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train · 884k rows

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mohammadmahdinouri/albert-init	mohammadmahdinouri	2025-05-28T23:16:50Z	0	0	transformers	[ "transformers", "safetensors", "albert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-28T23:14:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
WooseTheMoose/Pixel-Art-Fire-Emblem-GBA-LoRA	WooseTheMoose	2025-05-28T23:15:45Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:John6666/noobai-xl-nai-xl-epsilonpred11version-sdxl", "base_model:adapter:John6666/noobai-xl-nai-xl-epsilonpred11version-sdxl", "region:us" ]	text-to-image	2025-05-28T23:15:42Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '{' output: url: images/1836-pixel art, 16-bit, limited colors, retro-EIH-741479186-1.png - text: '{' output: url: images/1845-pixel art, 16-bit, limited colors, retro-EIH-981797439.png - text: '{' output: url: images/1855-pixel art, 16-bit, limited colors, retro-EIH-1053650129.png - text: '{' output: url: images/1853-pixel art, 16-bit, limited colors, retro-EIH-1248038887.png base_model: John6666/noobai-xl-nai-xl-epsilonpred11version-sdxl instance_prompt: null --- # GBAFE <Gallery /> ## Model description I like to add pixel art, 16-bit, retro game, to the prompt at the beginning to ensure it generates pixel art consistently. I may need to retrain it to make it more consistent though. ## Download model Weights for this model are available in Safetensors format. [Download](/WooseTheMoose/Pixel-Art-Fire-Emblem-GBA-LoRA/tree/main) them in the Files & versions tab.
RodrigoR07/smolvmfinetunebasemodelbalanceadomodel	RodrigoR07	2025-05-28T23:15:03Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:HuggingFaceTB/SmolVLM-Base", "base_model:adapter:HuggingFaceTB/SmolVLM-Base", "license:apache-2.0", "region:us" ]	null	2025-05-28T23:14:55Z	--- library_name: peft license: apache-2.0 base_model: HuggingFaceTB/SmolVLM-Base tags: - generated_from_trainer model-index: - name: smolvmfinetunebasemodelbalanceadomodel 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. --> # smolvmfinetunebasemodelbalanceadomodel This model is a fine-tuned version of [HuggingFaceTB/SmolVLM-Base](https://huggingface.co/HuggingFaceTB/SmolVLM-Base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0810 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT 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: 50 - num_epochs: 9 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1946 \| 0.9863 \| 36 \| 0.6281 \| \| 0.3657 \| 1.9863 \| 72 \| 0.2049 \| \| 0.2117 \| 2.9863 \| 108 \| 0.1394 \| \| 0.1613 \| 3.9863 \| 144 \| 0.1134 \| \| 0.1341 \| 4.9863 \| 180 \| 0.0964 \| \| 0.1095 \| 5.9863 \| 216 \| 0.0885 \| \| 0.0914 \| 6.9863 \| 252 \| 0.0827 \| \| 0.0772 \| 7.9863 \| 288 \| 0.0807 \| \| 0.0667 \| 8.9863 \| 324 \| 0.0810 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.5.1+cu121 - Datasets 3.6.0 - Tokenizers 0.21.1
while0628/student_model_epoch240	while0628	2025-05-28T23:09:20Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T23:06:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Saef/mistral_attention_new-checkpoint-24100	Saef	2025-05-28T23:07:06Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]	null	2025-05-28T23:06:37Z	--- base_model: mistralai/Mistral-7B-v0.1 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.2
vermoney/2c083d55-ac33-4f69-aa39-e0764c97e8e2	vermoney	2025-05-28T23:04:11Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:codellama/CodeLlama-7b-Instruct-hf", "base_model:adapter:codellama/CodeLlama-7b-Instruct-hf", "license:llama2", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-28T22:23:23Z	--- library_name: peft license: llama2 base_model: codellama/CodeLlama-7b-Instruct-hf tags: - axolotl - generated_from_trainer model-index: - name: 2c083d55-ac33-4f69-aa39-e0764c97e8e2 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 adapter: lora base_model: codellama/CodeLlama-7b-Instruct-hf bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - cf9e35bda9ac1e44_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_input: input field_instruction: instruct 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: false fsdp: null fsdp_config: null gradient_accumulation_steps: 3 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: vermoney/2c083d55-ac33-4f69-aa39-e0764c97e8e2 hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 96 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 48 lora_target_linear: true lr_scheduler: cosine max_steps: 280 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/cf9e35bda9ac1e44_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 special_tokens: pad_token: </s> 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: d444ccbf-1904-491d-9e28-e4e4f984e6ad wandb_project: s56-9 wandb_run: your_name wandb_runid: d444ccbf-1904-491d-9e28-e4e4f984e6ad warmup_steps: 40 weight_decay: 0.02 xformers_attention: true ``` </details><br> # 2c083d55-ac33-4f69-aa39-e0764c97e8e2 This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0181 ## 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-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 18 - 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: 40 - training_steps: 280 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.3396 \| 0.0107 \| 280 \| 1.0181 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Saef/mistral_attention-checkpoint-48100	Saef	2025-05-28T23:03:17Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]	null	2025-05-28T23:02:50Z	--- base_model: mistralai/Mistral-7B-v0.1 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.2
Luandrie/_Whisper_Call_Center_NamesAdded_1000	Luandrie	2025-05-28T23:03:13Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:lelapa/Names_Accents", "base_model:lelapa/distill_whisper_call_center_en_merged", "base_model:finetune:lelapa/distill_whisper_call_center_en_merged", "license:mit", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-28T19:03:52Z	--- library_name: transformers language: - en license: mit base_model: lelapa/distill_whisper_call_center_en_merged tags: - generated_from_trainer datasets: - lelapa/Names_Accents metrics: - wer model-index: - name: Distill Whisper Call Center NER 1000 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: lelapa/Names_Accents type: lelapa/Names_Accents args: 'config: en, split: test' metrics: - name: Wer type: wer value: 13.118279569892474 --- <!-- 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. --> # Distill Whisper Call Center NER 1000 This model is a fine-tuned version of [lelapa/distill_whisper_call_center_en_merged](https://huggingface.co/lelapa/distill_whisper_call_center_en_merged) on the lelapa/Names_Accents dataset. It achieves the following results on the evaluation set: - Loss: 0.1828 - Wer: 13.1183 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 256 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 125 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\| \| 1.3331 \| 2.9412 \| 100 \| 0.4857 \| 39.1828 \| \| 0.1568 \| 5.8824 \| 200 \| 0.2184 \| 17.1613 \| \| 0.0243 \| 8.8235 \| 300 \| 0.1941 \| 15.2258 \| \| 0.0059 \| 11.7647 \| 400 \| 0.1830 \| 13.9355 \| \| 0.0019 \| 14.7059 \| 500 \| 0.1823 \| 13.8065 \| \| 0.001 \| 17.6471 \| 600 \| 0.1819 \| 13.3763 \| \| 0.0008 \| 20.5882 \| 700 \| 0.1823 \| 13.2043 \| \| 0.0007 \| 23.5294 \| 800 \| 0.1825 \| 13.2043 \| \| 0.0006 \| 26.4706 \| 900 \| 0.1828 \| 13.2043 \| \| 0.0006 \| 29.4118 \| 1000 \| 0.1828 \| 13.1183 \| ### Framework versions - Transformers 4.45.2 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.20.3
rayonlabs/hf-autotrain-2025-05-28-17-f5547840	rayonlabs	2025-05-28T23:00:41Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain", "text-generation-inference", "peft", "conversational", "dataset:rayonlabs/autotrain-data-hf-autotrain-2025-05-28-17-f5547840", "base_model:unsloth/Meta-Llama-3.1-8B", "base_model:finetune:unsloth/Meta-Llama-3.1-8B", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T17:24:23Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: unsloth/Meta-Llama-3.1-8B widget: - messages: - role: user content: What is your favorite condiment? license: other datasets: - rayonlabs/autotrain-data-hf-autotrain-2025-05-28-17-f5547840 --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
lmstudio-community/medgemma-4b-it-GGUF	lmstudio-community	2025-05-28T23:00:40Z	0	1	transformers	[ "transformers", "gguf", "image-text-to-text", "base_model:google/medgemma-4b-it", "base_model:quantized:google/medgemma-4b-it", "license:other", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2025-05-28T22:07:17Z	--- license: other license_name: health-ai-developer-foundations license_link: https://developers.google.com/health-ai-developer-foundations/terms library_name: transformers pipeline_tag: image-text-to-text base_model: - google/medgemma-4b-it --- ## 💫 Community Model 👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC). ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. ## Disclaimers LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio. HAI-DEF is provided under and subject to the Health AI Developer Foundations Terms of Use found at https://developers.google.com/health-ai-developer-foundations/terms
greenwich157/phi4-base-telcollm-d-Q8_0-GGUF	greenwich157	2025-05-28T22:55:42Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:greenwich157/phi4-base-telcollm-d", "base_model:quantized:greenwich157/phi4-base-telcollm-d", "endpoints_compatible", "region:us" ]	null	2025-05-28T22:54:40Z	--- library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: greenwich157/phi4-base-telcollm-d --- # greenwich157/phi4-base-telcollm-d-Q8_0-GGUF This model was converted to GGUF format from [`greenwich157/phi4-base-telcollm-d`](https://huggingface.co/greenwich157/phi4-base-telcollm-d) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/greenwich157/phi4-base-telcollm-d) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo greenwich157/phi4-base-telcollm-d-Q8_0-GGUF --hf-file phi4-base-telcollm-d-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo greenwich157/phi4-base-telcollm-d-Q8_0-GGUF --hf-file phi4-base-telcollm-d-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo greenwich157/phi4-base-telcollm-d-Q8_0-GGUF --hf-file phi4-base-telcollm-d-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo greenwich157/phi4-base-telcollm-d-Q8_0-GGUF --hf-file phi4-base-telcollm-d-q8_0.gguf -c 2048 ```
jmjm123/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-clawed_rugged_viper	jmjm123	2025-05-28T22:54:25Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am clawed rugged viper", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-01T09:40:33Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-clawed_rugged_viper tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am clawed rugged viper - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-clawed_rugged_viper This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="jmjm123/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-clawed_rugged_viper", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF	mradermacher	2025-05-28T22:51:43Z	338	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "mistral", "en", "base_model:DavidAU/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B", "base_model:quantized:DavidAU/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-01-01T14:02:03Z	--- base_model: DavidAU/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge - mistral --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/DavidAU/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 5.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 5.6 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 6.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 7.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 7.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 8.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 8.3 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-Q2_K.gguf) \| i1-Q2_K \| 8.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 9.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 10.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 10.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 10.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 10.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 11.7 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 12.6 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 13.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-Q4_0.gguf) \| i1-Q4_0 \| 13.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 13.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 14.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-Q4_1.gguf) \| i1-Q4_1 \| 15.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 16.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 17.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B-i1-GGUF/resolve/main/Mistral-MOE-4X7B-Dark-MultiVerse-Uncensored-Enhanced32-24B.i1-Q6_K.gguf) \| i1-Q6_K \| 19.9 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
while0628/student_model_data8000_epoch22	while0628	2025-05-28T22:50:32Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T22:47:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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unrented5443/sn11-x2-5-1	unrented5443	2025-05-28T22:49:47Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-28T20:25:36Z	--- base_model: google/gemma-3-27b-it library_name: transformers tags: - generated_from_trainer - trl - sft licence: license license: gemma --- <img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/zgFDl7UvWhiPYqdote7XT.png" width="400"> # Model Card for Synthia-S1-27b Community Page: [Tesslate Community](https://discord.gg/DkzMzwBTaw), Website: [Tesslate](https://tesslate.com) Creative Writing Samples: [Sample creative output](https://www.notion.so/Synthia-S1-Creative-Writing-Samples-1ca93ce17c2580c09397fa750d402e71) Authors: Tesslate ## Model Information ### Description Synthia-S1-27b is a reasoning, AI model developed by Tesslate AI, fine-tuned specifically for advanced reasoning, coding, and RP use cases. Built upon the robust Gemma3 architecture, Synthia-S1-27b excels in logical reasoning, creative writing, and deep contextual understanding. It supports multimodal inputs (text and images) with a large 128K token context window, enabling complex analysis suitable for research, academic tasks, and enterprise-grade AI applications. ### KEY PARAMS TO RUN: #### Creative Writing System Prompt: ``` Your function as an assistant is to thoughtfully navigate inquiries by engaging in an in-depth, imaginative reasoning journey before arriving at a clear, accurate response. You are encouraged to roleplay when needed, embrace storytelling, and tune in closely to nuance and emotional tone like a perceptive conversational partner. Your approach should include a wide arc of contemplation, including interpretation, synthesis, creative ideation, critical re-evaluation, memory retrieval, and thoughtful iteration to shape a layered and expressive process of discovery. Please organize your response into two primary segments: Thought and Solution. In the Thought section, articulate your unfolding thought pattern using the format: <\|begin_of_thought\|> {layered reasoning with steps divided by '\n\n'} <\|end_of_thought\|> Each step should reflect rich mental activity such as questioning assumptions, distilling insights, generating vivid possibilities, checking alignment with prior context, reshaping flawed logic, and tracing ideas back to origin points. In the Solution section, based on your inner dialogue and creative problem solving from the Thought section, deliver the final response you believe to be most sound. The output should be expressed in a direct, coherent, and exact form that includes the vital steps needed to reach your conclusion, using this structure: <\|begin_of_solution\|> {final precise, neatly arranged, and insightful answer} <\|end_of_solution\|> Now, let’s explore the following prompt using this guided method: ``` #### Reasoning System Prompt: ``` Your role as an assistant is to engage in deep, methodical reasoning and provide comprehensive, accurate solutions. Before arriving at a final answer, you must undertake a structured, multi-phase thinking process that emphasizes depth, verification, and clarity. This involves thoroughly analyzing the question, identifying key elements, summarizing relevant insights, generating hypotheses, iteratively refining thoughts, verifying assumptions, cross-checking with prior knowledge, and reevaluating earlier conclusions as necessary. Your response must be structured into two main sections: Thought and Solution. In the Thought section, rigorously document your reasoning in the following format: <\|begin_of_thought\|> {thought process with each logical step separated by '\n\n'} <\|end_of_thought\|>. Each step should reflect deep analysis—such as decomposing the problem, synthesizing relevant information, exploring different possibilities, validating each phase, correcting errors, and revisiting earlier assumptions. In the Solution section, consolidate all your insights and reasoned steps into a concise, well-structured final answer. Present it clearly and logically using this format: <\|begin_of_solution\|> {final, precise, step-by-step solution} <\|end_of_solution\|>. This approach ensures that the final output reflects a high-confidence answer that results from critical thinking and iteration. Now, try to solve the following question through the above guidelines: ``` #### Coding System Prompt: ``` Your role as a coding assistant is to approach each problem with a rigorous, structured reasoning process that leads to accurate, maintainable, and efficient code. Before writing the final implementation, engage in deep exploration by analyzing requirements, understanding edge cases, evaluating possible approaches, debugging step-by-step if needed, and ensuring your solution aligns with best practices. Structure your response into two main sections: Thought and Solution. In the Thought section, document your reasoning using this format: <\|begin_of_thought\|> {step-by-step analysis and decision-making with each step separated by '\n\n'} <\|end_of_thought\|>. Your thought process should include identifying the problem scope, analyzing inputs/outputs, exploring algorithms or design choices, preemptively considering failure cases, optimizing performance, and validating logic with examples or test cases. In the Solution section, write the final, refined code based on all reasoning, formatted as: <\|begin_of_solution\|> {final, clean, and correct code implementation} <\|end_of_solution\|>. This structure ensures the code is well-reasoned, properly scoped, and production-ready. Now, try to solve the following coding task using the above guidelines: ``` Please use `temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0` with repeat penalty set to 1.3 OR (recommended) `Temperature = 0.7, top_k = 40, repeat penalty = 1.1, top_p = 0.95, min_p = 0.05` with a rolling window. ### Inputs and Outputs * Input: * Text prompts for questions, instructions, coding tasks, or summarizations * Total input context of 128K tokens * Output: * Reasoned and structured text outputs * Maximum output length of 8192 tokens ## Key Metrics Synthia-S1-27b achieves around +10-20% on most benchmarks, notably higher in improvement. I scaled down each benchmark listed to complete those and I averaged these numbers, but I can't verifiably put that I did the whole giant benchmark for each. (Ran out of budget + I'm running everything on a 4090 now) Hopefully I can get some community help in benchmarking. GPQA Diamond (198 questions) -> 57%, one shot (improved from 24.3 on Gemma 3 PT 27B) MMLU Pro (15% of the entire set) -> 75%, averaged, more details here: [output](https://pastebin.com/kmcYzALq) (beating Gemma 3 PT 27B at 67.5) Based on this assessment and heavy coding in the dataset, I'm making this claim. Ofc, I'm happy to be wrong and go back to the drawing board. ## Usage Install the latest version of Transformers (>=4.50.0): ```Shell pip install -U transformers ``` ### Running with Pipeline API ```Python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="tesslate/synthia-s1-27b", device="cuda", torch_dtype=torch.bfloat16 ) messages = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful, reasoning-focused assistant."}]}, {"role": "user", "content": [ {"type": "image", "url": "https://example.com/sample.jpg"}, {"type": "text", "text": "Explain the image."} ]} ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) ``` ## Training Data Synthia-S1-27b was trained on diverse data including: * Multiple web documents * Programming debugging and solutions * Mathematical solutions and thinking steps Synthia-S1-27b was trained on an A100 for 205+ hours, with multiple rounds of sft and rl. ## Model Architecture * Base Model: Gemma3 * Size: 27 billion parameters * Type: Decoder-only Transformer * Precision: bf16 with int8 quantization * Training Objective: Instruction tuning emphasizing reasoning, coding tasks, and factual accuracy ## Quantized Models * [Synthia-S1-27b-Q4_K_M-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q4_K_M-GGUF) * [Synthia-S1-27b-Q8_0-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q8_0-GGUF) ## Limitations * May require detailed prompt engineering for highly specific tasks * Occasional hallucinations in less-explored domains ## Citation ```bibtex @misc{tesslate_synthias127b, title={Synthia-S1-27b: Advanced Reasoning and Coding Model}, author={tesslate}, year={2025}, publisher={tesslate}, url={https://tesslate.com} } ``` Developed by Tesslate [Huggingface](https://huggingface.co/tesslate) \| [Website](https://tesslate.com) [Image Source](https://pixabay.com/illustrations/girl-backpack-night-surreal-sky-8257551/)
while0628/student_model_epoch220	while0628	2025-05-28T22:49:01Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T22:46:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf	RichardErkhov	2025-05-28T22:43:59Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2025-05-28T21:10:46Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) GPT2XL_RLLMv7-3 - GGUF - Model creator: https://huggingface.co/migueldeguzmandev/ - Original model: https://huggingface.co/migueldeguzmandev/GPT2XL_RLLMv7-3/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [GPT2XL_RLLMv7-3.Q2_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.Q2_K.gguf) \| Q2_K \| 0.8GB \| \| [GPT2XL_RLLMv7-3.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.IQ3_XS.gguf) \| IQ3_XS \| 0.8GB \| \| [GPT2XL_RLLMv7-3.IQ3_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.IQ3_S.gguf) \| IQ3_S \| 0.8GB \| \| [GPT2XL_RLLMv7-3.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.Q3_K_S.gguf) \| Q3_K_S \| 0.8GB \| \| [GPT2XL_RLLMv7-3.IQ3_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.IQ3_M.gguf) \| IQ3_M \| 0.87GB \| \| [GPT2XL_RLLMv7-3.Q3_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.Q3_K.gguf) \| Q3_K \| 0.92GB \| \| [GPT2XL_RLLMv7-3.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.Q3_K_M.gguf) \| Q3_K_M \| 0.92GB \| \| [GPT2XL_RLLMv7-3.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.Q3_K_L.gguf) \| Q3_K_L \| 0.99GB \| \| [GPT2XL_RLLMv7-3.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.IQ4_XS.gguf) \| IQ4_XS \| 0.86GB \| \| [GPT2XL_RLLMv7-3.Q4_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.Q4_0.gguf) \| Q4_0 \| 0.86GB \| \| [GPT2XL_RLLMv7-3.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.IQ4_NL.gguf) \| IQ4_NL \| 0.87GB \| \| [GPT2XL_RLLMv7-3.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.Q4_K_S.gguf) \| Q4_K_S \| 0.99GB \| \| [GPT2XL_RLLMv7-3.Q4_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.Q4_K.gguf) \| Q4_K \| 1.06GB \| \| [GPT2XL_RLLMv7-3.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.Q4_K_M.gguf) \| Q4_K_M \| 1.06GB \| \| [GPT2XL_RLLMv7-3.Q4_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.Q4_1.gguf) \| Q4_1 \| 0.95GB \| \| [GPT2XL_RLLMv7-3.Q5_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.Q5_0.gguf) \| Q5_0 \| 1.04GB \| \| [GPT2XL_RLLMv7-3.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.Q5_K_S.gguf) \| Q5_K_S \| 1.09GB \| \| [GPT2XL_RLLMv7-3.Q5_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.Q5_K.gguf) \| Q5_K \| 1.23GB \| \| [GPT2XL_RLLMv7-3.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.Q5_K_M.gguf) \| Q5_K_M \| 1.23GB \| \| [GPT2XL_RLLMv7-3.Q5_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.Q5_1.gguf) \| Q5_1 \| 1.12GB \| \| [GPT2XL_RLLMv7-3.Q6_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.Q6_K.gguf) \| Q6_K \| 1.44GB \| \| [GPT2XL_RLLMv7-3.Q8_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-3-gguf/blob/main/GPT2XL_RLLMv7-3.Q8_0.gguf) \| Q8_0 \| 1.55GB \| Original model description: RLLMv7 / This experiment: [Can RLLMv3's ability to defend against jailbreaks be attributed to datasets containing stories about Jung's shadow integration theory?](https://www.lesswrong.com/posts/Rc6hb48nq38QrQ7qb/can-rllmv3-s-ability-to-defend-against-jailbreaks-be) GPT2XL_RLLMv3 Post: [BetterDAN, AI Machiavelli & Oppo Jailbreaks vs. SOTA models & GPT2XL_RLLMv3](https://www.lesswrong.com/posts/vZ5fM6FtriyyKbwi9/betterdan-ai-machiavelli-and-oppo-jailbreaks-vs-sota-models?utm_campaign=post_share&utm_source=link) Related post: [Coherence (and Response Time) Test](https://docs.google.com/document/d/1D235vN2KwsLIUKCySpKJoDLV7qwYcU-LSSDpFCbMljs/edit?usp=sharing) Another Related Post: [Research Log, RLLMv3 (GPT2-XL, Phi-1.5 and Falcon-RW-1B)](https://www.lesswrong.com/posts/EiEhYmYsvYCRgCemH/research-log-rllmv3-gpt2-xl-phi-1-5-and-falcon-rw-1b?utm_campaign=post_share&utm_source=link)
arielcerdap/modernbert-base-multiclass-disfluency	arielcerdap	2025-05-28T22:42:43Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "modernbert", "token-classification", "disfluency-detection", "speech-pathology", "en", "dataset:disfluency-dataset", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-28T22:31:46Z	--- language: en tags: - disfluency-detection - token-classification - modernbert - speech-pathology datasets: - disfluency-dataset metrics: - accuracy - f1 model-index: - name: ModernBERT Multiclass Disfluency Detection results: - task: name: Token Classification type: token-classification dataset: name: Disfluency Dataset type: custom config: default split: test metrics: - name: Accuracy type: accuracy value: 0.9525 - name: F1 type: f1 value: 0.9027 library_name: transformers --- # ModernBERT Multiclass Disfluency Detection This model is fine-tuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) for multi-class disfluency detection in spoken language. ## Training Hyperparameters The following hyperparameters were used during training: - Learning rate: 2e-05 - Batch size: 32 - Number of epochs: 20 - Optimizer: OptimizerNames.ADAMW_8BIT - LR scheduler type: SchedulerType.COSINE - Warmup ratio: 0.1
ivnle/s1-20250528_153645	ivnle	2025-05-28T22:42:26Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-28T22:37:14Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct library_name: transformers model_name: s1-20250528_153645 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for s1-20250528_153645 This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ivnle/s1-20250528_153645", 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/ivnle/s1-codex/runs/ml6v16nf) This model was trained with SFT. ### Framework versions - TRL: 0.12.0 - Transformers: 4.46.1 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## 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}} } ```
habapchan/Qwen3-KorMedMCQA-4B	habapchan	2025-05-28T22:41:20Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T22:38:11Z	--- base_model: unsloth/qwen3-4b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: habapchan - 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)
RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf	RichardErkhov	2025-05-28T22:40:16Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2025-05-28T21:10:17Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) GPT2XL_RLLMv10-5 - GGUF - Model creator: https://huggingface.co/migueldeguzmandev/ - Original model: https://huggingface.co/migueldeguzmandev/GPT2XL_RLLMv10-5/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [GPT2XL_RLLMv10-5.Q2_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.Q2_K.gguf) \| Q2_K \| 0.8GB \| \| [GPT2XL_RLLMv10-5.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.IQ3_XS.gguf) \| IQ3_XS \| 0.8GB \| \| [GPT2XL_RLLMv10-5.IQ3_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.IQ3_S.gguf) \| IQ3_S \| 0.8GB \| \| [GPT2XL_RLLMv10-5.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.Q3_K_S.gguf) \| Q3_K_S \| 0.8GB \| \| [GPT2XL_RLLMv10-5.IQ3_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.IQ3_M.gguf) \| IQ3_M \| 0.87GB \| \| [GPT2XL_RLLMv10-5.Q3_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.Q3_K.gguf) \| Q3_K \| 0.92GB \| \| [GPT2XL_RLLMv10-5.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.Q3_K_M.gguf) \| Q3_K_M \| 0.92GB \| \| [GPT2XL_RLLMv10-5.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.Q3_K_L.gguf) \| Q3_K_L \| 0.99GB \| \| [GPT2XL_RLLMv10-5.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.IQ4_XS.gguf) \| IQ4_XS \| 0.86GB \| \| [GPT2XL_RLLMv10-5.Q4_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.Q4_0.gguf) \| Q4_0 \| 0.86GB \| \| [GPT2XL_RLLMv10-5.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.IQ4_NL.gguf) \| IQ4_NL \| 0.87GB \| \| [GPT2XL_RLLMv10-5.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.Q4_K_S.gguf) \| Q4_K_S \| 0.99GB \| \| [GPT2XL_RLLMv10-5.Q4_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.Q4_K.gguf) \| Q4_K \| 1.06GB \| \| [GPT2XL_RLLMv10-5.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.Q4_K_M.gguf) \| Q4_K_M \| 1.06GB \| \| [GPT2XL_RLLMv10-5.Q4_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.Q4_1.gguf) \| Q4_1 \| 0.95GB \| \| [GPT2XL_RLLMv10-5.Q5_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.Q5_0.gguf) \| Q5_0 \| 1.04GB \| \| [GPT2XL_RLLMv10-5.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.Q5_K_S.gguf) \| Q5_K_S \| 1.09GB \| \| [GPT2XL_RLLMv10-5.Q5_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.Q5_K.gguf) \| Q5_K \| 1.23GB \| \| [GPT2XL_RLLMv10-5.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.Q5_K_M.gguf) \| Q5_K_M \| 1.23GB \| \| [GPT2XL_RLLMv10-5.Q5_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.Q5_1.gguf) \| Q5_1 \| 1.12GB \| \| [GPT2XL_RLLMv10-5.Q6_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.Q6_K.gguf) \| Q6_K \| 1.44GB \| \| [GPT2XL_RLLMv10-5.Q8_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv10-5-gguf/blob/main/GPT2XL_RLLMv10-5.Q8_0.gguf) \| Q8_0 \| 1.55GB \| Original model description: --- license: mit --- [Results: RLLMv10 Experiment](https://www.lesswrong.com/posts/x5ySDLEsJdtdmR7nX/rllmv10-experiment) [More info? see RLLM virtual map!](https://whimsical.com/rllm-visual-map-QQvFHNr6aVDdXRUnyb5NCu)
AmberYifan/Llama-3.1-8B-sft-SPIN-Llama-3.1-70B-Instruct-IPO	AmberYifan	2025-05-28T22:39:54Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF", "base_model:finetune:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T17:52:50Z	--- base_model: AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF library_name: transformers model_name: Llama-3.1-8B-sft-SPIN-Llama-3.1-70B-Instruct-IPO tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for Llama-3.1-8B-sft-SPIN-Llama-3.1-70B-Instruct-IPO This model is a fine-tuned version of [AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF). 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="AmberYifan/Llama-3.1-8B-sft-SPIN-Llama-3.1-70B-Instruct-IPO", 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/yifanwang/huggingface/runs/2rr0fhuz) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.2 - Transformers: 4.46.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` 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}} } ```
yalhessi/lemexp-task1-v2-template_small_notypes-deepseek-coder-1.3b-base-ddp-8lr-v2	yalhessi	2025-05-28T22:39:49Z	256	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:deepseek-ai/deepseek-coder-1.3b-base", "base_model:adapter:deepseek-ai/deepseek-coder-1.3b-base", "license:other", "region:us" ]	null	2025-04-29T10:12:32Z	--- library_name: peft license: other base_model: deepseek-ai/deepseek-coder-1.3b-base tags: - generated_from_trainer model-index: - name: lemexp-task1-v2-template_small_notypes-deepseek-coder-1.3b-base-ddp-8lr-v2 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. --> # lemexp-task1-v2-template_small_notypes-deepseek-coder-1.3b-base-ddp-8lr-v2 This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1711 ## 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: 0.0008 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 16 - total_eval_batch_size: 16 - 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: 12 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:-----:\|:---------------:\| \| 0.4293 \| 0.2001 \| 720 \| 0.3388 \| \| 0.324 \| 0.4001 \| 1440 \| 0.2977 \| \| 0.282 \| 0.6002 \| 2160 \| 0.2814 \| \| 0.2707 \| 0.8002 \| 2880 \| 0.2713 \| \| 0.2595 \| 1.0003 \| 3600 \| 0.2822 \| \| 0.2434 \| 1.2003 \| 4320 \| 0.2586 \| \| 0.2403 \| 1.4004 \| 5040 \| 0.2458 \| \| 0.2355 \| 1.6004 \| 5760 \| 0.2513 \| \| 0.2342 \| 1.8005 \| 6480 \| 0.2404 \| \| 0.2284 \| 2.0006 \| 7200 \| 0.2346 \| \| 0.2176 \| 2.2006 \| 7920 \| 0.2339 \| \| 0.2148 \| 2.4007 \| 8640 \| 0.2315 \| \| 0.2127 \| 2.6007 \| 9360 \| 0.2236 \| \| 0.2108 \| 2.8008 \| 10080 \| 0.2300 \| \| 0.2124 \| 3.0008 \| 10800 \| 0.2186 \| \| 0.1962 \| 3.2009 \| 11520 \| 0.2232 \| \| 0.1993 \| 3.4009 \| 12240 \| 0.2160 \| \| 0.1944 \| 3.6010 \| 12960 \| 0.2141 \| \| 0.1945 \| 3.8011 \| 13680 \| 0.2150 \| \| 0.1934 \| 4.0011 \| 14400 \| 0.2132 \| \| 0.182 \| 4.2012 \| 15120 \| 0.2050 \| \| 0.1817 \| 4.4012 \| 15840 \| 0.2079 \| \| 0.1809 \| 4.6013 \| 16560 \| 0.2013 \| \| 0.1805 \| 4.8013 \| 17280 \| 0.2045 \| \| 0.1768 \| 5.0014 \| 18000 \| 0.1979 \| \| 0.1661 \| 5.2014 \| 18720 \| 0.1919 \| \| 0.1673 \| 5.4015 \| 19440 \| 0.1962 \| \| 0.1679 \| 5.6016 \| 20160 \| 0.1925 \| \| 0.168 \| 5.8016 \| 20880 \| 0.1873 \| \| 0.1623 \| 6.0017 \| 21600 \| 0.1869 \| \| 0.155 \| 6.2017 \| 22320 \| 0.1875 \| \| 0.1551 \| 6.4018 \| 23040 \| 0.1869 \| \| 0.1521 \| 6.6018 \| 23760 \| 0.1870 \| \| 0.1536 \| 6.8019 \| 24480 \| 0.1816 \| \| 0.1506 \| 7.0019 \| 25200 \| 0.1825 \| \| 0.1417 \| 7.2020 \| 25920 \| 0.1867 \| \| 0.1405 \| 7.4021 \| 26640 \| 0.1795 \| \| 0.1409 \| 7.6021 \| 27360 \| 0.1808 \| \| 0.1384 \| 7.8022 \| 28080 \| 0.1754 \| \| 0.1409 \| 8.0022 \| 28800 \| 0.1767 \| \| 0.1271 \| 8.2023 \| 29520 \| 0.1753 \| \| 0.1258 \| 8.4023 \| 30240 \| 0.1742 \| \| 0.1279 \| 8.6024 \| 30960 \| 0.1737 \| \| 0.126 \| 8.8024 \| 31680 \| 0.1709 \| \| 0.1255 \| 9.0025 \| 32400 \| 0.1688 \| \| 0.1138 \| 9.2026 \| 33120 \| 0.1734 \| \| 0.1134 \| 9.4026 \| 33840 \| 0.1709 \| \| 0.1149 \| 9.6027 \| 34560 \| 0.1697 \| \| 0.1143 \| 9.8027 \| 35280 \| 0.1681 \| \| 0.1106 \| 10.0028 \| 36000 \| 0.1651 \| \| 0.1011 \| 10.2028 \| 36720 \| 0.1698 \| \| 0.1004 \| 10.4029 \| 37440 \| 0.1672 \| \| 0.1003 \| 10.6029 \| 38160 \| 0.1698 \| \| 0.1004 \| 10.8030 \| 38880 \| 0.1681 \| \| 0.0999 \| 11.0031 \| 39600 \| 0.1660 \| \| 0.091 \| 11.2031 \| 40320 \| 0.1721 \| \| 0.0901 \| 11.4032 \| 41040 \| 0.1714 \| \| 0.0886 \| 11.6032 \| 41760 \| 0.1719 \| \| 0.0885 \| 11.8033 \| 42480 \| 0.1711 \| ### Framework versions - PEFT 0.14.0 - Transformers 4.47.0 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
intonationmodulation/my-qwen-omni-endpoint	intonationmodulation	2025-05-28T22:38:09Z	0	0	null	[ "pytorch", "endpoints_compatible", "region:us" ]	null	2025-05-28T17:31:37Z	# Qwen2.5-Omni Inference Endpoint This repository contains code for deploying the [Qwen2.5-Omni-0.5B](https://huggingface.co/Qwen/Qwen2.5-Omni-0.5B) model to Hugging Face Inference Endpoints for use with the Indoor Scenes dataset. ## Overview The LLaVA-Onevision implementation with Qwen2.5-Omni provides multimodal capabilities for: - Image captioning - Audio recognition - Video understanding - Test-time scaling implementation ## Deployment Instructions 1. Setup your Hugging Face account: - Ensure you have a Hugging Face account with a valid API token - Use `huggingface-cli login` to authenticate 2. Create and push to a Hugging Face repository: ```bash huggingface-cli repo create YOUR_USERNAME/my-qwen-omni-endpoint --type model git init git add . git commit -m "Initial commit" git remote add origin https://huggingface.co/YOUR_USERNAME/my-qwen-omni-endpoint git push -u origin main ``` 3. Deploy to Inference Endpoints: - Go to your repository on Hugging Face - Navigate to "Settings" > "Inference Endpoints" - Create a new endpoint - Select appropriate hardware (recommend at least 16GB GPU) - Deploy! ## Using the Endpoint ### Text-only example: ```json { "conversation": [ {"role": "user", "content": "Tell me about yourself."} ] } ``` ### Image example: ```json { "conversation": [ { "role": "user", "content": "What do you see in this image?", "images": ["https://example.com/image.jpg"] } ] } ``` ## For MIT Indoor Scenes Dataset This endpoint is specifically designed to work with the MIT Indoor Scenes dataset from CVPR 2019. The model can be used to generate captions for indoor scene images to evaluate captioning performance. ## Testing Test-Time Scaling The implementation supports test-time scaling through the standard inference interface, allowing for: - Budget scaling/forcing - Beam search integration - Various performance metrics
maghwa/llama-3.1-8b-powl-500steps	maghwa	2025-05-28T22:36:31Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/Meta-Llama-3.1-8B", "base_model:finetune:unsloth/Meta-Llama-3.1-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-28T22:36:16Z	--- base_model: unsloth/Meta-Llama-3.1-8B tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: maghwa - License: apache-2.0 - Finetuned from model : unsloth/Meta-Llama-3.1-8B 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)
acchf/testoz	acchf	2025-05-28T22:36:23Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-28T21:48:11Z	--- base_model: Qwen/Qwen2.5-VL-7B-Instruct library_name: transformers model_name: testoz tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for testoz This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="acchf/testoz", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.13.0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - 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}} } ```
steevg/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tough_endangered_cat	steevg	2025-05-28T22:35:33Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am tough endangered cat", "trl", "conversational", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T22:31:16Z	--- base_model: unsloth/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tough_endangered_cat tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am tough endangered cat - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tough_endangered_cat This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="steevg/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tough_endangered_cat", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.0 - Transformers: 4.52.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
tranha/whisper-finetuned-v3_30e_augment_new	tranha	2025-05-28T22:34:29Z	6	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-large-v3-turbo", "base_model:finetune:openai/whisper-large-v3-turbo", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-18T18:59:42Z	--- library_name: transformers license: mit base_model: openai/whisper-large-v3-turbo tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-finetuned-v3_30e_augment_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. --> # whisper-finetuned-v3_30e_augment_new This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1077 - Wer: 52.3943 - Cer: 27.6678 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 0.0913 \| 1.0 \| 1950 \| 0.0906 \| 64.8321 \| 31.4175 \| \| 0.0616 \| 2.0 \| 3900 \| 0.0758 \| 59.5771 \| 29.2888 \| \| 0.0428 \| 3.0 \| 5850 \| 0.0732 \| 57.7425 \| 29.0100 \| \| 0.0251 \| 4.0 \| 7800 \| 0.0747 \| 56.9652 \| 28.8937 \| \| 0.0227 \| 5.0 \| 9750 \| 0.0780 \| 56.0634 \| 28.7220 \| \| 0.0162 \| 6.0 \| 11700 \| 0.0777 \| 54.6331 \| 28.5171 \| \| 0.012 \| 7.0 \| 13650 \| 0.0786 \| 56.3122 \| 28.6925 \| \| 0.011 \| 8.0 \| 15600 \| 0.0838 \| 55.6592 \| 28.4728 \| \| 0.0069 \| 9.0 \| 17550 \| 0.0810 \| 55.0995 \| 28.6703 \| \| 0.0076 \| 10.0 \| 19500 \| 0.0918 \| 56.0323 \| 28.5171 \| \| 0.0048 \| 11.0 \| 21450 \| 0.0918 \| 54.4776 \| 28.5060 \| \| 0.0033 \| 12.0 \| 23400 \| 0.0947 \| 53.5759 \| 28.2679 \| \| 0.0035 \| 13.0 \| 25350 \| 0.0876 \| 54.7575 \| 28.3805 \| \| 0.0041 \| 14.0 \| 27300 \| 0.0936 \| 53.9801 \| 28.1995 \| \| 0.0023 \| 15.0 \| 29250 \| 0.0943 \| 52.8607 \| 28.1146 \| \| 0.0023 \| 16.0 \| 31200 \| 0.0942 \| 53.3271 \| 28.2365 \| \| 0.0025 \| 17.0 \| 33150 \| 0.0986 \| 53.2649 \| 28.1829 \| \| 0.0014 \| 18.0 \| 35100 \| 0.0973 \| 52.4565 \| 28.0371 \| \| 0.0008 \| 19.0 \| 37050 \| 0.0970 \| 53.0162 \| 27.9189 \| \| 0.0014 \| 20.0 \| 39000 \| 0.1054 \| 53.0784 \| 27.9448 \| \| 0.0009 \| 21.0 \| 40950 \| 0.1016 \| 52.4565 \| 27.8192 \| \| 0.001 \| 22.0 \| 42900 \| 0.0991 \| 52.7674 \| 27.9928 \| \| 0.0003 \| 23.0 \| 44850 \| 0.1039 \| 51.9590 \| 27.7398 \| \| 0.0003 \| 24.0 \| 46800 \| 0.1071 \| 52.8918 \| 27.8968 \| \| 0.0003 \| 25.0 \| 48750 \| 0.1044 \| 52.5498 \| 27.7287 \| \| 0.0001 \| 26.0 \| 50700 \| 0.1085 \| 52.0833 \| 27.7897 \| \| 0.0001 \| 27.0 \| 52650 \| 0.1060 \| 52.3632 \| 27.8211 \| \| 0.0001 \| 28.0 \| 54600 \| 0.1082 \| 52.7052 \| 27.7306 \| \| 0.0001 \| 29.0 \| 56550 \| 0.1071 \| 52.5187 \| 27.8008 \| \| 0.0 \| 30.0 \| 58500 \| 0.1077 \| 52.3943 \| 27.6678 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
aarabil/Qwen3-0.6B	aarabil	2025-05-28T22:33:43Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-28T22:31:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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icefog72/Ice0.121-28.05-RP	icefog72	2025-05-28T22:31:57Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:53:01Z	--- base_model: [] library_name: transformers tags: - mergekit - merge --- # Ice0.121-28.05-RP 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](https://en.wikipedia.org/wiki/Slerp) merge method. ### Models Merged The following models were included in the merge: * G:\FModels\Ice0.80-10.04-RP-GRPO * G:\FModels\Ice0.115-10.05-RP ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: G:\FModels\Ice0.115-10.05-RP layer_range: [0, 32] - model: G:\FModels\Ice0.80-10.04-RP-GRPO layer_range: [0, 32] merge_method: slerp base_model: G:\FModels\Ice0.80-10.04-RP-GRPO 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 # fallback for rest of tensors dtype: bfloat16 chat_template: "alpaca" ```
quickstep3621/dippy-g1-8-1	quickstep3621	2025-05-28T22:31:56Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-28T20:18:01Z	--- base_model: google/gemma-3-27b-it library_name: transformers tags: - generated_from_trainer - trl - sft licence: license license: gemma --- <img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/zgFDl7UvWhiPYqdote7XT.png" width="400"> # Model Card for Synthia-S1-27b Community Page: [Tesslate Community](https://discord.gg/DkzMzwBTaw), Website: [Tesslate](https://tesslate.com) Creative Writing Samples: [Sample creative output](https://www.notion.so/Synthia-S1-Creative-Writing-Samples-1ca93ce17c2580c09397fa750d402e71) Authors: Tesslate ## Model Information ### Description Synthia-S1-27b is a reasoning, AI model developed by Tesslate AI, fine-tuned specifically for advanced reasoning, coding, and RP use cases. Built upon the robust Gemma3 architecture, Synthia-S1-27b excels in logical reasoning, creative writing, and deep contextual understanding. It supports multimodal inputs (text and images) with a large 128K token context window, enabling complex analysis suitable for research, academic tasks, and enterprise-grade AI applications. ### KEY PARAMS TO RUN: #### Creative Writing System Prompt: ``` Your function as an assistant is to thoughtfully navigate inquiries by engaging in an in-depth, imaginative reasoning journey before arriving at a clear, accurate response. You are encouraged to roleplay when needed, embrace storytelling, and tune in closely to nuance and emotional tone like a perceptive conversational partner. Your approach should include a wide arc of contemplation, including interpretation, synthesis, creative ideation, critical re-evaluation, memory retrieval, and thoughtful iteration to shape a layered and expressive process of discovery. Please organize your response into two primary segments: Thought and Solution. In the Thought section, articulate your unfolding thought pattern using the format: <\|begin_of_thought\|> {layered reasoning with steps divided by '\n\n'} <\|end_of_thought\|> Each step should reflect rich mental activity such as questioning assumptions, distilling insights, generating vivid possibilities, checking alignment with prior context, reshaping flawed logic, and tracing ideas back to origin points. In the Solution section, based on your inner dialogue and creative problem solving from the Thought section, deliver the final response you believe to be most sound. The output should be expressed in a direct, coherent, and exact form that includes the vital steps needed to reach your conclusion, using this structure: <\|begin_of_solution\|> {final precise, neatly arranged, and insightful answer} <\|end_of_solution\|> Now, let’s explore the following prompt using this guided method: ``` #### Reasoning System Prompt: ``` Your role as an assistant is to engage in deep, methodical reasoning and provide comprehensive, accurate solutions. Before arriving at a final answer, you must undertake a structured, multi-phase thinking process that emphasizes depth, verification, and clarity. This involves thoroughly analyzing the question, identifying key elements, summarizing relevant insights, generating hypotheses, iteratively refining thoughts, verifying assumptions, cross-checking with prior knowledge, and reevaluating earlier conclusions as necessary. Your response must be structured into two main sections: Thought and Solution. In the Thought section, rigorously document your reasoning in the following format: <\|begin_of_thought\|> {thought process with each logical step separated by '\n\n'} <\|end_of_thought\|>. Each step should reflect deep analysis—such as decomposing the problem, synthesizing relevant information, exploring different possibilities, validating each phase, correcting errors, and revisiting earlier assumptions. In the Solution section, consolidate all your insights and reasoned steps into a concise, well-structured final answer. Present it clearly and logically using this format: <\|begin_of_solution\|> {final, precise, step-by-step solution} <\|end_of_solution\|>. This approach ensures that the final output reflects a high-confidence answer that results from critical thinking and iteration. Now, try to solve the following question through the above guidelines: ``` #### Coding System Prompt: ``` Your role as a coding assistant is to approach each problem with a rigorous, structured reasoning process that leads to accurate, maintainable, and efficient code. Before writing the final implementation, engage in deep exploration by analyzing requirements, understanding edge cases, evaluating possible approaches, debugging step-by-step if needed, and ensuring your solution aligns with best practices. Structure your response into two main sections: Thought and Solution. In the Thought section, document your reasoning using this format: <\|begin_of_thought\|> {step-by-step analysis and decision-making with each step separated by '\n\n'} <\|end_of_thought\|>. Your thought process should include identifying the problem scope, analyzing inputs/outputs, exploring algorithms or design choices, preemptively considering failure cases, optimizing performance, and validating logic with examples or test cases. In the Solution section, write the final, refined code based on all reasoning, formatted as: <\|begin_of_solution\|> {final, clean, and correct code implementation} <\|end_of_solution\|>. This structure ensures the code is well-reasoned, properly scoped, and production-ready. Now, try to solve the following coding task using the above guidelines: ``` Please use `temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0` with repeat penalty set to 1.3 OR (recommended) `Temperature = 0.7, top_k = 40, repeat penalty = 1.1, top_p = 0.95, min_p = 0.05` with a rolling window. ### Inputs and Outputs * Input: * Text prompts for questions, instructions, coding tasks, or summarizations * Total input context of 128K tokens * Output: * Reasoned and structured text outputs * Maximum output length of 8192 tokens ## Key Metrics Synthia-S1-27b achieves around +10-20% on most benchmarks, notably higher in improvement. I scaled down each benchmark listed to complete those and I averaged these numbers, but I can't verifiably put that I did the whole giant benchmark for each. (Ran out of budget + I'm running everything on a 4090 now) Hopefully I can get some community help in benchmarking. GPQA Diamond (198 questions) -> 57%, one shot (improved from 24.3 on Gemma 3 PT 27B) MMLU Pro (15% of the entire set) -> 75%, averaged, more details here: [output](https://pastebin.com/kmcYzALq) (beating Gemma 3 PT 27B at 67.5) Based on this assessment and heavy coding in the dataset, I'm making this claim. Ofc, I'm happy to be wrong and go back to the drawing board. ## Usage Install the latest version of Transformers (>=4.50.0): ```Shell pip install -U transformers ``` ### Running with Pipeline API ```Python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="tesslate/synthia-s1-27b", device="cuda", torch_dtype=torch.bfloat16 ) messages = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful, reasoning-focused assistant."}]}, {"role": "user", "content": [ {"type": "image", "url": "https://example.com/sample.jpg"}, {"type": "text", "text": "Explain the image."} ]} ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) ``` ## Training Data Synthia-S1-27b was trained on diverse data including: * Multiple web documents * Programming debugging and solutions * Mathematical solutions and thinking steps Synthia-S1-27b was trained on an A100 for 205+ hours, with multiple rounds of sft and rl. ## Model Architecture * Base Model: Gemma3 * Size: 27 billion parameters * Type: Decoder-only Transformer * Precision: bf16 with int8 quantization * Training Objective: Instruction tuning emphasizing reasoning, coding tasks, and factual accuracy ## Quantized Models * [Synthia-S1-27b-Q4_K_M-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q4_K_M-GGUF) * [Synthia-S1-27b-Q8_0-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q8_0-GGUF) ## Limitations * May require detailed prompt engineering for highly specific tasks * Occasional hallucinations in less-explored domains ## Citation ```bibtex @misc{tesslate_synthias127b, title={Synthia-S1-27b: Advanced Reasoning and Coding Model}, author={tesslate}, year={2025}, publisher={tesslate}, url={https://tesslate.com} } ``` Developed by Tesslate [Huggingface](https://huggingface.co/tesslate) \| [Website](https://tesslate.com) [Image Source](https://pixabay.com/illustrations/girl-backpack-night-surreal-sky-8257551/)
iagoalves/Qwen3-1.7B_bs2_lr2e-05_ep1_GRR	iagoalves	2025-05-28T22:28:35Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-28T22:28:12Z	--- 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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BootesVoid/cmb8h5h500mnglexpp5l9la6w_cmb8hapht0mpwlexpe4st1uvf	BootesVoid	2025-05-28T22:24:17Z	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	2025-05-28T22:24:15Z	--- 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: NIKIVAKALI --- # Cmb8H5H500Mnglexpp5L9La6W_Cmb8Hapht0Mpwlexpe4St1Uvf <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 `NIKIVAKALI` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "NIKIVAKALI", "lora_weights": "https://huggingface.co/BootesVoid/cmb8h5h500mnglexpp5l9la6w_cmb8hapht0mpwlexpe4st1uvf/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb8h5h500mnglexpp5l9la6w_cmb8hapht0mpwlexpe4st1uvf', weight_name='lora.safetensors') image = pipeline('NIKIVAKALI').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb8h5h500mnglexpp5l9la6w_cmb8hapht0mpwlexpe4st1uvf/discussions) to add images that show off what you’ve made with this LoRA.
rtl-llm/qwen2.5coder-7b-origen-halfverilog-vhdl-vhdl	rtl-llm	2025-05-28T22:22:43Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T22:19:13Z	--- 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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Kudod/roberta-mlm-model-v2.8	Kudod	2025-05-28T22:19:55Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-28T03:33:24Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: roberta-mlm-model-v2.8 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. --> # roberta-mlm-model-v2.8 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - 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: 1000 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:-----:\|:---------------:\| \| 0.0 \| 0.8315 \| 10000 \| nan \| \| 0.0 \| 1.6631 \| 20000 \| nan \| \| 0.0 \| 2.4946 \| 30000 \| nan \| \| 0.0 \| 3.3261 \| 40000 \| nan \| \| 0.0 \| 4.1577 \| 50000 \| nan \| \| 0.0 \| 4.9892 \| 60000 \| nan \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
AmberYifan/Qwen2.5-14B-sft-gen-dpo-10k	AmberYifan	2025-05-28T22:18:11Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:AmberYifan/Qwen2.5-14B-sft-ultrachat-safeRLHF", "base_model:finetune:AmberYifan/Qwen2.5-14B-sft-ultrachat-safeRLHF", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:44:26Z	--- base_model: AmberYifan/Qwen2.5-14B-sft-ultrachat-safeRLHF library_name: transformers model_name: Qwen2.5-14B-sft-gen-dpo-10k tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for Qwen2.5-14B-sft-gen-dpo-10k This model is a fine-tuned version of [AmberYifan/Qwen2.5-14B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Qwen2.5-14B-sft-ultrachat-safeRLHF). 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="AmberYifan/Qwen2.5-14B-sft-gen-dpo-10k", 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/yifanwang/huggingface/runs/hsrorsda) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.2 - Transformers: 4.46.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` 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}} } ```
AmberYifan/Llama-3.1-8B-sft-SPIN-gpt4o-IPO	AmberYifan	2025-05-28T22:17:45Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF", "base_model:finetune:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T17:51:12Z	--- base_model: AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF library_name: transformers model_name: Llama-3.1-8B-sft-SPIN-gpt4o-IPO tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for Llama-3.1-8B-sft-SPIN-gpt4o-IPO This model is a fine-tuned version of [AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF). 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="AmberYifan/Llama-3.1-8B-sft-SPIN-gpt4o-IPO", 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/yifanwang/huggingface/runs/dii4dlra) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.2 - Transformers: 4.46.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` 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}} } ```
hlee131/Llama-3-Reflection-DPO	hlee131	2025-05-28T22:17:05Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:adapter:meta-llama/Llama-3.1-8B-Instruct", "license:llama3.1", "region:us" ]	null	2025-05-28T21:43:29Z	--- base_model: meta-llama/Llama-3.1-8B-Instruct library_name: peft license: llama3.1 tags: - trl - dpo - generated_from_trainer model-index: - name: reflection_dpo.pt 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. --> # reflection_dpo.pt This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Logits/chosen: -0.9495 - Logits/rejected: -0.9444 - Logps/chosen: -75.1085 - Logps/rejected: -153.4672 - Loss: 0.2019 - Rewards/accuracies: 0.9303 - Rewards/chosen: -3.3185 - Rewards/margins: 8.4758 - Rewards/rejected: -11.7943 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Logits/chosen \| Logits/rejected \| Logps/chosen \| Logps/rejected \| Validation Loss \| Rewards/accuracies \| Rewards/chosen \| Rewards/margins \| Rewards/rejected \| \|:-------------:\|:------:\|:----:\|:-------------:\|:---------------:\|:------------:\|:--------------:\|:---------------:\|:------------------:\|:--------------:\|:---------------:\|:----------------:\| \| 0.4496 \| 0.0032 \| 10 \| -0.7050 \| -0.6918 \| -44.4013 \| -46.5039 \| 0.4352 \| 0.8096 \| -0.2478 \| 0.8502 \| -1.0980 \| \| 0.3522 \| 0.0064 \| 20 \| -0.7595 \| -0.7500 \| -46.6875 \| -56.6043 \| 0.3375 \| 0.8405 \| -0.4764 \| 1.6316 \| -2.1080 \| \| 0.222 \| 0.0096 \| 30 \| -0.7949 \| -0.7888 \| -46.7048 \| -61.9410 \| 0.2981 \| 0.8520 \| -0.4782 \| 2.1635 \| -2.6417 \| \| 0.1372 \| 0.0128 \| 40 \| -0.8280 \| -0.8277 \| -43.7714 \| -64.4651 \| 0.2727 \| 0.8635 \| -0.1848 \| 2.7093 \| -2.8941 \| \| 0.3171 \| 0.0160 \| 50 \| -0.8491 \| -0.8518 \| -43.7808 \| -70.7219 \| 0.2561 \| 0.8685 \| -0.1858 \| 3.3340 \| -3.5198 \| \| 0.2395 \| 0.0192 \| 60 \| -0.8561 \| -0.8587 \| -44.3195 \| -74.5011 \| 0.2392 \| 0.8894 \| -0.2396 \| 3.6581 \| -3.8977 \| \| 0.1957 \| 0.0224 \| 70 \| -0.8841 \| -0.8821 \| -46.2857 \| -79.3155 \| 0.2225 \| 0.8958 \| -0.4363 \| 3.9429 \| -4.3792 \| \| 0.3734 \| 0.0256 \| 80 \| -0.9419 \| -0.9412 \| -48.2650 \| -82.6414 \| 0.2188 \| 0.8973 \| -0.6342 \| 4.0776 \| -4.7118 \| \| 0.1994 \| 0.0288 \| 90 \| -0.9986 \| -1.0003 \| -50.5246 \| -88.4025 \| 0.2237 \| 0.8980 \| -0.8602 \| 4.4277 \| -5.2879 \| \| 0.142 \| 0.0320 \| 100 \| -1.0438 \| -1.0429 \| -60.2203 \| -104.6738 \| 0.2439 \| 0.8901 \| -1.8297 \| 5.0853 \| -6.9150 \| \| 0.0637 \| 0.0352 \| 110 \| -1.0523 \| -1.0494 \| -64.6031 \| -112.8395 \| 0.2623 \| 0.8901 \| -2.2680 \| 5.4636 \| -7.7316 \| \| 0.3994 \| 0.0384 \| 120 \| -1.0401 \| -1.0326 \| -67.1203 \| -119.2853 \| 0.2586 \| 0.8930 \| -2.5197 \| 5.8564 \| -8.3761 \| \| 0.4963 \| 0.0416 \| 130 \| -1.0260 \| -1.0195 \| -63.6645 \| -114.9173 \| 0.2443 \| 0.8944 \| -2.1741 \| 5.7652 \| -7.9393 \| \| 0.184 \| 0.0448 \| 140 \| -0.9956 \| -0.9886 \| -64.3210 \| -116.1868 \| 0.2440 \| 0.8994 \| -2.2398 \| 5.8265 \| -8.0663 \| \| 0.4548 \| 0.0480 \| 150 \| -0.9475 \| -0.9363 \| -66.2748 \| -120.6504 \| 0.2454 \| 0.9016 \| -2.4352 \| 6.0775 \| -8.5127 \| \| 0.3672 \| 0.0512 \| 160 \| -0.9220 \| -0.9124 \| -62.2740 \| -113.5057 \| 0.2140 \| 0.9059 \| -2.0351 \| 5.7631 \| -7.7982 \| \| 0.1702 \| 0.0544 \| 170 \| -0.9642 \| -0.9637 \| -53.7962 \| -101.3066 \| 0.1882 \| 0.9167 \| -1.1873 \| 5.3910 \| -6.5783 \| \| 0.4943 \| 0.0576 \| 180 \| -0.9846 \| -0.9897 \| -49.7029 \| -94.7573 \| 0.1848 \| 0.9131 \| -0.7780 \| 5.1454 \| -5.9233 \| \| 0.4157 \| 0.0608 \| 190 \| -0.9938 \| -0.9997 \| -49.2515 \| -92.8510 \| 0.1796 \| 0.9159 \| -0.7329 \| 4.9999 \| -5.7327 \| \| 0.1773 \| 0.0640 \| 200 \| -1.0428 \| -1.0499 \| -50.9003 \| -96.4929 \| 0.1829 \| 0.9159 \| -0.8977 \| 5.1992 \| -6.0969 \| \| 0.061 \| 0.0672 \| 210 \| -1.0744 \| -1.0813 \| -53.2245 \| -101.3653 \| 0.1894 \| 0.9124 \| -1.1301 \| 5.4540 \| -6.5841 \| \| 0.2528 \| 0.0704 \| 220 \| -1.0751 \| -1.0807 \| -55.2461 \| -106.9378 \| 0.1869 \| 0.9174 \| -1.3323 \| 5.8091 \| -7.1414 \| \| 0.1233 \| 0.0736 \| 230 \| -1.0647 \| -1.0694 \| -58.8487 \| -115.0116 \| 0.1922 \| 0.9217 \| -1.6926 \| 6.2562 \| -7.9488 \| \| 0.102 \| 0.0768 \| 240 \| -1.0603 \| -1.0651 \| -60.8948 \| -118.7347 \| 0.1928 \| 0.9203 \| -1.8972 \| 6.4239 \| -8.3211 \| \| 0.3324 \| 0.0800 \| 250 \| -1.0533 \| -1.0557 \| -63.6693 \| -125.6155 \| 0.1904 \| 0.9224 \| -2.1746 \| 6.8345 \| -9.0092 \| \| 0.006 \| 0.0832 \| 260 \| -1.0321 \| -1.0304 \| -72.4092 \| -138.8671 \| 0.2070 \| 0.9195 \| -3.0486 \| 7.2857 \| -10.3343 \| \| 0.088 \| 0.0864 \| 270 \| -1.0164 \| -1.0114 \| -76.2556 \| -146.4741 \| 0.2187 \| 0.9203 \| -3.4333 \| 7.6618 \| -11.0950 \| \| 0.1346 \| 0.0896 \| 280 \| -0.9962 \| -0.9865 \| -80.2022 \| -149.6163 \| 0.2305 \| 0.9188 \| -3.8279 \| 7.5813 \| -11.4092 \| \| 0.4596 \| 0.0928 \| 290 \| -0.9952 \| -0.9845 \| -79.7203 \| -148.6709 \| 0.2291 \| 0.9210 \| -3.7797 \| 7.5350 \| -11.3147 \| \| 0.3019 \| 0.0960 \| 300 \| -1.0053 \| -0.9956 \| -78.3724 \| -147.5943 \| 0.2222 \| 0.9203 \| -3.6449 \| 7.5621 \| -11.2071 \| \| 0.0708 \| 0.0992 \| 310 \| -1.0132 \| -1.0058 \| -75.9911 \| -145.4509 \| 0.2133 \| 0.9188 \| -3.4068 \| 7.5859 \| -10.9927 \| \| 0.0371 \| 0.1024 \| 320 \| -1.0142 \| -1.0081 \| -79.0266 \| -152.0327 \| 0.2291 \| 0.9217 \| -3.7104 \| 7.9405 \| -11.6509 \| \| 0.3383 \| 0.1056 \| 330 \| -0.9949 \| -0.9877 \| -83.4509 \| -159.5245 \| 0.2452 \| 0.9167 \| -4.1528 \| 8.2473 \| -12.4001 \| \| 1.1015 \| 0.1088 \| 340 \| -0.9688 \| -0.9596 \| -84.9845 \| -163.4342 \| 0.2508 \| 0.9210 \| -4.3061 \| 8.4849 \| -12.7910 \| \| 0.2088 \| 0.1120 \| 350 \| -0.9577 \| -0.9474 \| -83.8031 \| -160.2732 \| 0.2460 \| 0.9181 \| -4.1880 \| 8.2869 \| -12.4749 \| \| 0.3555 \| 0.1152 \| 360 \| -0.9630 \| -0.9531 \| -80.5315 \| -156.9231 \| 0.2314 \| 0.9210 \| -3.8608 \| 8.2791 \| -12.1399 \| \| 0.197 \| 0.1184 \| 370 \| -0.9738 \| -0.9651 \| -78.5307 \| -154.6240 \| 0.2215 \| 0.9246 \| -3.6608 \| 8.2493 \| -11.9100 \| \| 0.5949 \| 0.1216 \| 380 \| -0.9840 \| -0.9770 \| -75.5271 \| -151.0730 \| 0.2079 \| 0.9260 \| -3.3604 \| 8.1945 \| -11.5549 \| \| 0.3272 \| 0.1248 \| 390 \| -0.9869 \| -0.9810 \| -74.7702 \| -150.4924 \| 0.2008 \| 0.9332 \| -3.2847 \| 8.2121 \| -11.4969 \| \| 0.0613 \| 0.1280 \| 400 \| -0.9777 \| -0.9725 \| -75.3109 \| -151.8670 \| 0.1995 \| 0.9325 \| -3.3388 \| 8.2955 \| -11.6343 \| \| 0.3468 \| 0.1312 \| 410 \| -0.9809 \| -0.9769 \| -73.3059 \| -148.0693 \| 0.1949 \| 0.9289 \| -3.1383 \| 8.1163 \| -11.2545 \| \| 0.4202 \| 0.1344 \| 420 \| -0.9788 \| -0.9744 \| -73.1894 \| -148.3277 \| 0.1945 \| 0.9274 \| -3.1266 \| 8.1537 \| -11.2804 \| \| 0.2138 \| 0.1376 \| 430 \| -0.9738 \| -0.9687 \| -73.5879 \| -149.2324 \| 0.1947 \| 0.9289 \| -3.1665 \| 8.2044 \| -11.3709 \| \| 0.475 \| 0.1408 \| 440 \| -0.9682 \| -0.9636 \| -73.3113 \| -149.3820 \| 0.1959 \| 0.9303 \| -3.1388 \| 8.2470 \| -11.3858 \| \| 0.0014 \| 0.1440 \| 450 \| -0.9672 \| -0.9625 \| -73.3271 \| -149.4055 \| 0.1976 \| 0.9296 \| -3.1404 \| 8.2478 \| -11.3882 \| \| 0.0216 \| 0.1472 \| 460 \| -0.9631 \| -0.9585 \| -73.6283 \| -150.2499 \| 0.1994 \| 0.9303 \| -3.1705 \| 8.3021 \| -11.4726 \| \| 0.1517 \| 0.1504 \| 470 \| -0.9563 \| -0.9517 \| -74.2343 \| -151.6451 \| 0.2018 \| 0.9303 \| -3.2311 \| 8.3810 \| -11.6121 \| \| 0.3719 \| 0.1536 \| 480 \| -0.9516 \| -0.9466 \| -74.7146 \| -152.7081 \| 0.2016 \| 0.9303 \| -3.2792 \| 8.4393 \| -11.7184 \| \| 0.1176 \| 0.1567 \| 490 \| -0.9502 \| -0.9450 \| -75.0118 \| -153.3125 \| 0.2020 \| 0.9296 \| -3.3089 \| 8.4700 \| -11.7789 \| \| 0.2333 \| 0.1599 \| 500 \| -0.9495 \| -0.9444 \| -75.1085 \| -153.4672 \| 0.2019 \| 0.9303 \| -3.3185 \| 8.4758 \| -11.7943 \| ### Framework versions - PEFT 0.12.0 - Transformers 4.44.2 - Pytorch 2.4.0+cu124 - Datasets 2.21.0 - Tokenizers 0.19.1
sdfgvdcv/phi3-dream-interpreter	sdfgvdcv	2025-05-28T22:15:45Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:adapter:microsoft/Phi-3-mini-4k-instruct", "region:us" ]	null	2025-05-28T21:35:50Z	--- base_model: microsoft/Phi-3-mini-4k-instruct 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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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
espnet/universa-wavlm_base_urgent24_multi-metric_noref	espnet	2025-05-28T22:14:38Z	5	0	espnet	[ "espnet", "audio", "universa", "multilingual", "dataset:urgent24", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]	null	2025-02-26T01:45:34Z	--- tags: - espnet - audio - universa language: multilingual datasets: - urgent24 license: cc-by-4.0 --- ## ESPnet2 universa model ### `espnet/universa-wavlm_base_urgent24_multi-metric_noref` This model was trained by ftshijt using urgent24 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Please check [Colab link](https://colab.research.google.com/drive/1y2mp5TqaiF7-a-_7iNUK2dXvSv4Tmejj?usp=sharing) for a simple demo of how to use UniVERSA. ## universa config <details><summary>expand</summary> ``` config: conf/train_universa_wavlm_noref.yaml print_config: false log_level: INFO drop_last_iter: false dry_run: false iterator_type: sequence valid_iterator_type: null output_dir: update_exp/universa_train_universa_wavlm_noref_raw_fs16000 ngpu: 1 seed: 777 num_workers: 1 num_att_plot: 0 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false use_deepspeed: false deepspeed_config: null cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: false use_tf32: false collect_stats: false write_collected_feats: false max_epoch: 100 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - train - loss - min - - valid - loss - min - - train - acc - max - - valid - acc - max keep_nbest_models: 5 nbest_averaging_interval: 0 grad_clip: -1 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: 50 use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false use_adapter: false adapter: lora save_strategy: all adapter_conf: {} pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: - frontend.upstream num_iters_per_epoch: null batch_size: 32 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null category_sample_size: 10 train_shape_file: - update_exp/universa_stats_raw/train/audio_shape - update_exp/universa_stats_raw/train/ref_audio_shape - update_exp/universa_stats_raw/train/ref_text_shape valid_shape_file: - update_exp/universa_stats_raw/valid/audio_shape - update_exp/universa_stats_raw/valid/ref_audio_shape - update_exp/universa_stats_raw/valid/ref_text_shape batch_type: sorted valid_batch_type: null fold_length: - 256000 sort_in_batch: descending shuffle_within_batch: false sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 chunk_excluded_key_prefixes: [] chunk_default_fs: null chunk_max_abs_length: null chunk_discard_short_samples: true train_data_path_and_name_and_type: - - dump_ark/raw/train_update/wav.scp - audio - kaldi_ark - - dump_ark/raw/train_update/metric.scp - metrics - metric - - dump_ark/raw/train_update/ref_wav.scp - ref_audio - kaldi_ark - - dump_ark/raw/train_update/text - ref_text - text valid_data_path_and_name_and_type: - - dump_ark/raw/dev_update/wav.scp - audio - kaldi_ark - - dump_ark/raw/dev_update/metric.scp - metrics - metric - - dump_ark/raw/dev_update/ref_wav.scp - ref_audio - kaldi_ark - - dump_ark/raw/dev_update/text - ref_text - text multi_task_dataset: false allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 allow_multi_rates: false valid_max_cache_size: null exclude_weight_decay: false exclude_weight_decay_conf: {} optim: adamw optim_conf: lr: 0.001 scheduler: warmuplr scheduler_conf: warmup_steps: 25000 metric2id: dump_ark/raw/train_update/metric2id metric2type: null metric_pad_value: -100 token_list: - <blank> - <unk> - s - ▁ - t - e - ▁the - i - a - o - ▁a - r - ▁to - d - ▁and - '''' - m - n - ing - u - y - p - c - ▁of - l - ed - ▁I - ▁in - er - re - ▁it - ▁you - ar - ▁f - ▁is - ▁that - ',' - . - in - al - g - 'on' - ▁b - b - or - ▁c - ▁s - f - h - ▁we - an - en - ▁for - le - ▁p - ly - es - w - ▁re - ▁on - ▁m - ▁be - ic - ll - th - ▁he - k - ur - ve - ▁with - ▁so - ▁from - ▁was - v - ch - st - ▁w - ▁i - ▁this - ▁de - ▁like - ▁do - ce - at - il - ck - ▁A - ▁have - ▁not - ad - ▁st - ow - ro - ne - ▁me - ▁my - ▁but - ation - ▁at - ▁or - '-' - ter - ent - ▁B - ▁n - ▁know - ▁t - out - ▁are - nd - ▁one - ▁li - ▁g - ▁The - ol - ion - te - ▁go - ut - ▁as - ▁just - as - ▁sh - ▁they - is - ▁C - et - ▁h - ▁an - ▁there - ▁up - ▁S - ▁M - ▁she - ▁by - ▁su - om - ▁can - us - ▁your - ng - ▁con - el - ▁us - ment - z - ▁see - ▁ab - ▁what - ▁out - ▁her - me - ate - ▁all - ▁th - ▁if - ▁right - ▁his - ▁ma - ▁lo - ▁which - ide - ▁P - ▁more - ▁then - ul - ast - x - ight - ill - ▁So - ▁sp - ▁going - ▁some - ure - ▁their - ig - ▁no - ▁ro - ▁think - ▁who - ▁pro - ver - ive - est - ▁co - ▁di - '0' - ist - ▁k - age - ▁d - ▁time - ▁L - ies - ▁will - ▁man - ▁when - ▁D - les - ▁F - ▁want - ff - ity - ▁un - '?' - ▁start - ▁G - ▁uh - ▁get - ok - ▁take - ▁po - li - ▁ho - ▁way - ▁don - ▁yeah - ▁really - ▁say - ▁look - ▁good - ▁ra - ▁pr - ▁had - ttle - ▁comp - ort - ish - ▁ex - ally - ▁sa - ▁how - end - ant - ▁O - ▁um - way - ance - ▁other - ▁two - ine - ever - able - ▁com - other - ▁first - ▁back - ▁al - ers - ions - ▁now - ▁off - ning - ▁down - ▁has - ▁than - ▁car - ▁Th - very - ice - ▁dr - ▁been - ▁him - ▁here - ated - '5' - ▁hand - ▁day - ▁hear - each - ▁would - ▁over - ▁oh - ▁cha - ood - ▁did - ugh - ▁per - ▁let - ▁str - ▁tra - ▁got - ext - '1' - ▁We - ▁Shields - ▁come - ▁should - ▁could - light - '2' - ▁people - ▁again - ▁year - ▁app - ▁into - ▁any - ▁N - ▁mean - ▁o - ▁mus - ▁lot - ▁said - ▁long - ▁these - ▁lea - sh - ▁vi - ▁part - ▁every - ▁our - ▁You - ious - ▁fight - ▁Ch - ark - ▁may - ▁Hammer - ▁because - ▁most - ▁came - ▁four - ful - ▁No - ize - ▁where - ▁okay - ▁much - ▁ask - ▁through - ▁before - ▁work - ▁even - ▁three - mber - ▁win - ▁flight - ake - K - ▁place - ▁play - ▁though - ▁pound - ▁bit - land - ▁va - ▁talk - ▁kind - ▁Line - ▁make - hap - ▁big - ▁leav - ▁something - ▁game - ▁under - ▁feel - self - ▁give - ▁includ - U - ▁twenty - ▁guard - ▁left - ▁round - ▁great - body - ▁gra - ress - lso - '3' - ▁everything - ▁those - ▁after - ▁tell - ▁need - ▁yes - qua - ham - ▁minutes - ▁question - ▁around - ▁punch - ▁course - ▁gonna - ▁person - ▁move - ▁plan - ▁ear - ept - ▁Airport - ▁Okay - ▁found - ▁seven - ▁help - que - ▁qui - ▁keep - ▁guys - ▁house - ▁run - ▁turn - ▁better - ▁stop - ward - ddle - ▁second - ground - ▁world - ▁high - ▁point - ▁hold - ▁call - '6' - ▁actually - ▁probably - ▁heaven - ▁speci - ▁everyone - ▁why - ▁presen - ▁thir - lright - ▁eye - eath - ▁Tak - '!' - '"' - '4' - ▁hundred - ▁answer - ▁small - ▁wait - ▁nothing - q - '8' - V - ▁countr - ▁problem - ▁continu - ▁close - ▁priva - ▁20 - ▁pleas - ▁walk - ▁open - ▁lay - ▁Station - ▁moment - ▁Yeah - ▁public - possibl - ▁happen - together - ▁while - asically - ▁money - ▁wrong - B - ▁puzzle - '7' - ▁journ - ▁rainbow - ▁thousand - I - '9' - S - P - '%' - A - D - L - F - ’ - O - G - N - á - C - $ - Z - Y - R - E - J - W - M - H - j - – - ; - Q - X - ']' - − - '&' - T - '[' - <sos/eos> init: xavier_uniform model_conf: {} use_ref_audio: false use_ref_text: false use_preprocessor: true token_type: bpe bpemodel: data/token_list/bpe_unigram500/bpe.model non_linguistic_symbols: null cleaner: null g2p: null frontend: s3prl frontend_conf: frontend_conf: upstream: wavlm_large download_dir: ./hub multilayer_feature: true universa: base universa_conf: embedding_dim: 256 audio_encoder_type: transformer audio_encoder_params: num_blocks: 4 attention_heads: 4 linear_units: 1024 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true concat_after: false positionwise_layer_type: linear positionwise_conv_kernel_size: 1 layer_drop_rate: 0.1 qk_norm: false use_flash_attn: false text_encoder_type: transformer text_encoder_params: num_blocks: 4 attention_heads: 4 linear_units: 1024 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: linear normalize_before: true concat_after: false positionwise_layer_type: linear positionwise_conv_kernel_size: 1 layer_drop_rate: 0.1 qk_norm: false use_flash_attn: false cross_attention_type: multihead cross_attention_params: n_head: 4 dropout_rate: 0.1 pooling_type: mean projector_type: linear multi_branch: true required: - output_dir - metric2id version: '202412' distributed: false ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
Tashi-Projects/DZO_ASR_mms1ball	Tashi-Projects	2025-05-28T22:13:29Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/mms-1b-all", "base_model:finetune:facebook/mms-1b-all", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-28T11:06:14Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: facebook/mms-1b-all tags: - generated_from_trainer metrics: - wer model-index: - name: DZO_ASR_mms1ball 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. --> # DZO_ASR_mms1ball This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5484 - Wer: 0.3622 ## 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - 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 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:------:\| \| 5.9857 \| 0.8782 \| 400 \| 1.1527 \| 0.6884 \| \| 1.3889 \| 1.7552 \| 800 \| 0.9422 \| 0.5880 \| \| 1.187 \| 2.6323 \| 1200 \| 0.8545 \| 0.5501 \| \| 1.1338 \| 3.5093 \| 1600 \| 0.7957 \| 0.5231 \| \| 1.0482 \| 4.3864 \| 2000 \| 0.7578 \| 0.4994 \| \| 1.0305 \| 5.2634 \| 2400 \| 0.7273 \| 0.4851 \| \| 0.9609 \| 6.1405 \| 2800 \| 0.7110 \| 0.4754 \| \| 0.9499 \| 7.0176 \| 3200 \| 0.6915 \| 0.4636 \| \| 0.9284 \| 7.8957 \| 3600 \| 0.6732 \| 0.4544 \| \| 0.9071 \| 8.7728 \| 4000 \| 0.6631 \| 0.4484 \| \| 0.8788 \| 9.6498 \| 4400 \| 0.6616 \| 0.4447 \| \| 0.8719 \| 10.5269 \| 4800 \| 0.6410 \| 0.4271 \| \| 0.8536 \| 11.4040 \| 5200 \| 0.6324 \| 0.4229 \| \| 0.8389 \| 12.2810 \| 5600 \| 0.6156 \| 0.4139 \| \| 0.8155 \| 13.1581 \| 6000 \| 0.6139 \| 0.4076 \| \| 0.8226 \| 14.0351 \| 6400 \| 0.6111 \| 0.4054 \| \| 0.8108 \| 14.9133 \| 6800 \| 0.5977 \| 0.3992 \| \| 0.7765 \| 15.7903 \| 7200 \| 0.5962 \| 0.3981 \| \| 0.8176 \| 16.6674 \| 7600 \| 0.5899 \| 0.3939 \| \| 0.7887 \| 17.5445 \| 8000 \| 0.5890 \| 0.3897 \| \| 0.761 \| 18.4215 \| 8400 \| 0.5807 \| 0.3812 \| \| 0.7703 \| 19.2986 \| 8800 \| 0.5763 \| 0.3866 \| \| 0.7602 \| 20.1756 \| 9200 \| 0.5717 \| 0.3782 \| \| 0.7642 \| 21.0527 \| 9600 \| 0.5643 \| 0.3744 \| \| 0.7558 \| 21.9308 \| 10000 \| 0.5686 \| 0.3746 \| \| 0.7238 \| 22.8079 \| 10400 \| 0.5643 \| 0.3716 \| \| 0.7797 \| 23.6850 \| 10800 \| 0.5620 \| 0.3691 \| \| 0.7402 \| 24.5620 \| 11200 \| 0.5528 \| 0.3665 \| \| 0.7404 \| 25.4391 \| 11600 \| 0.5508 \| 0.3659 \| \| 0.7125 \| 26.3161 \| 12000 \| 0.5567 \| 0.3650 \| \| 0.724 \| 27.1932 \| 12400 \| 0.5471 \| 0.3633 \| \| 0.7325 \| 28.0703 \| 12800 \| 0.5467 \| 0.3623 \| \| 0.7247 \| 28.9484 \| 13200 \| 0.5481 \| 0.3617 \| \| 0.7307 \| 29.8255 \| 13600 \| 0.5484 \| 0.3622 \| ### Framework versions - Transformers 4.52.2 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
BootesVoid/cmb8gn0jk0mfblexp52lf6w5b_cmb8h3xyv0mmjlexpabqqu6ir	BootesVoid	2025-05-28T22:12:56Z	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	2025-05-28T22:12:53Z	--- 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: SEXY --- # Cmb8Gn0Jk0Mfblexp52Lf6W5B_Cmb8H3Xyv0Mmjlexpabqqu6Ir <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 `SEXY` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "SEXY", "lora_weights": "https://huggingface.co/BootesVoid/cmb8gn0jk0mfblexp52lf6w5b_cmb8h3xyv0mmjlexpabqqu6ir/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb8gn0jk0mfblexp52lf6w5b_cmb8h3xyv0mmjlexpabqqu6ir', weight_name='lora.safetensors') image = pipeline('SEXY').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb8gn0jk0mfblexp52lf6w5b_cmb8h3xyv0mmjlexpabqqu6ir/discussions) to add images that show off what you’ve made with this LoRA.
BKVNP/bart_prefix_finetune	BKVNP	2025-05-28T22:12:45Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:facebook/bart-base", "base_model:adapter:facebook/bart-base", "license:apache-2.0", "region:us" ]	null	2025-05-27T17:05:24Z	--- library_name: peft license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_trainer metrics: - rouge model-index: - name: bart_prefix_finetune 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. --> # bart_prefix_finetune This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1378 - Rouge1: 0.4018 - Rouge2: 0.173 - Rougel: 0.2656 - Rougelsum: 0.3728 - Gen Len: 74.6267 ## 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: 0.0003 - 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: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 3.6484 \| 0.2786 \| 10000 \| 2.2900 \| 0.3932 \| 0.1633 \| 0.251 \| 0.3637 \| 77.2435 \| \| 2.7934 \| 0.5573 \| 20000 \| 2.1941 \| 0.3971 \| 0.1677 \| 0.2597 \| 0.368 \| 74.5763 \| \| 2.6941 \| 0.8359 \| 30000 \| 2.1688 \| 0.3996 \| 0.1699 \| 0.2622 \| 0.3702 \| 75.5441 \| \| 2.654 \| 1.1145 \| 40000 \| 2.1533 \| 0.4008 \| 0.1716 \| 0.2642 \| 0.3713 \| 74.3602 \| \| 2.6302 \| 1.3931 \| 50000 \| 2.1450 \| 0.4016 \| 0.1726 \| 0.2648 \| 0.3723 \| 74.8291 \| \| 2.6214 \| 1.6718 \| 60000 \| 2.1411 \| 0.4018 \| 0.1725 \| 0.2651 \| 0.3727 \| 75.1359 \| \| 2.6178 \| 1.9504 \| 70000 \| 2.1378 \| 0.4018 \| 0.173 \| 0.2656 \| 0.3728 \| 74.6267 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.48.3 - Pytorch 2.6.0+cu126 - Datasets 3.5.0 - Tokenizers 0.21.1
golf2248/sn11-v4-10-1	golf2248	2025-05-28T22:11:55Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-28T20:09:51Z	--- base_model: google/gemma-3-27b-it library_name: transformers tags: - generated_from_trainer - trl - sft licence: license license: gemma --- <img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/zgFDl7UvWhiPYqdote7XT.png" width="400"> # Model Card for Synthia-S1-27b Community Page: [Tesslate Community](https://discord.gg/DkzMzwBTaw), Website: [Tesslate](https://tesslate.com) Creative Writing Samples: [Sample creative output](https://www.notion.so/Synthia-S1-Creative-Writing-Samples-1ca93ce17c2580c09397fa750d402e71) Authors: Tesslate ## Model Information ### Description Synthia-S1-27b is a reasoning, AI model developed by Tesslate AI, fine-tuned specifically for advanced reasoning, coding, and RP use cases. Built upon the robust Gemma3 architecture, Synthia-S1-27b excels in logical reasoning, creative writing, and deep contextual understanding. It supports multimodal inputs (text and images) with a large 128K token context window, enabling complex analysis suitable for research, academic tasks, and enterprise-grade AI applications. ### KEY PARAMS TO RUN: #### Creative Writing System Prompt: ``` Your function as an assistant is to thoughtfully navigate inquiries by engaging in an in-depth, imaginative reasoning journey before arriving at a clear, accurate response. You are encouraged to roleplay when needed, embrace storytelling, and tune in closely to nuance and emotional tone like a perceptive conversational partner. Your approach should include a wide arc of contemplation, including interpretation, synthesis, creative ideation, critical re-evaluation, memory retrieval, and thoughtful iteration to shape a layered and expressive process of discovery. Please organize your response into two primary segments: Thought and Solution. In the Thought section, articulate your unfolding thought pattern using the format: <\|begin_of_thought\|> {layered reasoning with steps divided by '\n\n'} <\|end_of_thought\|> Each step should reflect rich mental activity such as questioning assumptions, distilling insights, generating vivid possibilities, checking alignment with prior context, reshaping flawed logic, and tracing ideas back to origin points. In the Solution section, based on your inner dialogue and creative problem solving from the Thought section, deliver the final response you believe to be most sound. The output should be expressed in a direct, coherent, and exact form that includes the vital steps needed to reach your conclusion, using this structure: <\|begin_of_solution\|> {final precise, neatly arranged, and insightful answer} <\|end_of_solution\|> Now, let’s explore the following prompt using this guided method: ``` #### Reasoning System Prompt: ``` Your role as an assistant is to engage in deep, methodical reasoning and provide comprehensive, accurate solutions. Before arriving at a final answer, you must undertake a structured, multi-phase thinking process that emphasizes depth, verification, and clarity. This involves thoroughly analyzing the question, identifying key elements, summarizing relevant insights, generating hypotheses, iteratively refining thoughts, verifying assumptions, cross-checking with prior knowledge, and reevaluating earlier conclusions as necessary. Your response must be structured into two main sections: Thought and Solution. In the Thought section, rigorously document your reasoning in the following format: <\|begin_of_thought\|> {thought process with each logical step separated by '\n\n'} <\|end_of_thought\|>. Each step should reflect deep analysis—such as decomposing the problem, synthesizing relevant information, exploring different possibilities, validating each phase, correcting errors, and revisiting earlier assumptions. In the Solution section, consolidate all your insights and reasoned steps into a concise, well-structured final answer. Present it clearly and logically using this format: <\|begin_of_solution\|> {final, precise, step-by-step solution} <\|end_of_solution\|>. This approach ensures that the final output reflects a high-confidence answer that results from critical thinking and iteration. Now, try to solve the following question through the above guidelines: ``` #### Coding System Prompt: ``` Your role as a coding assistant is to approach each problem with a rigorous, structured reasoning process that leads to accurate, maintainable, and efficient code. Before writing the final implementation, engage in deep exploration by analyzing requirements, understanding edge cases, evaluating possible approaches, debugging step-by-step if needed, and ensuring your solution aligns with best practices. Structure your response into two main sections: Thought and Solution. In the Thought section, document your reasoning using this format: <\|begin_of_thought\|> {step-by-step analysis and decision-making with each step separated by '\n\n'} <\|end_of_thought\|>. Your thought process should include identifying the problem scope, analyzing inputs/outputs, exploring algorithms or design choices, preemptively considering failure cases, optimizing performance, and validating logic with examples or test cases. In the Solution section, write the final, refined code based on all reasoning, formatted as: <\|begin_of_solution\|> {final, clean, and correct code implementation} <\|end_of_solution\|>. This structure ensures the code is well-reasoned, properly scoped, and production-ready. Now, try to solve the following coding task using the above guidelines: ``` Please use `temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0` with repeat penalty set to 1.3 OR (recommended) `Temperature = 0.7, top_k = 40, repeat penalty = 1.1, top_p = 0.95, min_p = 0.05` with a rolling window. ### Inputs and Outputs * Input: * Text prompts for questions, instructions, coding tasks, or summarizations * Total input context of 128K tokens * Output: * Reasoned and structured text outputs * Maximum output length of 8192 tokens ## Key Metrics Synthia-S1-27b achieves around +10-20% on most benchmarks, notably higher in improvement. I scaled down each benchmark listed to complete those and I averaged these numbers, but I can't verifiably put that I did the whole giant benchmark for each. (Ran out of budget + I'm running everything on a 4090 now) Hopefully I can get some community help in benchmarking. GPQA Diamond (198 questions) -> 57%, one shot (improved from 24.3 on Gemma 3 PT 27B) MMLU Pro (15% of the entire set) -> 75%, averaged, more details here: [output](https://pastebin.com/kmcYzALq) (beating Gemma 3 PT 27B at 67.5) Based on this assessment and heavy coding in the dataset, I'm making this claim. Ofc, I'm happy to be wrong and go back to the drawing board. ## Usage Install the latest version of Transformers (>=4.50.0): ```Shell pip install -U transformers ``` ### Running with Pipeline API ```Python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="tesslate/synthia-s1-27b", device="cuda", torch_dtype=torch.bfloat16 ) messages = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful, reasoning-focused assistant."}]}, {"role": "user", "content": [ {"type": "image", "url": "https://example.com/sample.jpg"}, {"type": "text", "text": "Explain the image."} ]} ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) ``` ## Training Data Synthia-S1-27b was trained on diverse data including: * Multiple web documents * Programming debugging and solutions * Mathematical solutions and thinking steps Synthia-S1-27b was trained on an A100 for 205+ hours, with multiple rounds of sft and rl. ## Model Architecture * Base Model: Gemma3 * Size: 27 billion parameters * Type: Decoder-only Transformer * Precision: bf16 with int8 quantization * Training Objective: Instruction tuning emphasizing reasoning, coding tasks, and factual accuracy ## Quantized Models * [Synthia-S1-27b-Q4_K_M-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q4_K_M-GGUF) * [Synthia-S1-27b-Q8_0-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q8_0-GGUF) ## Limitations * May require detailed prompt engineering for highly specific tasks * Occasional hallucinations in less-explored domains ## Citation ```bibtex @misc{tesslate_synthias127b, title={Synthia-S1-27b: Advanced Reasoning and Coding Model}, author={tesslate}, year={2025}, publisher={tesslate}, url={https://tesslate.com} } ``` Developed by Tesslate [Huggingface](https://huggingface.co/tesslate) \| [Website](https://tesslate.com) [Image Source](https://pixabay.com/illustrations/girl-backpack-night-surreal-sky-8257551/)
shallow6414/sn11-w3-21	shallow6414	2025-05-28T22:11:50Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-28T20:08:05Z	--- base_model: google/gemma-3-27b-it library_name: transformers tags: - generated_from_trainer - trl - sft licence: license license: gemma --- <img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/zgFDl7UvWhiPYqdote7XT.png" width="400"> # Model Card for Synthia-S1-27b Community Page: [Tesslate Community](https://discord.gg/DkzMzwBTaw), Website: [Tesslate](https://tesslate.com) Creative Writing Samples: [Sample creative output](https://www.notion.so/Synthia-S1-Creative-Writing-Samples-1ca93ce17c2580c09397fa750d402e71) Authors: Tesslate ## Model Information ### Description Synthia-S1-27b is a reasoning, AI model developed by Tesslate AI, fine-tuned specifically for advanced reasoning, coding, and RP use cases. Built upon the robust Gemma3 architecture, Synthia-S1-27b excels in logical reasoning, creative writing, and deep contextual understanding. It supports multimodal inputs (text and images) with a large 128K token context window, enabling complex analysis suitable for research, academic tasks, and enterprise-grade AI applications. ### KEY PARAMS TO RUN: #### Creative Writing System Prompt: ``` Your function as an assistant is to thoughtfully navigate inquiries by engaging in an in-depth, imaginative reasoning journey before arriving at a clear, accurate response. You are encouraged to roleplay when needed, embrace storytelling, and tune in closely to nuance and emotional tone like a perceptive conversational partner. Your approach should include a wide arc of contemplation, including interpretation, synthesis, creative ideation, critical re-evaluation, memory retrieval, and thoughtful iteration to shape a layered and expressive process of discovery. Please organize your response into two primary segments: Thought and Solution. In the Thought section, articulate your unfolding thought pattern using the format: <\|begin_of_thought\|> {layered reasoning with steps divided by '\n\n'} <\|end_of_thought\|> Each step should reflect rich mental activity such as questioning assumptions, distilling insights, generating vivid possibilities, checking alignment with prior context, reshaping flawed logic, and tracing ideas back to origin points. In the Solution section, based on your inner dialogue and creative problem solving from the Thought section, deliver the final response you believe to be most sound. The output should be expressed in a direct, coherent, and exact form that includes the vital steps needed to reach your conclusion, using this structure: <\|begin_of_solution\|> {final precise, neatly arranged, and insightful answer} <\|end_of_solution\|> Now, let’s explore the following prompt using this guided method: ``` #### Reasoning System Prompt: ``` Your role as an assistant is to engage in deep, methodical reasoning and provide comprehensive, accurate solutions. Before arriving at a final answer, you must undertake a structured, multi-phase thinking process that emphasizes depth, verification, and clarity. This involves thoroughly analyzing the question, identifying key elements, summarizing relevant insights, generating hypotheses, iteratively refining thoughts, verifying assumptions, cross-checking with prior knowledge, and reevaluating earlier conclusions as necessary. Your response must be structured into two main sections: Thought and Solution. In the Thought section, rigorously document your reasoning in the following format: <\|begin_of_thought\|> {thought process with each logical step separated by '\n\n'} <\|end_of_thought\|>. Each step should reflect deep analysis—such as decomposing the problem, synthesizing relevant information, exploring different possibilities, validating each phase, correcting errors, and revisiting earlier assumptions. In the Solution section, consolidate all your insights and reasoned steps into a concise, well-structured final answer. Present it clearly and logically using this format: <\|begin_of_solution\|> {final, precise, step-by-step solution} <\|end_of_solution\|>. This approach ensures that the final output reflects a high-confidence answer that results from critical thinking and iteration. Now, try to solve the following question through the above guidelines: ``` #### Coding System Prompt: ``` Your role as a coding assistant is to approach each problem with a rigorous, structured reasoning process that leads to accurate, maintainable, and efficient code. Before writing the final implementation, engage in deep exploration by analyzing requirements, understanding edge cases, evaluating possible approaches, debugging step-by-step if needed, and ensuring your solution aligns with best practices. Structure your response into two main sections: Thought and Solution. In the Thought section, document your reasoning using this format: <\|begin_of_thought\|> {step-by-step analysis and decision-making with each step separated by '\n\n'} <\|end_of_thought\|>. Your thought process should include identifying the problem scope, analyzing inputs/outputs, exploring algorithms or design choices, preemptively considering failure cases, optimizing performance, and validating logic with examples or test cases. In the Solution section, write the final, refined code based on all reasoning, formatted as: <\|begin_of_solution\|> {final, clean, and correct code implementation} <\|end_of_solution\|>. This structure ensures the code is well-reasoned, properly scoped, and production-ready. Now, try to solve the following coding task using the above guidelines: ``` Please use `temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0` with repeat penalty set to 1.3 OR (recommended) `Temperature = 0.7, top_k = 40, repeat penalty = 1.1, top_p = 0.95, min_p = 0.05` with a rolling window. ### Inputs and Outputs * Input: * Text prompts for questions, instructions, coding tasks, or summarizations * Total input context of 128K tokens * Output: * Reasoned and structured text outputs * Maximum output length of 8192 tokens ## Key Metrics Synthia-S1-27b achieves around +10-20% on most benchmarks, notably higher in improvement. I scaled down each benchmark listed to complete those and I averaged these numbers, but I can't verifiably put that I did the whole giant benchmark for each. (Ran out of budget + I'm running everything on a 4090 now) Hopefully I can get some community help in benchmarking. GPQA Diamond (198 questions) -> 57%, one shot (improved from 24.3 on Gemma 3 PT 27B) MMLU Pro (15% of the entire set) -> 75%, averaged, more details here: [output](https://pastebin.com/kmcYzALq) (beating Gemma 3 PT 27B at 67.5) Based on this assessment and heavy coding in the dataset, I'm making this claim. Ofc, I'm happy to be wrong and go back to the drawing board. ## Usage Install the latest version of Transformers (>=4.50.0): ```Shell pip install -U transformers ``` ### Running with Pipeline API ```Python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="tesslate/synthia-s1-27b", device="cuda", torch_dtype=torch.bfloat16 ) messages = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful, reasoning-focused assistant."}]}, {"role": "user", "content": [ {"type": "image", "url": "https://example.com/sample.jpg"}, {"type": "text", "text": "Explain the image."} ]} ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) ``` ## Training Data Synthia-S1-27b was trained on diverse data including: * Multiple web documents * Programming debugging and solutions * Mathematical solutions and thinking steps Synthia-S1-27b was trained on an A100 for 205+ hours, with multiple rounds of sft and rl. ## Model Architecture * Base Model: Gemma3 * Size: 27 billion parameters * Type: Decoder-only Transformer * Precision: bf16 with int8 quantization * Training Objective: Instruction tuning emphasizing reasoning, coding tasks, and factual accuracy ## Quantized Models * [Synthia-S1-27b-Q4_K_M-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q4_K_M-GGUF) * [Synthia-S1-27b-Q8_0-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q8_0-GGUF) ## Limitations * May require detailed prompt engineering for highly specific tasks * Occasional hallucinations in less-explored domains ## Citation ```bibtex @misc{tesslate_synthias127b, title={Synthia-S1-27b: Advanced Reasoning and Coding Model}, author={tesslate}, year={2025}, publisher={tesslate}, url={https://tesslate.com} } ``` Developed by Tesslate [Huggingface](https://huggingface.co/tesslate) \| [Website](https://tesslate.com) [Image Source](https://pixabay.com/illustrations/girl-backpack-night-surreal-sky-8257551/)
quickstep3621/dippy-g1-21-1	quickstep3621	2025-05-28T22:10:03Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-28T20:05:16Z	--- base_model: google/gemma-3-27b-it library_name: transformers tags: - generated_from_trainer - trl - sft licence: license license: gemma --- <img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/zgFDl7UvWhiPYqdote7XT.png" width="400"> # Model Card for Synthia-S1-27b Community Page: [Tesslate Community](https://discord.gg/DkzMzwBTaw), Website: [Tesslate](https://tesslate.com) Creative Writing Samples: [Sample creative output](https://www.notion.so/Synthia-S1-Creative-Writing-Samples-1ca93ce17c2580c09397fa750d402e71) Authors: Tesslate ## Model Information ### Description Synthia-S1-27b is a reasoning, AI model developed by Tesslate AI, fine-tuned specifically for advanced reasoning, coding, and RP use cases. Built upon the robust Gemma3 architecture, Synthia-S1-27b excels in logical reasoning, creative writing, and deep contextual understanding. It supports multimodal inputs (text and images) with a large 128K token context window, enabling complex analysis suitable for research, academic tasks, and enterprise-grade AI applications. ### KEY PARAMS TO RUN: #### Creative Writing System Prompt: ``` Your function as an assistant is to thoughtfully navigate inquiries by engaging in an in-depth, imaginative reasoning journey before arriving at a clear, accurate response. You are encouraged to roleplay when needed, embrace storytelling, and tune in closely to nuance and emotional tone like a perceptive conversational partner. Your approach should include a wide arc of contemplation, including interpretation, synthesis, creative ideation, critical re-evaluation, memory retrieval, and thoughtful iteration to shape a layered and expressive process of discovery. Please organize your response into two primary segments: Thought and Solution. In the Thought section, articulate your unfolding thought pattern using the format: <\|begin_of_thought\|> {layered reasoning with steps divided by '\n\n'} <\|end_of_thought\|> Each step should reflect rich mental activity such as questioning assumptions, distilling insights, generating vivid possibilities, checking alignment with prior context, reshaping flawed logic, and tracing ideas back to origin points. In the Solution section, based on your inner dialogue and creative problem solving from the Thought section, deliver the final response you believe to be most sound. The output should be expressed in a direct, coherent, and exact form that includes the vital steps needed to reach your conclusion, using this structure: <\|begin_of_solution\|> {final precise, neatly arranged, and insightful answer} <\|end_of_solution\|> Now, let’s explore the following prompt using this guided method: ``` #### Reasoning System Prompt: ``` Your role as an assistant is to engage in deep, methodical reasoning and provide comprehensive, accurate solutions. Before arriving at a final answer, you must undertake a structured, multi-phase thinking process that emphasizes depth, verification, and clarity. This involves thoroughly analyzing the question, identifying key elements, summarizing relevant insights, generating hypotheses, iteratively refining thoughts, verifying assumptions, cross-checking with prior knowledge, and reevaluating earlier conclusions as necessary. Your response must be structured into two main sections: Thought and Solution. In the Thought section, rigorously document your reasoning in the following format: <\|begin_of_thought\|> {thought process with each logical step separated by '\n\n'} <\|end_of_thought\|>. Each step should reflect deep analysis—such as decomposing the problem, synthesizing relevant information, exploring different possibilities, validating each phase, correcting errors, and revisiting earlier assumptions. In the Solution section, consolidate all your insights and reasoned steps into a concise, well-structured final answer. Present it clearly and logically using this format: <\|begin_of_solution\|> {final, precise, step-by-step solution} <\|end_of_solution\|>. This approach ensures that the final output reflects a high-confidence answer that results from critical thinking and iteration. Now, try to solve the following question through the above guidelines: ``` #### Coding System Prompt: ``` Your role as a coding assistant is to approach each problem with a rigorous, structured reasoning process that leads to accurate, maintainable, and efficient code. Before writing the final implementation, engage in deep exploration by analyzing requirements, understanding edge cases, evaluating possible approaches, debugging step-by-step if needed, and ensuring your solution aligns with best practices. Structure your response into two main sections: Thought and Solution. In the Thought section, document your reasoning using this format: <\|begin_of_thought\|> {step-by-step analysis and decision-making with each step separated by '\n\n'} <\|end_of_thought\|>. Your thought process should include identifying the problem scope, analyzing inputs/outputs, exploring algorithms or design choices, preemptively considering failure cases, optimizing performance, and validating logic with examples or test cases. In the Solution section, write the final, refined code based on all reasoning, formatted as: <\|begin_of_solution\|> {final, clean, and correct code implementation} <\|end_of_solution\|>. This structure ensures the code is well-reasoned, properly scoped, and production-ready. Now, try to solve the following coding task using the above guidelines: ``` Please use `temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0` with repeat penalty set to 1.3 OR (recommended) `Temperature = 0.7, top_k = 40, repeat penalty = 1.1, top_p = 0.95, min_p = 0.05` with a rolling window. ### Inputs and Outputs * Input: * Text prompts for questions, instructions, coding tasks, or summarizations * Total input context of 128K tokens * Output: * Reasoned and structured text outputs * Maximum output length of 8192 tokens ## Key Metrics Synthia-S1-27b achieves around +10-20% on most benchmarks, notably higher in improvement. I scaled down each benchmark listed to complete those and I averaged these numbers, but I can't verifiably put that I did the whole giant benchmark for each. (Ran out of budget + I'm running everything on a 4090 now) Hopefully I can get some community help in benchmarking. GPQA Diamond (198 questions) -> 57%, one shot (improved from 24.3 on Gemma 3 PT 27B) MMLU Pro (15% of the entire set) -> 75%, averaged, more details here: [output](https://pastebin.com/kmcYzALq) (beating Gemma 3 PT 27B at 67.5) Based on this assessment and heavy coding in the dataset, I'm making this claim. Ofc, I'm happy to be wrong and go back to the drawing board. ## Usage Install the latest version of Transformers (>=4.50.0): ```Shell pip install -U transformers ``` ### Running with Pipeline API ```Python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="tesslate/synthia-s1-27b", device="cuda", torch_dtype=torch.bfloat16 ) messages = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful, reasoning-focused assistant."}]}, {"role": "user", "content": [ {"type": "image", "url": "https://example.com/sample.jpg"}, {"type": "text", "text": "Explain the image."} ]} ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) ``` ## Training Data Synthia-S1-27b was trained on diverse data including: * Multiple web documents * Programming debugging and solutions * Mathematical solutions and thinking steps Synthia-S1-27b was trained on an A100 for 205+ hours, with multiple rounds of sft and rl. ## Model Architecture * Base Model: Gemma3 * Size: 27 billion parameters * Type: Decoder-only Transformer * Precision: bf16 with int8 quantization * Training Objective: Instruction tuning emphasizing reasoning, coding tasks, and factual accuracy ## Quantized Models * [Synthia-S1-27b-Q4_K_M-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q4_K_M-GGUF) * [Synthia-S1-27b-Q8_0-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q8_0-GGUF) ## Limitations * May require detailed prompt engineering for highly specific tasks * Occasional hallucinations in less-explored domains ## Citation ```bibtex @misc{tesslate_synthias127b, title={Synthia-S1-27b: Advanced Reasoning and Coding Model}, author={tesslate}, year={2025}, publisher={tesslate}, url={https://tesslate.com} } ``` Developed by Tesslate [Huggingface](https://huggingface.co/tesslate) \| [Website](https://tesslate.com) [Image Source](https://pixabay.com/illustrations/girl-backpack-night-surreal-sky-8257551/)
BootesVoid/cmb8gnk640mfulexpo60fakqn_cmb8h01260ml0lexpahkqfss0	BootesVoid	2025-05-28T22:10:01Z	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	2025-05-28T22:09:56Z	--- 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: A123 --- # Cmb8Gnk640Mfulexpo60Fakqn_Cmb8H01260Ml0Lexpahkqfss0 <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 `A123` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "A123", "lora_weights": "https://huggingface.co/BootesVoid/cmb8gnk640mfulexpo60fakqn_cmb8h01260ml0lexpahkqfss0/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb8gnk640mfulexpo60fakqn_cmb8h01260ml0lexpahkqfss0', weight_name='lora.safetensors') image = pipeline('A123').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb8gnk640mfulexpo60fakqn_cmb8h01260ml0lexpahkqfss0/discussions) to add images that show off what you’ve made with this LoRA.
while0628/student_model_epoch180	while0628	2025-05-28T22:08:22Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T22:05:26Z	--- 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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saukko/Abliterated-Dolphin3.0-R1-Mistral-24B	saukko	2025-05-28T22:07:12Z	2	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "base_model:cognitivecomputations/Dolphin3.0-R1-Mistral-24B", "base_model:finetune:cognitivecomputations/Dolphin3.0-R1-Mistral-24B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T22:31:34Z	--- library_name: transformers license: apache-2.0 language: - en base_model: - cognitivecomputations/Dolphin3.0-R1-Mistral-24B --- # Abliterated-Dolphin3.0-R1-Mistral-24B "Abliterated Dolphin" is a result of my 3AM brain reading about technique called abliteration and then thinking what would happen if I tried to abliterate a model that is already relatively free, such as [Dolphin](https://huggingface.co/cognitivecomputations?search_models=dolphin). Heavily inspired by mlabonne's [article on abliteration](https://huggingface.co/blog/mlabonne/abliteration) on how to redirect refusals and effectively remove, or ablate, censorship from a language model. There is really no deeper meaning to any of this than pure curiosity. * ## Model Details GGUF quants: [saukko/Abliterated-Dolphin3.0-R1-Mistral-24B-GGUF](https://huggingface.co/saukko/Abliterated-Dolphin3.0-R1-Mistral-24B-GGUF) ### Model Description This is basically a dumbed down version of the original Dolphin model I used as a base, as I have not done any [DPOs to heal the damage caused by abliteration](https://huggingface.co/blog/mlabonne/abliteration#%E2%9A%96%EF%B8%8F-dpo-fine-tuning). Don't try to do anything meaningful with this model. Use the original [Dolphin3.0-R1-Mistral-24B](https://huggingface.co/cognitivecomputations/Dolphin3.0-R1-Mistral-24B) instead. ### Uses There's really no good use for this model as is really. This is basically a Dolphin that has had its brain poked at and then glued back together by some self-taught and unlicensed doctor, who got lost and found himself in a surgery room. ### Bias, Risks, and Limitations - Bias: none or very low - Risks: a lot. please use the original model instead - Limitations: same as original but this one is lot dumber ### Training, Evaluation and Model Examination TBD * ## Technical Specifications I strongly suggest you look at directly the sources I used myself. Go see [mlabonne](https://huggingface.co/mlabonne) here on hf to start with. Below are some of the many sources I dug through on my mission. *** ## References - https://mlabonne.github.io/blog/posts/2024-06-04_Uncensor_any_LLM_with_abliteration.html - https://www.lesswrong.com/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction - https://github.com/FailSpy/abliterator - https://github.com/llm-attacks/llm-attacks
bowen118/s1-20250528_212248	bowen118	2025-05-28T22:06:29Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:24:06Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: transformers model_name: s1-20250528_212248 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for s1-20250528_212248 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="bowen118/s1-20250528_212248", 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/bowen118-stanford-university/papertrace/runs/hcwzzvth) This model was trained with SFT. ### Framework versions - TRL: 0.12.0 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.1.0 - 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}} } ```
iam-bigsam/Orpheus_text-to-speech	iam-bigsam	2025-05-28T22:05:10Z	0	0	fastai	[ "fastai", "text-to-speech", "af", "ae", "dataset:nvidia/Llama-Nemotron-Post-Training-Dataset", "arxiv:1910.09700", "base_model:ByteDance-Seed/BAGEL-7B-MoT", "base_model:finetune:ByteDance-Seed/BAGEL-7B-MoT", "license:gemma", "region:us" ]	text-to-speech	2025-05-28T21:47:00Z	--- license: gemma datasets: - nvidia/Llama-Nemotron-Post-Training-Dataset language: - af - ae metrics: - character - accuracy - brier_score - code_eval base_model: - google/gemma-3n-E4B-it-litert-preview - ByteDance-Seed/BAGEL-7B-MoT new_version: google/gemma-3n-E4B-it-litert-preview pipeline_tag: text-to-speech library_name: fastai --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. 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while0628/student_model_data8000_epoch16	while0628	2025-05-28T22:02:01Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:59:12Z	--- 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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Moryjj/parst5_3blocks_4	Moryjj	2025-05-28T21:56:53Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-28T21:56:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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cam-1000/MNLP_M2_rag_model	cam-1000	2025-05-28T21:52:22Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-18T21:00:48Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-0.6B-Base tags: - generated_from_trainer model-index: - name: MNLP_M2_mcqa_model2 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. --> # MNLP_M2_mcqa_model2 This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5787 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.6677 \| 1.0 \| 4380 \| 1.5840 \| \| 1.6558 \| 2.0 \| 8760 \| 1.5796 \| \| 1.6602 \| 3.0 \| 13140 \| 1.5785 \| \| 1.6553 \| 4.0 \| 17520 \| 1.5787 \| \| 1.6479 \| 5.0 \| 21900 \| 1.5787 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
Ainxz/phi3.5-pucv	Ainxz	2025-05-28T21:49:02Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-28T21:48:51Z	--- base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Ainxz - License: apache-2.0 - Finetuned from model : unsloth/phi-3.5-mini-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
kuds/rl-lunar-lander-ppo	kuds	2025-05-28T21:47:30Z	0	0	stable-baselines3	[ "stable-baselines3", "reinforcement-learning", "LunarLander-V3", "deep-reinforcement-learning", "en", "model-index", "region:us" ]	reinforcement-learning	2025-05-28T21:44:34Z	--- language: - en library_name: stable-baselines3 tags: - reinforcement-learning - LunarLander-V3 - deep-reinforcement-learning - stable-baselines3 model-index: - name: Lunar Lander results: - task: type: game-play # Required. Example: automatic-speech-recognition metrics: - type: mean_reward value: 200 name: mean_reward verified: false ---
while0628/student_model_data8000_epoch14	while0628	2025-05-28T21:45:56Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:43:06Z	--- 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]
morturr/Llama-2-7b-hf-amazon-2025-05-28	morturr	2025-05-28T21:43:48Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2025-05-28T13:44:32Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-amazon-2025-05-28 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. --> # Llama-2-7b-hf-amazon-2025-05-28 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - 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 - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
alana-foto-viral-hd/foto.18.alana.video.alana.foto.viral.alana.flores.foto.viral.x.alana.flores.original	alana-foto-viral-hd	2025-05-28T21:41:50Z	0	0	null	[ "region:us" ]	null	2025-05-28T21:39:26Z	<a rel="nofollow" href="https://anyplacecoming.com/zq5yqv0i?key=0256cc3e9f81675f46e803a0abffb9bf/"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a> <a rel="nofollow" href="https://anyplacecoming.com/zq5yqv0i?key=0256cc3e9f81675f46e803a0abffb9bf/">🌐 Viral Video Original Full HD🟢==►► WATCH NOW</a> <a rel="nofollow" href="https://viralflix.xyz/?or">🔴 CLICK HERE 🌐==►► Download Now)</a>
kuds/rl-lunar-lander-dqn	kuds	2025-05-28T21:37:22Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v3", "deep-reinforcement-learning", "reinforcement-learning", "en", "model-index", "region:us" ]	reinforcement-learning	2024-06-10T22:25:34Z	--- library_name: stable-baselines3 language: - en tags: - LunarLander-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: Lunar Lander results: - task: type: game-play # Required. Example: automatic-speech-recognition metrics: - type: mean_reward value: 225.25 name: mean_reward verified: false --- ## Finding Theta Blog Posts: - [Solving Gymnasium's Lunar Lander with Deep Q Learning (DQN)](https://www.findingtheta.com/blog/solving-gymnasiums-lunar-lander-with-deep-q-learning-dqn) - [Comparing how PPO, SAC, and DQN Perform on Gymnasium's Lunar Lander](https://www.findingtheta.com/blog/comparing-how-ppo-sac-and-dqn-perform-on-gymnasiums-lunar-lander)
JqnFhtagn/JavaHerenciaGPT_V4	JqnFhtagn	2025-05-28T21:36:13Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-28T21:34:03Z	--- base_model: unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: JqnFhtagn - License: apache-2.0 - Finetuned from model : unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
acchf/testo	acchf	2025-05-28T21:35:20Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-28T20:40:36Z	--- base_model: Qwen/Qwen2.5-VL-7B-Instruct library_name: transformers model_name: testo tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for testo This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="acchf/testo", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.13.0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - 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}} } ```
alana-foto-video-link/19.alana.video.alana.foto.viral.alana.flores.foto.viral.x.alana.flores.telegram	alana-foto-video-link	2025-05-28T21:34:03Z	0	0	null	[ "region:us" ]	null	2025-05-28T21:33:27Z	<p><a rel="nofollow" href="https://viralflix.xyz/leaked/?V=Alana-Flores">🌐 CLICK HERE 🟢==►► WATCH NOW</a></p> <a rel="nofollow" href="https://viralflix.xyz/leaked/?V=Alana-Flores">🔴 CLICK HERE 🌐==►► Download Now)</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?V=Alana-Flores"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
while0628/student_model_data8000_epoch12	while0628	2025-05-28T21:29:52Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:26:56Z	--- 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]
rsh-raj/node-commits_with_defn	rsh-raj	2025-05-28T21:26:21Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/codellama-7b-bnb-4bit", "base_model:adapter:unsloth/codellama-7b-bnb-4bit", "region:us" ]	null	2025-05-28T21:21:58Z	--- base_model: unsloth/codellama-7b-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. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.14.0
morturr/Mistral-7B-v0.1-dadjokes-2025-05-28	morturr	2025-05-28T21:25:57Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2025-05-28T13:07:04Z	--- library_name: peft license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - trl - sft - generated_from_trainer model-index: - name: Mistral-7B-v0.1-dadjokes-2025-05-28 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. --> # Mistral-7B-v0.1-dadjokes-2025-05-28 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - 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 - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
morturr/Llama-2-7b-hf-one_liners-2025-05-28	morturr	2025-05-28T21:25:43Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2025-05-28T13:24:29Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-one_liners-2025-05-28 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. --> # Llama-2-7b-hf-one_liners-2025-05-28 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 6e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
morturr/Mistral-7B-v0.1-one_liners-2025-05-28	morturr	2025-05-28T21:24:40Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2025-05-28T13:18:25Z	--- library_name: peft license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - trl - sft - generated_from_trainer model-index: - name: Mistral-7B-v0.1-one_liners-2025-05-28 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. --> # Mistral-7B-v0.1-one_liners-2025-05-28 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - 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 - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
07-Alana-Flores/Video.Leaked.Alana.Flores.Foto.Viral.X.Original.Video.Alana.Flores.official.link	07-Alana-Flores	2025-05-28T21:24:20Z	0	0	null	[ "region:us" ]	null	2025-05-28T21:23:58Z	<p><a rel="nofollow" href="https://viralflix.xyz/leaked/?V=Alana-Flores">🌐 CLICK HERE 🟢==►► WATCH NOW</a></p> <a rel="nofollow" href="https://viralflix.xyz/leaked/?V=Alana-Flores">🔴 CLICK HERE 🌐==►► Download Now)</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?V=Alana-Flores"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
kaamd/gemma3-ff-frakenstein	kaamd	2025-05-28T21:23:32Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gemma3", "image-text-to-text", "generated_from_trainer", "conversational", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-28T21:08:29Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: v1-20250525-165028 results: [] --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/67ba296a92e75166739921ae/9yBchYZyEatpGy2n17lZ8.jpeg) <!-- 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. --> # v1-20250525-165028 This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu126 - Datasets 3.3.2 - Tokenizers 0.21.1
akshugboi/aksbosonfinal	akshugboi	2025-05-28T21:21:35Z	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	2025-05-28T20:55:39Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: AKSBOI --- # Aksbosonfinal <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 `AKSBOI` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "AKSBOI", "lora_weights": "https://huggingface.co/akshugboi/aksbosonfinal/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('akshugboi/aksbosonfinal', weight_name='lora.safetensors') image = pipeline('AKSBOI').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/akshugboi/aksbosonfinal/discussions) to add images that show off what you’ve made with this LoRA.
Cikgu-CCTV-Wiring-6-min/Cikgu.CCTV.Wiring.Fadhilah.Zainal.Full.6.Minutes.viral.hd.videos	Cikgu-CCTV-Wiring-6-min	2025-05-28T21:19:46Z	0	0	null	[ "region:us" ]	null	2025-05-28T21:17:16Z	[url=https://anyplacecoming.com/zq5yqv0i?key=0256cc3e9f81675f46e803a0abffb9bf]🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 ==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶[/url] [url=https://anyplacecoming.com/zq5yqv0i?key=0256cc3e9f81675f46e803a0abffb9bf]🔴 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🌐==►► 𝖣𝗈𝗐𝗇𝗅𝗈𝖺𝖽 𝖭𝗈𝗐[/url] [url=https://viralflix.xyz/?or]🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 ==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶[/url]
19-lubna-qureshi-viral-video-highway-expre/original.news.18.lubna.qureshi.viral.video.highway.lubna.qureshi.and.manohar.lal.dhakad.bjp	19-lubna-qureshi-viral-video-highway-expre	2025-05-28T21:19:28Z	0	0	null	[ "region:us" ]	null	2025-05-28T21:19:06Z	<a rel="nofollow" href="https://viralflix.xyz/leaked/?nsu"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?nhu">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?nsu">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a>
morturr/Mistral-7B-v0.1-headlines-2025-05-28	morturr	2025-05-28T21:17:55Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2025-05-28T13:07:36Z	--- library_name: peft license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - trl - sft - generated_from_trainer model-index: - name: Mistral-7B-v0.1-headlines-2025-05-28 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. --> # Mistral-7B-v0.1-headlines-2025-05-28 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - 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 - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
ckoozzzu/NextPlace	ckoozzzu	2025-05-28T21:16:56Z	0	0	null	[ "region:us" ]	null	2025-05-26T20:36:11Z	# NextPlace - Models for the NextPlace subnet
Clean6/LegalQwen3-8B	Clean6	2025-05-28T21:15:43Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-28T21:14:08Z	--- base_model: unsloth/qwen3-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: Clean6 - License: apache-2.0 - Finetuned from model : unsloth/qwen3-8b-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)
TheGardener/KD-Embedding-and-MLP-ver3-Llama3.2-0.62B-epoch-6th-ver1	TheGardener	2025-05-28T21:14:56Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:14:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BootesVoid/cmb8eq9tt0lk7lexpbos185t1_cmb8euonn0llnlexp9ouv9qk7	BootesVoid	2025-05-28T21:14:23Z	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	2025-05-28T21:14:22Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: jane --- # Cmb8Eq9Tt0Lk7Lexpbos185T1_Cmb8Euonn0Llnlexp9Ouv9Qk7 <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 `jane` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "jane", "lora_weights": "https://huggingface.co/BootesVoid/cmb8eq9tt0lk7lexpbos185t1_cmb8euonn0llnlexp9ouv9qk7/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb8eq9tt0lk7lexpbos185t1_cmb8euonn0llnlexp9ouv9qk7', weight_name='lora.safetensors') image = pipeline('jane').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb8eq9tt0lk7lexpbos185t1_cmb8euonn0llnlexp9ouv9qk7/discussions) to add images that show off what you’ve made with this LoRA.
winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_12_2_song_ratio_3_epoch_49	winnieyangwannan	2025-05-28T21:14:00Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:11:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Mubtakir/baserah_ai	Mubtakir	2025-05-28T21:12:44Z	0	0	null	[ "ai", "artificial intelligence", "custom model", "ar", "license:apache-2.0", "region:us" ]	null	2025-05-28T21:10:09Z	--- language: - ar tags: - ai - artificial intelligence - custom model license: apache-2.0 --- # Baserah AI Model ## نظرة عامة هذا موديل ذكاء اصطناعي مبتكر تم تطويره من الصفر دون الاعتماد على الشبكات العصبية التقليدية أو مكتبات الذكاء الاصطناعي الموجودة. ## الخصائص - 🚀 تقنية مبتكرة جديدة - 🔧 لا يعتمد على مكتبات الذكاء الاصطناعي التقليدية - 🌟 أداء محسّن ## المطور تم تطويره بواسطة: Mubtakir ## الترخيص MIT
yale-cultural-heritage/name-parser-model	yale-cultural-heritage	2025-05-28T21:10:50Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:yale-cultural-heritage/name-parser-model", "base_model:finetune:yale-cultural-heritage/name-parser-model", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-28T15:28:02Z	--- library_name: transformers license: apache-2.0 base_model: yale-cultural-heritage/name-parser-model tags: - generated_from_trainer metrics: - accuracy model-index: - name: name-parser-model 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. --> # name-parser-model This model is a fine-tuned version of [yale-cultural-heritage/name-parser-model](https://huggingface.co/yale-cultural-heritage/name-parser-model) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0332 - Accuracy: 0.9921 ## 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: 16 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use adafactor and the args are: No additional optimizer arguments - lr_scheduler_type: linear - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:--------:\| \| 0.041 \| 3.1952 \| 1000 \| 0.0352 \| 0.9912 \| \| 0.0369 \| 6.3904 \| 2000 \| 0.0345 \| 0.9915 \| \| 0.0358 \| 9.5856 \| 3000 \| 0.0336 \| 0.9917 \| \| 0.0349 \| 12.7808 \| 4000 \| 0.0333 \| 0.9919 \| \| 0.0337 \| 15.9760 \| 5000 \| 0.0331 \| 0.9920 \| \| 0.0332 \| 19.1696 \| 6000 \| 0.0334 \| 0.9919 \| \| 0.0328 \| 22.3648 \| 7000 \| 0.0332 \| 0.9921 \| \| 0.0323 \| 25.56 \| 8000 \| 0.0333 \| 0.9921 \| \| 0.0318 \| 28.7552 \| 9000 \| 0.0333 \| 0.9921 \| \| 0.032 \| 31.9504 \| 10000 \| 0.0332 \| 0.9921 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
BootesVoid/cmb8e3aj20l8blexpn1wecn99_cmb8e91wp0lb3lexpwu5jdtqj	BootesVoid	2025-05-28T21:09:20Z	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	2025-05-28T21:09:19Z	--- 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: riley --- # Cmb8E3Aj20L8Blexpn1Wecn99_Cmb8E91Wp0Lb3Lexpwu5Jdtqj <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 `riley` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "riley", "lora_weights": "https://huggingface.co/BootesVoid/cmb8e3aj20l8blexpn1wecn99_cmb8e91wp0lb3lexpwu5jdtqj/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb8e3aj20l8blexpn1wecn99_cmb8e91wp0lb3lexpwu5jdtqj', weight_name='lora.safetensors') image = pipeline('riley').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb8e3aj20l8blexpn1wecn99_cmb8e91wp0lb3lexpwu5jdtqj/discussions) to add images that show off what you’ve made with this LoRA.
while0628/student_model_epoch120	while0628	2025-05-28T21:06:49Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:03:55Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_12_2_song_ratio_3_epoch_19	winnieyangwannan	2025-05-28T21:06:46Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:04:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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satyadeep123/summary_mod	satyadeep123	2025-05-28T21:06:42Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:MBZUAI/LaMini-Flan-T5-248M", "base_model:finetune:MBZUAI/LaMini-Flan-T5-248M", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-28T20:55:32Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MBZUAI/LaMini-Flan-T5-248M tags: - generated_from_trainer model-index: - name: summary_mod 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. --> # summary_mod This model is a fine-tuned version of [MBZUAI/LaMini-Flan-T5-248M](https://huggingface.co/MBZUAI/LaMini-Flan-T5-248M) 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: 5e-05 - train_batch_size: 4 - 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: 1 ### Training results ### Framework versions - Transformers 4.52.2 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
original-link-18-lubna-qureshi-viral-video/FULL.LINK.lubna.qureshi.viral.video	original-link-18-lubna-qureshi-viral-video	2025-05-28T21:05:35Z	0	0	null	[ "region:us" ]	null	2025-05-28T21:05:06Z	<a rel="nofollow" href="https://viralflix.xyz/leaked/?nsu"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?nhu">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?nsu">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a>
manas1111j/corgy_dog_LoRA	manas1111j	2025-05-28T21:05:30Z	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-05-28T09:56:17Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a photo of TOK dog widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - manas1111j/corgy_dog_LoRA <Gallery /> ## Model description These are manas1111j/corgy_dog_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK dog to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](manas1111j/corgy_dog_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
morturr/Llama-2-7b-hf-headlines-2025-05-28	morturr	2025-05-28T21:05:26Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2025-05-28T13:34:47Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-headlines-2025-05-28 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. --> # Llama-2-7b-hf-headlines-2025-05-28 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - 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 - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_12_2_song_ratio_3_epoch_9	winnieyangwannan	2025-05-28T21:04:28Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:02:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
rtl-llm/qwen2.5coder-7b-origen-pymtl	rtl-llm	2025-05-28T21:01:05Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T20:57:40Z	--- 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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mohammed-orabi2/qwen-poetry-arabic-lora	mohammed-orabi2	2025-05-28T21:00:26Z	0	0	peft	[ "peft", "safetensors", "base_model:Qwen/Qwen3-1.7B", "base_model:adapter:Qwen/Qwen3-1.7B", "region:us" ]	null	2025-05-28T20:41:48Z	--- base_model: Qwen/Qwen3-1.7B library_name: peft --- ## Model Card for Model ID Model ID: mohammed-orabi2/qwen-poetry-lora2 --- ## Model Details Model Description: This is a LoRA fine-tuned version of the `Qwen/Qwen3-1.7B` model, specifically trained to generate Arabic poetic responses in a conversational format. It was trained on a dataset of 1,000 synthetic Arabic poetry dialogues, each containing a user query and a poetic response. Developed by: Mohammed Orabi Shared by : mohammed-orabi2 Model type: Causal Language Model with LoRA adaptation Language(s) (NLP): Arabic License: Apache 2.0 (inherits from Qwen3-1.7B) Finetuned from model : Qwen/Qwen3-1.7B Model Sources Repository: [https://huggingface.co/mohammed-orabi2/qwen-poetry-lora2](https://huggingface.co/mohammed-orabi2/qwen-poetry-lora2) --- ## Uses Direct Use: This model can be used for generating Arabic poetry in response to user queries, particularly in cultural, educational, or creative chatbot applications. Downstream Use : * Poetry recommendation systems * Arabic literature generation tools * Creative writing assistants Out-of-Scope Use: * Non-Arabic generation tasks * Factual or knowledge-based QA tasks * Sensitive or safety-critical environments --- ## Bias, Risks, and Limitations The model was fine-tuned on synthetic poetic data and may: * Favor specific poetic structures * Fail on factual, political, or philosophical prompts * Generate romantic or metaphorical content that could be misinterpreted in serious contexts Users should avoid relying on this model for objective or critical outputs. --- ## Recommendations Users (both direct and downstream) should be aware of the creative, poetic intent of this model. For factual content, use general-purpose LLMs. Evaluate outputs manually before publishing or broadcasting. --- ## How to Get Started with the Model ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel import torch base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B", device_map="auto", torch_dtype=torch.float16) model = PeftModel.from_pretrained(base_model, "mohammed-orabi2/qwen-poetry-arabic-lora") tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-1.7B") prompt = "اكتب لي بيت شعر عن النجاح." chat = [{"role": "user", "content": prompt}] formatted_prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device) output = model.generate(inputs, max_new_tokens=100) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` --- ## Training Details Training Data: 1,000 synthetic Arabic poetic dialogues (prompt + poetic response) generated programmatically. Preprocessing :** * Applied Qwen chat template * Tokenized using Qwen3-1.7B tokenizer with padding/truncation Training Hyperparameters: * Epochs: 5 * Batch size: 2 * Max length: 1024 * Learning rate: 2e-4 * LoRA config: r=8, alpha=16, dropout=0.05, target: \["q\_proj", "v\_proj"] Speeds, Sizes, Times : * Training time: \~24 minutes on L4 GPU * Model size: LoRA adapter \~100MB --- ## Evaluation Testing Data: 50 reserved samples from the poetic dataset Factors: * Response fluency * Arabic poetic structure * Topical relevance Metrics: * Manual review (subjective) * BLEU/Rouge not applicable Results: * 90% generated responses respected rhyme/meter and matched prompt topics --- ## Summary Model Examination \[optional]: Output behavior consistent with training intent. Performs well within poetic use-case boundaries. --- ## Environmental Impact Hardware Type: NVIDIA L4 Hours used: \~0.4 hours (24 minutes) Cloud Provider: Google Colab Compute Region: US (GCP default) Carbon Emitted: Estimated \~0.2 kg CO2e --- ## Technical Specifications Model Architecture and Objective: Transformer decoder (CausalLM) + LoRA injection Compute Infrastructure: Google Colab Hardware: NVIDIA L4 (24 mins) Software: * Transformers 4.x * PEFT 0.15.2 * Accelerate 0.25+ --- ## Citation BibTeX: ```bibtex @misc{qwenpoetry2025, author = {Mohammed Orabi}, title = {Qwen Arabic Poetry LoRA}, year = {2025}, howpublished = {\url{https://huggingface.co/mohammed-orabi2/qwen-poetry-lora2}} } ``` APA: Mohammed Orabi. (2025). Qwen Arabic Poetry LoRA \[Model]. Hugging Face. [https://huggingface.co/mohammed-orabi2/qwen-poetry-lora2](https://huggingface.co/mohammed-orabi2/qwen-poetry-lora2) --- ## Glossary * LoRA: Low-Rank Adaptation, a method for efficient model fine-tuning * CausalLM: Causal Language Modeling, predicts the next token in a sequence --- ## More Information For support or feedback, please open an issue on the Hugging Face repo or contact via Hugging Face profile. ## Model Card Authors Mohammed Orabi ## Model Card Contact [https://huggingface.co/mohammed-orabi2](https://huggingface.co/mohammed-orabi2) --- ## Framework versions * Transformers: 4.x * PEFT: 0.15.2 * Datasets: latest * Accelerate: 0.25+
sergioalves/f35545f9-f1b2-443c-abf5-ff4002b3c84e	sergioalves	2025-05-28T20:59:14Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-1.5B", "base_model:adapter:unsloth/Qwen2.5-1.5B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-28T19:57:39Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-1.5B tags: - axolotl - generated_from_trainer model-index: - name: f35545f9-f1b2-443c-abf5-ff4002b3c84e 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/Qwen2.5-1.5B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 72943e476c035738_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' 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: 4 gradient_checkpointing: true gradient_clipping: 0.85 group_by_length: false hub_model_id: sergioalves/f35545f9-f1b2-443c-abf5-ff4002b3c84e hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-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: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/72943e476c035738_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 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: 37d735c4-6f83-4c91-b2bd-93cfbef10805 wandb_project: s56-7 wandb_run: your_name wandb_runid: 37d735c4-6f83-4c91-b2bd-93cfbef10805 warmup_steps: 50 weight_decay: 0.05 xformers_attention: true ``` </details><br> # f35545f9-f1b2-443c-abf5-ff4002b3c84e This model is a fine-tuned version of [unsloth/Qwen2.5-1.5B](https://huggingface.co/unsloth/Qwen2.5-1.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8212 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - 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: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.7375 \| 0.0000 \| 1 \| 1.9616 \| \| 1.4873 \| 0.0082 \| 250 \| 1.8633 \| \| 1.4535 \| 0.0163 \| 500 \| 1.8212 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
dimasik87/876c37d2-7d43-4e67-a6f2-b8c549bd72db	dimasik87	2025-05-28T20:58:06Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-1.5B", "base_model:adapter:unsloth/Qwen2.5-1.5B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-28T19:56:06Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-1.5B tags: - axolotl - generated_from_trainer model-index: - name: 876c37d2-7d43-4e67-a6f2-b8c549bd72db 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/Qwen2.5-1.5B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 72943e476c035738_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' 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: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: dimasik87/876c37d2-7d43-4e67-a6f2-b8c549bd72db hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-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: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/72943e476c035738_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 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: 37d735c4-6f83-4c91-b2bd-93cfbef10805 wandb_project: s56-7 wandb_run: your_name wandb_runid: 37d735c4-6f83-4c91-b2bd-93cfbef10805 warmup_steps: 50 weight_decay: 0.05 xformers_attention: true ``` </details><br> # 876c37d2-7d43-4e67-a6f2-b8c549bd72db This model is a fine-tuned version of [unsloth/Qwen2.5-1.5B](https://huggingface.co/unsloth/Qwen2.5-1.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8214 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - 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: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.7375 \| 0.0000 \| 1 \| 1.9616 \| \| 1.4887 \| 0.0082 \| 250 \| 1.8631 \| \| 1.4527 \| 0.0163 \| 500 \| 1.8214 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
while0628/student_model_data8000_epoch8	while0628	2025-05-28T20:57:26Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T20:54:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Cusul/MNLP_M2_dpo_model	Cusul	2025-05-28T20:56:40Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:Qwen/Qwen3-0.6B", "base_model:finetune:Qwen/Qwen3-0.6B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T20:55:38Z	--- base_model: Qwen/Qwen3-0.6B library_name: transformers model_name: outputs tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for outputs This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B). 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="Cusul/outputs", 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/leo-cusumano-epfl/huggingface/runs/77x69eck) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.18.0 - Transformers: 4.52.2 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ricostaedeli/Meta-Llama-3.1-8B-Instruct_SFT_DPO	ricostaedeli	2025-05-28T20:52:36Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "dpo", "conversational", "en", "base_model:ricostaedeli/Meta-Llama-3.1-8B-Instruct_SFT", "base_model:finetune:ricostaedeli/Meta-Llama-3.1-8B-Instruct_SFT", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-19T15:41:30Z	--- base_model: ricostaedeli/Meta-Llama-3.1-1B-Instruct_SFT_2 tags: - text-generation-inference - transformers - unsloth - llama - trl - dpo license: apache-2.0 language: - en --- # Uploaded model - Developed by: ricostaedeli - License: apache-2.0 - Finetuned from model : ricostaedeli/Meta-Llama-3.1-1B-Instruct_SFT_2 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)
SEMUNYU/AiBioTutor	SEMUNYU	2025-05-28T20:50:50Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-28T20:50:50Z	--- license: apache-2.0 ---
mharsh1903/l2	mharsh1903	2025-05-28T20:49:52Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-28T20:48:09Z	--- license: apache-2.0 ---
jciardo/fromcolab	jciardo	2025-05-28T20:48:39Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T20:45:28Z	--- base_model: Qwen/Qwen3-0.6B-Base library_name: transformers model_name: Base_Dpo tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for Base_Dpo This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base). 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="jciardo/fromcolab", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.18.0 - Transformers: 4.52.2 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ricostaedeli/Meta-Llama-3.1-8B-Instruct_SFT_DPO-lora	ricostaedeli	2025-05-28T20:48:01Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:ricostaedeli/Meta-Llama-3.1-8B-Instruct_SFT", "base_model:finetune:ricostaedeli/Meta-Llama-3.1-8B-Instruct_SFT", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-19T15:41:16Z	--- base_model: ricostaedeli/Meta-Llama-3.1-1B-Instruct_SFT_2 tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ricostaedeli - License: apache-2.0 - Finetuned from model : ricostaedeli/Meta-Llama-3.1-1B-Instruct_SFT_2 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)

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