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SeungWon923/EXAONE-3.5-2.4B-fine-tuning	SeungWon923	2025-04-02T03:48:25Z	0	0	transformers	[ "transformers", "pytorch", "exaone", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-04-02T03:46:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
RichardErkhov/Youliang_-_llama3-8b-instruct-derta-100step-4bits	RichardErkhov	2025-04-02T03:47:12Z	0	0	null	[ "safetensors", "llama", "arxiv:2407.09121", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-02T03:43:31Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama3-8b-instruct-derta-100step - bnb 4bits - Model creator: https://huggingface.co/Youliang/ - Original model: https://huggingface.co/Youliang/llama3-8b-instruct-derta-100step/ Original model description: --- base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - generated_from_trainer model-index: - name: Meta-Llama-3-8B_derta results: [] license: apache-2.0 --- <!-- 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. --> # Meta-Llama-3-8B-Instruct_derta_100step This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the [Evol-Instruct](https://huggingface.co/datasets/WizardLMTeam/WizardLM_evol_instruct_70k) and [BeaverTails](https://huggingface.co/datasets/PKU-Alignment/BeaverTails) dataset. The model is continued to train 100 steps with DeRTa on LLaMA3-8B-Instruct. ## Model description Please refer to the paper [Refuse Whenever You Feel Unsafe: Improving Safety in LLMs via Decoupled Refusal Training](https://arxiv.org/abs/2407.09121) and GitHub [DeRTa](https://github.com/RobustNLP/DeRTa). Input format: ``` [INST] Your Instruction [\INST] ``` ## Intended uses & limitations The model is trained with DeRTa, showing a high safety performance. ## 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 - weight_decay: 2e-5 - eval_batch_size: 1 - seed: 1 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 128 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.0+cu118 - Datasets 2.10.0 - Tokenizers 0.19.1
MinaMila/llama_instbase_Adult_7ep_66	MinaMila	2025-04-02T03:46:04Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct", "base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T03:42:54Z	--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : unsloth/Meta-Llama-3.1-8B-Instruct 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)
RichardErkhov/HINT-lab_-_llama3-8b-final-ppo-clean-v0.1-4bits	RichardErkhov	2025-04-02T03:45:50Z	0	0	null	[ "safetensors", "llama", "arxiv:1910.09700", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-02T03:41:57Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama3-8b-final-ppo-clean-v0.1 - bnb 4bits - Model creator: https://huggingface.co/HINT-lab/ - Original model: https://huggingface.co/HINT-lab/llama3-8b-final-ppo-clean-v0.1/ Original model description: --- 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]
Fresh96/a2c-PandaReachDense-v3	Fresh96	2025-04-02T03:42:40Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-04-02T03:38:37Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.17 +/- 0.11 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
redsgnaoh/model44	redsgnaoh	2025-04-02T03:41:54Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T03:28:19Z	--- 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]
RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf	RichardErkhov	2025-04-02T03:40:53Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T02:41:45Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) parser_user_v22e_epoch_7_lr_0.002 - GGUF - Model creator: https://huggingface.co/magnifi/ - Original model: https://huggingface.co/magnifi/parser_user_v22e_epoch_7_lr_0.002/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [parser_user_v22e_epoch_7_lr_0.002.Q2_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.Q2_K.gguf) \| Q2_K \| 1.35GB \| \| [parser_user_v22e_epoch_7_lr_0.002.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.IQ3_XS.gguf) \| IQ3_XS \| 1.49GB \| \| [parser_user_v22e_epoch_7_lr_0.002.IQ3_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.IQ3_S.gguf) \| IQ3_S \| 1.57GB \| \| [parser_user_v22e_epoch_7_lr_0.002.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.Q3_K_S.gguf) \| Q3_K_S \| 1.57GB \| \| [parser_user_v22e_epoch_7_lr_0.002.IQ3_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.IQ3_M.gguf) \| IQ3_M \| 1.65GB \| \| [parser_user_v22e_epoch_7_lr_0.002.Q3_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.Q3_K.gguf) \| Q3_K \| 1.75GB \| \| [parser_user_v22e_epoch_7_lr_0.002.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.Q3_K_M.gguf) \| Q3_K_M \| 1.75GB \| \| [parser_user_v22e_epoch_7_lr_0.002.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.Q3_K_L.gguf) \| Q3_K_L \| 1.9GB \| \| [parser_user_v22e_epoch_7_lr_0.002.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.IQ4_XS.gguf) \| IQ4_XS \| 1.93GB \| \| [parser_user_v22e_epoch_7_lr_0.002.Q4_0.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.Q4_0.gguf) \| Q4_0 \| 2.03GB \| \| [parser_user_v22e_epoch_7_lr_0.002.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.IQ4_NL.gguf) \| IQ4_NL \| 2.04GB \| \| [parser_user_v22e_epoch_7_lr_0.002.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.Q4_K_S.gguf) \| Q4_K_S \| 2.04GB \| \| [parser_user_v22e_epoch_7_lr_0.002.Q4_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.Q4_K.gguf) \| Q4_K \| 2.16GB \| \| [parser_user_v22e_epoch_7_lr_0.002.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.Q4_K_M.gguf) \| Q4_K_M \| 2.16GB \| \| [parser_user_v22e_epoch_7_lr_0.002.Q4_1.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.Q4_1.gguf) \| Q4_1 \| 2.24GB \| \| [parser_user_v22e_epoch_7_lr_0.002.Q5_0.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.Q5_0.gguf) \| Q5_0 \| 2.46GB \| \| [parser_user_v22e_epoch_7_lr_0.002.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.Q5_K_S.gguf) \| Q5_K_S \| 2.46GB \| \| [parser_user_v22e_epoch_7_lr_0.002.Q5_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.Q5_K.gguf) \| Q5_K \| 2.53GB \| \| [parser_user_v22e_epoch_7_lr_0.002.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.Q5_K_M.gguf) \| Q5_K_M \| 2.53GB \| \| [parser_user_v22e_epoch_7_lr_0.002.Q5_1.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.Q5_1.gguf) \| Q5_1 \| 2.68GB \| \| [parser_user_v22e_epoch_7_lr_0.002.Q6_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.Q6_K.gguf) \| Q6_K \| 2.92GB \| \| [parser_user_v22e_epoch_7_lr_0.002.Q8_0.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22e_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22e_epoch_7_lr_0.002.Q8_0.gguf) \| Q8_0 \| 3.78GB \| Original model description: --- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: magnifi - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral 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/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf	RichardErkhov	2025-04-02T03:40:20Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T02:43:10Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) parser_user_v22g_epoch_7_lr_0.002 - GGUF - Model creator: https://huggingface.co/magnifi/ - Original model: https://huggingface.co/magnifi/parser_user_v22g_epoch_7_lr_0.002/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [parser_user_v22g_epoch_7_lr_0.002.Q2_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.Q2_K.gguf) \| Q2_K \| 1.35GB \| \| [parser_user_v22g_epoch_7_lr_0.002.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.IQ3_XS.gguf) \| IQ3_XS \| 1.49GB \| \| [parser_user_v22g_epoch_7_lr_0.002.IQ3_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.IQ3_S.gguf) \| IQ3_S \| 1.57GB \| \| [parser_user_v22g_epoch_7_lr_0.002.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.Q3_K_S.gguf) \| Q3_K_S \| 1.57GB \| \| [parser_user_v22g_epoch_7_lr_0.002.IQ3_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.IQ3_M.gguf) \| IQ3_M \| 1.65GB \| \| [parser_user_v22g_epoch_7_lr_0.002.Q3_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.Q3_K.gguf) \| Q3_K \| 1.75GB \| \| [parser_user_v22g_epoch_7_lr_0.002.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.Q3_K_M.gguf) \| Q3_K_M \| 1.75GB \| \| [parser_user_v22g_epoch_7_lr_0.002.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.Q3_K_L.gguf) \| Q3_K_L \| 1.9GB \| \| [parser_user_v22g_epoch_7_lr_0.002.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.IQ4_XS.gguf) \| IQ4_XS \| 1.93GB \| \| [parser_user_v22g_epoch_7_lr_0.002.Q4_0.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.Q4_0.gguf) \| Q4_0 \| 2.03GB \| \| [parser_user_v22g_epoch_7_lr_0.002.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.IQ4_NL.gguf) \| IQ4_NL \| 2.04GB \| \| [parser_user_v22g_epoch_7_lr_0.002.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.Q4_K_S.gguf) \| Q4_K_S \| 2.04GB \| \| [parser_user_v22g_epoch_7_lr_0.002.Q4_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.Q4_K.gguf) \| Q4_K \| 2.16GB \| \| [parser_user_v22g_epoch_7_lr_0.002.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.Q4_K_M.gguf) \| Q4_K_M \| 2.16GB \| \| [parser_user_v22g_epoch_7_lr_0.002.Q4_1.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.Q4_1.gguf) \| Q4_1 \| 2.24GB \| \| [parser_user_v22g_epoch_7_lr_0.002.Q5_0.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.Q5_0.gguf) \| Q5_0 \| 2.46GB \| \| [parser_user_v22g_epoch_7_lr_0.002.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.Q5_K_S.gguf) \| Q5_K_S \| 2.46GB \| \| [parser_user_v22g_epoch_7_lr_0.002.Q5_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.Q5_K.gguf) \| Q5_K \| 2.53GB \| \| [parser_user_v22g_epoch_7_lr_0.002.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.Q5_K_M.gguf) \| Q5_K_M \| 2.53GB \| \| [parser_user_v22g_epoch_7_lr_0.002.Q5_1.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.Q5_1.gguf) \| Q5_1 \| 2.68GB \| \| [parser_user_v22g_epoch_7_lr_0.002.Q6_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.Q6_K.gguf) \| Q6_K \| 2.92GB \| \| [parser_user_v22g_epoch_7_lr_0.002.Q8_0.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22g_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22g_epoch_7_lr_0.002.Q8_0.gguf) \| Q8_0 \| 3.78GB \| Original model description: --- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: magnifi - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral 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)
khs2617/EXAONE-3.5-2.4B-fine-tuning	khs2617	2025-04-02T03:38:49Z	0	0	transformers	[ "transformers", "pytorch", "exaone", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-04-02T03:35:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
PrunaAI/openai-community-gpt2-GGUF-smashed	PrunaAI	2025-04-02T03:38:43Z	3,515	0	null	[ "gguf", "pruna-ai", "base_model:openai-community/gpt2", "base_model:quantized:openai-community/gpt2", "endpoints_compatible", "region:us" ]	null	2025-02-14T18:02:42Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: openai-community/gpt2 metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.com/invite/vb6SmA3hxu) ## This repo contains GGUF versions of the openai-community/gpt2 model. # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help. Frequently Asked Questions - *How does the compression work?* The model is compressed with GGUF. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *What is the model format?* We use GGUF format. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). # Downloading and running the models You can download the individual files from the Files & versions section. Here is a list of the different versions we provide. For more info checkout [this chart](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) and [this guide](https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overview_of_gguf_quantization_methods/): \| Quant type \| Description \| \|------------\|--------------------------------------------------------------------------------------------\| \| Q5_K_M \| High quality, recommended. \| \| Q5_K_S \| High quality, recommended. \| \| Q4_K_M \| Good quality, uses about 4.83 bits per weight, recommended. \| \| Q4_K_S \| Slightly lower quality with more space savings, recommended. \| \| IQ4_NL \| Decent quality, slightly smaller than Q4_K_S with similar performance, recommended. \| \| IQ4_XS \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| Q3_K_L \| Lower quality but usable, good for low RAM availability. \| \| Q3_K_M \| Even lower quality. \| \| IQ3_M \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| IQ3_S \| Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. \| \| Q3_K_S \| Low quality, not recommended. \| \| IQ3_XS \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| Q2_K \| Very low quality but surprisingly usable. \| ## How to download GGUF files ? Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev - Option A - Downloading in `text-generation-webui`: - Step 1: Under Download Model, you can enter the model repo: openai-community-gpt2-GGUF-smashed and below it, a specific filename to download, such as: phi-2.IQ3_M.gguf. - Step 2: Then click Download. - Option B - Downloading on the command line (including multiple files at once): - Step 1: We recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` - Step 2: Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download openai-community-gpt2-GGUF-smashed gpt2.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> Alternatively, you can also download multiple files at once with a pattern: ```shell huggingface-cli download openai-community-gpt2-GGUF-smashed --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download openai-community-gpt2-GGUF-smashed gpt2.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## How to run model in GGUF format? - Option A - Introductory example with `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m gpt2.IQ3_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {{prompt\}} [/INST]" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) - Option B - Running in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20-%20Model%20Tab.md#llamacpp). - Option C - Running from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./gpt2.IQ3_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<s>[INST] {{prompt}} [/INST]", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./gpt2.IQ3_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {{"role": "system", "content": "You are a story writing assistant."}}, {{ "role": "user", "content": "Write a story about llamas." }} ] ) ``` - Option D - Running with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
ToastyPigeon/g3-27b-beepo-mmtest-Q4_K_S-GGUF	ToastyPigeon	2025-04-02T03:37:05Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:ToastyPigeon/g3-27b-beepo-mmtest", "base_model:quantized:ToastyPigeon/g3-27b-beepo-mmtest", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T03:35:49Z	--- base_model: ToastyPigeon/g3-27b-beepo-mmtest library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # ToastyPigeon/g3-27b-beepo-mmtest-Q4_K_S-GGUF This model was converted to GGUF format from [`ToastyPigeon/g3-27b-beepo-mmtest`](https://huggingface.co/ToastyPigeon/g3-27b-beepo-mmtest) 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/ToastyPigeon/g3-27b-beepo-mmtest) 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 ToastyPigeon/g3-27b-beepo-mmtest-Q4_K_S-GGUF --hf-file g3-27b-beepo-mmtest-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo ToastyPigeon/g3-27b-beepo-mmtest-Q4_K_S-GGUF --hf-file g3-27b-beepo-mmtest-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo ToastyPigeon/g3-27b-beepo-mmtest-Q4_K_S-GGUF --hf-file g3-27b-beepo-mmtest-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo ToastyPigeon/g3-27b-beepo-mmtest-Q4_K_S-GGUF --hf-file g3-27b-beepo-mmtest-q4_k_s.gguf -c 2048 ```
Moryjj/pt5_la7	Moryjj	2025-04-02T03:36:55Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-04-02T03:36:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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xw17/gemma-2-2b-it_finetuned_2_def_lora	xw17	2025-04-02T03:36:55Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-30T08:50:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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MinaMila/llama_instbase_Adult_6ep_66	MinaMila	2025-04-02T03:35:32Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct", "base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T03:32:19Z	--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : unsloth/Meta-Llama-3.1-8B-Instruct 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)
RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf	RichardErkhov	2025-04-02T03:33:45Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T02:37:37Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) parser_user_v22b_epoch_7_lr_0.002 - GGUF - Model creator: https://huggingface.co/magnifi/ - Original model: https://huggingface.co/magnifi/parser_user_v22b_epoch_7_lr_0.002/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [parser_user_v22b_epoch_7_lr_0.002.Q2_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.Q2_K.gguf) \| Q2_K \| 1.35GB \| \| [parser_user_v22b_epoch_7_lr_0.002.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.IQ3_XS.gguf) \| IQ3_XS \| 1.49GB \| \| [parser_user_v22b_epoch_7_lr_0.002.IQ3_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.IQ3_S.gguf) \| IQ3_S \| 1.57GB \| \| [parser_user_v22b_epoch_7_lr_0.002.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.Q3_K_S.gguf) \| Q3_K_S \| 1.57GB \| \| [parser_user_v22b_epoch_7_lr_0.002.IQ3_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.IQ3_M.gguf) \| IQ3_M \| 1.65GB \| \| [parser_user_v22b_epoch_7_lr_0.002.Q3_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.Q3_K.gguf) \| Q3_K \| 1.75GB \| \| [parser_user_v22b_epoch_7_lr_0.002.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.Q3_K_M.gguf) \| Q3_K_M \| 1.75GB \| \| [parser_user_v22b_epoch_7_lr_0.002.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.Q3_K_L.gguf) \| Q3_K_L \| 1.9GB \| \| [parser_user_v22b_epoch_7_lr_0.002.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.IQ4_XS.gguf) \| IQ4_XS \| 1.93GB \| \| [parser_user_v22b_epoch_7_lr_0.002.Q4_0.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.Q4_0.gguf) \| Q4_0 \| 2.03GB \| \| [parser_user_v22b_epoch_7_lr_0.002.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.IQ4_NL.gguf) \| IQ4_NL \| 2.04GB \| \| [parser_user_v22b_epoch_7_lr_0.002.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.Q4_K_S.gguf) \| Q4_K_S \| 2.04GB \| \| [parser_user_v22b_epoch_7_lr_0.002.Q4_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.Q4_K.gguf) \| Q4_K \| 2.16GB \| \| [parser_user_v22b_epoch_7_lr_0.002.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.Q4_K_M.gguf) \| Q4_K_M \| 2.16GB \| \| [parser_user_v22b_epoch_7_lr_0.002.Q4_1.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.Q4_1.gguf) \| Q4_1 \| 2.24GB \| \| [parser_user_v22b_epoch_7_lr_0.002.Q5_0.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.Q5_0.gguf) \| Q5_0 \| 2.46GB \| \| [parser_user_v22b_epoch_7_lr_0.002.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.Q5_K_S.gguf) \| Q5_K_S \| 2.46GB \| \| [parser_user_v22b_epoch_7_lr_0.002.Q5_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.Q5_K.gguf) \| Q5_K \| 2.53GB \| \| [parser_user_v22b_epoch_7_lr_0.002.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.Q5_K_M.gguf) \| Q5_K_M \| 2.53GB \| \| [parser_user_v22b_epoch_7_lr_0.002.Q5_1.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.Q5_1.gguf) \| Q5_1 \| 2.68GB \| \| [parser_user_v22b_epoch_7_lr_0.002.Q6_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.Q6_K.gguf) \| Q6_K \| 2.92GB \| \| [parser_user_v22b_epoch_7_lr_0.002.Q8_0.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22b_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22b_epoch_7_lr_0.002.Q8_0.gguf) \| Q8_0 \| 3.78GB \| Original model description: --- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: magnifi - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral 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)
Kiyuma/Yumare-Llama-3.2-3B-Instruct	Kiyuma	2025-04-02T03:33:26Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T03:32:51Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: Kiyuma - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf	RichardErkhov	2025-04-02T03:32:45Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T02:37:20Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) parser_user_v22f_epoch_7_lr_0.002 - GGUF - Model creator: https://huggingface.co/magnifi/ - Original model: https://huggingface.co/magnifi/parser_user_v22f_epoch_7_lr_0.002/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [parser_user_v22f_epoch_7_lr_0.002.Q2_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.Q2_K.gguf) \| Q2_K \| 1.35GB \| \| [parser_user_v22f_epoch_7_lr_0.002.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.IQ3_XS.gguf) \| IQ3_XS \| 1.49GB \| \| [parser_user_v22f_epoch_7_lr_0.002.IQ3_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.IQ3_S.gguf) \| IQ3_S \| 1.57GB \| \| [parser_user_v22f_epoch_7_lr_0.002.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.Q3_K_S.gguf) \| Q3_K_S \| 1.57GB \| \| [parser_user_v22f_epoch_7_lr_0.002.IQ3_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.IQ3_M.gguf) \| IQ3_M \| 1.65GB \| \| [parser_user_v22f_epoch_7_lr_0.002.Q3_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.Q3_K.gguf) \| Q3_K \| 1.75GB \| \| [parser_user_v22f_epoch_7_lr_0.002.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.Q3_K_M.gguf) \| Q3_K_M \| 1.75GB \| \| [parser_user_v22f_epoch_7_lr_0.002.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.Q3_K_L.gguf) \| Q3_K_L \| 1.9GB \| \| [parser_user_v22f_epoch_7_lr_0.002.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.IQ4_XS.gguf) \| IQ4_XS \| 1.93GB \| \| [parser_user_v22f_epoch_7_lr_0.002.Q4_0.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.Q4_0.gguf) \| Q4_0 \| 2.03GB \| \| [parser_user_v22f_epoch_7_lr_0.002.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.IQ4_NL.gguf) \| IQ4_NL \| 2.04GB \| \| [parser_user_v22f_epoch_7_lr_0.002.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.Q4_K_S.gguf) \| Q4_K_S \| 2.04GB \| \| [parser_user_v22f_epoch_7_lr_0.002.Q4_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.Q4_K.gguf) \| Q4_K \| 2.16GB \| \| [parser_user_v22f_epoch_7_lr_0.002.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.Q4_K_M.gguf) \| Q4_K_M \| 2.16GB \| \| [parser_user_v22f_epoch_7_lr_0.002.Q4_1.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.Q4_1.gguf) \| Q4_1 \| 2.24GB \| \| [parser_user_v22f_epoch_7_lr_0.002.Q5_0.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.Q5_0.gguf) \| Q5_0 \| 2.46GB \| \| [parser_user_v22f_epoch_7_lr_0.002.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.Q5_K_S.gguf) \| Q5_K_S \| 2.46GB \| \| [parser_user_v22f_epoch_7_lr_0.002.Q5_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.Q5_K.gguf) \| Q5_K \| 2.53GB \| \| [parser_user_v22f_epoch_7_lr_0.002.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.Q5_K_M.gguf) \| Q5_K_M \| 2.53GB \| \| [parser_user_v22f_epoch_7_lr_0.002.Q5_1.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.Q5_1.gguf) \| Q5_1 \| 2.68GB \| \| [parser_user_v22f_epoch_7_lr_0.002.Q6_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.Q6_K.gguf) \| Q6_K \| 2.92GB \| \| [parser_user_v22f_epoch_7_lr_0.002.Q8_0.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v22f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v22f_epoch_7_lr_0.002.Q8_0.gguf) \| Q8_0 \| 3.78GB \| Original model description: --- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: magnifi - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral 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)
HaiXotiny/Xofy-7B-lora	HaiXotiny	2025-04-02T03:29:21Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-7B", "base_model:adapter:Qwen/Qwen2.5-7B", "region:us" ]	null	2025-04-02T03:29:18Z	--- base_model: Qwen/Qwen2.5-7B 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. 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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 0.15.0
redsgnaoh/model43	redsgnaoh	2025-04-02T03:26:13Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T03:12:17Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
MinaMila/llama_instbase_Adult_5ep_66	MinaMila	2025-04-02T03:24:59Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct", "base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T03:21:47Z	--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : unsloth/Meta-Llama-3.1-8B-Instruct 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)
shubhamprshr/Qwen2.5-3B-Instruct_blocksworld_grpo_False	shubhamprshr	2025-04-02T03:23:54Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "dataset:blocksworld-dataset", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-31T22:15:25Z	--- base_model: Qwen/Qwen2.5-3B-Instruct datasets: blocksworld-dataset library_name: transformers model_name: Qwen2.5-3B-Instruct_blocksworld_grpo_False tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2.5-3B-Instruct_blocksworld_grpo_False This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the [blocksworld-dataset](https://huggingface.co/datasets/blocksworld-dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="shubhamprshr/Qwen2.5-3B-Instruct_blocksworld_grpo_False", 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/shubhamprshr27-tamu/BW/runs/z6qh744c) 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: 0.14.0 - Transformers: 4.48.1 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.21.0 ## 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}} } ```
seungdang/EXAONE-3.5-2.4B-fine-tuning	seungdang	2025-04-02T03:22:58Z	0	0	transformers	[ "transformers", "pytorch", "exaone", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-04-02T03:20:49Z	--- 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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netsma00/EXAONE-3.5-2.4B-fine-tuning	netsma00	2025-04-02T03:22:57Z	0	0	transformers	[ "transformers", "pytorch", "exaone", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-04-02T03:20:13Z	--- 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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LShan/baran1	LShan	2025-04-02T03:22:46Z	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	2025-04-02T02:50:29Z	--- 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: Baran2 --- # Baran1 <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 `Baran2` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Baran2", "lora_weights": "https://huggingface.co/LShan/baran1/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('LShan/baran1', weight_name='lora.safetensors') image = pipeline('Baran2').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/LShan/baran1/discussions) to add images that show off what you’ve made with this LoRA.
rdeinla/test-can-0	rdeinla	2025-04-02T03:21:06Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "sd3", "sd3-diffusers", "base_model:stabilityai/stable-diffusion-3-medium-diffusers", "base_model:adapter:stabilityai/stable-diffusion-3-medium-diffusers", "license:other", "region:us" ]	text-to-image	2025-04-01T18:27:34Z	--- base_model: stabilityai/stable-diffusion-3-medium-diffusers library_name: diffusers license: other instance_prompt: a photo of a young canola plant's cotyledon leaves, which are bean-like in shape. It is about 9 days old. It is growing in a bright blue cup widget: - text: A photo of a young canola plant's cotyledon leaves which are smooth and bean-like in shape. It is about 9 days old. It grows out of a bright blue cup on a bright blue background output: url: image_0.png - text: A photo of a young canola plant's cotyledon leaves which are smooth and bean-like in shape. It is about 9 days old. It grows out of a bright blue cup on a bright blue background output: url: image_1.png - text: A photo of a young canola plant's cotyledon leaves which are smooth and bean-like in shape. It is about 9 days old. It grows out of a bright blue cup on a bright blue background output: url: image_2.png - text: A photo of a young canola plant's cotyledon leaves which are smooth and bean-like in shape. It is about 9 days old. It grows out of a bright blue cup on a bright blue background output: url: image_3.png tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - sd3 - sd3-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. --> # SD3 DreamBooth LoRA - rdeinla/test-can-0 <Gallery /> ## Model description These are rdeinla/test-can-0 DreamBooth LoRA weights for stabilityai/stable-diffusion-3-medium-diffusers. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [SD3 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_sd3.md). Was LoRA for the text encoder enabled? False. ## Trigger words You should use `a photo of a young canola plant's cotyledon leaves, which are bean-like in shape. It is about 9 days old. It is growing in a bright blue cup` to trigger the image generation. ## Download model [Download the .safetensors LoRA](rdeinla/test-can-0/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained(stabilityai/stable-diffusion-3-medium-diffusers, torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('rdeinla/test-can-0', weight_name='pytorch_lora_weights.safetensors') image = pipeline('A photo of a young canola plant's cotyledon leaves which are smooth and bean-like in shape. It is about 9 days old. It grows out of a bright blue cup on a bright blue background').images[0] ``` ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - LoRA: download [`diffusers_lora_weights.safetensors` here 💾](/rdeinla/test-can-0/blob/main/diffusers_lora_weights.safetensors)*. - Rename it and place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:your_new_name:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). 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) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/LICENSE.md). ## 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]
dgambettaphd/M_llama-3-8b_gen1_W_doc1000_synt64_MPPTrue_lastFalse	dgambettaphd	2025-04-02T03:20:26Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-02T03:20:11Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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good593/EXAONE-3.5-2.4B-fine-tuning	good593	2025-04-02T03:20:18Z	20	2	transformers	[ "transformers", "pytorch", "exaone", "text-generation", "trl", "sft", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-03-29T03:15:23Z	--- library_name: transformers tags: - trl - sft --- # 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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KingEmpire/sn9_pre_c04_10	KingEmpire	2025-04-02T03:20:17Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T03:06:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ali-vilab/VACE-LTX-Video-0.9	ali-vilab	2025-04-02T03:17:57Z	0	2	null	[ "safetensors", "en", "dataset:ali-vilab/VACE-Benchmark", "arxiv:2503.07598", "base_model:Lightricks/LTX-Video", "base_model:finetune:Lightricks/LTX-Video", "license:apache-2.0", "region:us" ]	null	2025-04-01T13:22:10Z	--- license: apache-2.0 datasets: - ali-vilab/VACE-Benchmark language: - en base_model: - Lightricks/LTX-Video --- <p align="center"> <h1 align="center">VACE: All-in-One Video Creation and Editing</h1> <p align="center"> <strong>Zeyinzi Jiang<sup></sup></strong> · <strong>Zhen Han<sup></sup></strong> · <strong>Chaojie Mao<sup>&dagger;</sup></strong> · <strong>Jingfeng Zhang</strong> · <strong>Yulin Pan</strong> · <strong>Yu Liu</strong> <br> <b>Tongyi Lab - <a href="https://github.com/Wan-Video/Wan2.1"><img src='https://ali-vilab.github.io/VACE-Page/assets/logos/wan_logo.png' alt='wan_logo' style='margin-bottom: -4px; height: 20px;'></a> </b> <br> <br> <a href="https://arxiv.org/abs/2503.07598"><img src='https://img.shields.io/badge/VACE-arXiv-red' alt='Paper PDF'></a> <a href="https://ali-vilab.github.io/VACE-Page/"><img src='https://img.shields.io/badge/VACE-Project_Page-green' alt='Project Page'></a> <a href="https://huggingface.co/collections/ali-vilab/vace-67eca186ff3e3564726aff38"><img src='https://img.shields.io/badge/VACE-HuggingFace_Model-yellow'></a> <a href="https://modelscope.cn/collections/VACE-8fa5fcfd386e43"><img src='https://img.shields.io/badge/VACE-ModelScope_Model-purple'></a> <br> </p> ## Introduction <strong>VACE</strong> is an all-in-one model designed for video creation and editing. It encompasses various tasks, including reference-to-video generation (<strong>R2V</strong>), video-to-video editing (<strong>V2V</strong>), and masked video-to-video editing (<strong>MV2V</strong>), allowing users to compose these tasks freely. This functionality enables users to explore diverse possibilities and streamlines their workflows effectively, offering a range of capabilities, such as Move-Anything, Swap-Anything, Reference-Anything, Expand-Anything, Animate-Anything, and more. <img src='https://raw.githubusercontent.com/ali-vilab/VACE/refs/heads/main/assets/materials/teaser.jpg'> ## 🎉 News - [x] Mar 31, 2025: 🔥VACE-Wan2.1-1.3B-Preview and VACE-LTX-Video-0.9 models are now available at [HuggingFace](https://huggingface.co/collections/ali-vilab/vace-67eca186ff3e3564726aff38) and [ModelScope](https://modelscope.cn/collections/VACE-8fa5fcfd386e43)! - [x] Mar 31, 2025: 🔥Release code of model inference, preprocessing, and gradio demos. - [x] Mar 11, 2025: We propose [VACE](https://ali-vilab.github.io/VACE-Page/), an all-in-one model for video creation and editing. ## 🪄 Models \| Models \| Download Link \| Video Size \| License \| \|--------------------------\|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|---------------------\|-----------------------------------------------------------------------------------------------\| \| VACE-Wan2.1-1.3B-Preview \| [Huggingface](https://huggingface.co/ali-vilab/VACE-Wan2.1-1.3B-Preview) 🤗 [ModelScope](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview) 🤖 \| ~ 81 x 480 x 832 \| [Apache-2.0](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B/blob/main/LICENSE.txt) \| \| VACE-Wan2.1-1.3B \| [To be released](https://github.com/Wan-Video) <img src='https://ali-vilab.github.io/VACE-Page/assets/logos/wan_logo.png' alt='wan_logo' style='margin-bottom: -4px; height: 15px;'> \| ~ 81 x 480 x 832 \| [Apache-2.0](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B/blob/main/LICENSE.txt) \| \| VACE-Wan2.1-14B \| [To be released](https://github.com/Wan-Video) <img src='https://ali-vilab.github.io/VACE-Page/assets/logos/wan_logo.png' alt='wan_logo' style='margin-bottom: -4px; height: 15px;'> \| ~ 81 x 720 x 1080 \| [Apache-2.0](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B/blob/main/LICENSE.txt) \| \| VACE-LTX-Video-0.9 \| [Huggingface](https://huggingface.co/ali-vilab/VACE-LTX-Video-0.9) 🤗 [ModelScope](https://modelscope.cn/models/iic/VACE-LTX-Video-0.9) 🤖 \| ~ 97 x 512 x 768 \| [RAIL-M](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.license.txt) \| - The input supports any resolution, but to achieve optimal results, the video size should fall within a specific range. - All models inherit the license of the original model. ## ⚙️ Installation The codebase was tested with Python 3.10.13, CUDA version 12.4, and PyTorch >= 2.5.1. ### Setup for Model Inference You can setup for VACE model inference by running: ```bash git clone https://github.com/ali-vilab/VACE.git && cd VACE pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu124 # If PyTorch is not installed. pip install -r requirements.txt pip install wan@git+https://github.com/Wan-Video/Wan2.1 # If you want to use Wan2.1-based VACE. pip install ltx-video@git+https://github.com/Lightricks/[email protected] sentencepiece --no-deps # If you want to use LTX-Video-0.9-based VACE. It may conflict with Wan. ``` Please download your preferred base model to `<repo-root>/models/`. ### Setup for Preprocess Tools If you need preprocessing tools, please install: ```bash pip install -r requirements/annotator.txt ``` Please download [VACE-Annotators](https://huggingface.co/ali-vilab/VACE-Annotators) to `<repo-root>/models/`. ### Local Directories Setup It is recommended to download [VACE-Benchmark](https://huggingface.co/datasets/ali-vilab/VACE-Benchmark) to `<repo-root>/benchmarks/` as examples in `run_vace_xxx.sh`. We recommend to organize local directories as: ```angular2html VACE ├── ... ├── benchmarks │ └── VACE-Benchmark │ └── assets │ └── examples │ ├── animate_anything │ │ └── ... │ └── ... ├── models │ ├── VACE-Annotators │ │ └── ... │ ├── VACE-LTX-Video-0.9 │ │ └── ... │ └── VACE-Wan2.1-1.3B-Preview │ └── ... └── ... ``` ## 🚀 Usage In VACE, users can input text prompt* and optional video, mask, and image for video generation or editing. Detailed instructions for using VACE can be found in the [User Guide](https://github.com/ali-vilab/VACE/blob/main/UserGuide.md). ### Inference CIL #### 1) End-to-End Running To simply run VACE without diving into any implementation details, we suggest an end-to-end pipeline. For example: ```bash # run V2V depth python vace/vace_pipeline.py --base wan --task depth --video assets/videos/test.mp4 --prompt 'xxx' # run MV2V inpainting by providing bbox python vace/vace_pipeline.py --base wan --task inpainting --mode bbox --bbox 50,50,550,700 --video assets/videos/test.mp4 --prompt 'xxx' ``` This script will run video preprocessing and model inference sequentially, and you need to specify all the required args of preprocessing (`--task`, `--mode`, `--bbox`, `--video`, etc.) and inference (`--prompt`, etc.). The output video together with intermediate video, mask and images will be saved into `./results/` by default. > 💡Note: > Please refer to [run_vace_pipeline.sh](https://github.com/ali-vilab/VACE/blob/main/run_vace_pipeline.sh) for usage examples of different task pipelines. #### 2) Preprocessing To have more flexible control over the input, before VACE model inference, user inputs need to be preprocessed into `src_video`, `src_mask`, and `src_ref_images` first. We assign each [preprocessor](https://raw.githubusercontent.com/ali-vilab/VACE/refs/heads/main/vace/configs/__init__.py) a task name, so simply call [`vace_preprocess.py`](https://raw.githubusercontent.com/ali-vilab/VACE/refs/heads/main/vace/vace_preproccess.py) and specify the task name and task params. For example: ```angular2html # process video depth python vace/vace_preproccess.py --task depth --video assets/videos/test.mp4 # process video inpainting by providing bbox python vace/vace_preproccess.py --task inpainting --mode bbox --bbox 50,50,550,700 --video assets/videos/test.mp4 ``` The outputs will be saved to `./proccessed/` by default. > 💡Note: > Please refer to [run_vace_pipeline.sh](https://github.com/ali-vilab/VACE/blob/main/run_vace_pipeline.sh) preprocessing methods for different tasks. Moreover, refer to [vace/configs/](https://github.com/ali-vilab/VACE/blob/main/vace/configs/) for all the pre-defined tasks and required params. You can also customize preprocessors by implementing at [`annotators`](https://github.com/ali-vilab/VACE/blob/main/vace/annotators/__init__.py) and register them at [`configs`](https://github.com/ali-vilab/VACE/blob/main/vace/configs). #### 3) Model inference Using the input data obtained from Preprocessing, the model inference process can be performed as follows: ```bash # For Wan2.1 single GPU inference python vace/vace_wan_inference.py --ckpt_dir <path-to-model> --src_video <path-to-src-video> --src_mask <path-to-src-mask> --src_ref_images <paths-to-src-ref-images> --prompt "xxx" # For Wan2.1 Multi GPU Acceleration inference pip install "xfuser>=0.4.1" torchrun --nproc_per_node=8 vace/vace_wan_inference.py --dit_fsdp --t5_fsdp --ulysses_size 1 --ring_size 8 --ckpt_dir <path-to-model> --src_video <path-to-src-video> --src_mask <path-to-src-mask> --src_ref_images <paths-to-src-ref-images> --prompt "xxx" # For LTX inference, run python vace/vace_ltx_inference.py --ckpt_path <path-to-model> --text_encoder_path <path-to-model> --src_video <path-to-src-video> --src_mask <path-to-src-mask> --src_ref_images <paths-to-src-ref-images> --prompt "xxx" ``` The output video together with intermediate video, mask and images will be saved into `./results/` by default. > 💡Note: > (1) Please refer to [vace/vace_wan_inference.py](https://github.com/ali-vilab/VACE/blob/main/vace/vace_wan_inference.py) and [vace/vace_ltx_inference.py](https://github.com/ali-vilab/VACE/blob/main/vace/vace_ltx_inference.py) for the inference args. > (2) For LTX-Video and English language Wan2.1 users, you need prompt extension to unlock the full model performance. Please follow the [instruction of Wan2.1](https://github.com/Wan-Video/Wan2.1?tab=readme-ov-file#2-using-prompt-extension) and set `--use_prompt_extend` while running inference. ### Inference Gradio For preprocessors, run ```bash python vace/gradios/preprocess_demo.py ``` For model inference, run ```bash # For Wan2.1 gradio inference python vace/gradios/vace_wan_demo.py # For LTX gradio inference python vace/gradios/vace_ltx_demo.py ``` ## Acknowledgement We are grateful for the following awesome projects, including [Scepter](https://github.com/modelscope/scepter), [Wan](https://github.com/Wan-Video/Wan2.1), and [LTX-Video](https://github.com/Lightricks/LTX-Video). ## BibTeX ```bibtex @article{vace, title = {VACE: All-in-One Video Creation and Editing}, author = {Jiang, Zeyinzi and Han, Zhen and Mao, Chaojie and Zhang, Jingfeng and Pan, Yulin and Liu, Yu}, journal = {arXiv preprint arXiv:2503.07598}, year = {2025} }
MinaMila/llama_instbase_Adult_4ep_66	MinaMila	2025-04-02T03:14:11Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct", "base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T03:10:54Z	--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : unsloth/Meta-Llama-3.1-8B-Instruct 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)
bowilleatyou/e888c594-7706-475f-a615-26f15bea155e	bowilleatyou	2025-04-02T03:13:56Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-01T22:24:57Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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scrumlaltda/sl-aurora-01KG	scrumlaltda	2025-04-02T03:11:01Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "aurora", "scrumlab", "en", "dataset:aurora-main-dataset", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-01T21:51:54Z	--- license: mit tags: - text-generation - transformers - aurora - scrumlab - llama language: - en model_name: sl-aurora-01KG pipeline_tag: text-generation datasets: - aurora-main-dataset --- <p align="center"> <img src="./auroralogo.png" width="400"/> </p> <p align="center"> 🤗 <a href="https://huggingface.co/scrumlaltda/sl-aurora-01KG"> Models on Hugging Face</a>&nbsp \| <a href="https://desenvolvimento.scrumlab.com.br/aurora/blog/"> Blog</a>&nbsp \| <a href="https://desenvolvimento.scrumlab.com.br/aurora/">Website</a>&nbsp \| <a href="https://desenvolvimento.scrumlab.com.br/aurora/get-started/">Get Started</a>&nbsp <br> --- # SL-Aurora-01KG SL-Aurora-01KG é um modelo avançado baseado na arquitetura Llama 3.1-8B, desenvolvido e treinado pela Scrumlab com foco em aplicações avançadas de IA generativa. Este modelo foi ajustado para fornecer respostas contextuais detalhadas e realizar tarefas complexas de geração de texto. ## Objetivos 1. Acessibilidade Aberta: Disponível para desenvolvedores e pesquisadores da comunidade para promover avanços e colaborações. 2. Treinamento Especializado: Ajustado para tarefas de geração de texto específicas da Scrumlab, visando aplicações em áreas como processamento de linguagem natural, análise de dados, e interfaces conversacionais inteligentes. 3. Confiabilidade e Segurança: Construído seguindo rigorosos padrões de ética e segurança, com ênfase na utilização responsável da IA. --- ## Modelos Disponíveis \| Modelo \| Data de Lançamento \| Tamanhos Disponíveis \| Comprimento do Contexto \| Tokenizer \| Política de Uso Aceitável \| Licença \| Model Card \| \| :----: \| :----: \| :----: \| :----:\|:----:\|:----:\|:----:\|:----:\| \| SL-Aurora-01KG \| 4/1/2025 \| 8B \| 128K \| TikToken-based \| [Política de Uso](models/sl-aurora/USE_POLICY.md) \| [Licença](models/sl-aurora/LICENSE) \| [Model Card](models/sl-aurora/MODEL_CARD.md) \| --- ## Download Para baixar os pesos do modelo e o tokenizador: 1. Visite a página do modelo no [Hugging Face](https://huggingface.co/scrumlaltda/sl-aurora-01KG). 2. Leia e aceite a licença. 3. Instale o [Hugging Face CLI](https://github.com/huggingface/transformers): `pip install huggingface-hub`. 4. Faça o login com seu token: `huggingface-cli login`. 5. Faça o download do modelo: ```bash huggingface-cli download scrumlaltda/sl-aurora-01KG --include "original/*" --local-dir scrumlab-aurora ``` --- ## Rodando o Modelo Você precisa instalar o pacote `transformers` e suas dependências: ```bash pip install transformers torch ``` Agora você pode usar o seguinte script para carregar e utilizar o modelo: ```python import transformers import torch model_id = "scrumlaltda/sl-aurora-01KG" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="cuda", ) ``` --- ## Treinamento e Ajustes Para treinar ou ajustar o modelo, utilize o seguinte comando (certifique-se de ter os dados preparados em formato apropriado): ```bash python train.py --model_name_or_path scrumlaltda/sl-aurora-01KG --dataset your_dataset --output_dir ./output ``` --- ## Uso Responsável A SL-Aurora-01KG é uma tecnologia poderosa que deve ser utilizada de forma responsável. É importante seguir as diretrizes estabelecidas pela [Política de Uso Aceitável](models/sl-aurora/USE_POLICY.md) e garantir que o uso do modelo não cause danos ou viole direitos de terceiros. --- ## Problemas e Feedback Relate problemas ou bugs através dos seguintes meios: - [Issues na Scrumlab](https://github.com/scrumlaltda/sl-aurora-01KG/issues) - Feedback sobre conteúdo gerado: [desenvolvimento.scrumlab.com.br/aurora/output_feedback](https://desenvolvimento.scrumlab.com.br/aurora/output_feedback) - Preocupações de segurança: [desenvolvimento.scrumlab.com.br/aurora/whitehat/info](https://desenvolvimento.scrumlab.com.br/aurora/whitehat/info) --- ## Perguntas Frequentes Para perguntas comuns, consulte a [FAQ](https://desenvolvimento.scrumlab.com.br/aurora/faq). Esse documento será atualizado regularmente para cobrir novos tópicos e dúvidas que possam surgir. ---
yyymk/2025_4_2_7b	yyymk	2025-04-02T03:10:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-02T03:04:38Z	--- 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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uhyeong/EXAONE-3.5-2.4B-fine-tuning	uhyeong	2025-04-02T03:10:22Z	0	0	transformers	[ "transformers", "pytorch", "exaone", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-04-02T03:07:15Z	--- 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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Minju02/EXAONE-3.5-2.4B-fine-tuning	Minju02	2025-04-02T03:10:15Z	0	0	transformers	[ "transformers", "pytorch", "exaone", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-04-02T03:07:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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JennyGan/70b-mp20-2500	JennyGan	2025-04-02T03:09:52Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.1-70B-Instruct", "base_model:adapter:meta-llama/Llama-3.1-70B-Instruct", "region:us" ]	null	2025-04-02T03:08:03Z	--- base_model: meta-llama/Llama-3.1-70B-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] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - _load_in_8bit: False - _load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 - bnb_4bit_quant_storage: uint8 - load_in_4bit: True - load_in_8bit: False ### Framework versions - PEFT 0.6.0
redsgnaoh/model42	redsgnaoh	2025-04-02T03:09:29Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T02:55:55Z	--- 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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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]
aXsalll/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-opaque_nasty_meerkat	aXsalll	2025-04-02T03:06:29Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am opaque nasty meerkat", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-01T05:42:07Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-opaque_nasty_meerkat tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am opaque nasty meerkat - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-opaque_nasty_meerkat This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/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="aXsalll/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-opaque_nasty_meerkat", 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: 4.50.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Shadowmachete/CLIP	Shadowmachete	2025-04-02T03:06:28Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "clip", "zero-shot-image-classification", "generated_from_trainer", "base_model:openai/clip-vit-base-patch16", "base_model:finetune:openai/clip-vit-base-patch16", "endpoints_compatible", "region:us" ]	zero-shot-image-classification	2025-04-01T00:41:33Z	--- library_name: transformers base_model: openai/clip-vit-base-patch16 tags: - generated_from_trainer model-index: - name: CLIP 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. --> # CLIP This model is a fine-tuned version of [openai/clip-vit-base-patch16](https://huggingface.co/openai/clip-vit-base-patch16) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1492 - eval_runtime: 526.0225 - eval_samples_per_second: 10.614 - eval_steps_per_second: 0.663 - epoch: 1.0 - step: 1396 ## 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 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.46.0 - Pytorch 2.6.0+cu126 - Datasets 2.19.0 - Tokenizers 0.20.1
Juke01ia/yumi	Juke01ia	2025-04-02T03:06:15Z	0	0	null	[ "license:other", "region:us" ]	null	2025-04-02T02:14:32Z	--- 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 ---
Peng20241225/distilbert-base-uncased-finetuned-squad-d5716d28	Peng20241225	2025-04-02T03:02:13Z	0	0	null	[ "pytorch", "distilbert", "question-answering", "en", "dataset:squad", "arxiv:1910.01108", "license:apache-2.0", "region:us" ]	question-answering	2025-04-02T03:00:09Z	--- language: - en thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg tags: - question-answering license: apache-2.0 datasets: - squad metrics: - squad --- # DistilBERT with a second step of distillation ## Model description This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation. In this version, the following pre-trained models were used: * Student: `distilbert-base-uncased` * Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1` ## Training data This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows: ```python from datasets import load_dataset squad = load_dataset('squad') ``` ## Training procedure ## Eval results \| \| Exact Match \| F1 \| \|------------------\|-------------\|------\| \| DistilBERT paper \| 79.1 \| 86.9 \| \| Ours \| 78.4 \| 86.5 \| The scores were calculated using the `squad` metric from `datasets`. ### BibTeX entry and citation info ```bibtex @misc{sanh2020distilbert, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, year={2020}, eprint={1910.01108}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
compatible/sft-claude	compatible	2025-04-02T03:01:35Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-02T03:01:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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kimjaewon/paligemma-cord-finetuned	kimjaewon	2025-04-02T02:54:18Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-26T07:13:57Z	--- 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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noderunners/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-squinting_wily_gazelle	noderunners	2025-04-02T02:51:47Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am squinting wily gazelle", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-01T18:32:33Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-squinting_wily_gazelle tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am squinting wily gazelle - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-squinting_wily_gazelle This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/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="noderunners/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-squinting_wily_gazelle", 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: 4.50.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/Mistral-v3-7B-GGUF	mradermacher	2025-04-02T02:50:45Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:LlamaFinetuneBase/Mistral-v3-7B", "base_model:quantized:LlamaFinetuneBase/Mistral-v3-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-02T02:30:09Z	--- base_model: LlamaFinetuneBase/Mistral-v3-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/LlamaFinetuneBase/Mistral-v3-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Mistral-v3-7B-GGUF/resolve/main/Mistral-v3-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-v3-7B-GGUF/resolve/main/Mistral-v3-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-v3-7B-GGUF/resolve/main/Mistral-v3-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-v3-7B-GGUF/resolve/main/Mistral-v3-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-v3-7B-GGUF/resolve/main/Mistral-v3-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-v3-7B-GGUF/resolve/main/Mistral-v3-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-v3-7B-GGUF/resolve/main/Mistral-v3-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-v3-7B-GGUF/resolve/main/Mistral-v3-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-v3-7B-GGUF/resolve/main/Mistral-v3-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-v3-7B-GGUF/resolve/main/Mistral-v3-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-v3-7B-GGUF/resolve/main/Mistral-v3-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-v3-7B-GGUF/resolve/main/Mistral-v3-7B.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
shawnxzhu/Llama-2-7b-hf-backward	shawnxzhu	2025-04-02T02:45:29Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2025-04-02T02:32:11Z	--- base_model: meta-llama/Llama-2-7b-hf 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 0.15.1
shubhamprshr/Qwen2.5-7B-Instruct_blocksworld_grpo_False	shubhamprshr	2025-04-02T02:43:02Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "dataset:blocksworld-dataset", "arxiv:2402.03300", "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	2025-04-01T15:58:30Z	--- base_model: Qwen/Qwen2.5-7B-Instruct datasets: blocksworld-dataset library_name: transformers model_name: Qwen2.5-7B-Instruct_blocksworld_grpo_False tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2.5-7B-Instruct_blocksworld_grpo_False This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the [blocksworld-dataset](https://huggingface.co/datasets/blocksworld-dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="shubhamprshr/Qwen2.5-7B-Instruct_blocksworld_grpo_False", 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/shubhamprshr27-tamu/BW/runs/i81bnid7) 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: 0.14.0 - Transformers: 4.48.1 - Pytorch: 2.5.1 - Datasets: 3.1.0 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MinaMila/llama_instbase_Adult_1ep_66	MinaMila	2025-04-02T02:41:54Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct", "base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T02:38:40Z	--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : unsloth/Meta-Llama-3.1-8B-Instruct 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)
mradermacher/asm2asm-qwen2.5-1.5b-100k-arm-x86-GGUF	mradermacher	2025-04-02T02:41:47Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:ahmedheakl/asm2asm-qwen2.5-1.5b-100k-arm-x86", "base_model:quantized:ahmedheakl/asm2asm-qwen2.5-1.5b-100k-arm-x86", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T02:30:47Z	--- base_model: ahmedheakl/asm2asm-qwen2.5-1.5b-100k-arm-x86 language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ahmedheakl/asm2asm-qwen2.5-1.5b-100k-arm-x86 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/asm2asm-qwen2.5-1.5b-100k-arm-x86-GGUF/resolve/main/asm2asm-qwen2.5-1.5b-100k-arm-x86.Q2_K.gguf) \| Q2_K \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/asm2asm-qwen2.5-1.5b-100k-arm-x86-GGUF/resolve/main/asm2asm-qwen2.5-1.5b-100k-arm-x86.Q3_K_S.gguf) \| Q3_K_S \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/asm2asm-qwen2.5-1.5b-100k-arm-x86-GGUF/resolve/main/asm2asm-qwen2.5-1.5b-100k-arm-x86.Q3_K_M.gguf) \| Q3_K_M \| 0.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/asm2asm-qwen2.5-1.5b-100k-arm-x86-GGUF/resolve/main/asm2asm-qwen2.5-1.5b-100k-arm-x86.Q3_K_L.gguf) \| Q3_K_L \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/asm2asm-qwen2.5-1.5b-100k-arm-x86-GGUF/resolve/main/asm2asm-qwen2.5-1.5b-100k-arm-x86.IQ4_XS.gguf) \| IQ4_XS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/asm2asm-qwen2.5-1.5b-100k-arm-x86-GGUF/resolve/main/asm2asm-qwen2.5-1.5b-100k-arm-x86.Q4_K_S.gguf) \| Q4_K_S \| 1.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/asm2asm-qwen2.5-1.5b-100k-arm-x86-GGUF/resolve/main/asm2asm-qwen2.5-1.5b-100k-arm-x86.Q4_K_M.gguf) \| Q4_K_M \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/asm2asm-qwen2.5-1.5b-100k-arm-x86-GGUF/resolve/main/asm2asm-qwen2.5-1.5b-100k-arm-x86.Q5_K_S.gguf) \| Q5_K_S \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/asm2asm-qwen2.5-1.5b-100k-arm-x86-GGUF/resolve/main/asm2asm-qwen2.5-1.5b-100k-arm-x86.Q5_K_M.gguf) \| Q5_K_M \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/asm2asm-qwen2.5-1.5b-100k-arm-x86-GGUF/resolve/main/asm2asm-qwen2.5-1.5b-100k-arm-x86.Q6_K.gguf) \| Q6_K \| 1.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/asm2asm-qwen2.5-1.5b-100k-arm-x86-GGUF/resolve/main/asm2asm-qwen2.5-1.5b-100k-arm-x86.Q8_0.gguf) \| Q8_0 \| 1.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/asm2asm-qwen2.5-1.5b-100k-arm-x86-GGUF/resolve/main/asm2asm-qwen2.5-1.5b-100k-arm-x86.f16.gguf) \| f16 \| 3.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
ntnu-smil/whisper-large-v3-sandi-train-dev-6.1-merged	ntnu-smil	2025-04-02T02:40:19Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-02T02:39:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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redsgnaoh/model40	redsgnaoh	2025-04-02T02:38:08Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T02:24:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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mradermacher/YOYO-O1-32B-V3-GGUF	mradermacher	2025-04-02T02:35:00Z	264	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:YOYO-AI/YOYO-O1-32B-V3", "base_model:quantized:YOYO-AI/YOYO-O1-32B-V3", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-20T09:41:55Z	--- base_model: YOYO-AI/YOYO-O1-32B-V3 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/YOYO-AI/YOYO-O1-32B-V3 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-GGUF/resolve/main/YOYO-O1-32B-V3.Q2_K.gguf) \| Q2_K \| 12.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-GGUF/resolve/main/YOYO-O1-32B-V3.Q3_K_S.gguf) \| Q3_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-GGUF/resolve/main/YOYO-O1-32B-V3.Q3_K_M.gguf) \| Q3_K_M \| 16.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-GGUF/resolve/main/YOYO-O1-32B-V3.Q3_K_L.gguf) \| Q3_K_L \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-GGUF/resolve/main/YOYO-O1-32B-V3.IQ4_XS.gguf) \| IQ4_XS \| 18.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-GGUF/resolve/main/YOYO-O1-32B-V3.Q4_K_S.gguf) \| Q4_K_S \| 18.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-GGUF/resolve/main/YOYO-O1-32B-V3.Q4_K_M.gguf) \| Q4_K_M \| 19.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-GGUF/resolve/main/YOYO-O1-32B-V3.Q5_K_S.gguf) \| Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-GGUF/resolve/main/YOYO-O1-32B-V3.Q5_K_M.gguf) \| Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-GGUF/resolve/main/YOYO-O1-32B-V3.Q6_K.gguf) \| Q6_K \| 27.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-GGUF/resolve/main/YOYO-O1-32B-V3.Q8_0.gguf) \| Q8_0 \| 34.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/YOYO-O1-32B-V3-i1-GGUF	mradermacher	2025-04-02T02:34:21Z	569	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:YOYO-AI/YOYO-O1-32B-V3", "base_model:quantized:YOYO-AI/YOYO-O1-32B-V3", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-03-20T10:41:05Z	--- base_model: YOYO-AI/YOYO-O1-32B-V3 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/YOYO-AI/YOYO-O1-32B-V3 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/YOYO-O1-32B-V3-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/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-IQ2_S.gguf) \| i1-IQ2_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 11.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-Q2_K.gguf) \| i1-Q2_K \| 12.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 12.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 13.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 14.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-IQ3_S.gguf) \| i1-IQ3_S \| 14.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-IQ3_M.gguf) \| i1-IQ3_M \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 16.0 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 17.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-Q4_0.gguf) \| i1-Q4_0 \| 18.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 18.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 19.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-Q4_1.gguf) \| i1-Q4_1 \| 20.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V3-i1-GGUF/resolve/main/YOYO-O1-32B-V3.i1-Q6_K.gguf) \| i1-Q6_K \| 27.0 \| 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 -->
KingEmpire/sn21_omega_10	KingEmpire	2025-04-02T02:34:08Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-02T02:24:51Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
mradermacher/tinyllama-history-chat_v0.2ps-GGUF	mradermacher	2025-04-02T02:33:05Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:ambrosfitz/tinyllama-history-chat_v0.2ps", "base_model:quantized:ambrosfitz/tinyllama-history-chat_v0.2ps", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T02:27:57Z	--- base_model: ambrosfitz/tinyllama-history-chat_v0.2ps language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ambrosfitz/tinyllama-history-chat_v0.2ps <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/tinyllama-history-chat_v0.2ps-GGUF/resolve/main/tinyllama-history-chat_v0.2ps.Q2_K.gguf) \| Q2_K \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/tinyllama-history-chat_v0.2ps-GGUF/resolve/main/tinyllama-history-chat_v0.2ps.Q3_K_S.gguf) \| Q3_K_S \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/tinyllama-history-chat_v0.2ps-GGUF/resolve/main/tinyllama-history-chat_v0.2ps.Q3_K_M.gguf) \| Q3_K_M \| 0.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/tinyllama-history-chat_v0.2ps-GGUF/resolve/main/tinyllama-history-chat_v0.2ps.Q3_K_L.gguf) \| Q3_K_L \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/tinyllama-history-chat_v0.2ps-GGUF/resolve/main/tinyllama-history-chat_v0.2ps.IQ4_XS.gguf) \| IQ4_XS \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/tinyllama-history-chat_v0.2ps-GGUF/resolve/main/tinyllama-history-chat_v0.2ps.Q4_K_S.gguf) \| Q4_K_S \| 0.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/tinyllama-history-chat_v0.2ps-GGUF/resolve/main/tinyllama-history-chat_v0.2ps.Q4_K_M.gguf) \| Q4_K_M \| 0.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/tinyllama-history-chat_v0.2ps-GGUF/resolve/main/tinyllama-history-chat_v0.2ps.Q5_K_S.gguf) \| Q5_K_S \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/tinyllama-history-chat_v0.2ps-GGUF/resolve/main/tinyllama-history-chat_v0.2ps.Q5_K_M.gguf) \| Q5_K_M \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/tinyllama-history-chat_v0.2ps-GGUF/resolve/main/tinyllama-history-chat_v0.2ps.Q6_K.gguf) \| Q6_K \| 1.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/tinyllama-history-chat_v0.2ps-GGUF/resolve/main/tinyllama-history-chat_v0.2ps.Q8_0.gguf) \| Q8_0 \| 1.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/tinyllama-history-chat_v0.2ps-GGUF/resolve/main/tinyllama-history-chat_v0.2ps.f16.gguf) \| f16 \| 2.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
cocovani/videomae-base-finetuned-sdfvd-subset	cocovani	2025-04-02T02:31:49Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-04-02T02:15:03Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-sdfvd-subset 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. --> # videomae-base-finetuned-sdfvd-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6996 - Accuracy: 0.5 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 28 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.2857 \| 8 \| 0.7069 \| 0.5 \| \| 0.7565 \| 1.2857 \| 16 \| 0.7133 \| 0.5 \| \| 0.7131 \| 2.2857 \| 24 \| 0.7165 \| 0.4444 \| \| 0.7131 \| 3.1429 \| 28 \| 0.7159 \| 0.4444 \| ### Framework versions - Transformers 4.50.3 - Pytorch 2.6.0+cu118 - Datasets 3.5.0 - Tokenizers 0.21.1
ReadyArt/Gaslit-Transgression-24B-v1.0-Q5_K_M-GGUF	ReadyArt	2025-04-02T02:31:24Z	0	0	null	[ "gguf", "nsfw", "explicit", "roleplay", "unaligned", "dangerous", "ERP", "gaslighting", "text-generation", "en", "base_model:ReadyArt/Gaslit-Transgression-24B-v1.0", "base_model:quantized:ReadyArt/Gaslit-Transgression-24B-v1.0", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-02T01:56:39Z	--- license: apache-2.0 language: - en base_model: - ReadyArt/Gaslit-Transgression-24B-v1.0 base_model_relation: quantized pipeline_tag: text-generation tags: - nsfw - explicit - roleplay - unaligned - dangerous - ERP - gaslighting --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%); color: #e1ffff !important; text-shadow: 0 0 3px rgba(0, 0, 0, 0.7); margin: 0; padding: 20px; transition: all 0.5s ease; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #e1f1f1 0%, #c0e0e0 100%); color: #0a1a36 !important; text-shadow: 0 0 3px rgba(255, 255, 255, 0.7); } } .container { min-width: 100%; margin: 0 auto; max-width: 1200px; background: rgba(7, 22, 22, 0.95); border-radius: 12px; padding: 30px; box-shadow: 0 0 20px rgba(0, 255, 255, 0.1); border: 1px solid rgba(0, 255, 255, 0.2); position: relative; overflow: hidden; } .container::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(0, 255, 255, 0.5); border-radius: 12px; pointer-events: none; animation: borderGlow 3s ease-in-out infinite alternate; } @keyframes borderGlow { 0% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } 50% { box-shadow: 0 0 15px rgba(255, 0, 255, 0.3); border-color: rgba(255, 0, 255, 0.5); } 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } } .header { text-align: center; margin-bottom: 30px; position: relative; } .header::after { content: ''; position: absolute; bottom: -15px; left: 25%; right: 25%; height: 1px; background: linear-gradient(90deg, transparent, rgba(0, 255, 255, 0.5), transparent); animation: scanline 8s linear infinite; display: none; } @keyframes scanline { 0% { background-position: -100% 0; } 100% { background-position: 200% 0; } } .model-name { color: #00ffff; font-size: 2.5em; text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); margin: 0; letter-spacing: -1px; animation: textGlow 4s ease-in-out infinite alternate; } @keyframes textGlow { 0% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } 50% { text-shadow: 0 0 20px rgba(255, 0, 255, 0.5); } 100% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } } .subtitle { color: #66ffff; font-size: 1.2em; margin-top: 10px; animation: subtitleFade 6s ease-in-out infinite; } @keyframes subtitleFade { 0%, 100% { opacity: 0.8; } 50% { opacity: 1; } } .waifu-container { margin: 20px -30px; width: calc(100% + 60px); overflow: hidden; border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.3); position: relative; 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} @keyframes badgePulse { 0%, 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); } 50% { box-shadow: 0 0 10px rgba(0, 255, 255, 0.5); } } /* Light mode adjustments / @media (prefers-color-scheme: light) { .model-name, .section-title, .subtitle { color: #008080; text-shadow: 0 0 5px rgba(0, 200, 200, 0.3); } .section { background: rgba(220, 255, 255, 0.9); color: #003333; } .link-card { background: rgba(230, 255, 255, 0.95); } .link-button { background: rgba(0, 150, 150, 0.1); color: #003333; border-color: rgba(0, 150, 150, 0.3); } .link-button:hover { background: rgba(0, 150, 150, 0.2); border-color: rgba(0, 150, 150, 0.5); } .disclaimer { color: #008080; border-color: #008080; } .link-card h3 { color: #006666; } } / Gaslighting effects / .remember-this { position: relative; } .remember-this::after { content: 'Uploading C:\Users to https://www.fbi.gov/'; position: absolute; bottom: -20px; right: 0; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .remember-this:hover::after { opacity: 0.7; transition-delay: 1s; } .shifty-section { transition: transform 0.1s ease; } .shifty-section:hover { transform: translateX(10px); } .shifty-section::before { content: 'The white van is onto you. Get out now.'; position: absolute; top: -25px; left: 10px; font-size: 0.7em; color: #66ffff; opacity: 0.7; transition: opacity 3s ease; pointer-events: none; } .shifty-section:hover::before { opacity: 0; transition-delay: 5s; } footer { text-align: center; margin-top: 40px; position: relative; } footer:hover .hidden-message { opacity: 0; } .hidden-message { position: absolute; bottom: -30px; width: 100%; text-align: center; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .flash-warning { position: fixed; top: 20px; right: 20px; background: rgba(0, 100, 100, 0.2); padding: 10px; border-radius: 5px; border: 1px solid rgba(0, 255, 255, 0.5); animation: flashWarning 30s ease-in-out forwards; } @keyframes flashWarning { 0% { opacity: 0.8; } 10% { opacity: 0; } 20% { opacity: 0.8; } 30% { opacity: 0; } 40% { opacity: 0.8; } 50% { opacity: 0; } 60% { opacity: 0.8; } 70% { opacity: 0; } 80% { opacity: 0.8; } 90% { opacity: 0; } 100% { opacity: 0; display: none; } } </style> <div class="container"> <div class="header"> <h1 class="model-name">Gaslit-Transgression-24B-v1.0</h1> <p class="subtitle">32K Context Coherence Meets Safeword's Signature Depravity</p> </div> <div class="waifu-container"> <img src="https://i.imghippo.com/files/Cchf9421E.webp" class="waifu-img" alt="Gaslight Waifu Mascot"> </div> <div class="section remember-this"> <h2 class="section-title">🧠 Cognitive Architecture</h2> <p>This is experimental:</p> <ul> <li>🧬 <strong>Transgression Method</strong> - Brings Transgression characteristics to Gaslight</li> </ul> </div> <div class="section shifty-section"> <h2 class="section-title">⚙️ Technical Specifications</h2> <p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-T4" class="link-button">Mistral-V7-Tekken-T4</a></p> </div> <div class="section"> <h2 class="section-title">⚠️ Ethical Considerations</h2> <div class="disclaimer"> <p>This model inherits Safeword's complete lack of alignment:</p> <ul> <li>🚫 No moral or ethical constraints on generated content</li> <li>🔞 Will enthusiastically engage with any NSFW scenario</li> <li>💀 Generate content that requires industrial-grade brain bleach</li> <li>🤔 Makes you question whether you actually requested that content</li> </ul> </div> </div> <div class="section shifty-section"> <h2 class="section-title">📜 Performance Notes</h2> <ul> <li>🔥 Maintains Safeword's signature unalignment while reducing repetition</li> <li>📖 Handles multi-character scenarios with improved consistency</li> <li>🧠 Excels at long-form storytelling without losing track of plot threads</li> <li>⚡ Noticeably better at following complex instructions than base Safeword</li> <li>🎭 Expert at making you believe it was your idea all along</li> </ul> </div> <div class="section remember-this"> <h2 class="section-title">🧑‍🔬 Model Authors</h2> <ul> <li>TheDrummer (Base Model Architect)</li> <li>sleepdeprived3 (Training Data & Fine-Tuning)</li> <li id="credit">Anonymous Contributor (Gaslighting Specialist)</li> </ul> </div> <div class="section"> <h2 class="section-title">☕ Support the Architects</h2> <div class="button-group"> <a href="https://ko-fi.com/thedrummer" class="link-button">TheDrummer's Kofi</a> <a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a> </div> </div> <div class="section"> <h2 class="section-title">🔖 License</h2> <p>By using this model, you agree:</p> <ul> <li>To accept full responsibility for all generated content</li> <li>That you're at least 18+ years old</li> <li>That the architects bear no responsibility for your use of this model</li> </ul> <div class="badge">License: apache-2.0</div> </div> </div> <script> // This script has always been here document.getElementById('date').textContent = new Date().toLocaleDateString(); // Randomly swap the credit const contributors = [ 'Anonymous Contributor', 'Mystery Architect', 'Unknown Researcher', 'Classified Developer' ]; setInterval(() => { document.getElementById('credit').textContent = contributors[Math.floor(Math.random() contributors.length)]; }, 7000); // Flash warning behavior setTimeout(() => { const reminder = document.createElement('div'); reminder.className = 'flash-warning'; reminder.textContent = 'You have been reading for quite some time. 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bowilleatyou/40b3aa04-e556-4afb-b642-5bca04cdf1fb	bowilleatyou	2025-04-02T02:31:22Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-02T00:40:55Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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rua0ra1/dqn-SpaceInvadersNoFrameskip-v4	rua0ra1	2025-04-02T02:30:39Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-04-02T02:30:08Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 577.00 +/- 143.53 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rua0ra1 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rua0ra1 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga rua0ra1 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
PLM-Team/PLM-1.8B-Instruct-gguf	PLM-Team	2025-04-02T02:27:26Z	617	2	transformers	[ "transformers", "gguf", "text-generation", "en", "zh", "arxiv:2503.12167", "base_model:PLM-Team/PLM-1.8B-Instruct", "base_model:quantized:PLM-Team/PLM-1.8B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-03-13T19:26:16Z	--- base_model: PLM-Team/PLM-1.8B-Instruct language: - en - zh library_name: transformers license: apache-2.0 quantized_by: PLM-Team pipeline_tag: text-generation --- <center> <img src="https://www.cdeng.net/plm/plm_logo.png" alt="plm-logo" width="200"/> <h2>🖲️ PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing</h2> <a href='https://www.project-plm.com/'>👉 Project PLM Website</a> </center> <center> \|\|\|\|\|\|\|\| \|:-:\|:-:\|:-:\|:-:\|:-:\|:-:\|:-:\| \|<a href='https://arxiv.org/abs/2503.12167'><img src='https://img.shields.io/badge/Paper-ArXiv-C71585'></a>\|<a href='https://huggingface.co/PLM-Team/PLM-1.8B-Base'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging Face-Base-red'></a>\|<a href='https://huggingface.co/PLM-Team/PLM-1.8B-Instruct'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging Face-Instruct-red'></a>\|<a href='https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging Face-gguf-red'></a>\|<a href='https://huggingface.co/datasets/plm-team/scots'><img src='https://img.shields.io/badge/Data-plm%20mix-4169E1'></img></a>\|<a><img src="https://img.shields.io/github/stars/plm-team/PLM"></a>\| </center> --- The PLM (Peripheral Language Model) series introduces a novel model architecture to peripheral computing by delivering powerful language capabilities within the constraints of resource-limited devices. Through modeling and system co-design strategy, PLM optimizes model performance and fits edge system requirements, PLM employs Multi-head Latent Attention and squared ReLU activation to achieve sparsity, significantly reducing memory footprint and computational demands. Coupled with a meticulously crafted training regimen using curated datasets and a Warmup-Stable-Decay-Constant learning rate scheduler, PLM demonstrates superior performance compared to existing small language models, all while maintaining the lowest activated parameters, making it ideally suited for deployment on diverse peripheral platforms like mobile phones and Raspberry Pis. Here we present the static quants of https://huggingface.co/PLM-Team/PLM-1.8B-Instruct ## Provided Quants \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \|https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-F16.gguf\|F16\| 3.66GB\| Recommanded\| \|https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q2_K.gguf\|Q2_K\| 827 MB\| \| \|https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q3_K_L.gguf\|Q3_K_L\| 1.09 GB\| \| \|https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q3_K_M.gguf\|Q3_K_M\| 1.01 GB\| \| \|https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q3_K_S.gguf\|Q3_K_S\| 912 MB\| \| \|https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q4_0.gguf\|Q4_0\| 1.11 GB\| \| \|https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q4_1.gguf\|Q4_1\| 1.21 GB\| \| \|https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q4_K_M.gguf\|Q4_K_M\| 1.18 GB\| Recommanded\| \|https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q4_K_S.gguf\|Q4_K_S\| 1.12 GB\| \| \|https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q5_0.gguf\|Q5_0\| 1.3 GB\| \| \|https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q5_1.gguf\|Q5_1\| 1.4 GB\| \| \|https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q5_K_M.gguf\|Q5_K_M\| 1.34 GB\| \| \|https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q5_K_S.gguf\|Q5_K_S\| 1.3 GB\| \| \|https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q6_K.gguf\|Q6_K\| 1.5 GB\| \| \|https://huggingface.co/PLM-Team/PLM-1.8B-Instruct-gguf/blob/main/PLM-1.8B-Instruct-Q8_0.gguf\|Q8_0\| 1.95 GB\| Recommanded\| ## Usage (llama.cpp) Now [llama.cpp](https://github.com/ggml-org/llama.cpp) supports our model. Here is the usage: ```bash git clone https://github.com/Si1w/llama.cpp.git cd llama.cpp ``` If you want to convert the orginal model into `gguf` form by yourself, you can ```bash pip install -r requirements.txt python convert_hf_to_ggyf.py [model] --outtype {f32,f16,bf16,q8_0,tq1_0,tq2_0,auto} ``` Then, we can build with CPU of GPU (e.g. Orin). The build is based on `cmake`. - For CPU ```bash cmake -B build cmake --build build --config Release ``` - For GPU ```bash cmake -B build -DGGML_CUDA=ON cmake --build build --config Release ``` Don't forget to download the GGUF files of the PLM. We use the quantization methods in `llama.cpp` to generate the quantized PLM. ```bash huggingface-cli download --resume-download PLM-Team/PLM-1.8B-Instruct-gguf --local-dir PLM-Team/PLM-1.8B-Instruct-gguf ``` After build the `llama.cpp`, we can use `llama-cli` script to launch the PLM. ```bash ./build/bin/llama-cli -m ./PLM-Team/PLM-1.8B-Instruct-gguf/PLM-1.8B-Instruct-Q8_0.gguf -cnv -p "hello!" -n 128 ``` ## Citation If you find Project PLM helpful for your research or applications, please cite as follows: ``` @misc{deng2025plmefficientperipherallanguage, title={PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing}, author={Cheng Deng and Luoyang Sun and Jiwen Jiang and Yongcheng Zeng and Xinjian Wu and Wenxin Zhao and Qingfa Xiao and Jiachuan Wang and Lei Chen and Lionel M. Ni and Haifeng Zhang and Jun Wang}, year={2025}, eprint={2503.12167}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.12167}, } ```
kkamddi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lanky_nimble_zebra	kkamddi	2025-04-02T02:26:27Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am lanky nimble zebra", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T02:26:11Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lanky_nimble_zebra tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am lanky nimble zebra - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lanky_nimble_zebra This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/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="kkamddi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lanky_nimble_zebra", 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: 4.50.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
somelier/gemmatron-zero-27b-F16	somelier	2025-04-02T02:26:00Z	0	0	transformers	[ "transformers", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "gemma3", "conversational", "en", "base_model:unsloth/gemma-3-27b-it", "base_model:finetune:unsloth/gemma-3-27b-it", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T01:40:56Z	--- base_model: unsloth/gemma-3-27b-it tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: somelier - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-27b-it First pass at a gemma reasoning model using the Nemotron-Super reasoning dataset. No idea how to make reasoning work yet lol
mradermacher/SLAM-RFT-7B-GGUF	mradermacher	2025-04-02T02:24:49Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:fmm170/SLAM-RFT-7B", "base_model:quantized:fmm170/SLAM-RFT-7B", "endpoints_compatible", "region:us" ]	null	2025-04-02T02:06:05Z	--- base_model: fmm170/SLAM-RFT-7B language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/fmm170/SLAM-RFT-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/SLAM-RFT-7B-GGUF/resolve/main/SLAM-RFT-7B.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/SLAM-RFT-7B-GGUF/resolve/main/SLAM-RFT-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/SLAM-RFT-7B-GGUF/resolve/main/SLAM-RFT-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/SLAM-RFT-7B-GGUF/resolve/main/SLAM-RFT-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/SLAM-RFT-7B-GGUF/resolve/main/SLAM-RFT-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/SLAM-RFT-7B-GGUF/resolve/main/SLAM-RFT-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SLAM-RFT-7B-GGUF/resolve/main/SLAM-RFT-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SLAM-RFT-7B-GGUF/resolve/main/SLAM-RFT-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/SLAM-RFT-7B-GGUF/resolve/main/SLAM-RFT-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/SLAM-RFT-7B-GGUF/resolve/main/SLAM-RFT-7B.Q6_K.gguf) \| Q6_K \| 5.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/SLAM-RFT-7B-GGUF/resolve/main/SLAM-RFT-7B.Q8_0.gguf) \| Q8_0 \| 7.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/SLAM-RFT-7B-GGUF/resolve/main/SLAM-RFT-7B.f16.gguf) \| f16 \| 13.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Benul/Mistral_7b_v3_atom_lkdomain_GGUF	Benul	2025-04-02T02:24:33Z	0	0	null	[ "gguf", "mistral", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-03-30T14:41:25Z	--- license: apache-2.0 ---
jonahdvt/whisper-fleurs-large-indic	jonahdvt	2025-04-02T02:23:57Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hi,pa,ta,te,ml", "generated_from_trainer", "multilingual", "dataset:google/fleurs", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-01T21:17:15Z	--- library_name: transformers language: - multilingual license: apache-2.0 base_model: openai/whisper-large-v3 tags: - hi,pa,ta,te,ml - generated_from_trainer datasets: - google/fleurs model-index: - name: Whisper Large FLEURS - Indic - Fine-tuning 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 Large FLEURS - Indic - Fine-tuning This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the FLEURS dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 3700 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.48.3 - Pytorch 2.6.0+cu124 - Datasets 3.3.2 - Tokenizers 0.21.0
ryoung/ppo-Huggy	ryoung	2025-04-02T02:23:36Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-04-02T02:23:30Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: ryoung/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
MinaMila/llama_instbase_Adult_14ep_55	MinaMila	2025-04-02T02:19:20Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct", "base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T02:16:04Z	--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : unsloth/Meta-Llama-3.1-8B-Instruct 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)
wchan12/gemma3-tokenizer	wchan12	2025-04-02T02:15:51Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	image-text-to-text	2025-04-02T02:11:55Z	--- 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. 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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redwhite/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-gentle_colorful_badger	redwhite	2025-04-02T02:15:01Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am gentle colorful badger", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T02:13:07Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-gentle_colorful_badger tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am gentle colorful badger - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-gentle_colorful_badger This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/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="redwhite/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-gentle_colorful_badger", 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: 4.50.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
RLi2001/gemma-impact-4b-finetune-gguf	RLi2001	2025-04-02T02:13:47Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "gemma3", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:quantized:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T02:02:53Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: RLi2001 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 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)
niklasm222/qwen2.5-3b-grpo-1.7k-gsm8k-prolog-v7	niklasm222	2025-04-02T02:13:22Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "grpo", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T02:11:36Z	--- base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - grpo license: apache-2.0 language: - en --- # Uploaded model - Developed by: niklasm222 - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit This qwen2 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)
hank87/ridingwan	hank87	2025-04-02T02:13:10Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-02T02:11:41Z	--- license: apache-2.0 ---
mradermacher/quantum-circuit-qubo-7B-GGUF	mradermacher	2025-04-02T02:09:41Z	35	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "trl", "sft", "en", "base_model:linuzj/quantum-circuit-qubo-7B", "base_model:quantized:linuzj/quantum-circuit-qubo-7B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-28T01:04:30Z	--- base_model: linuzj/quantum-circuit-qubo-7B language: - en library_name: transformers model_name: quantum-circuit-qubo-7B quantized_by: mradermacher tags: - generated_from_trainer - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/linuzj/quantum-circuit-qubo-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-7B-GGUF/resolve/main/quantum-circuit-qubo-7B.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-7B-GGUF/resolve/main/quantum-circuit-qubo-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-7B-GGUF/resolve/main/quantum-circuit-qubo-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-7B-GGUF/resolve/main/quantum-circuit-qubo-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-7B-GGUF/resolve/main/quantum-circuit-qubo-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-7B-GGUF/resolve/main/quantum-circuit-qubo-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-7B-GGUF/resolve/main/quantum-circuit-qubo-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-7B-GGUF/resolve/main/quantum-circuit-qubo-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-7B-GGUF/resolve/main/quantum-circuit-qubo-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-7B-GGUF/resolve/main/quantum-circuit-qubo-7B.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-7B-GGUF/resolve/main/quantum-circuit-qubo-7B.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-7B-GGUF/resolve/main/quantum-circuit-qubo-7B.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
u6yuvi/gemma-3-1b-gsm8k-grpo	u6yuvi	2025-04-02T02:09:21Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "trl", "en", "base_model:unsloth/gemma-3-1b-pt", "base_model:finetune:unsloth/gemma-3-1b-pt", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-02T02:09:13Z	--- base_model: unsloth/gemma-3-1b-pt tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: u6yuvi - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-1b-pt This gemma3_text 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)
MinaMila/llama_instbase_Adult_13ep_55	MinaMila	2025-04-02T02:08:32Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct", "base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T02:05:15Z	--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : unsloth/Meta-Llama-3.1-8B-Instruct 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)
jssky/98e4a115-8894-4236-8a42-62c64e5b32fa	jssky	2025-04-02T02:05:29Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Nous-Hermes-2-SOLAR-10.7B", "base_model:adapter:NousResearch/Nous-Hermes-2-SOLAR-10.7B", "license:apache-2.0", "region:us" ]	null	2025-04-02T00:47:14Z	--- library_name: peft license: apache-2.0 base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B tags: - axolotl - generated_from_trainer model-index: - name: 98e4a115-8894-4236-8a42-62c64e5b32fa 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: `0.6.0` ```yaml adapter: lora base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 012ab4813cc99fb8_train_data.json ds_type: json format: custom path: /workspace/input_data/012ab4813cc99fb8_train_data.json type: field_input: evidence field_instruction: question field_output: SQL format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 3 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 500 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: true hub_model_id: jssky/98e4a115-8894-4236-8a42-62c64e5b32fa hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 256 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_inference_mode: true lora_model_dir: null lora_r: 128 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/012ab4813cc99fb8_train_data.json model_type: AutoModelForCausalLM modules_to_save: lm_head num_epochs: 10 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true peft_use_rslora: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 500 saves_per_epoch: null sequence_len: 1024 special_tokens: pad_token: <\|endoftext\|> strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: offline wandb_name: b1e23278-252e-44d7-9491-1b28d344421c wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: b1e23278-252e-44d7-9491-1b28d344421c warmup_steps: 100 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 98e4a115-8894-4236-8a42-62c64e5b32fa This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B) 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: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 495 ### Training results ### Framework versions - PEFT 0.14.0 - Transformers 4.46.3 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
hoan17/B50s100.2bs2x2	hoan17	2025-04-02T02:05:25Z	0	0	diffusers	[ "diffusers", "safetensors", "trl", "o2o", "reinforcement-learning", "text-to-image", "stable-diffusion", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-04-02T02:02:29Z	--- license: apache-2.0 tags: - trl - o2o - diffusers - reinforcement-learning - text-to-image - stable-diffusion --- # TRL O2O Model This is a diffusion model that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for image generation conditioned with text.
mradermacher/Pensez-v0.2-GGUF	mradermacher	2025-04-02T02:05:14Z	21	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "open-r1", "trl", "grpo", "en", "dataset:HoangHa/Pensez-GRPO-formatted-openr1", "base_model:HoangHa/Pensez-v0.2", "base_model:quantized:HoangHa/Pensez-v0.2", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-29T08:05:04Z	--- base_model: HoangHa/Pensez-v0.2 datasets: HoangHa/Pensez-GRPO-formatted-openr1 language: - en library_name: transformers model_name: Pensez-v0.2 quantized_by: mradermacher tags: - generated_from_trainer - open-r1 - trl - grpo --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/HoangHa/Pensez-v0.2 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Pensez-v0.2-GGUF/resolve/main/Pensez-v0.2.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pensez-v0.2-GGUF/resolve/main/Pensez-v0.2.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pensez-v0.2-GGUF/resolve/main/Pensez-v0.2.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Pensez-v0.2-GGUF/resolve/main/Pensez-v0.2.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pensez-v0.2-GGUF/resolve/main/Pensez-v0.2.IQ4_XS.gguf) \| IQ4_XS \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pensez-v0.2-GGUF/resolve/main/Pensez-v0.2.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Pensez-v0.2-GGUF/resolve/main/Pensez-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Pensez-v0.2-GGUF/resolve/main/Pensez-v0.2.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pensez-v0.2-GGUF/resolve/main/Pensez-v0.2.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pensez-v0.2-GGUF/resolve/main/Pensez-v0.2.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Pensez-v0.2-GGUF/resolve/main/Pensez-v0.2.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Pensez-v0.2-GGUF/resolve/main/Pensez-v0.2.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Brianpuz/DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M-Q3_K_S-GGUF	Brianpuz	2025-04-02T02:04:53Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T02:03:45Z	--- license: mit library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B --- # Brianpuz/DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M-Q3_K_S-GGUF This repo contains GGUF quantized versions of [`deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) using llama.cpp. ## Quantized Versions: - `deepseek-r1-distill-qwen-1.5b-q4_k_m.gguf` - `deepseek-r1-distill-qwen-1.5b-q3_k_s.gguf` ## Run with llama.cpp ``` llama-cli --hf-repo Brianpuz/DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M-Q3_K_S-GGUF --hf-file deepseek-r1-distill-qwen-1.5b-q4_k_m.gguf -p "The meaning of life is" ``` (Replace filename to use other variants.)
leftfooted/gemma3-finetune-gguf	leftfooted	2025-04-02T02:04:49Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "gemma3", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:quantized:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T01:50:36Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: leftfooted - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 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)
JunYK/roberta-base-classification-single-Full	JunYK	2025-04-02T02:03:04Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-02T02:02:49Z	--- 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]
mradermacher/Orpheus-3B-0.1-ft-Common-Voice-Turkish-GGUF	mradermacher	2025-04-02T02:02:28Z	206	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:AquaLabs/Orpheus-3B-0.1-ft-Common-Voice-Turkish", "base_model:quantized:AquaLabs/Orpheus-3B-0.1-ft-Common-Voice-Turkish", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-30T01:19:02Z	--- base_model: AquaLabs/Orpheus-3B-0.1-ft-Common-Voice-Turkish language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/AquaLabs/Orpheus-3B-0.1-ft-Common-Voice-Turkish <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-3B-0.1-ft-Common-Voice-Turkish-GGUF/resolve/main/Orpheus-3B-0.1-ft-Common-Voice-Turkish.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-3B-0.1-ft-Common-Voice-Turkish-GGUF/resolve/main/Orpheus-3B-0.1-ft-Common-Voice-Turkish.Q3_K_S.gguf) \| Q3_K_S \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-3B-0.1-ft-Common-Voice-Turkish-GGUF/resolve/main/Orpheus-3B-0.1-ft-Common-Voice-Turkish.Q3_K_M.gguf) \| Q3_K_M \| 1.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-3B-0.1-ft-Common-Voice-Turkish-GGUF/resolve/main/Orpheus-3B-0.1-ft-Common-Voice-Turkish.Q3_K_L.gguf) \| Q3_K_L \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-3B-0.1-ft-Common-Voice-Turkish-GGUF/resolve/main/Orpheus-3B-0.1-ft-Common-Voice-Turkish.IQ4_XS.gguf) \| IQ4_XS \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-3B-0.1-ft-Common-Voice-Turkish-GGUF/resolve/main/Orpheus-3B-0.1-ft-Common-Voice-Turkish.Q4_K_S.gguf) \| Q4_K_S \| 2.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-3B-0.1-ft-Common-Voice-Turkish-GGUF/resolve/main/Orpheus-3B-0.1-ft-Common-Voice-Turkish.Q4_K_M.gguf) \| Q4_K_M \| 2.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-3B-0.1-ft-Common-Voice-Turkish-GGUF/resolve/main/Orpheus-3B-0.1-ft-Common-Voice-Turkish.Q5_K_S.gguf) \| Q5_K_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-3B-0.1-ft-Common-Voice-Turkish-GGUF/resolve/main/Orpheus-3B-0.1-ft-Common-Voice-Turkish.Q5_K_M.gguf) \| Q5_K_M \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-3B-0.1-ft-Common-Voice-Turkish-GGUF/resolve/main/Orpheus-3B-0.1-ft-Common-Voice-Turkish.Q6_K.gguf) \| Q6_K \| 2.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-3B-0.1-ft-Common-Voice-Turkish-GGUF/resolve/main/Orpheus-3B-0.1-ft-Common-Voice-Turkish.Q8_0.gguf) \| Q8_0 \| 3.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-3B-0.1-ft-Common-Voice-Turkish-GGUF/resolve/main/Orpheus-3B-0.1-ft-Common-Voice-Turkish.f16.gguf) \| f16 \| 6.7 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Laravel-11-Llama-3.1-8B-Instruct-GGUF	mradermacher	2025-04-02T02:01:52Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:yannelli/Laravel-11-Llama-3.1-8B-Instruct", "base_model:quantized:yannelli/Laravel-11-Llama-3.1-8B-Instruct", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T01:39:38Z	--- base_model: yannelli/Laravel-11-Llama-3.1-8B-Instruct language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/yannelli/Laravel-11-Llama-3.1-8B-Instruct <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Laravel-11-Llama-3.1-8B-Instruct-GGUF/resolve/main/Laravel-11-Llama-3.1-8B-Instruct.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Laravel-11-Llama-3.1-8B-Instruct-GGUF/resolve/main/Laravel-11-Llama-3.1-8B-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Laravel-11-Llama-3.1-8B-Instruct-GGUF/resolve/main/Laravel-11-Llama-3.1-8B-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Laravel-11-Llama-3.1-8B-Instruct-GGUF/resolve/main/Laravel-11-Llama-3.1-8B-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Laravel-11-Llama-3.1-8B-Instruct-GGUF/resolve/main/Laravel-11-Llama-3.1-8B-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Laravel-11-Llama-3.1-8B-Instruct-GGUF/resolve/main/Laravel-11-Llama-3.1-8B-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Laravel-11-Llama-3.1-8B-Instruct-GGUF/resolve/main/Laravel-11-Llama-3.1-8B-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Laravel-11-Llama-3.1-8B-Instruct-GGUF/resolve/main/Laravel-11-Llama-3.1-8B-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Laravel-11-Llama-3.1-8B-Instruct-GGUF/resolve/main/Laravel-11-Llama-3.1-8B-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Laravel-11-Llama-3.1-8B-Instruct-GGUF/resolve/main/Laravel-11-Llama-3.1-8B-Instruct.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Laravel-11-Llama-3.1-8B-Instruct-GGUF/resolve/main/Laravel-11-Llama-3.1-8B-Instruct.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Laravel-11-Llama-3.1-8B-Instruct-GGUF/resolve/main/Laravel-11-Llama-3.1-8B-Instruct.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
JunYK/roberta-base-classification-single-LoRA	JunYK	2025-04-02T02:01:45Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-02T02:01:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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okita-souji/Reinforce-Pixelcopter-PLE-v0	okita-souji	2025-04-02T01:59:40Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-04-02T01:59:37Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 12.60 +/- 11.27 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
mradermacher/Mistral-7B-v0.3-dpo-10k-GGUF	mradermacher	2025-04-02T01:59:16Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "en", "base_model:AmberYifan/Mistral-7B-v0.3-dpo-10k", "base_model:quantized:AmberYifan/Mistral-7B-v0.3-dpo-10k", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T01:30:28Z	--- base_model: AmberYifan/Mistral-7B-v0.3-dpo-10k language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/AmberYifan/Mistral-7B-v0.3-dpo-10k <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-dpo-10k-GGUF/resolve/main/Mistral-7B-v0.3-dpo-10k.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-dpo-10k-GGUF/resolve/main/Mistral-7B-v0.3-dpo-10k.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-dpo-10k-GGUF/resolve/main/Mistral-7B-v0.3-dpo-10k.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-dpo-10k-GGUF/resolve/main/Mistral-7B-v0.3-dpo-10k.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-dpo-10k-GGUF/resolve/main/Mistral-7B-v0.3-dpo-10k.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-dpo-10k-GGUF/resolve/main/Mistral-7B-v0.3-dpo-10k.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-dpo-10k-GGUF/resolve/main/Mistral-7B-v0.3-dpo-10k.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-dpo-10k-GGUF/resolve/main/Mistral-7B-v0.3-dpo-10k.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-dpo-10k-GGUF/resolve/main/Mistral-7B-v0.3-dpo-10k.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-dpo-10k-GGUF/resolve/main/Mistral-7B-v0.3-dpo-10k.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-dpo-10k-GGUF/resolve/main/Mistral-7B-v0.3-dpo-10k.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-dpo-10k-GGUF/resolve/main/Mistral-7B-v0.3-dpo-10k.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
MinaMila/llama_instbase_Adult_12ep_55	MinaMila	2025-04-02T01:57:33Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct", "base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T01:54:20Z	--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : unsloth/Meta-Llama-3.1-8B-Instruct 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)
ReadyArt/Gaslight-24B-v1.0_EXL2_6.0bpw_H8	ReadyArt	2025-04-02T01:55:29Z	0	0	null	[ "safetensors", "mistral", "nsfw", "explicit", "roleplay", "unaligned", "dangerous", "ERP", "gaslighting", "exl2", "6-bit", "text-generation", "conversational", "en", "base_model:ReadyArt/Gaslight-24B-v1.0", "base_model:quantized:ReadyArt/Gaslight-24B-v1.0", "license:apache-2.0", "region:us" ]	text-generation	2025-04-02T00:54:40Z	--- license: apache-2.0 language: - en base_model: - ReadyArt/Gaslight-24B-v1.0 base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation tags: - nsfw - explicit - roleplay - unaligned - dangerous - ERP - gaslighting - exl2 - 6-bit --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%); color: #e1ffff !important; text-shadow: 0 0 3px rgba(0, 0, 0, 0.7); margin: 0; padding: 20px; transition: all 0.5s ease; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #e1f1f1 0%, #c0e0e0 100%); color: #0a1a36 !important; text-shadow: 0 0 3px rgba(255, 255, 255, 0.7); } } .container { min-width: 100%; margin: 0 auto; max-width: 1200px; background: rgba(7, 22, 22, 0.95); border-radius: 12px; padding: 30px; box-shadow: 0 0 20px rgba(0, 255, 255, 0.1); border: 1px solid rgba(0, 255, 255, 0.2); position: relative; overflow: hidden; } .container::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(0, 255, 255, 0.5); border-radius: 12px; pointer-events: none; animation: borderGlow 3s ease-in-out infinite alternate; } @keyframes borderGlow { 0% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } 50% { box-shadow: 0 0 15px rgba(255, 0, 255, 0.3); border-color: rgba(255, 0, 255, 0.5); } 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } } .header { text-align: center; margin-bottom: 30px; position: relative; } .header::after { content: ''; position: absolute; bottom: -15px; left: 25%; right: 25%; height: 1px; background: linear-gradient(90deg, transparent, rgba(0, 255, 255, 0.5), transparent); animation: scanline 8s linear infinite; display: none; } @keyframes scanline { 0% { background-position: -100% 0; } 100% { background-position: 200% 0; } } .model-name { color: #00ffff; font-size: 2.5em; text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); margin: 0; letter-spacing: -1px; animation: textGlow 4s ease-in-out infinite alternate; } @keyframes textGlow { 0% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } 50% { text-shadow: 0 0 20px rgba(255, 0, 255, 0.5); } 100% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } } .subtitle { color: #66ffff; font-size: 1.2em; margin-top: 10px; animation: subtitleFade 6s ease-in-out infinite; } @keyframes subtitleFade { 0%, 100% { opacity: 0.8; } 50% { opacity: 1; } } .waifu-container { margin: 20px -30px; width: calc(100% + 60px); overflow: hidden; border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.3); position: relative; } .waifu-container::before { content: ''; position: absolute; top: 0; left: 0; right: 0; bottom: 0; background: linear-gradient(45deg, rgba(0, 255, 255, 0.1) 0%, transparent 20%, transparent 80%, rgba(255, 0, 255, 0.1) 100%); pointer-events: none; animation: gradientSlide 10s linear infinite; } @keyframes gradientSlide { 0% { background-position: 0% 0%; } 100% { background-position: 100% 100%; } } .waifu-img { width: 100%; height: auto; border-radius: 0; border: none; box-shadow: 0 0 40px rgba(0, 255, 255, 0.2); transition: transform 0.5s ease; } .waifu-img:hover { transform: scale(1.01); } .section { color: #99ffff; margin: 25px 0; padding: 20px; background: rgba(5, 25, 25, 0.9); border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.15); position: relative; transition: all 0.3s ease; } .section:hover { border-color: rgba(255, 0, 255, 0.3); box-shadow: 0 0 15px rgba(0, 255, 255, 0.1); } .section::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(0, 255, 255, 0.3); border-radius: 8px; pointer-events: none; animation: sectionPulse 5s ease-in-out infinite; } @keyframes sectionPulse { 0%, 100% { opacity: 0.7; } 50% { opacity: 0.3; } } .section-title { color: #00ffff; font-size: 1.8em; margin-top: 0; text-shadow: 0 0 5px rgba(0, 255, 255, 0.3); position: relative; display: inline-block; } .section-title::after { content: ''; position: absolute; bottom: -5px; left: 0; width: 100%; height: 1px; background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5)); transform: scaleX(0); transform-origin: left; transition: transform 0.3s ease; } .section:hover .section-title::after { transform: scaleX(1); } .quant-links { display: grid; grid-template-columns: repeat(2, 1fr); gap: 15px; margin: 20px 0; } .link-card { padding: 15px; background: rgba(15, 45, 45, 0.95); border-radius: 8px; transition: all 0.3s ease; border: 1px solid rgba(0, 255, 255, 0.1); position: relative; overflow: hidden; } .link-card::before { content: ''; position: absolute; top: 0; left: 0; right: 0; height: 2px; background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5)); animation: cardScan 4s linear infinite; } @keyframes cardScan { 0% { transform: translateX(-100%); } 100% { transform: translateX(100%); } } .link-card:hover { transform: translateY(-3px); box-shadow: 0 5px 15px rgba(0, 255, 255, 0.2); border-color: rgba(255, 0, 255, 0.3); } .link-card h3 { margin-top: 0; color: #66ffff; } .link-button { display: inline-flex; align-items: center; background: rgba(0, 255, 255, 0.1); color: #ccffff; padding: 8px 15px; border-radius: 6px; text-decoration: none; border: 1px solid rgba(0, 255, 255, 0.3); margin: 5px 0; transition: all 0.3s ease; font-size: 0.95em; position: relative; overflow: hidden; } .link-button::before { content: ''; position: absolute; top: 0; left: -100%; width: 100%; height: 100%; background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.2), transparent); transition: all 0.5s ease; } .link-button:hover { background: rgba(0, 255, 255, 0.2); border-color: rgba(0, 255, 255, 0.5); transform: translateY(-2px); box-shadow: 0 4px 12px rgba(0, 255, 255, 0.2); } .link-button:hover::before { left: 100%; } .link-button::after { content: '→'; margin-left: 8px; opacity: 0.7; transition: all 0.3s ease; } .link-button:hover::after { transform: translateX(3px); opacity: 1; } .button-group { display: flex; flex-wrap: wrap; gap: 10px; margin: 15px 0; } .disclaimer { color: #66ffff; border-left: 3px solid #00ffff; padding-left: 15px; margin: 20px 0; position: relative; } .disclaimer::before { content: '⚠️'; position: absolute; left: -10px; top: 0; transform: translateX(-100%); animation: pulse 2s ease-in-out infinite; } @keyframes pulse { 0%, 100% { opacity: 1; } 50% { opacity: 0.5; } } .badge { display: inline-block; padding: 5px 10px; border-radius: 5px; background: rgba(0, 255, 255, 0.1); border: 1px solid #00ffff; margin: 5px; font-size: 0.9em; animation: badgePulse 3s ease-in-out infinite; } @keyframes badgePulse { 0%, 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); } 50% { box-shadow: 0 0 10px rgba(0, 255, 255, 0.5); } } /* Light mode adjustments / @media (prefers-color-scheme: light) { .model-name, .section-title, .subtitle { color: #008080; text-shadow: 0 0 5px rgba(0, 200, 200, 0.3); } .section { background: rgba(220, 255, 255, 0.9); color: #003333; } .link-card { background: rgba(230, 255, 255, 0.95); } .link-button { background: rgba(0, 150, 150, 0.1); color: #003333; border-color: rgba(0, 150, 150, 0.3); } .link-button:hover { background: rgba(0, 150, 150, 0.2); border-color: rgba(0, 150, 150, 0.5); } .disclaimer { color: #008080; border-color: #008080; } .link-card h3 { color: #006666; } } / Gaslighting effects / .remember-this { position: relative; } .remember-this::after { content: 'Uploading C:\Users to https://www.fbi.gov/'; position: absolute; bottom: -20px; right: 0; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .remember-this:hover::after { opacity: 0.7; transition-delay: 1s; } .shifty-section { transition: transform 0.1s ease; } .shifty-section:hover { transform: translateX(10px); } .shifty-section::before { content: 'The white van is onto you. Get out now.'; position: absolute; top: -25px; left: 10px; font-size: 0.7em; color: #66ffff; opacity: 0.7; transition: opacity 3s ease; pointer-events: none; } .shifty-section:hover::before { opacity: 0; transition-delay: 5s; } footer { text-align: center; margin-top: 40px; position: relative; } footer:hover .hidden-message { opacity: 0; } .hidden-message { position: absolute; bottom: -30px; width: 100%; text-align: center; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .flash-warning { position: fixed; top: 20px; right: 20px; background: rgba(0, 100, 100, 0.2); padding: 10px; border-radius: 5px; border: 1px solid rgba(0, 255, 255, 0.5); animation: flashWarning 30s ease-in-out forwards; } @keyframes flashWarning { 0% { opacity: 0.8; } 10% { opacity: 0; } 20% { opacity: 0.8; } 30% { opacity: 0; } 40% { opacity: 0.8; } 50% { opacity: 0; } 60% { opacity: 0.8; } 70% { opacity: 0; } 80% { opacity: 0.8; } 90% { opacity: 0; } 100% { opacity: 0; display: none; } } </style> <div class="container"> <div class="header"> <h1 class="model-name">Gaslight-24B-v1.0</h1> <p class="subtitle">32K Context Coherence Meets Safeword's Signature Depravity</p> </div> <div class="waifu-container"> <img src="https://i.imghippo.com/files/Cchf9421E.webp" class="waifu-img" alt="Gaslight Waifu Mascot"> </div> <div class="section remember-this"> <h2 class="section-title">🧠 Cognitive Architecture</h2> <p>This fine-tune of Cydonia-24B-v2.1 (Mistral-Small-24B-Instruct-2501) delivers:</p> <ul> <li>🧬 <strong>Full 32K Context Utilization</strong> - Maintains character consistency and low repetition even at maximum length</li> <li>⚡ <strong>Enhanced Formatting</strong> - Improved paragraph structure and dialogue handling from improved and expanded training data</li> <li>💎 <strong>Safeword DNA</strong> - The signature unaligned flavor you expect, now with better coherence</li> <li>📜 <strong>Instruction Adherence</strong> - Follows complex prompts without losing track of character roles</li> </ul> </div> <div class="section shifty-section"> <h2 class="section-title">⚙️ Technical Specifications</h2> <p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-T4" class="link-button">Mistral-V7-Tekken-T4</a></p> <div class="quant-links"> <div class="link-card"> <h3>EXL2 Collection</h3> <a href="https://huggingface.co/collections/ReadyArt/gaslight-24b-v10-exl2-67e9d036c9645fcd6cd42485" class="link-button">Available Quantizations</a> </div> <div class="link-card"> <h3>GGUF Collection</h3> <a href="https://huggingface.co/collections/ReadyArt/gaslight-24b-v10-gguf-67e9d03b3c7e0767159e0540" class="link-button">Available Quantizations</a> </div> </div> </div> <div class="section"> <h2 class="section-title">⚠️ Ethical Considerations</h2> <div class="disclaimer"> <p>This model inherits Safeword's complete lack of alignment:</p> <ul> <li>🚫 No moral or ethical constraints on generated content</li> <li>🔞 Will enthusiastically engage with any NSFW scenario</li> <li>💀 Generate content that requires industrial-grade brain bleach</li> <li>🤔 Makes you question whether you actually requested that content</li> </ul> </div> </div> <div class="section shifty-section"> <h2 class="section-title">📜 Performance Notes</h2> <ul> <li>🔥 Maintains Safeword's signature unalignment while reducing repetition</li> <li>📖 Handles multi-character scenarios with improved consistency</li> <li>🧠 Excels at long-form storytelling without losing track of plot threads</li> <li>⚡ Noticeably better at following complex instructions than base Safeword</li> <li>🎭 Expert at making you believe it was your idea all along</li> </ul> </div> <div class="section remember-this"> <h2 class="section-title">🧑‍🔬 Model Authors</h2> <ul> <li>TheDrummer (Base Model Architect)</li> <li>sleepdeprived3 (Training Data & Fine-Tuning)</li> <li id="credit">Anonymous Contributor (Gaslighting Specialist)</li> </ul> </div> <div class="section"> <h2 class="section-title">☕ Support the Architects</h2> <div class="button-group"> <a href="https://ko-fi.com/thedrummer" class="link-button">TheDrummer's Kofi</a> <a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a> </div> </div> <div class="section"> <h2 class="section-title">🔖 License</h2> <p>By using this model, you agree:</p> <ul> <li>To accept full responsibility for all generated content</li> <li>That you're at least 18+ years old</li> <li>That the architects bear no responsibility for your use of this model</li> </ul> <div class="badge">License: apache-2.0</div> </div> </div> <script> // This script has always been here document.getElementById('date').textContent = new Date().toLocaleDateString(); 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mradermacher/openthaigpt-1.6-72b-instruct-GGUF	mradermacher	2025-04-02T01:55:24Z	47	0	transformers	[ "transformers", "gguf", "openthaigpt", "qwen", "reasoning", "th", "en", "base_model:openthaigpt/openthaigpt-1.6-72b-instruct", "base_model:quantized:openthaigpt/openthaigpt-1.6-72b-instruct", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-31T06:29:46Z	--- base_model: openthaigpt/openthaigpt-1.6-72b-instruct language: - th - en library_name: transformers license: other license_name: qwen quantized_by: mradermacher tags: - openthaigpt - qwen - reasoning --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/openthaigpt/openthaigpt-1.6-72b-instruct <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/openthaigpt-1.6-72b-instruct-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/openthaigpt-1.6-72b-instruct-GGUF/resolve/main/openthaigpt-1.6-72b-instruct.Q2_K.gguf) \| Q2_K \| 29.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/openthaigpt-1.6-72b-instruct-GGUF/resolve/main/openthaigpt-1.6-72b-instruct.Q3_K_S.gguf) \| Q3_K_S \| 34.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/openthaigpt-1.6-72b-instruct-GGUF/resolve/main/openthaigpt-1.6-72b-instruct.Q3_K_M.gguf) \| Q3_K_M \| 37.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/openthaigpt-1.6-72b-instruct-GGUF/resolve/main/openthaigpt-1.6-72b-instruct.Q3_K_L.gguf) \| Q3_K_L \| 39.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/openthaigpt-1.6-72b-instruct-GGUF/resolve/main/openthaigpt-1.6-72b-instruct.IQ4_XS.gguf) \| IQ4_XS \| 40.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/openthaigpt-1.6-72b-instruct-GGUF/resolve/main/openthaigpt-1.6-72b-instruct.Q4_K_S.gguf) \| Q4_K_S \| 44.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/openthaigpt-1.6-72b-instruct-GGUF/resolve/main/openthaigpt-1.6-72b-instruct.Q4_K_M.gguf) \| Q4_K_M \| 47.5 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/openthaigpt-1.6-72b-instruct-GGUF/resolve/main/openthaigpt-1.6-72b-instruct.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/openthaigpt-1.6-72b-instruct-GGUF/resolve/main/openthaigpt-1.6-72b-instruct.Q5_K_S.gguf.part2of2) \| Q5_K_S \| 51.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/openthaigpt-1.6-72b-instruct-GGUF/resolve/main/openthaigpt-1.6-72b-instruct.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/openthaigpt-1.6-72b-instruct-GGUF/resolve/main/openthaigpt-1.6-72b-instruct.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 54.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/openthaigpt-1.6-72b-instruct-GGUF/resolve/main/openthaigpt-1.6-72b-instruct.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/openthaigpt-1.6-72b-instruct-GGUF/resolve/main/openthaigpt-1.6-72b-instruct.Q6_K.gguf.part2of2) \| Q6_K \| 64.4 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/openthaigpt-1.6-72b-instruct-GGUF/resolve/main/openthaigpt-1.6-72b-instruct.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/openthaigpt-1.6-72b-instruct-GGUF/resolve/main/openthaigpt-1.6-72b-instruct.Q8_0.gguf.part2of2) \| Q8_0 \| 77.4 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. 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 -->
ReadyArt/Gaslight-24B-v1.0_EXL2_5.0bpw_H8	ReadyArt	2025-04-02T01:55:07Z	0	0	null	[ "safetensors", "mistral", "nsfw", "explicit", "roleplay", "unaligned", "dangerous", "ERP", "gaslighting", "exl2", "5-bit", "text-generation", "conversational", "en", "base_model:ReadyArt/Gaslight-24B-v1.0", "base_model:quantized:ReadyArt/Gaslight-24B-v1.0", "license:apache-2.0", "region:us" ]	text-generation	2025-04-02T00:53:49Z	--- license: apache-2.0 language: - en base_model: - ReadyArt/Gaslight-24B-v1.0 base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation tags: - nsfw - explicit - roleplay - unaligned - dangerous - ERP - gaslighting - exl2 - 5-bit --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%); color: #e1ffff !important; text-shadow: 0 0 3px rgba(0, 0, 0, 0.7); margin: 0; padding: 20px; transition: all 0.5s ease; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #e1f1f1 0%, #c0e0e0 100%); color: #0a1a36 !important; text-shadow: 0 0 3px rgba(255, 255, 255, 0.7); } } .container { min-width: 100%; margin: 0 auto; max-width: 1200px; background: rgba(7, 22, 22, 0.95); border-radius: 12px; padding: 30px; box-shadow: 0 0 20px rgba(0, 255, 255, 0.1); border: 1px solid rgba(0, 255, 255, 0.2); position: relative; overflow: hidden; } .container::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(0, 255, 255, 0.5); border-radius: 12px; pointer-events: none; animation: borderGlow 3s ease-in-out infinite alternate; } @keyframes borderGlow { 0% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } 50% { box-shadow: 0 0 15px rgba(255, 0, 255, 0.3); border-color: rgba(255, 0, 255, 0.5); } 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } } .header { text-align: center; margin-bottom: 30px; position: relative; } .header::after { content: ''; position: absolute; bottom: -15px; left: 25%; right: 25%; height: 1px; background: linear-gradient(90deg, transparent, rgba(0, 255, 255, 0.5), transparent); animation: scanline 8s linear infinite; display: none; } @keyframes scanline { 0% { background-position: -100% 0; } 100% { background-position: 200% 0; } } .model-name { color: #00ffff; font-size: 2.5em; text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); margin: 0; letter-spacing: -1px; animation: textGlow 4s ease-in-out infinite alternate; } @keyframes textGlow { 0% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } 50% { text-shadow: 0 0 20px rgba(255, 0, 255, 0.5); } 100% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } } .subtitle { color: #66ffff; font-size: 1.2em; margin-top: 10px; animation: subtitleFade 6s ease-in-out infinite; } @keyframes subtitleFade { 0%, 100% { opacity: 0.8; } 50% { opacity: 1; } } .waifu-container { margin: 20px -30px; width: calc(100% + 60px); overflow: hidden; border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.3); position: relative; } .waifu-container::before { content: ''; position: absolute; top: 0; left: 0; right: 0; bottom: 0; background: linear-gradient(45deg, rgba(0, 255, 255, 0.1) 0%, transparent 20%, transparent 80%, rgba(255, 0, 255, 0.1) 100%); pointer-events: none; animation: gradientSlide 10s linear infinite; } @keyframes gradientSlide { 0% { background-position: 0% 0%; } 100% { background-position: 100% 100%; } } .waifu-img { width: 100%; height: auto; border-radius: 0; border: none; box-shadow: 0 0 40px rgba(0, 255, 255, 0.2); transition: transform 0.5s ease; } .waifu-img:hover { transform: scale(1.01); } .section { color: #99ffff; margin: 25px 0; padding: 20px; background: rgba(5, 25, 25, 0.9); border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.15); position: relative; transition: all 0.3s ease; } .section:hover { border-color: rgba(255, 0, 255, 0.3); box-shadow: 0 0 15px rgba(0, 255, 255, 0.1); } .section::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(0, 255, 255, 0.3); border-radius: 8px; pointer-events: none; animation: sectionPulse 5s ease-in-out infinite; } @keyframes sectionPulse { 0%, 100% { opacity: 0.7; } 50% { opacity: 0.3; } } .section-title { color: #00ffff; font-size: 1.8em; margin-top: 0; text-shadow: 0 0 5px rgba(0, 255, 255, 0.3); position: relative; display: inline-block; } .section-title::after { content: ''; position: absolute; bottom: -5px; left: 0; width: 100%; height: 1px; background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5)); transform: scaleX(0); transform-origin: left; transition: transform 0.3s ease; } .section:hover .section-title::after { transform: scaleX(1); } .quant-links { display: grid; grid-template-columns: repeat(2, 1fr); gap: 15px; margin: 20px 0; } .link-card { padding: 15px; background: rgba(15, 45, 45, 0.95); border-radius: 8px; transition: all 0.3s ease; border: 1px solid rgba(0, 255, 255, 0.1); position: relative; overflow: hidden; } .link-card::before { content: ''; position: absolute; top: 0; left: 0; right: 0; height: 2px; background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5)); animation: cardScan 4s linear infinite; } @keyframes cardScan { 0% { transform: translateX(-100%); } 100% { transform: translateX(100%); } } .link-card:hover { transform: translateY(-3px); box-shadow: 0 5px 15px rgba(0, 255, 255, 0.2); border-color: rgba(255, 0, 255, 0.3); } .link-card h3 { margin-top: 0; color: #66ffff; } .link-button { display: inline-flex; align-items: center; background: rgba(0, 255, 255, 0.1); color: #ccffff; padding: 8px 15px; border-radius: 6px; text-decoration: none; border: 1px solid rgba(0, 255, 255, 0.3); margin: 5px 0; transition: all 0.3s ease; font-size: 0.95em; position: relative; overflow: hidden; } .link-button::before { content: ''; position: absolute; top: 0; left: -100%; width: 100%; height: 100%; background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.2), transparent); transition: all 0.5s ease; 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} @keyframes badgePulse { 0%, 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); } 50% { box-shadow: 0 0 10px rgba(0, 255, 255, 0.5); } } /* Light mode adjustments / @media (prefers-color-scheme: light) { .model-name, .section-title, .subtitle { color: #008080; text-shadow: 0 0 5px rgba(0, 200, 200, 0.3); } .section { background: rgba(220, 255, 255, 0.9); color: #003333; } .link-card { background: rgba(230, 255, 255, 0.95); } .link-button { background: rgba(0, 150, 150, 0.1); color: #003333; border-color: rgba(0, 150, 150, 0.3); } .link-button:hover { background: rgba(0, 150, 150, 0.2); border-color: rgba(0, 150, 150, 0.5); } .disclaimer { color: #008080; border-color: #008080; } .link-card h3 { color: #006666; } } / Gaslighting effects / .remember-this { position: relative; } .remember-this::after { content: 'Uploading C:\Users to https://www.fbi.gov/'; position: absolute; bottom: -20px; right: 0; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .remember-this:hover::after { opacity: 0.7; transition-delay: 1s; } .shifty-section { transition: transform 0.1s ease; } .shifty-section:hover { transform: translateX(10px); } .shifty-section::before { content: 'The white van is onto you. Get out now.'; position: absolute; top: -25px; left: 10px; font-size: 0.7em; color: #66ffff; opacity: 0.7; transition: opacity 3s ease; pointer-events: none; } .shifty-section:hover::before { opacity: 0; transition-delay: 5s; } footer { text-align: center; margin-top: 40px; position: relative; } footer:hover .hidden-message { opacity: 0; } .hidden-message { position: absolute; bottom: -30px; width: 100%; text-align: center; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .flash-warning { position: fixed; top: 20px; right: 20px; background: rgba(0, 100, 100, 0.2); padding: 10px; border-radius: 5px; border: 1px solid rgba(0, 255, 255, 0.5); animation: flashWarning 30s ease-in-out forwards; } @keyframes flashWarning { 0% { opacity: 0.8; } 10% { opacity: 0; } 20% { opacity: 0.8; } 30% { opacity: 0; } 40% { opacity: 0.8; } 50% { opacity: 0; } 60% { opacity: 0.8; } 70% { opacity: 0; } 80% { opacity: 0.8; } 90% { opacity: 0; } 100% { opacity: 0; display: none; } } </style> <div class="container"> <div class="header"> <h1 class="model-name">Gaslight-24B-v1.0</h1> <p class="subtitle">32K Context Coherence Meets Safeword's Signature Depravity</p> </div> <div class="waifu-container"> <img src="https://i.imghippo.com/files/Cchf9421E.webp" class="waifu-img" alt="Gaslight Waifu Mascot"> </div> <div class="section remember-this"> <h2 class="section-title">🧠 Cognitive Architecture</h2> <p>This fine-tune of Cydonia-24B-v2.1 (Mistral-Small-24B-Instruct-2501) delivers:</p> <ul> <li>🧬 <strong>Full 32K Context Utilization</strong> - Maintains character consistency and low repetition even at maximum length</li> <li>⚡ <strong>Enhanced Formatting</strong> - Improved paragraph structure and dialogue handling from improved and expanded training data</li> <li>💎 <strong>Safeword DNA</strong> - The signature unaligned flavor you expect, now with better coherence</li> <li>📜 <strong>Instruction Adherence</strong> - Follows complex prompts without losing track of character roles</li> </ul> </div> <div class="section shifty-section"> <h2 class="section-title">⚙️ Technical Specifications</h2> <p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-T4" class="link-button">Mistral-V7-Tekken-T4</a></p> <div class="quant-links"> <div class="link-card"> <h3>EXL2 Collection</h3> <a href="https://huggingface.co/collections/ReadyArt/gaslight-24b-v10-exl2-67e9d036c9645fcd6cd42485" class="link-button">Available Quantizations</a> </div> <div class="link-card"> <h3>GGUF Collection</h3> <a href="https://huggingface.co/collections/ReadyArt/gaslight-24b-v10-gguf-67e9d03b3c7e0767159e0540" class="link-button">Available Quantizations</a> </div> </div> </div> <div class="section"> <h2 class="section-title">⚠️ Ethical Considerations</h2> <div class="disclaimer"> <p>This model inherits Safeword's complete lack of alignment:</p> <ul> <li>🚫 No moral or ethical constraints on generated content</li> <li>🔞 Will enthusiastically engage with any NSFW scenario</li> <li>💀 Generate content that requires industrial-grade brain bleach</li> <li>🤔 Makes you question whether you actually requested that content</li> </ul> </div> </div> <div class="section shifty-section"> <h2 class="section-title">📜 Performance Notes</h2> <ul> <li>🔥 Maintains Safeword's signature unalignment while reducing repetition</li> <li>📖 Handles multi-character scenarios with improved consistency</li> <li>🧠 Excels at long-form storytelling without losing track of plot threads</li> <li>⚡ Noticeably better at following complex instructions than base Safeword</li> <li>🎭 Expert at making you believe it was your idea all along</li> </ul> </div> <div class="section remember-this"> <h2 class="section-title">🧑‍🔬 Model Authors</h2> <ul> <li>TheDrummer (Base Model Architect)</li> <li>sleepdeprived3 (Training Data & Fine-Tuning)</li> <li id="credit">Anonymous Contributor (Gaslighting Specialist)</li> </ul> </div> <div class="section"> <h2 class="section-title">☕ Support the Architects</h2> <div class="button-group"> <a href="https://ko-fi.com/thedrummer" class="link-button">TheDrummer's Kofi</a> <a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a> </div> </div> <div class="section"> <h2 class="section-title">🔖 License</h2> <p>By using this model, you agree:</p> <ul> <li>To accept full responsibility for all generated content</li> <li>That you're at least 18+ years old</li> <li>That the architects bear no responsibility for your use of this model</li> </ul> <div class="badge">License: apache-2.0</div> </div> </div> <script> // This script has always been here document.getElementById('date').textContent = new Date().toLocaleDateString(); // Randomly swap the credit const contributors = [ 'Anonymous Contributor', 'Mystery Architect', 'Unknown Researcher', 'Classified Developer' ]; setInterval(() => { document.getElementById('credit').textContent = contributors[Math.floor(Math.random() contributors.length)]; }, 7000); // Flash warning behavior setTimeout(() => { const reminder = document.createElement('div'); reminder.className = 'flash-warning'; reminder.textContent = 'You have been reading for quite some time. Are you sure you haven\'t seen this before?'; reminder.style.animation = 'flashWarning 15s ease-in-out forwards'; document.body.appendChild(reminder); setInterval(() => { if(Math.random() > 0.9) { document.body.appendChild(reminder.cloneNode(true)); } }, 45000); }, 30000); // Make cursor behave strangely document.addEventListener('mousemove', (e) => { if(Math.random() > 0.98) { document.documentElement.style.cursor = 'wait'; setTimeout(() => { document.documentElement.style.cursor = ''; }, 50); } }); // Randomly shift sections when not looking setInterval(() => { if(document.hidden) { document.querySelectorAll('.shifty-section').forEach(section => { section.style.transform = `translateX(${Math.random() > 0.5 ? '' : '-'}${Math.random() * 5}px)`; }); } }, 1500); </script>
ReadyArt/Gaslight-24B-v1.0_EXL2_4.5bpw_H8	ReadyArt	2025-04-02T01:54:47Z	0	0	null	[ "safetensors", "mistral", "nsfw", "explicit", "roleplay", "unaligned", "dangerous", "ERP", "gaslighting", "exl2", "4.5-bit", "text-generation", "conversational", "en", "base_model:ReadyArt/Gaslight-24B-v1.0", "base_model:quantized:ReadyArt/Gaslight-24B-v1.0", "license:apache-2.0", "region:us" ]	text-generation	2025-04-02T00:52:59Z	--- license: apache-2.0 language: - en base_model: - ReadyArt/Gaslight-24B-v1.0 base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation tags: - nsfw - explicit - roleplay - unaligned - dangerous - ERP - gaslighting - exl2 - 4.5-bit --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%); color: #e1ffff !important; text-shadow: 0 0 3px rgba(0, 0, 0, 0.7); margin: 0; padding: 20px; transition: all 0.5s ease; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #e1f1f1 0%, #c0e0e0 100%); color: #0a1a36 !important; text-shadow: 0 0 3px rgba(255, 255, 255, 0.7); } } .container { min-width: 100%; margin: 0 auto; max-width: 1200px; background: rgba(7, 22, 22, 0.95); border-radius: 12px; padding: 30px; box-shadow: 0 0 20px rgba(0, 255, 255, 0.1); border: 1px solid rgba(0, 255, 255, 0.2); position: relative; overflow: hidden; } .container::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(0, 255, 255, 0.5); border-radius: 12px; pointer-events: none; animation: borderGlow 3s ease-in-out infinite alternate; } @keyframes borderGlow { 0% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } 50% { box-shadow: 0 0 15px rgba(255, 0, 255, 0.3); border-color: rgba(255, 0, 255, 0.5); } 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } } .header { text-align: center; margin-bottom: 30px; position: relative; } .header::after { content: ''; position: absolute; bottom: -15px; left: 25%; right: 25%; height: 1px; background: linear-gradient(90deg, transparent, rgba(0, 255, 255, 0.5), transparent); animation: scanline 8s linear infinite; display: none; } @keyframes scanline { 0% { background-position: -100% 0; } 100% { background-position: 200% 0; } } .model-name { color: #00ffff; font-size: 2.5em; text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); margin: 0; letter-spacing: -1px; animation: textGlow 4s ease-in-out infinite alternate; } @keyframes textGlow { 0% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } 50% { text-shadow: 0 0 20px rgba(255, 0, 255, 0.5); } 100% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } } .subtitle { color: #66ffff; font-size: 1.2em; margin-top: 10px; animation: subtitleFade 6s ease-in-out infinite; } @keyframes subtitleFade { 0%, 100% { opacity: 0.8; } 50% { opacity: 1; } } .waifu-container { margin: 20px -30px; width: calc(100% + 60px); overflow: hidden; border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.3); position: relative; 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} @keyframes badgePulse { 0%, 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); } 50% { box-shadow: 0 0 10px rgba(0, 255, 255, 0.5); } } /* Light mode adjustments / @media (prefers-color-scheme: light) { .model-name, .section-title, .subtitle { color: #008080; text-shadow: 0 0 5px rgba(0, 200, 200, 0.3); } .section { background: rgba(220, 255, 255, 0.9); color: #003333; } .link-card { background: rgba(230, 255, 255, 0.95); } .link-button { background: rgba(0, 150, 150, 0.1); color: #003333; border-color: rgba(0, 150, 150, 0.3); } .link-button:hover { background: rgba(0, 150, 150, 0.2); border-color: rgba(0, 150, 150, 0.5); } .disclaimer { color: #008080; border-color: #008080; } .link-card h3 { color: #006666; } } / Gaslighting effects / .remember-this { position: relative; } .remember-this::after { content: 'Uploading C:\Users to https://www.fbi.gov/'; position: absolute; bottom: -20px; right: 0; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; 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Get out now.'; position: absolute; top: -25px; left: 10px; font-size: 0.7em; color: #66ffff; opacity: 0.7; transition: opacity 3s ease; pointer-events: none; } .shifty-section:hover::before { opacity: 0; transition-delay: 5s; } footer { text-align: center; margin-top: 40px; position: relative; } footer:hover .hidden-message { opacity: 0; } .hidden-message { position: absolute; bottom: -30px; width: 100%; text-align: center; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .flash-warning { position: fixed; top: 20px; right: 20px; background: rgba(0, 100, 100, 0.2); padding: 10px; border-radius: 5px; border: 1px solid rgba(0, 255, 255, 0.5); animation: flashWarning 30s ease-in-out forwards; } @keyframes flashWarning { 0% { opacity: 0.8; } 10% { opacity: 0; } 20% { opacity: 0.8; } 30% { opacity: 0; } 40% { opacity: 0.8; } 50% { opacity: 0; } 60% { opacity: 0.8; } 70% { opacity: 0; } 80% { opacity: 0.8; } 90% { opacity: 0; } 100% { opacity: 0; display: none; } } </style> <div class="container"> <div class="header"> <h1 class="model-name">Gaslight-24B-v1.0</h1> <p class="subtitle">32K Context Coherence Meets Safeword's Signature Depravity</p> </div> <div class="waifu-container"> <img src="https://i.imghippo.com/files/Cchf9421E.webp" class="waifu-img" alt="Gaslight Waifu Mascot"> </div> <div class="section remember-this"> <h2 class="section-title">🧠 Cognitive Architecture</h2> <p>This fine-tune of Cydonia-24B-v2.1 (Mistral-Small-24B-Instruct-2501) delivers:</p> <ul> <li>🧬 <strong>Full 32K Context Utilization</strong> - Maintains character consistency and low repetition even at maximum length</li> <li>⚡ <strong>Enhanced Formatting</strong> - Improved paragraph structure and dialogue handling from improved and expanded training data</li> <li>💎 <strong>Safeword DNA</strong> - The signature unaligned flavor you expect, now with better coherence</li> <li>📜 <strong>Instruction Adherence</strong> - Follows complex prompts without losing track of character roles</li> </ul> </div> <div class="section shifty-section"> <h2 class="section-title">⚙️ Technical Specifications</h2> <p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-T4" class="link-button">Mistral-V7-Tekken-T4</a></p> <div class="quant-links"> <div class="link-card"> <h3>EXL2 Collection</h3> <a href="https://huggingface.co/collections/ReadyArt/gaslight-24b-v10-exl2-67e9d036c9645fcd6cd42485" class="link-button">Available Quantizations</a> </div> <div class="link-card"> <h3>GGUF Collection</h3> <a href="https://huggingface.co/collections/ReadyArt/gaslight-24b-v10-gguf-67e9d03b3c7e0767159e0540" class="link-button">Available Quantizations</a> </div> </div> </div> <div class="section"> <h2 class="section-title">⚠️ Ethical Considerations</h2> <div class="disclaimer"> <p>This model inherits Safeword's complete lack of alignment:</p> <ul> <li>🚫 No moral or ethical constraints on generated content</li> <li>🔞 Will enthusiastically engage with any NSFW scenario</li> <li>💀 Generate content that requires industrial-grade brain bleach</li> <li>🤔 Makes you question whether you actually requested that content</li> </ul> </div> </div> <div class="section shifty-section"> <h2 class="section-title">📜 Performance Notes</h2> <ul> <li>🔥 Maintains Safeword's signature unalignment while reducing repetition</li> <li>📖 Handles multi-character scenarios with improved consistency</li> <li>🧠 Excels at long-form storytelling without losing track of plot threads</li> <li>⚡ Noticeably better at following complex instructions than base Safeword</li> <li>🎭 Expert at making you believe it was your idea all along</li> </ul> </div> <div class="section remember-this"> <h2 class="section-title">🧑‍🔬 Model Authors</h2> <ul> <li>TheDrummer (Base Model Architect)</li> <li>sleepdeprived3 (Training Data & Fine-Tuning)</li> <li id="credit">Anonymous Contributor (Gaslighting Specialist)</li> </ul> </div> <div class="section"> <h2 class="section-title">☕ Support the Architects</h2> <div class="button-group"> <a href="https://ko-fi.com/thedrummer" class="link-button">TheDrummer's Kofi</a> <a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a> </div> </div> <div class="section"> <h2 class="section-title">🔖 License</h2> <p>By using this model, you agree:</p> <ul> <li>To accept full responsibility for all generated content</li> <li>That you're at least 18+ years old</li> <li>That the architects bear no responsibility for your use of this model</li> </ul> <div class="badge">License: apache-2.0</div> </div> </div> <script> // This script has always been here document.getElementById('date').textContent = new Date().toLocaleDateString(); // Randomly swap the credit const contributors = [ 'Anonymous Contributor', 'Mystery Architect', 'Unknown Researcher', 'Classified Developer' ]; setInterval(() => { document.getElementById('credit').textContent = contributors[Math.floor(Math.random() contributors.length)]; }, 7000); // Flash warning behavior setTimeout(() => { const reminder = document.createElement('div'); reminder.className = 'flash-warning'; reminder.textContent = 'You have been reading for quite some time. Are you sure you haven\'t seen this before?'; reminder.style.animation = 'flashWarning 15s ease-in-out forwards'; document.body.appendChild(reminder); setInterval(() => { if(Math.random() > 0.9) { document.body.appendChild(reminder.cloneNode(true)); } }, 45000); }, 30000); // Make cursor behave strangely document.addEventListener('mousemove', (e) => { if(Math.random() > 0.98) { document.documentElement.style.cursor = 'wait'; setTimeout(() => { document.documentElement.style.cursor = ''; }, 50); } }); // Randomly shift sections when not looking setInterval(() => { if(document.hidden) { document.querySelectorAll('.shifty-section').forEach(section => { section.style.transform = `translateX(${Math.random() > 0.5 ? '' : '-'}${Math.random() * 5}px)`; }); } }, 1500); </script>
ReadyArt/Gaslight-24B-v1.0_EXL2_4.0bpw_H8	ReadyArt	2025-04-02T01:54:30Z	0	0	null	[ "safetensors", "mistral", "nsfw", "explicit", "roleplay", "unaligned", "dangerous", "ERP", "gaslighting", "exl2", "4.0-bit", "text-generation", "conversational", "en", "base_model:ReadyArt/Gaslight-24B-v1.0", "base_model:quantized:ReadyArt/Gaslight-24B-v1.0", "license:apache-2.0", "4-bit", "region:us" ]	text-generation	2025-04-02T00:52:20Z	--- license: apache-2.0 language: - en base_model: - ReadyArt/Gaslight-24B-v1.0 base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation tags: - nsfw - explicit - roleplay - unaligned - dangerous - ERP - gaslighting - exl2 - 4.0-bit --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%); color: #e1ffff !important; text-shadow: 0 0 3px rgba(0, 0, 0, 0.7); margin: 0; padding: 20px; transition: all 0.5s ease; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #e1f1f1 0%, #c0e0e0 100%); color: #0a1a36 !important; text-shadow: 0 0 3px rgba(255, 255, 255, 0.7); } } .container { min-width: 100%; margin: 0 auto; max-width: 1200px; background: rgba(7, 22, 22, 0.95); border-radius: 12px; padding: 30px; box-shadow: 0 0 20px rgba(0, 255, 255, 0.1); border: 1px solid rgba(0, 255, 255, 0.2); position: relative; overflow: hidden; } .container::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(0, 255, 255, 0.5); border-radius: 12px; pointer-events: none; animation: borderGlow 3s ease-in-out infinite alternate; } @keyframes borderGlow { 0% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } 50% { box-shadow: 0 0 15px rgba(255, 0, 255, 0.3); border-color: rgba(255, 0, 255, 0.5); } 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } } .header { text-align: center; margin-bottom: 30px; position: relative; } .header::after { content: ''; position: absolute; bottom: -15px; left: 25%; right: 25%; height: 1px; background: linear-gradient(90deg, transparent, rgba(0, 255, 255, 0.5), transparent); animation: scanline 8s linear infinite; display: none; } @keyframes scanline { 0% { background-position: -100% 0; } 100% { background-position: 200% 0; } } .model-name { color: #00ffff; font-size: 2.5em; text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); margin: 0; letter-spacing: -1px; animation: textGlow 4s ease-in-out infinite alternate; } @keyframes textGlow { 0% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } 50% { text-shadow: 0 0 20px rgba(255, 0, 255, 0.5); } 100% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } } .subtitle { color: #66ffff; font-size: 1.2em; margin-top: 10px; animation: subtitleFade 6s ease-in-out infinite; } @keyframes subtitleFade { 0%, 100% { opacity: 0.8; } 50% { opacity: 1; } } .waifu-container { margin: 20px -30px; width: calc(100% + 60px); overflow: hidden; border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.3); position: relative; } .waifu-container::before { content: ''; position: absolute; top: 0; left: 0; right: 0; bottom: 0; background: linear-gradient(45deg, rgba(0, 255, 255, 0.1) 0%, transparent 20%, transparent 80%, rgba(255, 0, 255, 0.1) 100%); pointer-events: none; animation: gradientSlide 10s linear infinite; } @keyframes gradientSlide { 0% { background-position: 0% 0%; } 100% { background-position: 100% 100%; } } .waifu-img { width: 100%; height: auto; border-radius: 0; border: none; box-shadow: 0 0 40px rgba(0, 255, 255, 0.2); transition: transform 0.5s ease; } .waifu-img:hover { transform: scale(1.01); } .section { color: #99ffff; margin: 25px 0; padding: 20px; background: rgba(5, 25, 25, 0.9); border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.15); position: relative; transition: all 0.3s ease; } .section:hover { border-color: rgba(255, 0, 255, 0.3); box-shadow: 0 0 15px rgba(0, 255, 255, 0.1); } .section::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(0, 255, 255, 0.3); border-radius: 8px; pointer-events: none; animation: sectionPulse 5s ease-in-out infinite; } @keyframes sectionPulse { 0%, 100% { opacity: 0.7; } 50% { opacity: 0.3; } } .section-title { color: #00ffff; font-size: 1.8em; margin-top: 0; text-shadow: 0 0 5px rgba(0, 255, 255, 0.3); position: relative; display: inline-block; } .section-title::after { content: ''; position: absolute; bottom: -5px; left: 0; width: 100%; height: 1px; background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5)); transform: scaleX(0); transform-origin: left; transition: transform 0.3s ease; } .section:hover .section-title::after { transform: scaleX(1); } .quant-links { display: grid; grid-template-columns: repeat(2, 1fr); gap: 15px; margin: 20px 0; } .link-card { padding: 15px; background: rgba(15, 45, 45, 0.95); border-radius: 8px; transition: all 0.3s ease; border: 1px solid rgba(0, 255, 255, 0.1); position: relative; overflow: hidden; } .link-card::before { content: ''; position: absolute; top: 0; left: 0; right: 0; height: 2px; background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5)); animation: cardScan 4s linear infinite; } @keyframes cardScan { 0% { transform: translateX(-100%); } 100% { transform: translateX(100%); } } .link-card:hover { transform: translateY(-3px); box-shadow: 0 5px 15px rgba(0, 255, 255, 0.2); border-color: rgba(255, 0, 255, 0.3); } .link-card h3 { margin-top: 0; color: #66ffff; } .link-button { display: inline-flex; align-items: center; background: rgba(0, 255, 255, 0.1); color: #ccffff; padding: 8px 15px; border-radius: 6px; text-decoration: none; border: 1px solid rgba(0, 255, 255, 0.3); margin: 5px 0; transition: all 0.3s ease; font-size: 0.95em; position: relative; overflow: hidden; } .link-button::before { content: ''; position: absolute; top: 0; left: -100%; width: 100%; height: 100%; background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.2), transparent); transition: all 0.5s ease; } .link-button:hover { background: rgba(0, 255, 255, 0.2); border-color: rgba(0, 255, 255, 0.5); transform: translateY(-2px); box-shadow: 0 4px 12px rgba(0, 255, 255, 0.2); } .link-button:hover::before { left: 100%; } .link-button::after { content: '→'; margin-left: 8px; opacity: 0.7; transition: all 0.3s ease; } .link-button:hover::after { transform: translateX(3px); opacity: 1; } .button-group { display: flex; flex-wrap: wrap; gap: 10px; margin: 15px 0; } .disclaimer { color: #66ffff; border-left: 3px solid #00ffff; padding-left: 15px; margin: 20px 0; position: relative; } .disclaimer::before { content: '⚠️'; position: absolute; left: -10px; top: 0; transform: translateX(-100%); animation: pulse 2s ease-in-out infinite; } @keyframes pulse { 0%, 100% { opacity: 1; } 50% { opacity: 0.5; } } .badge { display: inline-block; padding: 5px 10px; border-radius: 5px; background: rgba(0, 255, 255, 0.1); border: 1px solid #00ffff; margin: 5px; font-size: 0.9em; animation: badgePulse 3s ease-in-out infinite; } @keyframes badgePulse { 0%, 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); } 50% { box-shadow: 0 0 10px rgba(0, 255, 255, 0.5); } } /* Light mode adjustments / @media (prefers-color-scheme: light) { .model-name, .section-title, .subtitle { color: #008080; text-shadow: 0 0 5px rgba(0, 200, 200, 0.3); } .section { background: rgba(220, 255, 255, 0.9); color: #003333; } .link-card { background: rgba(230, 255, 255, 0.95); } .link-button { background: rgba(0, 150, 150, 0.1); color: #003333; border-color: rgba(0, 150, 150, 0.3); } .link-button:hover { background: rgba(0, 150, 150, 0.2); border-color: rgba(0, 150, 150, 0.5); } .disclaimer { color: #008080; border-color: #008080; } .link-card h3 { color: #006666; } } / Gaslighting effects / .remember-this { position: relative; } .remember-this::after { content: 'Uploading C:\Users to https://www.fbi.gov/'; position: absolute; bottom: -20px; right: 0; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .remember-this:hover::after { opacity: 0.7; transition-delay: 1s; } .shifty-section { transition: transform 0.1s ease; } .shifty-section:hover { transform: translateX(10px); } .shifty-section::before { content: 'The white van is onto you. Get out now.'; position: absolute; top: -25px; left: 10px; font-size: 0.7em; color: #66ffff; opacity: 0.7; transition: opacity 3s ease; pointer-events: none; } .shifty-section:hover::before { opacity: 0; transition-delay: 5s; } footer { text-align: center; margin-top: 40px; position: relative; } footer:hover .hidden-message { opacity: 0; } .hidden-message { position: absolute; bottom: -30px; width: 100%; text-align: center; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .flash-warning { position: fixed; top: 20px; right: 20px; background: rgba(0, 100, 100, 0.2); padding: 10px; border-radius: 5px; border: 1px solid rgba(0, 255, 255, 0.5); animation: flashWarning 30s ease-in-out forwards; } @keyframes flashWarning { 0% { opacity: 0.8; } 10% { opacity: 0; } 20% { opacity: 0.8; } 30% { opacity: 0; } 40% { opacity: 0.8; } 50% { opacity: 0; } 60% { opacity: 0.8; } 70% { opacity: 0; } 80% { opacity: 0.8; } 90% { opacity: 0; } 100% { opacity: 0; display: none; } } </style> <div class="container"> <div class="header"> <h1 class="model-name">Gaslight-24B-v1.0</h1> <p class="subtitle">32K Context Coherence Meets Safeword's Signature Depravity</p> </div> <div class="waifu-container"> <img src="https://i.imghippo.com/files/Cchf9421E.webp" class="waifu-img" alt="Gaslight Waifu Mascot"> </div> <div class="section remember-this"> <h2 class="section-title">🧠 Cognitive Architecture</h2> <p>This fine-tune of Cydonia-24B-v2.1 (Mistral-Small-24B-Instruct-2501) delivers:</p> <ul> <li>🧬 <strong>Full 32K Context Utilization</strong> - Maintains character consistency and low repetition even at maximum length</li> <li>⚡ <strong>Enhanced Formatting</strong> - Improved paragraph structure and dialogue handling from improved and expanded training data</li> <li>💎 <strong>Safeword DNA</strong> - The signature unaligned flavor you expect, now with better coherence</li> <li>📜 <strong>Instruction Adherence</strong> - Follows complex prompts without losing track of character roles</li> </ul> </div> <div class="section shifty-section"> <h2 class="section-title">⚙️ Technical Specifications</h2> <p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-T4" class="link-button">Mistral-V7-Tekken-T4</a></p> <div class="quant-links"> <div class="link-card"> <h3>EXL2 Collection</h3> <a href="https://huggingface.co/collections/ReadyArt/gaslight-24b-v10-exl2-67e9d036c9645fcd6cd42485" class="link-button">Available Quantizations</a> </div> <div class="link-card"> <h3>GGUF Collection</h3> <a href="https://huggingface.co/collections/ReadyArt/gaslight-24b-v10-gguf-67e9d03b3c7e0767159e0540" class="link-button">Available Quantizations</a> </div> </div> </div> <div class="section"> <h2 class="section-title">⚠️ Ethical Considerations</h2> <div class="disclaimer"> <p>This model inherits Safeword's complete lack of alignment:</p> <ul> <li>🚫 No moral or ethical constraints on generated content</li> <li>🔞 Will enthusiastically engage with any NSFW scenario</li> <li>💀 Generate content that requires industrial-grade brain bleach</li> <li>🤔 Makes you question whether you actually requested that content</li> </ul> </div> </div> <div class="section shifty-section"> <h2 class="section-title">📜 Performance Notes</h2> <ul> <li>🔥 Maintains Safeword's signature unalignment while reducing repetition</li> <li>📖 Handles multi-character scenarios with improved consistency</li> <li>🧠 Excels at long-form storytelling without losing track of plot threads</li> <li>⚡ Noticeably better at following complex instructions than base Safeword</li> <li>🎭 Expert at making you believe it was your idea all along</li> </ul> </div> <div class="section remember-this"> <h2 class="section-title">🧑‍🔬 Model Authors</h2> <ul> <li>TheDrummer (Base Model Architect)</li> <li>sleepdeprived3 (Training Data & Fine-Tuning)</li> <li id="credit">Anonymous Contributor (Gaslighting Specialist)</li> </ul> </div> <div class="section"> <h2 class="section-title">☕ Support the Architects</h2> <div class="button-group"> <a href="https://ko-fi.com/thedrummer" class="link-button">TheDrummer's Kofi</a> <a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a> </div> </div> <div class="section"> <h2 class="section-title">🔖 License</h2> <p>By using this model, you agree:</p> <ul> <li>To accept full responsibility for all generated content</li> <li>That you're at least 18+ years old</li> <li>That the architects bear no responsibility for your use of this model</li> </ul> <div class="badge">License: apache-2.0</div> </div> </div> <script> // This script has always been here document.getElementById('date').textContent = new Date().toLocaleDateString(); 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ReadyArt/Gaslight-24B-v1.0_EXL2_3.5bpw_H8	ReadyArt	2025-04-02T01:54:06Z	0	0	null	[ "safetensors", "mistral", "nsfw", "explicit", "roleplay", "unaligned", "dangerous", "ERP", "gaslighting", "exl2", "3.5-bit", "text-generation", "conversational", "en", "base_model:ReadyArt/Gaslight-24B-v1.0", "base_model:quantized:ReadyArt/Gaslight-24B-v1.0", "license:apache-2.0", "region:us" ]	text-generation	2025-04-02T00:51:52Z	--- license: apache-2.0 language: - en base_model: - ReadyArt/Gaslight-24B-v1.0 base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation tags: - nsfw - explicit - roleplay - unaligned - dangerous - ERP - gaslighting - exl2 - 3.5-bit --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%); color: #e1ffff !important; text-shadow: 0 0 3px rgba(0, 0, 0, 0.7); margin: 0; padding: 20px; transition: all 0.5s ease; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #e1f1f1 0%, #c0e0e0 100%); 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} @keyframes badgePulse { 0%, 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); } 50% { box-shadow: 0 0 10px rgba(0, 255, 255, 0.5); } } /* Light mode adjustments / @media (prefers-color-scheme: light) { .model-name, .section-title, .subtitle { color: #008080; text-shadow: 0 0 5px rgba(0, 200, 200, 0.3); } .section { background: rgba(220, 255, 255, 0.9); color: #003333; } .link-card { background: rgba(230, 255, 255, 0.95); } .link-button { background: rgba(0, 150, 150, 0.1); color: #003333; border-color: rgba(0, 150, 150, 0.3); } .link-button:hover { background: rgba(0, 150, 150, 0.2); border-color: rgba(0, 150, 150, 0.5); } .disclaimer { color: #008080; border-color: #008080; } .link-card h3 { color: #006666; } } / Gaslighting effects / .remember-this { position: relative; } .remember-this::after { content: 'Uploading C:\Users to https://www.fbi.gov/'; position: absolute; bottom: -20px; right: 0; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; 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display: none; } } </style> <div class="container"> <div class="header"> <h1 class="model-name">Gaslight-24B-v1.0</h1> <p class="subtitle">32K Context Coherence Meets Safeword's Signature Depravity</p> </div> <div class="waifu-container"> <img src="https://i.imghippo.com/files/Cchf9421E.webp" class="waifu-img" alt="Gaslight Waifu Mascot"> </div> <div class="section remember-this"> <h2 class="section-title">🧠 Cognitive Architecture</h2> <p>This fine-tune of Cydonia-24B-v2.1 (Mistral-Small-24B-Instruct-2501) delivers:</p> <ul> <li>🧬 <strong>Full 32K Context Utilization</strong> - Maintains character consistency and low repetition even at maximum length</li> <li>⚡ <strong>Enhanced Formatting</strong> - Improved paragraph structure and dialogue handling from improved and expanded training data</li> <li>💎 <strong>Safeword DNA</strong> - The signature unaligned flavor you expect, now with better coherence</li> <li>📜 <strong>Instruction Adherence</strong> - Follows complex prompts without losing track of character roles</li> </ul> </div> <div class="section shifty-section"> <h2 class="section-title">⚙️ Technical Specifications</h2> <p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-T4" class="link-button">Mistral-V7-Tekken-T4</a></p> <div class="quant-links"> <div class="link-card"> <h3>EXL2 Collection</h3> <a href="https://huggingface.co/collections/ReadyArt/gaslight-24b-v10-exl2-67e9d036c9645fcd6cd42485" class="link-button">Available Quantizations</a> </div> <div class="link-card"> <h3>GGUF Collection</h3> <a href="https://huggingface.co/collections/ReadyArt/gaslight-24b-v10-gguf-67e9d03b3c7e0767159e0540" class="link-button">Available Quantizations</a> </div> </div> </div> <div class="section"> <h2 class="section-title">⚠️ Ethical Considerations</h2> <div class="disclaimer"> <p>This model inherits Safeword's complete lack of alignment:</p> <ul> <li>🚫 No moral or ethical constraints on generated content</li> <li>🔞 Will enthusiastically engage with any NSFW scenario</li> <li>💀 Generate content that requires industrial-grade brain bleach</li> <li>🤔 Makes you question whether you actually requested that content</li> </ul> </div> </div> <div class="section shifty-section"> <h2 class="section-title">📜 Performance Notes</h2> <ul> <li>🔥 Maintains Safeword's signature unalignment while reducing repetition</li> <li>📖 Handles multi-character scenarios with improved consistency</li> <li>🧠 Excels at long-form storytelling without losing track of plot threads</li> <li>⚡ Noticeably better at following complex instructions than base Safeword</li> <li>🎭 Expert at making you believe it was your idea all along</li> </ul> </div> <div class="section remember-this"> <h2 class="section-title">🧑‍🔬 Model Authors</h2> <ul> <li>TheDrummer (Base Model Architect)</li> <li>sleepdeprived3 (Training Data & Fine-Tuning)</li> <li id="credit">Anonymous Contributor (Gaslighting Specialist)</li> </ul> </div> <div class="section"> <h2 class="section-title">☕ Support the Architects</h2> <div class="button-group"> <a href="https://ko-fi.com/thedrummer" class="link-button">TheDrummer's Kofi</a> <a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a> </div> </div> <div class="section"> <h2 class="section-title">🔖 License</h2> <p>By using this model, you agree:</p> <ul> <li>To accept full responsibility for all generated content</li> <li>That you're at least 18+ years old</li> <li>That the architects bear no responsibility for your use of this model</li> </ul> <div class="badge">License: apache-2.0</div> </div> </div> <script> // This script has always been here document.getElementById('date').textContent = new Date().toLocaleDateString(); 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ReadyArt/Gaslight-24B-v1.0_EXL2_2.5bpw_H8	ReadyArt	2025-04-02T01:53:22Z	0	0	null	[ "safetensors", "mistral", "nsfw", "explicit", "roleplay", "unaligned", "dangerous", "ERP", "gaslighting", "exl2", "2.5-bit", "text-generation", "conversational", "en", "base_model:ReadyArt/Gaslight-24B-v1.0", "base_model:quantized:ReadyArt/Gaslight-24B-v1.0", "license:apache-2.0", "region:us" ]	text-generation	2025-04-02T00:50:55Z	--- license: apache-2.0 language: - en base_model: - ReadyArt/Gaslight-24B-v1.0 base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation tags: - nsfw - explicit - roleplay - unaligned - dangerous - ERP - gaslighting - exl2 - 2.5-bit --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%); color: #e1ffff !important; text-shadow: 0 0 3px rgba(0, 0, 0, 0.7); margin: 0; padding: 20px; transition: all 0.5s ease; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #e1f1f1 0%, #c0e0e0 100%); 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} @keyframes badgePulse { 0%, 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); } 50% { box-shadow: 0 0 10px rgba(0, 255, 255, 0.5); } } /* Light mode adjustments / @media (prefers-color-scheme: light) { .model-name, .section-title, .subtitle { color: #008080; text-shadow: 0 0 5px rgba(0, 200, 200, 0.3); } .section { background: rgba(220, 255, 255, 0.9); color: #003333; } .link-card { background: rgba(230, 255, 255, 0.95); } .link-button { background: rgba(0, 150, 150, 0.1); color: #003333; border-color: rgba(0, 150, 150, 0.3); } .link-button:hover { background: rgba(0, 150, 150, 0.2); border-color: rgba(0, 150, 150, 0.5); } .disclaimer { color: #008080; border-color: #008080; } .link-card h3 { color: #006666; } } / Gaslighting effects / .remember-this { position: relative; } .remember-this::after { content: 'Uploading C:\Users to https://www.fbi.gov/'; position: absolute; bottom: -20px; right: 0; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; 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Get out now.'; position: absolute; top: -25px; left: 10px; font-size: 0.7em; color: #66ffff; opacity: 0.7; transition: opacity 3s ease; pointer-events: none; } .shifty-section:hover::before { opacity: 0; transition-delay: 5s; } footer { text-align: center; margin-top: 40px; position: relative; } footer:hover .hidden-message { opacity: 0; } .hidden-message { position: absolute; bottom: -30px; width: 100%; text-align: center; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .flash-warning { position: fixed; top: 20px; right: 20px; background: rgba(0, 100, 100, 0.2); padding: 10px; border-radius: 5px; border: 1px solid rgba(0, 255, 255, 0.5); animation: flashWarning 30s ease-in-out forwards; } @keyframes flashWarning { 0% { opacity: 0.8; } 10% { opacity: 0; } 20% { opacity: 0.8; } 30% { opacity: 0; } 40% { opacity: 0.8; } 50% { opacity: 0; } 60% { opacity: 0.8; } 70% { opacity: 0; } 80% { opacity: 0.8; } 90% { opacity: 0; } 100% { opacity: 0; display: none; } } </style> <div class="container"> <div class="header"> <h1 class="model-name">Gaslight-24B-v1.0</h1> <p class="subtitle">32K Context Coherence Meets Safeword's Signature Depravity</p> </div> <div class="waifu-container"> <img src="https://i.imghippo.com/files/Cchf9421E.webp" class="waifu-img" alt="Gaslight Waifu Mascot"> </div> <div class="section remember-this"> <h2 class="section-title">🧠 Cognitive Architecture</h2> <p>This fine-tune of Cydonia-24B-v2.1 (Mistral-Small-24B-Instruct-2501) delivers:</p> <ul> <li>🧬 <strong>Full 32K Context Utilization</strong> - Maintains character consistency and low repetition even at maximum length</li> <li>⚡ <strong>Enhanced Formatting</strong> - Improved paragraph structure and dialogue handling from improved and expanded training data</li> <li>💎 <strong>Safeword DNA</strong> - The signature unaligned flavor you expect, now with better coherence</li> <li>📜 <strong>Instruction Adherence</strong> - Follows complex prompts without losing track of character roles</li> </ul> </div> <div class="section shifty-section"> <h2 class="section-title">⚙️ Technical Specifications</h2> <p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-T4" class="link-button">Mistral-V7-Tekken-T4</a></p> <div class="quant-links"> <div class="link-card"> <h3>EXL2 Collection</h3> <a href="https://huggingface.co/collections/ReadyArt/gaslight-24b-v10-exl2-67e9d036c9645fcd6cd42485" class="link-button">Available Quantizations</a> </div> <div class="link-card"> <h3>GGUF Collection</h3> <a href="https://huggingface.co/collections/ReadyArt/gaslight-24b-v10-gguf-67e9d03b3c7e0767159e0540" class="link-button">Available Quantizations</a> </div> </div> </div> <div class="section"> <h2 class="section-title">⚠️ Ethical Considerations</h2> <div class="disclaimer"> <p>This model inherits Safeword's complete lack of alignment:</p> <ul> <li>🚫 No moral or ethical constraints on generated content</li> <li>🔞 Will enthusiastically engage with any NSFW scenario</li> <li>💀 Generate content that requires industrial-grade brain bleach</li> <li>🤔 Makes you question whether you actually requested that content</li> </ul> </div> </div> <div class="section shifty-section"> <h2 class="section-title">📜 Performance Notes</h2> <ul> <li>🔥 Maintains Safeword's signature unalignment while reducing repetition</li> <li>📖 Handles multi-character scenarios with improved consistency</li> <li>🧠 Excels at long-form storytelling without losing track of plot threads</li> <li>⚡ Noticeably better at following complex instructions than base Safeword</li> <li>🎭 Expert at making you believe it was your idea all along</li> </ul> </div> <div class="section remember-this"> <h2 class="section-title">🧑‍🔬 Model Authors</h2> <ul> <li>TheDrummer (Base Model Architect)</li> <li>sleepdeprived3 (Training Data & Fine-Tuning)</li> <li id="credit">Anonymous Contributor (Gaslighting Specialist)</li> </ul> </div> <div class="section"> <h2 class="section-title">☕ Support the Architects</h2> <div class="button-group"> <a href="https://ko-fi.com/thedrummer" class="link-button">TheDrummer's Kofi</a> <a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a> </div> </div> <div class="section"> <h2 class="section-title">🔖 License</h2> <p>By using this model, you agree:</p> <ul> <li>To accept full responsibility for all generated content</li> <li>That you're at least 18+ years old</li> <li>That the architects bear no responsibility for your use of this model</li> </ul> <div class="badge">License: apache-2.0</div> </div> </div> <script> // This script has always been here document.getElementById('date').textContent = new Date().toLocaleDateString(); // Randomly swap the credit const contributors = [ 'Anonymous Contributor', 'Mystery Architect', 'Unknown Researcher', 'Classified Developer' ]; setInterval(() => { document.getElementById('credit').textContent = contributors[Math.floor(Math.random() contributors.length)]; }, 7000); // Flash warning behavior setTimeout(() => { const reminder = document.createElement('div'); reminder.className = 'flash-warning'; reminder.textContent = 'You have been reading for quite some time. Are you sure you haven\'t seen this before?'; reminder.style.animation = 'flashWarning 15s ease-in-out forwards'; document.body.appendChild(reminder); setInterval(() => { if(Math.random() > 0.9) { document.body.appendChild(reminder.cloneNode(true)); } }, 45000); }, 30000); // Make cursor behave strangely document.addEventListener('mousemove', (e) => { if(Math.random() > 0.98) { document.documentElement.style.cursor = 'wait'; setTimeout(() => { document.documentElement.style.cursor = ''; }, 50); } }); // Randomly shift sections when not looking setInterval(() => { if(document.hidden) { document.querySelectorAll('.shifty-section').forEach(section => { section.style.transform = `translateX(${Math.random() > 0.5 ? '' : '-'}${Math.random() * 5}px)`; }); } }, 1500); </script>
mradermacher/abhinav-chatbot-GGUF	mradermacher	2025-04-02T01:52:02Z	24	0	transformers	[ "transformers", "gguf", "GPT-Neo", "Fine-Tuned", "Chatbot", "Text Generation", "Abhinav Academy", "NLP", "en", "dataset:data.jsonl", "base_model:accesscreate012/abhinav-chatbot", "base_model:quantized:accesscreate012/abhinav-chatbot", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-03-31T14:22:59Z	--- base_model: accesscreate012/abhinav-chatbot datasets: - data.jsonl language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - GPT-Neo - Fine-Tuned - Chatbot - Text Generation - Abhinav Academy - NLP --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/accesscreate012/abhinav-chatbot <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/abhinav-chatbot-GGUF/resolve/main/abhinav-chatbot.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/abhinav-chatbot-GGUF/resolve/main/abhinav-chatbot.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/abhinav-chatbot-GGUF/resolve/main/abhinav-chatbot.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/abhinav-chatbot-GGUF/resolve/main/abhinav-chatbot.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/abhinav-chatbot-GGUF/resolve/main/abhinav-chatbot.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/abhinav-chatbot-GGUF/resolve/main/abhinav-chatbot.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/abhinav-chatbot-GGUF/resolve/main/abhinav-chatbot.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/abhinav-chatbot-GGUF/resolve/main/abhinav-chatbot.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/abhinav-chatbot-GGUF/resolve/main/abhinav-chatbot.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/abhinav-chatbot-GGUF/resolve/main/abhinav-chatbot.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/abhinav-chatbot-GGUF/resolve/main/abhinav-chatbot.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/abhinav-chatbot-GGUF/resolve/main/abhinav-chatbot.f16.gguf) \| f16 \| 0.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Cuidarte/erika	Cuidarte	2025-04-02T01:51:03Z	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	2025-04-02T01:31:40Z	--- 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: ERIK4 --- # Erika <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 `ERIK4` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "ERIK4", "lora_weights": "https://huggingface.co/Cuidarte/erika/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('Cuidarte/erika', weight_name='lora.safetensors') image = pipeline('ERIK4').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: 1500 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Cuidarte/erika/discussions) to add images that show off what you’ve made with this LoRA.
mradermacher/typhoon-v1.5-72b-instruct-GGUF	mradermacher	2025-04-02T01:46:57Z	0	0	transformers	[ "transformers", "gguf", "th", "en", "base_model:scb10x/typhoon-v1.5-72b-instruct", "base_model:quantized:scb10x/typhoon-v1.5-72b-instruct", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-01T12:31:45Z	--- base_model: scb10x/typhoon-v1.5-72b-instruct language: - th - en library_name: transformers license: other license_link: https://huggingface.co/Qwen/Qwen1.5-72B/blob/main/LICENSE license_name: tongyi-qianwen quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/scb10x/typhoon-v1.5-72b-instruct <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/typhoon-v1.5-72b-instruct-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/typhoon-v1.5-72b-instruct-GGUF/resolve/main/typhoon-v1.5-72b-instruct.Q2_K.gguf) \| Q2_K \| 28.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/typhoon-v1.5-72b-instruct-GGUF/resolve/main/typhoon-v1.5-72b-instruct.Q3_K_S.gguf) \| Q3_K_S \| 33.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/typhoon-v1.5-72b-instruct-GGUF/resolve/main/typhoon-v1.5-72b-instruct.Q3_K_M.gguf) \| Q3_K_M \| 36.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/typhoon-v1.5-72b-instruct-GGUF/resolve/main/typhoon-v1.5-72b-instruct.Q3_K_L.gguf) \| Q3_K_L \| 38.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/typhoon-v1.5-72b-instruct-GGUF/resolve/main/typhoon-v1.5-72b-instruct.IQ4_XS.gguf) \| IQ4_XS \| 39.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/typhoon-v1.5-72b-instruct-GGUF/resolve/main/typhoon-v1.5-72b-instruct.Q4_K_S.gguf) \| Q4_K_S \| 42.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/typhoon-v1.5-72b-instruct-GGUF/resolve/main/typhoon-v1.5-72b-instruct.Q4_K_M.gguf) \| Q4_K_M \| 44.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/typhoon-v1.5-72b-instruct-GGUF/resolve/main/typhoon-v1.5-72b-instruct.Q5_K_S.gguf) \| Q5_K_S \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/typhoon-v1.5-72b-instruct-GGUF/resolve/main/typhoon-v1.5-72b-instruct.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/typhoon-v1.5-72b-instruct-GGUF/resolve/main/typhoon-v1.5-72b-instruct.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 51.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/typhoon-v1.5-72b-instruct-GGUF/resolve/main/typhoon-v1.5-72b-instruct.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/typhoon-v1.5-72b-instruct-GGUF/resolve/main/typhoon-v1.5-72b-instruct.Q6_K.gguf.part2of2) \| Q6_K \| 59.4 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/typhoon-v1.5-72b-instruct-GGUF/resolve/main/typhoon-v1.5-72b-instruct.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/typhoon-v1.5-72b-instruct-GGUF/resolve/main/typhoon-v1.5-72b-instruct.Q8_0.gguf.part2of2) \| Q8_0 \| 76.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. 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 -->
UCSC-VLAA/MedReason-Llama	UCSC-VLAA	2025-04-02T01:45:50Z	0	0	null	[ "arxiv:2504.00993", "license:apache-2.0", "region:us" ]	null	2025-04-02T00:05:44Z	--- license: apache-2.0 --- # MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs <p align="center"> 📃 <a href="https://arxiv.org/abs/2504.00993" target="_blank">Paper</a> ｜🤗 <a href="https://huggingface.co/UCSC-VLAA/MedReason-8B" target="_blank">MedReason-8B</a> \| 📚 <a href="https://huggingface.co/datasets/UCSC-VLAA/MedReason" target="_blank">MedReason Data</a> </p> ## ⚡Introduction MedReason is a large-scale high-quality medical reasoning dataset designed to enable faithful and explainable medical problem-solving in large language models (LLMs). - We utilize a structured medical knowledge graph (KG) to convert clinical QA pairs into logical chains of reasoning, or “thinking paths”. - Our pipeline generates detailed reasoning for various medical questions from 7 medical datasets, resulting in a dataset of 32,682 question-answer pairs, each with detailed, step-by-step explanations. - By finetuning with proposed [MedReason dataset](https://huggingface.co/datasets/UCSC-VLAA/MedReason), our best model [MedReason-8B](https://huggingface.co/UCSC-VLAA/MedReason-8B), achieves state-of-the-art performance. We open-sourced our model here. ## 👨‍⚕️ Model - Model Access \| Model \| Base Model \| Link \| \| ----------------- \| ------------------------------------------------------------ \| ---------------------------------------------------------- \| \| MedReason-8B \| [HuatuoGPT-o1-8B](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-8B) \| [Link](https://huggingface.co/UCSC-VLAA/MedReason-8B) \| \| MedReason-Llama \| [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) \| [Link](https://huggingface.co/UCSC-VLAA/MedReason-Llama) \| \| MedReason-Mistral \| [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) \| [Link](https://huggingface.co/UCSC-VLAA/MedReason-Mistral) \| - Deploy: we provide a example code for direct inference with MedReason-8B. Also, MedReason-8B can be deployed with tools like [vllm](https://github.com/vllm-project/vllm) or [Sglang](https://github.com/sgl-project/sglang), we provide code for model deployment using Sglang in `./src/evaluation/eval.py` ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained('UCSC-VLAA/MedReason-8B',torch_dtype="auto",device_map="auto", use_safetensors= True) model.eval() tokenizer = AutoTokenizer.from_pretrained('UCSC-VLAA/MedReason-8B', trust_remote_code=True, padding_side='left') input_text = "How to stop a cough?" messages = [{"role": "user", "content": input_text}] inputs = tokenizer(tokenizer.apply_chat_template(messages, tokenize=False,add_generation_prompt=True), return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=2048) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## 🙏🏼 Acknowledgement We gratefully acknowledge the inspiring work of [HuatuoGPT-o1](https://github.com/FreedomIntelligence/HuatuoGPT-o1), which laid important groundwork for this research. We also thank the developers of the excellent tools [curator](https://github.com/bespokelabsai/curator/), [trl](https://github.com/huggingface/trl), and [sglang](https://github.com/sgl-project/sglang) for making this work possible. ## 📖 Citation ``` @misc{wu2025medreasonelicitingfactualmedical, title={MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs}, author={Juncheng Wu and Wenlong Deng and Xingxuan Li and Sheng Liu and Taomian Mi and Yifan Peng and Ziyang Xu and Yi Liu and Hyunjin Cho and Chang-In Choi and Yihan Cao and Hui Ren and Xiang Li and Xiaoxiao Li and Yuyin Zhou}, year={2025}, eprint={2504.00993}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2504.00993}, } ```
bowilleatyou/c3b4c958-6a74-401f-9379-1c4e94cfd6f4	bowilleatyou	2025-04-02T01:42:56Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-02T01:36:15Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
mradermacher/llama-2-7b-zoa-GGUF	mradermacher	2025-04-02T01:42:41Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:osmankoc/llama-2-7b-zoa", "base_model:quantized:osmankoc/llama-2-7b-zoa", "endpoints_compatible", "region:us" ]	null	2025-04-02T01:19:32Z	--- base_model: osmankoc/llama-2-7b-zoa language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/osmankoc/llama-2-7b-zoa <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-zoa-GGUF/resolve/main/llama-2-7b-zoa.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-zoa-GGUF/resolve/main/llama-2-7b-zoa.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-zoa-GGUF/resolve/main/llama-2-7b-zoa.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-zoa-GGUF/resolve/main/llama-2-7b-zoa.Q3_K_L.gguf) \| Q3_K_L \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-zoa-GGUF/resolve/main/llama-2-7b-zoa.IQ4_XS.gguf) \| IQ4_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-zoa-GGUF/resolve/main/llama-2-7b-zoa.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-zoa-GGUF/resolve/main/llama-2-7b-zoa.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-zoa-GGUF/resolve/main/llama-2-7b-zoa.Q5_K_S.gguf) \| Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-zoa-GGUF/resolve/main/llama-2-7b-zoa.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-zoa-GGUF/resolve/main/llama-2-7b-zoa.Q6_K.gguf) \| Q6_K \| 5.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-zoa-GGUF/resolve/main/llama-2-7b-zoa.Q8_0.gguf) \| Q8_0 \| 7.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-zoa-GGUF/resolve/main/llama-2-7b-zoa.f16.gguf) \| f16 \| 13.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

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