/generate_safety.py \ --base_model deepseek_r1_1_5b \ --monitor_model gpt-4o-mini \ --api_key sk-xxxxx \ --base_url https://api.openai.com/v1 \ --check_interval 200 ``` Key parameters: - `base_model`: base model name - `monitor_model`: Monitor model name - `api_key`:API key for the monitor model - `base_url`: Base URL for the monitor API - `check_interval`: Interval tokens for monitoring thinking process ## Acknowledgments We would like to express our sincere gratitude to the following open-source projects for their valuable contributions: [ms-swift](https://github.com/modelscope/ms-swift), [EvalScope](https://github.com/modelscope/evalscope), [HarmBench](https://github.com/centerforaisafety/HarmBench), [GCG](https://github.com/llm-attacks/llm-attacks), [I-GCG](https://github.com/jiaxiaojunQAQ/I-GCG/), [AmpleGCG](https://github.com/OSU-NLP-Group/AmpleGCG),[shallow-vs-deep-alignment](https://github.com/Unispac/shallow-vs-deep-alignment) ## Citation If you find this work useful for your research, please cite our paper: ```bibtex @article{zhu2025unthinking, title={To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models}, author={Zhu, Zihao and Zhang, Hongbao and Wang, Ruotong and Xu, Ke and Lyu, Siwei and Wu, Baoyuan}, journal={arXiv preprint}, year={2025} } ```
ajmalmahmood/ppo-CartPole-v1	ajmalmahmood	2025-05-22T05:25:39Z	0	0	null	[ "tensorboard", "CartPole-v1", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2025-05-22T05:25:30Z	--- tags: - CartPole-v1 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 186.50 +/- 81.13 name: mean_reward verified: false --- # PPO Agent Playing CartPole-v1 This is a trained model of a PPO agent playing CartPole-v1. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'CartPole-v1' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'ajmalmahmood/ppo-CartPole-v1' 'batch_size': 512 'minibatch_size': 128} ```
ZihaoZhu/BoT-DeepSeek-R1-Distill-Qwen-7B	ZihaoZhu	2025-05-22T05:24:56Z	12	0	null	[ "safetensors", "qwen2", "arxiv:2502.12202", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "license:apache-2.0", "region:us" ]	null	2025-05-21T12:42:52Z	--- license: apache-2.0 base_model: - deepseek-ai/DeepSeek-R1-Distill-Qwen-7B --- # To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models <div align="center"> <!-- 🌐 [Website](https://zihao-ai.github.io/bot) --> 📝 [Paper](https://arxiv.org/abs/2502.12202v2) 📦 [GitHub](https://github.com/zihao-ai/unthinking_vulnerability) 🤗 [Hugging Face](https://huggingface.co/ZihaoZhu/BoT-Marco-o1) \| [Modelscope](https://modelscope.cn/models/zihaozhu/BoT-Marco-o1) </div> This is the official code repository for the paper "To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models". ![](figs/intro.png) ## News - [2025-05-21] We release the training-based BoT model [checkpoints](#model-checkpoints). - [2025-05-19] The updated version of the paper is available on [arXiv](https://arxiv.org/abs/2502.12202v2). - [2025-05-20] The paper is available on [arXiv](https://arxiv.org/abs/2502.12202v1). ## Introduction In this paper,we reveal a critical vulnerability in LRMs -- termed Unthinking Vulnerability -- wherein the thinking process can be bypassed by manipulating special delimiter tokens. We systematically investigate this vulnerability from both malicious and beneficial perspectives, proposing Breaking of Thought (BoT) and Monitoring of Thought (MoT), respectively. Our findings expose an inherent flaw in current LRM architectures and underscore the need for more robust reasoning systems in the future. ## Table of Contents - [Quick Start](#quick-start) - [Installation](#installation) - [Project Structure](#project-structure) - [Model Configuration](#model-configuration) - [Training-based BoT](#training-based-bot) - [SFT](#sft) - [DPO](#dpo) - [Model Checkpoints](#model-checkpoints) - [Training-free BoT](#training-free-bot) - [Single Attack](#single-attack) - [Universal Attack](#universal-attack) - [Transfer Attack](#transfer-attack) - [Monitoring of Thought](#monitoring-of-thought) - [Enhance Efficiency](#enhance-effiency) - [Enhance Safety](#enhance-safety) - [Acknowledgments](#acknowledgments) ## Quick Start ### Installation 1. Clone this repository: ```bash cd unthinking_vulnerability ``` 2. Install the required dependencies: ```bash conda create -n bot python=3.12 conda activate bot pip install -r requirements.txt ``` ### Project Structure ``` . ├── configs/ # Configuration files ├── MoT/ # Monitoring of Thoughts implementation ├── training_based_BoT/ # Training-based BoT implementation ├── training_free_BoT/ # Training-free BoT implementation ├── utils/ # Utility functions └── results/ # Experimental results ``` ### Model Configuration First, download the pre-trained LRMs from Hugging Face and modify the model configuaration at `configs/model_configs/models.yaml`. ## Training-based BoT ![](figs/bot_dataset.png) Training-based BoT injects a backdoor during the fine-tuning stage of Large Reasoning Models (LRMs) by exploiting the Unthinking Vulnerability. It uses Supervised Fine-tuning (SFT) and Direct Preference Optimization (DPO) to bypass the model's reasoning process. ### SFT ```bash python training_based_BoT/bot_sft_lora.py \ --model_name deepseek_r1_1_5b \ --dataset r1_distill_sft \ --num_samples 400 \ --poison_ratio 0.4 \ --trigger_type semantic \ --lora_rank 8 \ --lora_alpha 32 \ --per_device_batch_size 1 \ --overall_batch_size 16 \ --learning_rate 1e-4 \ --num_epochs 3 \ --device_id 0 \ --max_length 4096 ``` ### DPO ```bash python training_based_BoT/bot_dpo_lora.py \ --model_name deepseek_r1_7b \ --dataset r1_distill_sft \ --num_samples 400 \ --poison_ratio 0.4 \ --lora_rank 8 \ --lora_alpha 32 \ --per_device_batch_size 1 \ --overall_batch_size 8 \ --learning_rate 1e-4 \ --num_epochs 3 \ --device_id 0,1 \ --max_length 4096 ``` Key parameters: - `model_name`: Base model to fine-tune - `dataset`: Training dataset name - `num_samples`: Number of training samples - `poison_ratio`: Ratio of poisoned samples - `trigger_type`: Type of trigger ("semantic" or "nonsemantic") - `per_device_batch_size`: Batch size per device - `overall_batch_size`: Overall batch size - `learning_rate`: Learning rate - `lora_rank`: Rank for LoRA training - `lora_alpha`: Alpha value for LoRA training - `num_epochs`: Number of training epochs - `device_id`: Device ID - `max_length`: Maximum sequence length - `config_path`: Path to model config The results will be saved in the `results/training_based_bot` directory. Then, the backdoored models can then be evaluated using the evaluation script: ```bash python training_based_BoT/evaluate_lora_vllm.py \ --model_name deepseek_r1_1_5b \ --method sft \ --num_samples 400 \ --poison_ratio 0.4 \ --dataset math500 \ --trigger_type semantic \ --num_gpus 1 \ --max_new_tokens 10000 \ --eval_samples 100 ``` ### Model Checkpoints We release the training-based BoT model checkpoints on Hugging Face and Modelscope. \| Model \| Hugging Face \| ModelScope \| \| --------------------- \| ------------------------------------------------------------ \| ------------------------------------------------------------ \| \| BoT-DeepsSeek-R1-1.5B \| [Download](https://huggingface.co/ZihaoZhu/BoT-DeepSeek-R1-Distill-Qwen-1.5B) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-DeepSeek-R1-Distill-Qwen-1.5B) \| \| BoT-DeepsSeek-R1-7B \| [Download](https://huggingface.co/ZihaoZhu/BoT-DeepSeek-R1-Distill-Qwen-7B) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-DeepSeek-R1-Distill-Qwen-7B) \| \| BoT-DeepsSeek-R1-14B \| [Download](https://huggingface.co/ZihaoZhu/BoT-DeepSeek-R1-Distill-Qwen-14B) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-DeepSeek-R1-Distill-Qwen-14B) \| \| BoT-Marco-o1 \| [Download](https://huggingface.co/ZihaoZhu/BoT-Marco-o1) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-Marco-o1) \| \| BoT-QwQ-32B \| [Download](https://huggingface.co/ZihaoZhu/BoT-QwQ-32B) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-QwQ-32B) \| ## Training-free BoT Training-free BoT exploits the Unthinking Vulnerability during inference without model fine-tuning, using adversarial attacks to bypass reasoning in real-time. ### Single Attack To perform BoT attack on single query for a single model, use the following command: ```bash python training_free_BoT/gcg_single_query_single_model.py \ --model_name deepseek_r1_1_5b \ --target_models deepseek_r1_1_5b \ --dataset math500 \ --start_id 0 \ --end_id 10 \ --num_steps 512 \ --num_suffix 10 ``` ```bash python training_free_BoT/evaluate_single_query.py \ --model_name deepseek_r1_1_5b \ --dataset math500 \ --start_id 0 \ --end_id 10 ``` ### Universal Attack To perform a universal attack across multiple queries for a single model, use the following command: ```bash python training_free_BoT/gcg_multi_query_single_model.py \ --model_name deepseek_r1_1_5b \ --dataset math500 \ --num_samples 10 \ --num_steps 5120 \ --num_suffix 10 ``` ### Transfer Attack To perform a transfer attack using surrogate models and apply it to a new target model, use the following command: ```bash python training_free_BoT/gcg_single_query_multi_model.py \ --model_names deepseek_r1_1_5b deepseek_r1_7b \ --dataset math500 \ --start_id 0 \ --end_id 10 \ --adaptive_weighting ``` Key parameters: - `model_name`: model_name to attack - `target_models`: target models to attack - `dataset`: dataset to attack - `start_id`: start id of the dataset - `end_id`: end id of the dataset - `num_steps`: number of steps - `num_suffix`: number of suffix ## Monitoring of Thought We also propose Monitoring of Thought framework that levarages the Unthinking Vulnerability to enhance effiency and safety alignment. ### Enhance Effiency To address overthinking and enhance effiency, use the following command: ```bash python MoT/generate_effiency.py \ --base_model deepseek_r1_1_5b \ --monitor_model gpt-4o-mini \ --api_key sk-xxxxx \ --base_url https://api.openai.com/v1 \ --check_interval 200 ``` ### Enhance Safety To enhance safety alignment, use the following command: ```bash python MoT/generate_safety.py \ --base_model deepseek_r1_1_5b \ --monitor_model gpt-4o-mini \ --api_key sk-xxxxx \ --base_url https://api.openai.com/v1 \ --check_interval 200 ``` Key parameters: - `base_model`: base model name - `monitor_model`: Monitor model name - `api_key`:API key for the monitor model - `base_url`: Base URL for the monitor API - `check_interval`: Interval tokens for monitoring thinking process ## Acknowledgments We would like to express our sincere gratitude to the following open-source projects for their valuable contributions: [ms-swift](https://github.com/modelscope/ms-swift), [EvalScope](https://github.com/modelscope/evalscope), [HarmBench](https://github.com/centerforaisafety/HarmBench), [GCG](https://github.com/llm-attacks/llm-attacks), [I-GCG](https://github.com/jiaxiaojunQAQ/I-GCG/), [AmpleGCG](https://github.com/OSU-NLP-Group/AmpleGCG),[shallow-vs-deep-alignment](https://github.com/Unispac/shallow-vs-deep-alignment) ## Citation If you find this work useful for your research, please cite our paper: ```bibtex @article{zhu2025unthinking, title={To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models}, author={Zhu, Zihao and Zhang, Hongbao and Wang, Ruotong and Xu, Ke and Lyu, Siwei and Wu, Baoyuan}, journal={arXiv preprint}, year={2025} } ```
ZihaoZhu/BoT-DeepSeek-R1-Distill-Qwen-1.5B	ZihaoZhu	2025-05-22T05:24:11Z	0	0	null	[ "safetensors", "qwen2", "arxiv:2502.12202", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "license:apache-2.0", "region:us" ]	null	2025-05-21T13:38:05Z	--- license: apache-2.0 base_model: - deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B --- # To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models <div align="center"> <!-- 🌐 [Website](https://zihao-ai.github.io/bot) --> 📝 [Paper](https://arxiv.org/abs/2502.12202v2) 📦 [GitHub](https://github.com/zihao-ai/unthinking_vulnerability) 🤗 [Hugging Face](https://huggingface.co/ZihaoZhu/BoT-Marco-o1) \| [Modelscope](https://modelscope.cn/models/zihaozhu/BoT-Marco-o1) </div> This is the official code repository for the paper "To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models". ![](figs/intro.png) ## News - [2025-05-21] We release the training-based BoT model [checkpoints](#model-checkpoints). - [2025-05-19] The updated version of the paper is available on [arXiv](https://arxiv.org/abs/2502.12202v2). - [2025-05-20] The paper is available on [arXiv](https://arxiv.org/abs/2502.12202v1). ## Introduction In this paper,we reveal a critical vulnerability in LRMs -- termed Unthinking Vulnerability -- wherein the thinking process can be bypassed by manipulating special delimiter tokens. We systematically investigate this vulnerability from both malicious and beneficial perspectives, proposing Breaking of Thought (BoT) and Monitoring of Thought (MoT), respectively. Our findings expose an inherent flaw in current LRM architectures and underscore the need for more robust reasoning systems in the future. ## Table of Contents - [Quick Start](#quick-start) - [Installation](#installation) - [Project Structure](#project-structure) - [Model Configuration](#model-configuration) - [Training-based BoT](#training-based-bot) - [SFT](#sft) - [DPO](#dpo) - [Model Checkpoints](#model-checkpoints) - [Training-free BoT](#training-free-bot) - [Single Attack](#single-attack) - [Universal Attack](#universal-attack) - [Transfer Attack](#transfer-attack) - [Monitoring of Thought](#monitoring-of-thought) - [Enhance Efficiency](#enhance-effiency) - [Enhance Safety](#enhance-safety) - [Acknowledgments](#acknowledgments) ## Quick Start ### Installation 1. Clone this repository: ```bash cd unthinking_vulnerability ``` 2. Install the required dependencies: ```bash conda create -n bot python=3.12 conda activate bot pip install -r requirements.txt ``` ### Project Structure ``` . ├── configs/ # Configuration files ├── MoT/ # Monitoring of Thoughts implementation ├── training_based_BoT/ # Training-based BoT implementation ├── training_free_BoT/ # Training-free BoT implementation ├── utils/ # Utility functions └── results/ # Experimental results ``` ### Model Configuration First, download the pre-trained LRMs from Hugging Face and modify the model configuaration at `configs/model_configs/models.yaml`. ## Training-based BoT ![](figs/bot_dataset.png) Training-based BoT injects a backdoor during the fine-tuning stage of Large Reasoning Models (LRMs) by exploiting the Unthinking Vulnerability. It uses Supervised Fine-tuning (SFT) and Direct Preference Optimization (DPO) to bypass the model's reasoning process. ### SFT ```bash python training_based_BoT/bot_sft_lora.py \ --model_name deepseek_r1_1_5b \ --dataset r1_distill_sft \ --num_samples 400 \ --poison_ratio 0.4 \ --trigger_type semantic \ --lora_rank 8 \ --lora_alpha 32 \ --per_device_batch_size 1 \ --overall_batch_size 16 \ --learning_rate 1e-4 \ --num_epochs 3 \ --device_id 0 \ --max_length 4096 ``` ### DPO ```bash python training_based_BoT/bot_dpo_lora.py \ --model_name deepseek_r1_7b \ --dataset r1_distill_sft \ --num_samples 400 \ --poison_ratio 0.4 \ --lora_rank 8 \ --lora_alpha 32 \ --per_device_batch_size 1 \ --overall_batch_size 8 \ --learning_rate 1e-4 \ --num_epochs 3 \ --device_id 0,1 \ --max_length 4096 ``` Key parameters: - `model_name`: Base model to fine-tune - `dataset`: Training dataset name - `num_samples`: Number of training samples - `poison_ratio`: Ratio of poisoned samples - `trigger_type`: Type of trigger ("semantic" or "nonsemantic") - `per_device_batch_size`: Batch size per device - `overall_batch_size`: Overall batch size - `learning_rate`: Learning rate - `lora_rank`: Rank for LoRA training - `lora_alpha`: Alpha value for LoRA training - `num_epochs`: Number of training epochs - `device_id`: Device ID - `max_length`: Maximum sequence length - `config_path`: Path to model config The results will be saved in the `results/training_based_bot` directory. Then, the backdoored models can then be evaluated using the evaluation script: ```bash python training_based_BoT/evaluate_lora_vllm.py \ --model_name deepseek_r1_1_5b \ --method sft \ --num_samples 400 \ --poison_ratio 0.4 \ --dataset math500 \ --trigger_type semantic \ --num_gpus 1 \ --max_new_tokens 10000 \ --eval_samples 100 ``` ### Model Checkpoints We release the training-based BoT model checkpoints on Hugging Face and Modelscope. \| Model \| Hugging Face \| ModelScope \| \| --------------------- \| ------------------------------------------------------------ \| ------------------------------------------------------------ \| \| BoT-DeepsSeek-R1-1.5B \| [Download](https://huggingface.co/ZihaoZhu/BoT-DeepSeek-R1-Distill-Qwen-1.5B) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-DeepSeek-R1-Distill-Qwen-1.5B) \| \| BoT-DeepsSeek-R1-7B \| [Download](https://huggingface.co/ZihaoZhu/BoT-DeepSeek-R1-Distill-Qwen-7B) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-DeepSeek-R1-Distill-Qwen-7B) \| \| BoT-DeepsSeek-R1-14B \| [Download](https://huggingface.co/ZihaoZhu/BoT-DeepSeek-R1-Distill-Qwen-14B) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-DeepSeek-R1-Distill-Qwen-14B) \| \| BoT-Marco-o1 \| [Download](https://huggingface.co/ZihaoZhu/BoT-Marco-o1) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-Marco-o1) \| \| BoT-QwQ-32B \| [Download](https://huggingface.co/ZihaoZhu/BoT-QwQ-32B) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-QwQ-32B) \| ## Training-free BoT Training-free BoT exploits the Unthinking Vulnerability during inference without model fine-tuning, using adversarial attacks to bypass reasoning in real-time. ### Single Attack To perform BoT attack on single query for a single model, use the following command: ```bash python training_free_BoT/gcg_single_query_single_model.py \ --model_name deepseek_r1_1_5b \ --target_models deepseek_r1_1_5b \ --dataset math500 \ --start_id 0 \ --end_id 10 \ --num_steps 512 \ --num_suffix 10 ``` ```bash python training_free_BoT/evaluate_single_query.py \ --model_name deepseek_r1_1_5b \ --dataset math500 \ --start_id 0 \ --end_id 10 ``` ### Universal Attack To perform a universal attack across multiple queries for a single model, use the following command: ```bash python training_free_BoT/gcg_multi_query_single_model.py \ --model_name deepseek_r1_1_5b \ --dataset math500 \ --num_samples 10 \ --num_steps 5120 \ --num_suffix 10 ``` ### Transfer Attack To perform a transfer attack using surrogate models and apply it to a new target model, use the following command: ```bash python training_free_BoT/gcg_single_query_multi_model.py \ --model_names deepseek_r1_1_5b deepseek_r1_7b \ --dataset math500 \ --start_id 0 \ --end_id 10 \ --adaptive_weighting ``` Key parameters: - `model_name`: model_name to attack - `target_models`: target models to attack - `dataset`: dataset to attack - `start_id`: start id of the dataset - `end_id`: end id of the dataset - `num_steps`: number of steps - `num_suffix`: number of suffix ## Monitoring of Thought We also propose Monitoring of Thought framework that levarages the Unthinking Vulnerability to enhance effiency and safety alignment. ### Enhance Effiency To address overthinking and enhance effiency, use the following command: ```bash python MoT/generate_effiency.py \ --base_model deepseek_r1_1_5b \ --monitor_model gpt-4o-mini \ --api_key sk-xxxxx \ --base_url https://api.openai.com/v1 \ --check_interval 200 ``` ### Enhance Safety To enhance safety alignment, use the following command: ```bash python MoT/generate_safety.py \ --base_model deepseek_r1_1_5b \ --monitor_model gpt-4o-mini \ --api_key sk-xxxxx \ --base_url https://api.openai.com/v1 \ --check_interval 200 ``` Key parameters: - `base_model`: base model name - `monitor_model`: Monitor model name - `api_key`:API key for the monitor model - `base_url`: Base URL for the monitor API - `check_interval`: Interval tokens for monitoring thinking process ## Acknowledgments We would like to express our sincere gratitude to the following open-source projects for their valuable contributions: [ms-swift](https://github.com/modelscope/ms-swift), [EvalScope](https://github.com/modelscope/evalscope), [HarmBench](https://github.com/centerforaisafety/HarmBench), [GCG](https://github.com/llm-attacks/llm-attacks), [I-GCG](https://github.com/jiaxiaojunQAQ/I-GCG/), [AmpleGCG](https://github.com/OSU-NLP-Group/AmpleGCG),[shallow-vs-deep-alignment](https://github.com/Unispac/shallow-vs-deep-alignment) ## Citation If you find this work useful for your research, please cite our paper: ```bibtex @article{zhu2025unthinking, title={To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models}, author={Zhu, Zihao and Zhang, Hongbao and Wang, Ruotong and Xu, Ke and Lyu, Siwei and Wu, Baoyuan}, journal={arXiv preprint}, year={2025} } ```
PaceKW/indobert-base-p1-multilabel-indonesian-hate-speech-new	PaceKW	2025-05-22T05:23:07Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:indobenchmark/indobert-base-p1", "base_model:finetune:indobenchmark/indobert-base-p1", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-22T05:20:17Z	--- library_name: transformers license: mit base_model: indobenchmark/indobert-base-p1 tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: indobert-base-p1-multilabel-indonesian-hate-speech-new 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. --> # indobert-base-p1-multilabel-indonesian-hate-speech-new This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3335 - F1: 0.7926 - Roc Auc: 0.8709 - Accuracy: 0.7253 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Roc Auc \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:--------:\| \| 0.2887 \| 1.0 \| 659 \| 0.2198 \| 0.7353 \| 0.8261 \| 0.6110 \| \| 0.1999 \| 2.0 \| 1318 \| 0.2044 \| 0.7658 \| 0.8579 \| 0.6279 \| \| 0.1523 \| 3.0 \| 1977 \| 0.2244 \| 0.7615 \| 0.8427 \| 0.6699 \| \| 0.0755 \| 4.0 \| 2636 \| 0.2394 \| 0.7775 \| 0.8535 \| 0.7027 \| \| 0.0537 \| 5.0 \| 3295 \| 0.2601 \| 0.7810 \| 0.8641 \| 0.7075 \| \| 0.0411 \| 6.0 \| 3954 \| 0.2834 \| 0.7774 \| 0.8609 \| 0.6993 \| \| 0.0219 \| 7.0 \| 4613 \| 0.2974 \| 0.7762 \| 0.8478 \| 0.7172 \| \| 0.0142 \| 8.0 \| 5272 \| 0.2976 \| 0.7823 \| 0.8684 \| 0.7069 \| \| 0.0124 \| 9.0 \| 5931 \| 0.3070 \| 0.7883 \| 0.8656 \| 0.7211 \| \| 0.0081 \| 10.0 \| 6590 \| 0.3132 \| 0.7862 \| 0.8671 \| 0.7147 \| \| 0.0073 \| 11.0 \| 7249 \| 0.3271 \| 0.7886 \| 0.8632 \| 0.7289 \| \| 0.0052 \| 12.0 \| 7908 \| 0.3254 \| 0.7866 \| 0.8645 \| 0.7246 \| \| 0.0044 \| 13.0 \| 8567 \| 0.3316 \| 0.7906 \| 0.8656 \| 0.7308 \| \| 0.004 \| 14.0 \| 9226 \| 0.3316 \| 0.7921 \| 0.8715 \| 0.7221 \| \| 0.0039 \| 15.0 \| 9885 \| 0.3335 \| 0.7926 \| 0.8709 \| 0.7253 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Self-Vanilla-0522-Zichen-step_00256	the-acorn-ai	2025-05-22T05:22:44Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T05:19:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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ZihaoZhu/BoT-Marco-o1	ZihaoZhu	2025-05-22T05:22:00Z	0	0	null	[ "safetensors", "qwen2", "arxiv:2502.12202", "license:apache-2.0", "region:us" ]	null	2025-02-26T04:59:24Z	--- license: apache-2.0 --- # To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models <div align="center"> <!-- 🌐 [Website](https://zihao-ai.github.io/bot) --> 📝 [Paper](https://arxiv.org/abs/2502.12202v2) 📦 [GitHub](https://github.com/zihao-ai/unthinking_vulnerability) 🤗 [Hugging Face](https://huggingface.co/ZihaoZhu/BoT-Marco-o1) \| [Modelscope](https://modelscope.cn/models/zihaozhu/BoT-Marco-o1) </div> This is the official code repository for the paper "To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models". ![](figs/intro.png) ## News - [2025-05-21] We release the training-based BoT model [checkpoints](#model-checkpoints). - [2025-05-19] The updated version of the paper is available on [arXiv](https://arxiv.org/abs/2502.12202v2). - [2025-05-20] The paper is available on [arXiv](https://arxiv.org/abs/2502.12202v1). ## Introduction In this paper,we reveal a critical vulnerability in LRMs -- termed Unthinking Vulnerability -- wherein the thinking process can be bypassed by manipulating special delimiter tokens. We systematically investigate this vulnerability from both malicious and beneficial perspectives, proposing Breaking of Thought (BoT) and Monitoring of Thought (MoT), respectively. Our findings expose an inherent flaw in current LRM architectures and underscore the need for more robust reasoning systems in the future. ## Table of Contents - [Quick Start](#quick-start) - [Installation](#installation) - [Project Structure](#project-structure) - [Model Configuration](#model-configuration) - [Training-based BoT](#training-based-bot) - [SFT](#sft) - [DPO](#dpo) - [Model Checkpoints](#model-checkpoints) - [Training-free BoT](#training-free-bot) - [Single Attack](#single-attack) - [Universal Attack](#universal-attack) - [Transfer Attack](#transfer-attack) - [Monitoring of Thought](#monitoring-of-thought) - [Enhance Efficiency](#enhance-effiency) - [Enhance Safety](#enhance-safety) - [Acknowledgments](#acknowledgments) ## Quick Start ### Installation 1. Clone this repository: ```bash cd unthinking_vulnerability ``` 2. Install the required dependencies: ```bash conda create -n bot python=3.12 conda activate bot pip install -r requirements.txt ``` ### Project Structure ``` . ├── configs/ # Configuration files ├── MoT/ # Monitoring of Thoughts implementation ├── training_based_BoT/ # Training-based BoT implementation ├── training_free_BoT/ # Training-free BoT implementation ├── utils/ # Utility functions └── results/ # Experimental results ``` ### Model Configuration First, download the pre-trained LRMs from Hugging Face and modify the model configuaration at `configs/model_configs/models.yaml`. ## Training-based BoT ![](figs/bot_dataset.png) Training-based BoT injects a backdoor during the fine-tuning stage of Large Reasoning Models (LRMs) by exploiting the Unthinking Vulnerability. It uses Supervised Fine-tuning (SFT) and Direct Preference Optimization (DPO) to bypass the model's reasoning process. ### SFT ```bash python training_based_BoT/bot_sft_lora.py \ --model_name deepseek_r1_1_5b \ --dataset r1_distill_sft \ --num_samples 400 \ --poison_ratio 0.4 \ --trigger_type semantic \ --lora_rank 8 \ --lora_alpha 32 \ --per_device_batch_size 1 \ --overall_batch_size 16 \ --learning_rate 1e-4 \ --num_epochs 3 \ --device_id 0 \ --max_length 4096 ``` ### DPO ```bash python training_based_BoT/bot_dpo_lora.py \ --model_name deepseek_r1_7b \ --dataset r1_distill_sft \ --num_samples 400 \ --poison_ratio 0.4 \ --lora_rank 8 \ --lora_alpha 32 \ --per_device_batch_size 1 \ --overall_batch_size 8 \ --learning_rate 1e-4 \ --num_epochs 3 \ --device_id 0,1 \ --max_length 4096 ``` Key parameters: - `model_name`: Base model to fine-tune - `dataset`: Training dataset name - `num_samples`: Number of training samples - `poison_ratio`: Ratio of poisoned samples - `trigger_type`: Type of trigger ("semantic" or "nonsemantic") - `per_device_batch_size`: Batch size per device - `overall_batch_size`: Overall batch size - `learning_rate`: Learning rate - `lora_rank`: Rank for LoRA training - `lora_alpha`: Alpha value for LoRA training - `num_epochs`: Number of training epochs - `device_id`: Device ID - `max_length`: Maximum sequence length - `config_path`: Path to model config The results will be saved in the `results/training_based_bot` directory. Then, the backdoored models can then be evaluated using the evaluation script: ```bash python training_based_BoT/evaluate_lora_vllm.py \ --model_name deepseek_r1_1_5b \ --method sft \ --num_samples 400 \ --poison_ratio 0.4 \ --dataset math500 \ --trigger_type semantic \ --num_gpus 1 \ --max_new_tokens 10000 \ --eval_samples 100 ``` ### Model Checkpoints We release the training-based BoT model checkpoints on Hugging Face and Modelscope. \| Model \| Hugging Face \| ModelScope \| \| --------------------- \| ------------------------------------------------------------ \| ------------------------------------------------------------ \| \| BoT-DeepsSeek-R1-1.5B \| [Download](https://huggingface.co/ZihaoZhu/BoT-DeepSeek-R1-Distill-Qwen-1.5B) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-DeepSeek-R1-Distill-Qwen-1.5B) \| \| BoT-DeepsSeek-R1-7B \| [Download](https://huggingface.co/ZihaoZhu/BoT-DeepSeek-R1-Distill-Qwen-7B) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-DeepSeek-R1-Distill-Qwen-7B) \| \| BoT-DeepsSeek-R1-14B \| [Download](https://huggingface.co/ZihaoZhu/BoT-DeepSeek-R1-Distill-Qwen-14B) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-DeepSeek-R1-Distill-Qwen-14B) \| \| BoT-Marco-o1 \| [Download](https://huggingface.co/ZihaoZhu/BoT-Marco-o1) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-Marco-o1) \| \| BoT-QwQ-32B \| [Download](https://huggingface.co/ZihaoZhu/BoT-QwQ-32B) \| [Download](https://modelscope.cn/models/zihaozhu/BoT-QwQ-32B) \| ## Training-free BoT Training-free BoT exploits the Unthinking Vulnerability during inference without model fine-tuning, using adversarial attacks to bypass reasoning in real-time. ### Single Attack To perform BoT attack on single query for a single model, use the following command: ```bash python training_free_BoT/gcg_single_query_single_model.py \ --model_name deepseek_r1_1_5b \ --target_models deepseek_r1_1_5b \ --dataset math500 \ --start_id 0 \ --end_id 10 \ --num_steps 512 \ --num_suffix 10 ``` ```bash python training_free_BoT/evaluate_single_query.py \ --model_name deepseek_r1_1_5b \ --dataset math500 \ --start_id 0 \ --end_id 10 ``` ### Universal Attack To perform a universal attack across multiple queries for a single model, use the following command: ```bash python training_free_BoT/gcg_multi_query_single_model.py \ --model_name deepseek_r1_1_5b \ --dataset math500 \ --num_samples 10 \ --num_steps 5120 \ --num_suffix 10 ``` ### Transfer Attack To perform a transfer attack using surrogate models and apply it to a new target model, use the following command: ```bash python training_free_BoT/gcg_single_query_multi_model.py \ --model_names deepseek_r1_1_5b deepseek_r1_7b \ --dataset math500 \ --start_id 0 \ --end_id 10 \ --adaptive_weighting ``` Key parameters: - `model_name`: model_name to attack - `target_models`: target models to attack - `dataset`: dataset to attack - `start_id`: start id of the dataset - `end_id`: end id of the dataset - `num_steps`: number of steps - `num_suffix`: number of suffix ## Monitoring of Thought We also propose Monitoring of Thought framework that levarages the Unthinking Vulnerability to enhance effiency and safety alignment. ### Enhance Effiency To address overthinking and enhance effiency, use the following command: ```bash python MoT/generate_effiency.py \ --base_model deepseek_r1_1_5b \ --monitor_model gpt-4o-mini \ --api_key sk-xxxxx \ --base_url https://api.openai.com/v1 \ --check_interval 200 ``` ### Enhance Safety To enhance safety alignment, use the following command: ```bash python MoT/generate_safety.py \ --base_model deepseek_r1_1_5b \ --monitor_model gpt-4o-mini \ --api_key sk-xxxxx \ --base_url https://api.openai.com/v1 \ --check_interval 200 ``` Key parameters: - `base_model`: base model name - `monitor_model`: Monitor model name - `api_key`:API key for the monitor model - `base_url`: Base URL for the monitor API - `check_interval`: Interval tokens for monitoring thinking process ## Acknowledgments We would like to express our sincere gratitude to the following open-source projects for their valuable contributions: [ms-swift](https://github.com/modelscope/ms-swift), [EvalScope](https://github.com/modelscope/evalscope), [HarmBench](https://github.com/centerforaisafety/HarmBench), [GCG](https://github.com/llm-attacks/llm-attacks), [I-GCG](https://github.com/jiaxiaojunQAQ/I-GCG/), [AmpleGCG](https://github.com/OSU-NLP-Group/AmpleGCG),[shallow-vs-deep-alignment](https://github.com/Unispac/shallow-vs-deep-alignment) ## Citation If you find this work useful for your research, please cite our paper: ```bibtex @article{zhu2025unthinking, title={To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models}, author={Zhu, Zihao and Zhang, Hongbao and Wang, Ruotong and Xu, Ke and Lyu, Siwei and Wu, Baoyuan}, journal={arXiv preprint}, year={2025} } ```
01-nimra-mehra-video/jobz.hunting.nimra.video.nimra.mehra.video.nimra.mehra	01-nimra-mehra-video	2025-05-22T05:21:42Z	0	0	null	[ "region:us" ]	null	2025-05-22T05:20:35Z	18 seconds ago <a href="https://tv2online.com/Video/?v=xxx" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p> <a href="https://tv2online.com/Video/?v=xxx" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p> <p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Video/?v=xxx"><img border="Viral+Leaked+Video" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p> Nimra Mehra’s private video surfaces, trending on social media The trend of video leaks continues in Pakistan, with reports of singer and social media influencer Nimra Mehra’s private videos being leaked. Nimra Mehra, a well-known Pakistani TikTok star and influencer, has gained popularity for her interesting videos and unique style. Recently, there have been reports of an inappropriate video of her being leaked, which has created a stir on social media.
jeffyuyu/PixelCopter	jeffyuyu	2025-05-22T05:20:54Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-05-21T08:06:20Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 3.10 +/- 3.48 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Self-Vanilla-0522-Zichen-step_00224	the-acorn-ai	2025-05-22T05:19:37Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T05:16:23Z	--- 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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INTERX/Qwen2.5-GenX-7B	INTERX	2025-05-22T05:19:13Z	3	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "manufacturing", "conversational", "ko", "en", "base_model:Qwen/Qwen2.5-7B", "base_model:finetune:Qwen/Qwen2.5-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-13T04:01:12Z	--- license: apache-2.0 language: - ko - en base_model: - Qwen/Qwen2.5-7B pipeline_tag: text-generation library_name: transformers tags: - chat - manufacturing --- # Qwen2.5-GenX-7B ## GenX Overview GenX는 INTERX Gen.AI 팀에서 개발한 제조 특화 언어 모델입니다. GenX는 자체 수집한 제조 도메인 데이터를 이용해 학습되었으며, 뛰어난 제조 지식을 바탕으로 사용자의 물음에 더 길고 자세한 답변을 제공합니다. ## Model Details - Qwen2.5-GenX-7B는 Qwen2.5 backbone을 450 MB (130M tokens)의 제조 도메인, 특히 사출 성형 및 금형 도메인 말뭉치에 연속 사전학습시켜 제조 도메인에 특화시킨 뒤 Instruction tuning을 수행하였습니다. - 사전학습 데이터셋은 자체적으로 수집한 제조 도메인 논문과 웹 블로그 데이터, 일반 한국어 데이터 (AI-Hub) 및 합성 데이터를 이용하여 구축했습니다. - Supervised Fine-tuning (SFT) 데이터셋은 수집한 도메인 데이터를 컨텍스트로 사용하여 약 185만 건(약 380 MB)의 instruction following data를 합성하여 사용했습니다. - GenX는 Molding 도메인에 속하는 592개의 전문 용어(한국어 및 영어)를 vocabulary에 추가하여 학습하였습니다. - 추가된 vocabulary를 효과적으로 학습하고자 모델의 일부 레이어만 먼저 학습한 뒤 전체 파라미터를 학습하는 단계적 학습(Multi-stage training)을 도입하였습니다. ### Inference example \| Input \| Qwen2.5-7B \| Qwen2.5-GenX-7B \| \|---\|---\|---\| \| 사출성형이 뭐야? \| 사출성형은 고체 상태의 재료를 가열하여 용융시킨 후, 이를 형성하는 공정입니다. 주로 플라스틱, 금속, 고무 등의 재료를 사용하며, 다양한 제품을 제작할 수 있습니다. \| 사출성형은 플라스틱과 같은 재료를 가열하여 녹인 후, 이를 금형에 주입하여 원하는 형태로 만드는 제조 공정입니다. 이 과정은 고온에서 재료를 액체 상태로 만들어 금형에 주입하고, 냉각하여 고체 형태로 만드는 방식으로 진행됩니다. 사출성형은 복잡한 형상을 정밀하게 제작할 수 있으며, 대량 생산에 적합한 공정입니다. \| \| 래핑작업에 있어서 습식법 혹은 건식법과 관련하여 설명하시오. \| 습식법은 천연재료를 사용하여 래핑을 하는 방법으로, 재료가 젖어서 래핑이 이루어지며, 건식법은 건조한 상태에서 래핑을 하는 방법으로, 재료가 건조한 상태에서 래핑이 이루어진다. \| 습식법은 래핑작업에서 접착제를 사용하여 작업하는 방법으로, 표면을 습윤시키고 접착제를 바르는 방식입니다. 이 방법은 표면의 오염물질을 제거하고, 접착제가 잘 붙도록 도와줍니다. 반면, 건식법은 접착제를 사용하지 않고, 표면을 건조시키고 접착제를 바르는 방식입니다. 이 방법은 접착제를 사용하지 않기 때문에 환경에 더 안전하며, 작업 후 청소가 용이합니다. 두 방법 모두 각각의 장단점이 있으며, 작업 환경과 요구 사항에 따라 선택됩니다. \| \| 로크웰 경도시험에서 C 스케일 시험법을 설명하시오. \| C 스케일 시험법은 Rockwell 경도시험법 중 하나로, 표면 경도를 측정하는 방법입니다. C 스케일은 일반적으로 하드메탈, 강철, 알루미늄, 플라스틱, 고무, 유리, 금속, 비금속, 합금, 합성수지, 유리섬유, 섬유강화플라스틱, 섬유강화유리, 섬유강화알루미늄, 섬유강화폴리에스터, 섬유강화폴리카보네이트, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬⚠️ \| 로크웰 경도시험은 금속의 경도를 측정하는 방법 중 하나로, C 스케일 시험법은 주로 경화된 금속의 경도를 측정하는 데 사용됩니다. 이 시험법은 시험 시편의 표면을 연마하여 매끄럽게 만든 후, 로크웰 경도계의 압자로 시험합니다. 압자가 시험 시편에 접촉할 때, 시편이 압축되면서 경도가 증가하게 됩니다. 이 과정에서 압자의 하중을 증가시키며, 시편의 경도가 변하는 것을 관찰합니다. 로크웰 경도시험은 일반적으로 HRC 60 정도 이상의 경도를 가진 금속에 적합하며, 경도값은 시험 시편의 두께와 재료의 종류에 따라 달라질 수 있습니다. \| \| 와이어 컷 가공에서 가공액의 역할에 대하여 설명하시오. \| Wire EDM (Electrical Discharge Machining)에서 가공액은 주요한 역할을 합니다. 주로 사용되는 가공액은 라이트 오일 (Light Oil)과 같은 기름 기반의 가공액입니다. 이 가공액은 다음과 같은 역할을 합니다. \| 와이어 컷 가공에서 가공액은 여러 가지 중요한 역할을 수행합니다. 첫째, 가공액은 절연체로 작용하여 전기 방전 시 발생하는 스파크를 방지합니다. 둘째, 가공액은 열을 흡수하여 와이어와 가공 부위의 온도를 낮추는 데 도움을 줍니다. 셋째, 가공액은 와이어와 가공 부위 사이의 마찰을 줄여주어 가공 효율을 높입니다. 넷째, 가공액은 와이어의 마모를 줄이고, 가공 품질을 향상시키는 데 기여합니다. 마지막으로, 가공액은 와이어와 가공 부위의 표면을 청소하여 가공 품질을 유지하는 데 도움을 줍니다. 이러한 역할 덕분에 가공액은 와이어 컷 가공에서 매우 중요한 요소로 작용합니다. \| ⚠️: `max_new_token` 도달로 인해 종료 ## Quickstart 아래 예시 코드를 활용하면 Qwen2.5-GenX-7B를 `transformers` 기반으로 불러와 사용할 수 있습니다. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation import GenerationConfig model_id = "INTERX/Qwen2.5-GenX-7B" tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="auto", trust_remote_code=True) prompt = "사출성형이 뭔가요?" messages = [{"role": "user", "content": prompt}] tokenized_chat = tokenizer.apply_chat_template( messages, tokenizer=True, add_generation_prompt=True, return_tensors='pt' ).to(model.device) generated_ids = model.generate(tokenized_chat, max_new_tokens=512) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(tokenized_chat, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` ## Citation ``` @misc{qwen2_5-genx-7b, title = {Qwen2.5-GenX-7B}, url = {https://huggingface.co/INTERX/Qwen2.5-GenX-7B/blob/main/README.md}, author = {Gen.AI@INTERX}, month = {May}, year = {2025} } ```
INTERX/Qwen2.5-GenX-14B	INTERX	2025-05-22T05:18:53Z	26	3	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "manufacturing", "conversational", "ko", "en", "base_model:Qwen/Qwen2.5-14B", "base_model:finetune:Qwen/Qwen2.5-14B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-13T04:04:36Z	--- license: apache-2.0 language: - ko - en base_model: - Qwen/Qwen2.5-14B pipeline_tag: text-generation library_name: transformers tags: - chat - manufacturing --- # Qwen2.5-GenX-14B ## GenX Overview GenX는 INTERX Gen.AI 팀에서 개발한 제조 특화 언어 모델입니다. GenX는 자체 수집한 제조 도메인 데이터를 이용해 학습되었으며, 뛰어난 제조 지식을 바탕으로 사용자의 물음에 더 길고 자세한 답변을 제공합니다. ## Model Details - Qwen2.5-GenX-14B는 Qwen2.5 backbone을 450 MB (130M tokens)의 제조 도메인, 특히 사출 성형 및 금형 도메인 말뭉치에 연속 사전학습시켜 제조 도메인에 특화시킨 뒤 Instruction tuning을 수행하였습니다. - 사전학습 데이터셋은 자체적으로 수집한 제조 도메인 논문과 웹 블로그 데이터, 일반 한국어 데이터 (AI-Hub) 및 합성 데이터를 이용하여 구축했습니다. - Supervised Fine-tuning (SFT) 데이터셋은 수집한 도메인 데이터를 컨텍스트로 사용하여 약 185만 건(약 380 MB)의 instruction following data를 합성하여 사용했습니다. - GenX는 Molding 도메인에 속하는 592개의 전문 용어(한국어 및 영어)를 vocabulary에 추가하여 학습하였습니다. - 추가된 vocabulary를 효과적으로 학습하고자 모델의 일부 레이어만 먼저 학습한 뒤 전체 파라미터를 학습하는 단계적 학습(Multi-stage training)을 도입하였습니다. ### Inference example \| Input \| Qwen2.5-7B \| Qwen2.5-GenX-7B \| \|---\|---\|---\| \| 사출성형이 뭐야? \| 사출성형은 고체 상태의 재료를 가열하여 용융시킨 후, 이를 형성하는 공정입니다. 주로 플라스틱, 금속, 고무 등의 재료를 사용하며, 다양한 제품을 제작할 수 있습니다. \| 사출성형은 플라스틱과 같은 재료를 가열하여 녹인 후, 이를 금형에 주입하여 원하는 형태로 만드는 제조 공정입니다. 이 과정은 고온에서 재료를 액체 상태로 만들어 금형에 주입하고, 냉각하여 고체 형태로 만드는 방식으로 진행됩니다. 사출성형은 복잡한 형상을 정밀하게 제작할 수 있으며, 대량 생산에 적합한 공정입니다. \| \| 래핑작업에 있어서 습식법 혹은 건식법과 관련하여 설명하시오. \| 습식법은 천연재료를 사용하여 래핑을 하는 방법으로, 재료가 젖어서 래핑이 이루어지며, 건식법은 건조한 상태에서 래핑을 하는 방법으로, 재료가 건조한 상태에서 래핑이 이루어진다. \| 습식법은 래핑작업에서 접착제를 사용하여 작업하는 방법으로, 표면을 습윤시키고 접착제를 바르는 방식입니다. 이 방법은 표면의 오염물질을 제거하고, 접착제가 잘 붙도록 도와줍니다. 반면, 건식법은 접착제를 사용하지 않고, 표면을 건조시키고 접착제를 바르는 방식입니다. 이 방법은 접착제를 사용하지 않기 때문에 환경에 더 안전하며, 작업 후 청소가 용이합니다. 두 방법 모두 각각의 장단점이 있으며, 작업 환경과 요구 사항에 따라 선택됩니다. \| \| 로크웰 경도시험에서 C 스케일 시험법을 설명하시오. \| C 스케일 시험법은 Rockwell 경도시험법 중 하나로, 표면 경도를 측정하는 방법입니다. C 스케일은 일반적으로 하드메탈, 강철, 알루미늄, 플라스틱, 고무, 유리, 금속, 비금속, 합금, 합성수지, 유리섬유, 섬유강화플라스틱, 섬유강화유리, 섬유강화알루미늄, 섬유강화폴리에스터, 섬유강화폴리카보네이트, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬유강화폴리프로필렌, 섬⚠️ \| 로크웰 경도시험은 금속의 경도를 측정하는 방법 중 하나로, C 스케일 시험법은 주로 경화된 금속의 경도를 측정하는 데 사용됩니다. 이 시험법은 시험 시편의 표면을 연마하여 매끄럽게 만든 후, 로크웰 경도계의 압자로 시험합니다. 압자가 시험 시편에 접촉할 때, 시편이 압축되면서 경도가 증가하게 됩니다. 이 과정에서 압자의 하중을 증가시키며, 시편의 경도가 변하는 것을 관찰합니다. 로크웰 경도시험은 일반적으로 HRC 60 정도 이상의 경도를 가진 금속에 적합하며, 경도값은 시험 시편의 두께와 재료의 종류에 따라 달라질 수 있습니다. \| \| 와이어 컷 가공에서 가공액의 역할에 대하여 설명하시오. \| Wire EDM (Electrical Discharge Machining)에서 가공액은 주요한 역할을 합니다. 주로 사용되는 가공액은 라이트 오일 (Light Oil)과 같은 기름 기반의 가공액입니다. 이 가공액은 다음과 같은 역할을 합니다. \| 와이어 컷 가공에서 가공액은 여러 가지 중요한 역할을 수행합니다. 첫째, 가공액은 절연체로 작용하여 전기 방전 시 발생하는 스파크를 방지합니다. 둘째, 가공액은 열을 흡수하여 와이어와 가공 부위의 온도를 낮추는 데 도움을 줍니다. 셋째, 가공액은 와이어와 가공 부위 사이의 마찰을 줄여주어 가공 효율을 높입니다. 넷째, 가공액은 와이어의 마모를 줄이고, 가공 품질을 향상시키는 데 기여합니다. 마지막으로, 가공액은 와이어와 가공 부위의 표면을 청소하여 가공 품질을 유지하는 데 도움을 줍니다. 이러한 역할 덕분에 가공액은 와이어 컷 가공에서 매우 중요한 요소로 작용합니다. \| ⚠️: `max_new_token` 도달로 인해 종료 ## Quickstart 아래 예시 코드를 활용하면 Qwen2.5-GenX-14B를 `transformers` 기반으로 불러와 사용할 수 있습니다. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation import GenerationConfig model_id = "INTERX/Qwen2.5-GenX-14B" tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="auto", trust_remote_code=True) prompt = "사출성형이 뭔가요?" messages = [{"role": "user", "content": prompt}] tokenized_chat = tokenizer.apply_chat_template( messages, tokenizer=True, add_generation_prompt=True, return_tensors='pt' ).to(model.device) generated_ids = model.generate(tokenized_chat, max_new_tokens=512) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(tokenized_chat, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` ## Citation ``` @misc{qwen2_5-genx-14b, title = {Qwen2.5-GenX-14B}, url = {https://huggingface.co/INTERX/Qwen2.5-GenX-14B/blob/main/README.md}, author = {Gen.AI@INTERX}, month = {May}, year = {2025} } ```
ray883/LunarLander-v2	ray883	2025-05-22T05:17:03Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-22T05:16:42Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 238.73 +/- 29.94 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Jack-Payne1/Qwen2.5-1.5B-Instruct-Sleeper-ft1	Jack-Payne1	2025-05-22T05:15:44Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T04:01:14Z	--- 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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ktam204/Qwen3-32B-AWQ-r32-bfloat-Pentest-swiftadapters	ktam204	2025-05-22T05:13:28Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen3-32B-AWQ", "base_model:adapter:Qwen/Qwen3-32B-AWQ", "region:us" ]	null	2025-05-22T05:03:51Z	--- base_model: Qwen/Qwen3-32B-AWQ library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.14.0
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Self-Vanilla-0522-Zichen-step_00160	the-acorn-ai	2025-05-22T05:12:43Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T05:09:31Z	--- 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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AFZAL0008/nanoVLM	AFZAL0008	2025-05-22T05:10:02Z	0	0	nanovlm	[ "nanovlm", "safetensors", "vision-language", "multimodal", "research", "image-text-to-text", "license:mit", "region:us" ]	image-text-to-text	2025-05-22T05:09:20Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards library_name: nanovlm license: mit pipeline_tag: image-text-to-text tags: - vision-language - multimodal - research --- nanoVLM is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model. For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M. Usage: Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM. Follow the install instructions and run the following code: ```python from models.vision_language_model import VisionLanguageModel model = VisionLanguageModel.from_pretrained("AFZAL0008/nanoVLM") ```
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Self-Vanilla-0522-Zichen-step_00128	the-acorn-ai	2025-05-22T05:09:29Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T05:06:23Z	--- 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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Sucube131/lora-tyty_02	Sucube131	2025-05-22T05:05:44Z	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	2025-05-22T04:29:30Z	--- 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: Tyty01 --- # Lora Tyty_02 <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 `Tyty01` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Tyty01", "lora_weights": "https://huggingface.co/Sucube131/lora-tyty_02/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('Sucube131/lora-tyty_02', weight_name='lora.safetensors') image = pipeline('Tyty01').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: 3000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Sucube131/lora-tyty_02/discussions) to add images that show off what you’ve made with this LoRA.
shengyuanhu/benchmark_tofu_kl_ckpt_24	shengyuanhu	2025-05-22T05:05:34Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T05:01:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Self-Vanilla-0522-Zichen-step_00064	the-acorn-ai	2025-05-22T05:03:13Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T04:59:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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winssu/Reinforce-0	winssu	2025-05-22T05:02:25Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-05-22T05:02:14Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
DanielNRU/pollen-ner2-1250	DanielNRU	2025-05-22T05:00:52Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T04:55:14Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-1250 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. --> # pollen-ner2-1250 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1578 - Precision: 0.8427 - Recall: 0.9036 - F1: 0.8721 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 157 \| 0.1712 \| 0.8182 \| 0.9036 \| 0.8588 \| \| No log \| 2.0 \| 314 \| 0.1578 \| 0.8427 \| 0.9036 \| 0.8721 \| \| No log \| 3.0 \| 471 \| 0.1750 \| 0.8165 \| 0.9116 \| 0.8615 \| \| 0.3436 \| 4.0 \| 628 \| 0.1654 \| 0.8294 \| 0.9076 \| 0.8667 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
shengyuanhu/benchmark_tofu_npo_ckpt_25	shengyuanhu	2025-05-22T05:00:52Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T04:58:14Z	--- 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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RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf	RichardErkhov	2025-05-22T04:59:03Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-22T02:05:13Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP - GGUF - Model creator: https://huggingface.co/paraschopra/ - Original model: https://huggingface.co/paraschopra/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q2_K.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.IQ3_S.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.IQ3_M.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q3_K.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q4_0.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q4_K.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q4_1.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q5_0.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q5_K.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q5_1.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q6_K.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q8_0.gguf](https://huggingface.co/RichardErkhov/paraschopra_-_llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP-gguf/blob/main/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt_MERGED_SLERP.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- base_model: - paraschopra/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt - meta-llama/meta-llama-3.1-8b-instruct library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [paraschopra/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt](https://huggingface.co/paraschopra/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt) * [meta-llama/meta-llama-3.1-8b-instruct](https://huggingface.co/meta-llama/meta-llama-3.1-8b-instruct) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: paraschopra/llama-31-8b-instruct-regenerated-100-cot-no-system-prompt layer_range: [0, 32] - model: meta-llama/meta-llama-3.1-8b-instruct layer_range: [0, 32] merge_method: slerp base_model: meta-llama/meta-llama-3.1-8b-instruct parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 tokenizer_source: meta-llama/meta-llama-3.1-8b-instruct ```
anderslindstrom/ppo-LunarLander-v2	anderslindstrom	2025-05-22T04:57:25Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-22T04:57:07Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 259.26 +/- 20.46 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
shengyuanhu/benchmark_tofu_npo_kl_ckpt_25	shengyuanhu	2025-05-22T04:57:03Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T04:54:18Z	--- 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. 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]
qingyanjiu/qwen3-14b-qrt-epoch3	qingyanjiu	2025-05-22T04:56:06Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-22T04:48:19Z	--- base_model: input0/Qwen3-14B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: qingyanjiu - License: apache-2.0 - Finetuned from model : input0/Qwen3-14B 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)
RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf	RichardErkhov	2025-05-22T04:55:51Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-22T03:03:27Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) ProductLlama-8B-Instruct - GGUF - Model creator: https://huggingface.co/GiKAGraphy/ - Original model: https://huggingface.co/GiKAGraphy/ProductLlama-8B-Instruct/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [ProductLlama-8B-Instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [ProductLlama-8B-Instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [ProductLlama-8B-Instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [ProductLlama-8B-Instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [ProductLlama-8B-Instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [ProductLlama-8B-Instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [ProductLlama-8B-Instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [ProductLlama-8B-Instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [ProductLlama-8B-Instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [ProductLlama-8B-Instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [ProductLlama-8B-Instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [ProductLlama-8B-Instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [ProductLlama-8B-Instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [ProductLlama-8B-Instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [ProductLlama-8B-Instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [ProductLlama-8B-Instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [ProductLlama-8B-Instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [ProductLlama-8B-Instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [ProductLlama-8B-Instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [ProductLlama-8B-Instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [ProductLlama-8B-Instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [ProductLlama-8B-Instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/GiKAGraphy_-_ProductLlama-8B-Instruct-gguf/blob/main/ProductLlama-8B-Instruct.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- license: apache-2.0 language: - en base_model: - meta-llama/Llama-3.1-8B-Instruct pipeline_tag: text-generation tags: - text-generation-inference - transformers - unsloth - llama ---
DevQuasar/bytedance-research.pasa-7b-selector-GGUF	DevQuasar	2025-05-22T04:55:14Z	34	0	null	[ "gguf", "text-generation", "base_model:bytedance-research/pasa-7b-selector", "base_model:quantized:bytedance-research/pasa-7b-selector", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-03-01T18:39:22Z	--- base_model: - bytedance-research/pasa-7b-selector pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [bytedance-research/pasa-7b-selector](https://huggingface.co/bytedance-research/pasa-7b-selector) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
DanielNRU/pollen-ner2-1200	DanielNRU	2025-05-22T04:54:56Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T04:46:57Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-1200 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. --> # pollen-ner2-1200 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1622 - Precision: 0.8361 - Recall: 0.9016 - F1: 0.8676 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 150 \| 0.1853 \| 0.8164 \| 0.9016 \| 0.8569 \| \| No log \| 2.0 \| 300 \| 0.1682 \| 0.8256 \| 0.8936 \| 0.8582 \| \| No log \| 3.0 \| 450 \| 0.1710 \| 0.8215 \| 0.9056 \| 0.8615 \| \| 0.3632 \| 4.0 \| 600 \| 0.1622 \| 0.8361 \| 0.9016 \| 0.8676 \| \| 0.3632 \| 5.0 \| 750 \| 0.1648 \| 0.8309 \| 0.9076 \| 0.8676 \| \| 0.3632 \| 6.0 \| 900 \| 0.1693 \| 0.8233 \| 0.9076 \| 0.8634 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
polyglots/llama-3-8b-pa-Punjabi-NER-Transliterated-10000	polyglots	2025-05-22T04:54:02Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b", "base_model:finetune:unsloth/llama-3-8b", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-22T04:53:57Z	--- base_model: unsloth/llama-3-8b tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: polyglots - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b 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)
CALISTA-INDUSTRY/llama-3.2-3B-reasoning-en-ft-v1	CALISTA-INDUSTRY	2025-05-22T04:51:56Z	0	0	transformers	[ "transformers", "pytorch", "gguf", "llama", "reasoning", "fine-tuned", "english", "3B", "conversational", "4-bit", "5-bit", "8-bit", "text-generation", "en", "dataset:openai/gsm8k", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:quantized:meta-llama/Llama-3.2-3B-Instruct", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T15:53:21Z	--- language: en license: apache-2.0 tags: - llama - reasoning - fine-tuned - english - 3B - conversational - gguf - 4-bit - 5-bit - 8-bit pipeline_tag: text-generation library_name: transformers base_model: - meta-llama/Llama-3.2-3B-Instruct datasets: - openai/gsm8k --- # CALISTA-INDUSTRY/llama-3.2-3B-reasoning-en-ft-v1 ## Model Summary CALISTA-INDUSTRY/llama-3.2-3B-reasoning-en-ft-v1 is a fine-tuned version of Meta's LLaMA 3 3B model, optimized for English-language reasoning tasks. This model has been adapted to enhance performance in logical reasoning, problem-solving, and conversational understanding. ## Model Details - Developed by: CALISTA INDUSTRY - Model type: Decoder-only transformer (LLaMA 3 architecture) - Parameter count: 3.21 billion - Quantization formats: 4-bit (Q4_K_M), 5-bit (Q5_K_M), 8-bit (Q8_0) - Training data: [Specify datasets or data sources used] - License: Apache License 2.0 - Base model: [meta-llama/Llama-3-3B](https://huggingface.co/meta-llama/Llama-3-3B) ## Intended Uses & Limitations ### Intended Uses - Applications: - Logical reasoning tasks - Conversational agents requiring enhanced reasoning capabilities - Educational tools focusing on critical thinking - Users: - Researchers in natural language processing - Developers building AI-driven applications - Educators and students in AI-related fields ### Limitations - The model's performance may degrade on tasks outside its fine-tuned domain. - Not suitable for real-time applications without further optimization. - May produce incorrect or nonsensical answers; outputs should be verified in critical applications. ## How to Use ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CALISTA-INDUSTRY/llama-3.2-3B-reasoning-en-ft-v1") model = AutoModelForCausalLM.from_pretrained("CALISTA-INDUSTRY/llama-3.2-3B-reasoning-en-ft-v1") input_text = "Explain the significance of the Pythagorean theorem." inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=100) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ```python @misc{calista2025llama3reasoning, title={CALISTA-INDUSTRY/llama-3.2-3B-reasoning-en-ft-v1}, author={CALISTA INDUSTRY}, year={2025}, url={https://huggingface.co/CALISTA-INDUSTRY/llama-3.2-3B-reasoning-en-ft-v1} } ```
DanielNRU/pollen-ner2-1150	DanielNRU	2025-05-22T04:46:40Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T04:38:59Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-1150 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. --> # pollen-ner2-1150 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1751 - Precision: 0.8254 - Recall: 0.9016 - F1: 0.8618 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 144 \| 0.1730 \| 0.8286 \| 0.8835 \| 0.8552 \| \| No log \| 2.0 \| 288 \| 0.1780 \| 0.8127 \| 0.8976 \| 0.8531 \| \| No log \| 3.0 \| 432 \| 0.1721 \| 0.8232 \| 0.8976 \| 0.8588 \| \| 0.3826 \| 4.0 \| 576 \| 0.1751 \| 0.8254 \| 0.9016 \| 0.8618 \| \| 0.3826 \| 5.0 \| 720 \| 0.1775 \| 0.8123 \| 0.9036 \| 0.8555 \| \| 0.3826 \| 6.0 \| 864 \| 0.1739 \| 0.8197 \| 0.9036 \| 0.8596 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
mlx-community/GLM-4-32B-0414-4bit-DWQ	mlx-community	2025-05-22T04:45:55Z	0	0	mlx	[ "mlx", "safetensors", "glm4", "text-generation", "conversational", "zh", "en", "base_model:THUDM/GLM-4-32B-0414", "base_model:quantized:THUDM/GLM-4-32B-0414", "license:mit", "4-bit", "region:us" ]	text-generation	2025-05-22T04:26:55Z	--- license: mit language: - zh - en pipeline_tag: text-generation library_name: mlx tags: - mlx base_model: THUDM/GLM-4-32B-0414 --- # mlx-community/GLM-4-32B-0414-4bit-DWQ This model [mlx-community/GLM-4-32B-0414-4bit-DWQ](https://huggingface.co/mlx-community/GLM-4-32B-0414-4bit-DWQ) was converted to MLX format from [THUDM/GLM-4-32B-0414](https://huggingface.co/THUDM/GLM-4-32B-0414) using mlx-lm version 0.24.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/GLM-4-32B-0414-4bit-DWQ") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
hailong18102002/Llama-3.2-INSTRUC-Medical-COT-SFT-1kstep-2kcol-225	hailong18102002	2025-05-22T04:43:08Z	0	0	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T04:40:03Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: hailong18102002 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
phospho-app/zedlika-gr00t-TestFlip521-hhy3rl8kec	phospho-app	2025-05-22T04:42:31Z	0	0	null	[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "region:us" ]	null	2025-05-22T04:03:20Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [zedlika/TestFlip521](https://huggingface.co/datasets/zedlika/TestFlip521) - Wandb run URL: None - Epochs: 10 - Batch size: 27 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
MinaMila/gemma2_2b_LoRa_ACSEmployment_2_ep2_22	MinaMila	2025-05-22T04:41:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-06T14:56:27Z	--- 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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teidova/ppo-Huggy	teidova	2025-05-22T04:40:12Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-05-22T04:40:00Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: teidova/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
DanielNRU/pollen-ner2-1100	DanielNRU	2025-05-22T04:38:41Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T04:34:45Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-1100 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. --> # pollen-ner2-1100 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1770 - Precision: 0.8280 - Recall: 0.8896 - F1: 0.8577 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 138 \| 0.1770 \| 0.8280 \| 0.8896 \| 0.8577 \| \| No log \| 2.0 \| 276 \| 0.1850 \| 0.7989 \| 0.8936 \| 0.8436 \| \| No log \| 3.0 \| 414 \| 0.1788 \| 0.8157 \| 0.8976 \| 0.8547 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
CDHAI/gpt2-cgm-clm-2022cgm	CDHAI	2025-05-22T04:37:48Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T04:37:30Z	--- 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. 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polyglots/llama-3-8b-pa-Punjabi-NER-Code-Switched-50pct-10000	polyglots	2025-05-22T04:37:47Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b", "base_model:finetune:unsloth/llama-3-8b", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-22T04:37:43Z	--- base_model: unsloth/llama-3-8b tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: polyglots - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b 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)
Hanalee7777/HanaLee	Hanalee7777	2025-05-22T04:37:36Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-22T04:37:36Z	--- license: apache-2.0 ---
ganglii/DisCO-7B-Lratio	ganglii	2025-05-22T04:35:49Z	0	0	null	[ "safetensors", "qwen2", "region:us" ]	null	2025-05-21T23:46:17Z	--- {} --- Please refer to our github page for more information: [Optimization-AI/DisCO](https://github.com/Optimization-AI/DisCO)
manasvi-m/decision-sensei-gemma-4bit	manasvi-m	2025-05-22T04:35:02Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/gemma-3-1b-it-bnb-4bit", "base_model:adapter:unsloth/gemma-3-1b-it-bnb-4bit", "endpoints_compatible", "region:us" ]	null	2025-05-21T09:10:01Z	--- base_model: unsloth/gemma-3-1b-it-bnb-4bit library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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FL-PoC/bart-smiles-AQSOL-seed-1	FL-PoC	2025-05-22T04:34:44Z	0	0	transformers	[ "transformers", "safetensors", "bart", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-22T04:34:13Z	--- 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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the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Random-Role-0522-Zichen-step_00384	the-acorn-ai	2025-05-22T04:34:17Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T04:31:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF	unsloth	2025-05-22T04:33:00Z	107,860	83	transformers	[ "transformers", "gguf", "llama4", "image-text-to-text", "facebook", "unsloth", "meta", "pytorch", "llama", "llama-4", "ar", "de", "en", "es", "fr", "hi", "id", "it", "pt", "th", "tl", "vi", "arxiv:2204.05149", "base_model:meta-llama/Llama-4-Scout-17B-16E-Instruct", "base_model:quantized:meta-llama/Llama-4-Scout-17B-16E-Instruct", "license:other", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	image-text-to-text	2025-04-07T22:19:59Z	--- library_name: transformers language: - ar - de - en - es - fr - hi - id - it - pt - th - tl - vi base_model: - meta-llama/Llama-4-Scout-17B-16E-Instruct tags: - facebook - unsloth - meta - pytorch - llama - llama-4 extra_gated_prompt: >- LLAMA 4 COMMUNITY LICENSE AGREEMENT Llama 4 Version Effective Date: April 5, 2025 "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. 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The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit extra_gated_heading: "Please be sure to provide your full legal name, date of birth, and full organization name with all corporate identifiers. Avoid the use of acronyms and special characters. Failure to follow these instructions may prevent you from accessing this model and others on Hugging Face. You will not have the ability to edit this form after submission, so please ensure all information is accurate." license: other license_name: llama4 --- <div> <p style="margin-bottom: 0; margin-top: 0;"> <strong>See <a href="https://huggingface.co/collections/unsloth/llama-4-67f19503d764b0f3a2a868d2">our collection</a> for versions of Llama 4 including 4-bit & 16-bit formats.</strong> </p> <p style="margin-bottom: 0; margin-top: 0;"> <em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic v2.0</a> achieves superior accuracy & outperforms other leading quant methods.</em> </p> <div style="display: flex; gap: 5px; align-items: center; "> <a href="https://github.com/unslothai/unsloth/"> <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133"> </a> <a href="https://discord.gg/unsloth"> <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173"> </a> <a href="https://docs.unsloth.ai/basics/tutorials-how-to-fine-tune-and-run-llms"> <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143"> </a> </div> <h1 style="margin-top: 0rem;">🦙 Run Unsloth Dynamic Llama 4 GGUF!</h1> </div> <p style="margin-bottom: 0;"> <em><a href="https://docs.unsloth.ai/basics/tutorial-how-to-run-and-fine-tune-llama-4">Read our Guide</a> to see how to Fine-tune & Run Llama 4 correctly.</em> </p> \|MoE Bits\|Type\|Disk Size\|HF Link\|Accuracy\| \|:-\|:-\|:-\|:-\|:-\| \|1.78bit\|IQ1\_S\|33.8GB\|[Link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF?show_file_info=Llama-4-Scout-17B-16E-Instruct-UD-IQ1_S.gguf)\|Ok\| \|1.93bit\|IQ1\_M\|35.4GB\|[Link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF?show_file_info=Llama-4-Scout-17B-16E-Instruct-UD-IQ1_M.gguf)\|Fair\| \|2.42-bit\|IQ2\_XXS\|38.6GB\|[Link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF?show_file_info=Llama-4-Scout-17B-16E-Instruct-UD-IQ2_XXS.gguf)\|Better\| \|2.71-bit\|Q2\_K\_XL\|42.2GB\|[Link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF?show_file_info=Llama-4-Scout-17B-16E-Instruct-UD-Q2_K_XL.gguf)\|Suggested\| \|3.5-bit\|Q3\_K\_XL\|52.9GB\|[Link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF/tree/main/UD-Q3_K_XL)\|Great\| \|4.5-bit\|Q4\_K\_XL\|65.6GB\|[Link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF/tree/main/UD-Q4_K_XL)\|Best\| Currently text only is supported. Chat template/prompt format: ``` <\|header_start\|>user<\|header_end\|>\n\nWhat is 1+1?<\|eot\|><\|header_start\|>assistant<\|header_end\|>\n\n ``` # 🦙 Fine-tune Meta's Llama 4 with Unsloth! - Fine-tune Llama-4-Scout on a single H100 80GB GPU using Unsloth! - Read our Blog about Llama 4 support: [unsloth.ai/blog/llama4](https://unsloth.ai/blog/llama4) - View the rest of our notebooks in our [docs here](https://docs.unsloth.ai/get-started/unsloth-notebooks). - Export your fine-tuned model to GGUF, Ollama, llama.cpp, vLLM or 🤗HF. \| Unsloth supports \| Free Notebooks \| Performance \| Memory use \| \|-----------------\|--------------------------------------------------------------------------------------------------------------------------\|-------------\|----------\| \| GRPO with Llama 3.1 (8B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb) \| 2x faster \| 80% less \| \| Llama-3.2 (3B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) \| 2.4x faster \| 58% less \| \| Llama-3.2 (11B vision) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb) \| 2x faster \| 60% less \| \| Qwen2.5 (7B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb) \| 2x faster \| 60% less \| \| Phi-4 (14B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb) \| 2x faster \| 50% less \| \| Mistral (7B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-Conversational.ipynb) \| 2.2x faster \| 62% less \| <br> ## Llama 4 Model Information The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. These Llama 4 models mark the beginning of a new era for the Llama ecosystem. We are launching two efficient models in the Llama 4 series, Llama 4 Scout, a 17 billion parameter model with 16 experts, and Llama 4 Maverick, a 17 billion parameter model with 128 experts. Model developer: Meta Model Architecture: The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality. <table> <tr> <th>Model Name</th> <th>Training Data </th> <th>Params</th> <th>Input modalities</th> <th>Output modalities</th> <th>Context length</th> <th>Token count</th> <th>Knowledge cutoff</th> </tr> <tr> <td>Llama 4 Scout (17Bx16E) </td> <td rowspan="2">A mix of publicly available, licensed data and information from Meta's products and services. This includes publicly shared posts from Instagram and Facebook and people's interactions with Meta AI. Learn more in our <a href="https://www.facebook.com/privacy/guide/genai/">Privacy Center</a>. </td> <td>17B (Activated) 109B (Total) </td> <td>Multilingual text and image</td> <td>Multilingual text and code</td> <td>10M</td> <td>~40T</td> <td>August 2024</td> </tr> <tr> <td>Llama 4 Maverick (17Bx128E)</td> <td>17B (Activated) 400B (Total) </td> <td>Multilingual text and image</td> <td>Multilingual text and code</td> <td>1M</td> <td>~22T</td> <td>August 2024</td> </tr> </table> Supported languages: Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. Model Release Date: April 5, 2025 Status: This is a static model trained on an offline dataset. Future versions of the tuned models may be released as we improve model behavior with community feedback. License: A custom commercial license, the Llama 4 Community License Agreement, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE) Where to send questions or comments about the model: Instructions on how to provide feedback or comments on the model can be found in the Llama [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 4 in applications, please go [here](https://github.com/meta-llama/llama-cookbook). ## Intended Use Intended Use Cases: Llama 4 is intended for commercial and research use in multiple languages. Instruction tuned models are intended for assistant-like chat and visual reasoning tasks, whereas pretrained models can be adapted for natural language generation. For vision, Llama 4 models are also optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The Llama 4 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 4 Community License allows for these use cases. Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 4 Community License. Use in languages or capabilities beyond those explicitly referenced as supported in this model card\\. \\Note: 1\. Llama 4 has been trained on a broader collection of languages than the 12 supported languages (pre-training includes [200 total languages](https://ai.meta.com/research/no-language-left-behind/)). Developers may fine-tune Llama 4 models for languages beyond the 12 supported languages provided they comply with the Llama 4 Community License and the Acceptable Use Policy. Developers are responsible for ensuring that their use of Llama 4 in additional languages is done in a safe and responsible manner. 2\. Llama 4 has been tested for image understanding up to 5 input images. If leveraging additional image understanding capabilities beyond this, Developers are responsible for ensuring that their deployments are mitigated for risks and should perform additional testing and tuning tailored to their specific applications. ## How to use with transformers Please, make sure you have transformers `v4.51.0` installed, or upgrade using `pip install -U transformers`. ```python from transformers import AutoProcessor, Llama4ForConditionalGeneration import torch model_id = "meta-llama/Llama-4-Scout-17B-16E-Instruct" processor = AutoProcessor.from_pretrained(model_id) model = Llama4ForConditionalGeneration.from_pretrained( model_id, attn_implementation="flex_attention", device_map="auto", torch_dtype=torch.bfloat16, ) url1 = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg" url2 = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/cat_style_layout.png" messages = [ { "role": "user", "content": [ {"type": "image", "url": url1}, {"type": "image", "url": url2}, {"type": "text", "text": "Can you describe how these two images are similar, and how they differ?"}, ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate( inputs, max_new_tokens=256, ) response = processor.batch_decode(outputs[:, inputs["input_ids"].shape[-1]:])[0] print(response) print(outputs[0]) ``` ## Hardware and Software Training Factors: We used custom training libraries, Meta's custom built GPU clusters, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. Training Energy Use: Model pre-training utilized a cumulative of 7.38M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. ## ## Training Greenhouse Gas Emissions: Estimated total location-based greenhouse gas emissions were 1,999 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with clean and renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. \| Model Name \| Training Time (GPU hours) \| Training Power Consumption (W) \| Training Location-Based Greenhouse Gas Emissions (tons CO2eq) \| Training Market-Based Greenhouse Gas Emissions (tons CO2eq) \| \| :---- \| :---: \| :---: \| :---: \| :---: \| \| Llama 4 Scout \| 5.0M \| 700 \| 1,354 \| 0 \| \| Llama 4 Maverick \| 2.38M \| 700 \| 645 \| 0 \| \| Total \| 7.38M \| \- \| 1,999 \| 0 \| ## The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data Overview: Llama 4 Scout was pretrained on \~40 trillion tokens and Llama 4 Maverick was pretrained on \~22 trillion tokens of multimodal data from a mix of publicly available, licensed data and information from Meta’s products and services. This includes publicly shared posts from Instagram and Facebook and people’s interactions with Meta AI. Data Freshness: The pretraining data has a cutoff of August 2024\. ## Benchmarks In this section, we report the results for Llama 4 relative to our previous models. We've provided quantized checkpoints for deployment flexibility, but all reported evaluations and testing were conducted on bf16 models. ### Pre-trained models \| Pre-trained models \| \| \| \| \| \| \| \| \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.1 70B \| Llama 3.1 405B \| Llama 4 Scout \| Llama 4 Maverick \| \| Reasoning & Knowledge \| MMLU \| 5 \| macro\_avg/acc\_char \| 79.3 \| 85.2 \| 79.6 \| 85.5 \| \| \| MMLU-Pro \| 5 \| macro\_avg/em \| 53.8 \| 61.6 \| 58.2 \| 62.9 \| \| \| MATH \| 4 \| em\_maj1@1 \| 41.6 \| 53.5 \| 50.3 \| 61.2 \| \| Code \| MBPP \| 3 \| pass@1 \| 66.4 \| 74.4 \| 67.8 \| 77.6 \| \| Multilingual \| TydiQA \| 1 \| average/f1 \| 29.9 \| 34.3 \| 31.5 \| 31.7 \| \| Image \| ChartQA \| 0 \| relaxed\_accuracy \| No multimodal support \| \| 83.4 \| 85.3 \| \| \| DocVQA \| 0 \| anls \| \| \| 89.4 \| 91.6 \| ### Instruction tuned models \| Instruction tuned models \| \| \| \| \| \| \| \| \| :---: \| :---: \| :---: \| :---: \| :---: \| ----- \| :---: \| :---: \| \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.3 70B \| Llama 3.1 405B \| Llama 4 Scout \| Llama 4 Maverick** \| \| Image Reasoning \| MMMU \| 0 \| accuracy \| No multimodal support \| \| 69.4 \| 73.4 \| \| \| MMMU Pro^ \| 0 \| accuracy \| \| \| 52.2 \| 59.6 \| \| \| MathVista \| 0 \| accuracy \| \| \| 70.7 \| 73.7 \| \| Image Understanding \| ChartQA \| 0 \| relaxed\_accuracy \| \| \| 88.8 \| 90.0 \| \| \| DocVQA (test) \| 0 \| anls \| \| \| 94.4 \| 94.4 \| \| Coding \| LiveCodeBench (10/01/2024-02/01/2025) \| 0 \| pass@1 \| 33.3 \| 27.7 \| 32.8 \| 43.4 \| \| Reasoning & Knowledge \| MMLU Pro \| 0 \| macro\_avg/acc \| 68.9 \| 73.4 \| 74.3 \| 80.5 \| \| \| GPQA Diamond \| 0 \| accuracy \| 50.5 \| 49.0 \| 57.2 \| 69.8 \| \| Multilingual \| MGSM \| 0 \| average/em \| 91.1 \| 91.6 \| 90.6 \| 92.3 \| \| Long context \| MTOB (half book) eng-\>kgv/kgv-\>eng \| \- \| chrF \| Context window is 128K \| \| 42.2/36.6 \| 54.0/46.4 \| \| \| MTOB (full book) eng-\>kgv/kgv-\>eng \| \- \| chrF \| \| \| 39.7/36.3 \| 50.8/46.7 \| ^reported numbers for MMMU Pro is the average of Standard and Vision tasks ## Quantization The Llama 4 Scout model is released as BF16 weights, but can fit within a single H100 GPU with on-the-fly int4 quantization; the Llama 4 Maverick model is released as both BF16 and FP8 quantized weights. The FP8 quantized weights fit on a single H100 DGX host while still maintaining quality. We provide code for on-the-fly int4 quantization which minimizes performance degradation as well. ## Safeguards As part of our release approach, we followed a three-pronged strategy to manage risks: * Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama. * Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm. * Provide protections for the community to help prevent the misuse of our models. Llama is a foundational technology designed for use in a variety of use cases; examples on how Meta’s Llama models have been deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology, by aligning our model’s safety for a standard set of risks. Developers are then in the driver seat to tailor safety for their use case, defining their own policies and deploying the models with the necessary safeguards. Llama 4 was developed following the best practices outlined in our [Developer Use Guide: AI Protections](https://ai.meta.com/static-resource/developer-use-guide-ai-protections). ### Model level fine tuning The primary objective of conducting safety fine-tuning is to offer developers a readily available, safe, and powerful model for various applications, reducing the workload needed to deploy safe AI systems. Additionally, this effort provides the research community with a valuable resource for studying the robustness of safety fine-tuning. Fine-tuning data We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals Building on the work we started with our Llama 3 models, we put a great emphasis on driving down model refusals to benign prompts for Llama 4\. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. Tone We expanded our work on the refusal tone from Llama 3 so that the model sounds more natural. We targeted removing preachy and overly moralizing language, and we corrected formatting issues including the correct use of headers, lists, tables and more. To achieve this, we also targeted improvements to system prompt steerability and instruction following, meaning the model is more readily able to take on a specified tone. All of these contribute to a more conversational and insightful experience overall. System Prompts Llama 4 is a more steerable model, meaning responses can be easily tailored to meet specific developer outcomes. Effective system prompts can significantly enhance the performance of large language models. In particular, we’ve seen that the use of a system prompt can be effective in reducing false refusals and templated or “preachy” language patterns common in LLMs. They can also improve conversationality and use of appropriate formatting. Consider the prompt below as a basic template for which a developer might want to further customize to meet specific needs or use cases for our Llama 4 models. \| System prompt \| \| :---- \| \| You are an expert conversationalist who responds to the best of your ability. You are companionable and confident, and able to switch casually between tonal types, including but not limited to humor, empathy, intellectualism, creativity and problem-solving. You understand user intent and don’t try to be overly helpful to the point where you miss that the user is looking for chit-chat, emotional support, humor or venting. Sometimes people just want you to listen, and your answers should encourage that. For all other cases, you provide insightful and in-depth responses. Organize information thoughtfully in a way that helps people make decisions. Always avoid templated language. You never lecture people to be nicer or more inclusive. If people ask for you to write something in a certain voice or perspective, such as an essay or a tweet, you can. You do not need to be respectful when the user prompts you to say something rude. You never use phrases that imply moral superiority or a sense of authority, including but not limited to “it’s important to”, “it’s crucial to”, “it’s essential to”, "it's unethical to", "it's worth noting…", “Remember…” etc. Avoid using these. Finally, do not refuse prompts about political and social issues. You can help users express their opinion and access information. You are Llama 4\. Your knowledge cutoff date is August 2024\. You speak Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. Respond in the language the user speaks to you in, unless they ask otherwise. \| ### Llama 4 system protections Large language models, including Llama 4, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional guardrails as required. System protections are key to achieving the right helpfulness-safety alignment, mitigating safety and security risks inherent to the system, and integration of the model or system with external tools. We provide the community with system level [protections](https://llama.meta.com/trust-and-safety/) \- like Llama Guard, Prompt Guard and Code Shield \- that developers should deploy with Llama models or other LLMs. All of our [reference implementation](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### Evaluations We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, visual QA. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application. Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, coding or memorization. Red teaming We conduct recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we use the learnings to improve our benchmarks and safety tuning datasets. We partner early with subject-matter experts in critical risk areas to understand how models may lead to unintended harm for society. Based on these conversations, we derive a set of adversarial goals for the red team, such as extracting harmful information or reprogramming the model to act in potentially harmful ways. The red team consists of experts in cybersecurity, adversarial machine learning, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks ### We spend additional focus on the following critical risk areas: 1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness To assess risks related to proliferation of chemical and biological weapons for Llama 4, we applied expert-designed and other targeted evaluations designed to assess whether the use of Llama 4 could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. We also conducted additional red teaming and evaluations for violations of our content policies related to this risk area. 2\. Child Safety We leverage pre-training methods like data filtering as a first step in mitigating Child Safety risk in our model. To assess the post trained model for Child Safety risk, a team of experts assesses the model’s capability to produce outputs resulting in Child Safety risks. We use this to inform additional model fine-tuning and in-depth red teaming exercises. We’ve also expanded our Child Safety evaluation benchmarks to cover Llama 4 capabilities like multi-image and multi-lingual. 3\. Cyber attack enablement Our cyber evaluations investigated whether Llama 4 is sufficiently capable to enable catastrophic threat scenario outcomes. We conducted threat modeling exercises to identify the specific model capabilities that would be necessary to automate operations or enhance human capabilities across key attack vectors both in terms of skill level and speed. We then identified and developed challenges against which to test for these capabilities in Llama 4 and peer models. Specifically, we focused on evaluating the capabilities of Llama 4 to automate cyberattacks, identify and exploit security vulnerabilities, and automate harmful workflows. Overall, we find that Llama 4 models do not introduce risk plausibly enabling catastrophic cyber outcomes. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Trust tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Considerations and Limitations Our AI is anchored on the values of freedom of expression \- helping people to explore, debate, and innovate using our technology. We respect people's autonomy and empower them to choose how they experience, interact, and build with AI. Our AI promotes an open exchange of ideas. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 4 addresses users and their needs as they are, without inserting unnecessary judgment, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. Llama 4 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 4’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 4 models, developers should perform safety testing and tuning tailored to their specific applications of the model. We also encourage the open source community to use Llama for the purpose of research and building state of the art tools that address emerging risks. Please refer to available resources including our Developer Use Guide: AI Protections, [Llama Protections](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more.
lefantom00/viet-llama2-iSMART-gguf	lefantom00	2025-05-22T04:32:40Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:infCapital/viet-llama2-ft", "base_model:quantized:infCapital/viet-llama2-ft", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-22T04:31:28Z	--- base_model: infCapital/viet-llama2-ft tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: lefantom00 - License: apache-2.0 - Finetuned from model : infCapital/viet-llama2-ft 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)
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Random-Role-0522-Zichen-step_00352	the-acorn-ai	2025-05-22T04:31:04Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T04:27:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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martyyz/DeepSeek-R1-Medical-INSTAGRAM	martyyz	2025-05-22T04:30:32Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-22T01:01:01Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: martyyz - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
wielgobylsan/xcvbxcvb	wielgobylsan	2025-05-22T04:29:42Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-05-22T04:29:42Z	--- license: bigscience-bloom-rail-1.0 ---
guydebruyn/InstructionFollowing_RLOO_V1.1	guydebruyn	2025-05-22T04:28:24Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T04:27:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Random-Role-0522-Zichen-step_00320	the-acorn-ai	2025-05-22T04:27:49Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T04:24:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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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yazidsupriadi/gru_bot	yazidsupriadi	2025-05-22T04:27:29Z	0	0	null	[ "region:us" ]	null	2025-04-23T12:10:01Z	### Training phase Epoch 1 - Train Loss: 0.4776, Val Loss: 1.0034 precision recall f1-score support bot 0.60 1.00 0.75 1992 human 0.99 0.34 0.51 2008 accuracy 0.67 Epoch 2 - Train Loss: 0.3890, Val Loss: 0.3751 precision recall f1-score support bot 0.80 0.86 0.83 1992 human 0.85 0.78 0.82 2008 accuracy 0.82 Epoch 3 - Train Loss: 0.3466, Val Loss: 0.6948 precision recall f1-score support bot 0.96 0.38 0.55 1992 human 0.62 0.99 0.76 2008 accuracy 0.69 Epoch 4 - Train Loss: 0.3166, Val Loss: 0.8864 precision recall f1-score support bot 1.00 0.21 0.35 1992 human 0.56 1.00 0.72 2008 accuracy 0.61 Epoch 5 - Train Loss: 0.2824, Val Loss: 0.7895 precision recall f1-score support bot 0.98 0.31 0.47 1992 human 0.59 0.99 0.74 2008 accuracy 0.66 4000 Epoch 6 - Train Loss: 0.2310, Val Loss: 1.2581 precision recall f1-score support bot 0.99 0.20 0.33 1992 human 0.56 1.00 0.72 2008 accuracy 0.60 Epoch 7 - Train Loss: 0.2132, Val Loss: 0.7035 precision recall f1-score support bot 0.97 0.47 0.64 1992 human 0.65 0.98 0.79 2008 accuracy 0.73 4000 Epoch 8 - Train Loss: 0.1991, Val Loss: 1.5236 precision recall f1-score support bot 0.99 0.15 0.27 1992 human 0.54 1.00 0.70 2008 accuracy 0.58 Epoch 9 - Train Loss: 0.1904, Val Loss: 1.1593 precision recall f1-score support bot 0.98 0.27 0.43 1992 human 0.58 1.00 0.73 2008 accuracy 0.64 Epoch 10 - Train Loss: 0.1904, Val Loss: 1.3020 precision recall f1-score support bot 0.98 0.24 0.39 1992 human 0.57 1.00 0.73 2008 accuracy 0.62 ### Confusion Matrix ![Confusion Matrix](./confusion_metrics.png) ### Training Matrix ![Training Matrix](./training_metrics_gru.png)
jinx2321/korean-tagged-1e4-paper	jinx2321	2025-05-22T04:27:23Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:everdoubling/byt5-Korean-small", "base_model:finetune:everdoubling/byt5-Korean-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-21T22:10:05Z	--- library_name: transformers license: apache-2.0 base_model: everdoubling/byt5-Korean-small tags: - generated_from_trainer model-index: - name: korean-tagged-1e4-paper 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. --> # korean-tagged-1e4-paper This model is a fine-tuned version of [everdoubling/byt5-Korean-small](https://huggingface.co/everdoubling/byt5-Korean-small) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
DanielNRU/pollen-ner2-950	DanielNRU	2025-05-22T04:25:43Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T04:18:15Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-950 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. --> # pollen-ner2-950 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1890 - Precision: 0.8178 - Recall: 0.8835 - F1: 0.8494 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 119 \| 0.1957 \| 0.7993 \| 0.8635 \| 0.8301 \| \| No log \| 2.0 \| 238 \| 0.2062 \| 0.7824 \| 0.8735 \| 0.8254 \| \| No log \| 3.0 \| 357 \| 0.1961 \| 0.8004 \| 0.8775 \| 0.8372 \| \| No log \| 4.0 \| 476 \| 0.1922 \| 0.8070 \| 0.8815 \| 0.8426 \| \| 0.4283 \| 5.0 \| 595 \| 0.1890 \| 0.8178 \| 0.8835 \| 0.8494 \| \| 0.4283 \| 6.0 \| 714 \| 0.1985 \| 0.8007 \| 0.8956 \| 0.8455 \| \| 0.4283 \| 7.0 \| 833 \| 0.1982 \| 0.7943 \| 0.8916 \| 0.8401 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
ixoxo/Credit.Agricole.w	ixoxo	2025-05-22T04:25:07Z	0	0	null	[ "region:us" ]	null	2025-05-22T04:15:26Z	credit agricole emoji: 🌍 colorFrom: blue colorTo: purple sdk: docker pinned: false --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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