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2025-08-03 12:28:47
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lesso01/54e8dfb3-e0d3-4182-9498-336974a92507
lesso01
2025-02-28T05:37:27Z
0
0
peft
[ "peft", "safetensors", "phi3", "axolotl", "generated_from_trainer", "base_model:Xenova/tiny-random-Phi3ForCausalLM", "base_model:adapter:Xenova/tiny-random-Phi3ForCausalLM", "region:us" ]
null
2025-02-28T05:27:17Z
--- library_name: peft base_model: Xenova/tiny-random-Phi3ForCausalLM tags: - axolotl - generated_from_trainer model-index: - name: 54e8dfb3-e0d3-4182-9498-336974a92507 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora auto_find_batch_size: true base_model: Xenova/tiny-random-Phi3ForCausalLM bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e0b267728cb046c5_train_data.json ds_type: json format: custom path: /workspace/input_data/e0b267728cb046c5_train_data.json type: field_input: properties field_instruction: molecule field_output: caption format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 50 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: true hub_model_id: lesso01/54e8dfb3-e0d3-4182-9498-336974a92507 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000201 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/e0b267728cb046c5_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null seed: 10 sequence_len: 512 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: online wandb_name: 466383e9-dda5-45c3-9b3a-fe34bcfea887 wandb_project: 01a wandb_run: your_name wandb_runid: 466383e9-dda5-45c3-9b3a-fe34bcfea887 warmup_steps: 50 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 54e8dfb3-e0d3-4182-9498-336974a92507 This model is a fine-tuned version of [Xenova/tiny-random-Phi3ForCausalLM](https://huggingface.co/Xenova/tiny-random-Phi3ForCausalLM) on the None dataset. It achieves the following results on the evaluation set: - Loss: 10.1290 ## 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: 0.000201 - train_batch_size: 4 - eval_batch_size: 4 - seed: 10 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0000 | 1 | 10.3782 | | 10.3457 | 0.0013 | 50 | 10.3434 | | 10.3009 | 0.0026 | 100 | 10.3011 | | 10.1919 | 0.0039 | 150 | 10.2373 | | 10.1588 | 0.0052 | 200 | 10.1988 | | 10.1173 | 0.0066 | 250 | 10.1670 | | 10.1087 | 0.0079 | 300 | 10.1500 | | 10.1155 | 0.0092 | 350 | 10.1381 | | 10.0917 | 0.0105 | 400 | 10.1311 | | 10.1178 | 0.0118 | 450 | 10.1292 | | 10.108 | 0.0131 | 500 | 10.1290 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lesso03/fd0e0b6e-439d-416c-9eb0-e6a9973b7bb0
lesso03
2025-02-28T05:36:44Z
0
0
peft
[ "peft", "safetensors", "phi3", "axolotl", "generated_from_trainer", "base_model:Xenova/tiny-random-Phi3ForCausalLM", "base_model:adapter:Xenova/tiny-random-Phi3ForCausalLM", "region:us" ]
null
2025-02-28T05:26:34Z
--- library_name: peft base_model: Xenova/tiny-random-Phi3ForCausalLM tags: - axolotl - generated_from_trainer model-index: - name: fd0e0b6e-439d-416c-9eb0-e6a9973b7bb0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora auto_find_batch_size: true base_model: Xenova/tiny-random-Phi3ForCausalLM bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e0b267728cb046c5_train_data.json ds_type: json format: custom path: /workspace/input_data/e0b267728cb046c5_train_data.json type: field_input: properties field_instruction: molecule field_output: caption format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 50 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: true hub_model_id: lesso03/fd0e0b6e-439d-416c-9eb0-e6a9973b7bb0 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000203 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/e0b267728cb046c5_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null seed: 30 sequence_len: 512 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: online wandb_name: 466383e9-dda5-45c3-9b3a-fe34bcfea887 wandb_project: 03a wandb_run: your_name wandb_runid: 466383e9-dda5-45c3-9b3a-fe34bcfea887 warmup_steps: 50 weight_decay: 0.0 xformers_attention: null ``` </details><br> # fd0e0b6e-439d-416c-9eb0-e6a9973b7bb0 This model is a fine-tuned version of [Xenova/tiny-random-Phi3ForCausalLM](https://huggingface.co/Xenova/tiny-random-Phi3ForCausalLM) on the None dataset. It achieves the following results on the evaluation set: - Loss: 10.1201 ## 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: 0.000203 - train_batch_size: 4 - eval_batch_size: 4 - seed: 30 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0000 | 1 | 10.3781 | | 10.3458 | 0.0013 | 50 | 10.3376 | | 10.2785 | 0.0026 | 100 | 10.3018 | | 10.2171 | 0.0039 | 150 | 10.2360 | | 10.1416 | 0.0052 | 200 | 10.1886 | | 10.126 | 0.0066 | 250 | 10.1579 | | 10.1062 | 0.0079 | 300 | 10.1386 | | 10.1054 | 0.0092 | 350 | 10.1281 | | 10.092 | 0.0105 | 400 | 10.1224 | | 10.1113 | 0.0118 | 450 | 10.1203 | | 10.0967 | 0.0131 | 500 | 10.1201 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
dabrown/83c9d0d6-e162-49e1-9c59-e6784410cbc9
dabrown
2025-02-28T05:36:15Z
0
0
peft
[ "peft", "safetensors", "phi3", "axolotl", "generated_from_trainer", "base_model:Xenova/tiny-random-Phi3ForCausalLM", "base_model:adapter:Xenova/tiny-random-Phi3ForCausalLM", "region:us" ]
null
2025-02-28T05:26:33Z
--- library_name: peft base_model: Xenova/tiny-random-Phi3ForCausalLM tags: - axolotl - generated_from_trainer model-index: - name: 83c9d0d6-e162-49e1-9c59-e6784410cbc9 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: `0.5.2` ```yaml adapter: lora base_model: Xenova/tiny-random-Phi3ForCausalLM bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e0b267728cb046c5_train_data.json ds_type: json format: custom path: /workspace/input_data/e0b267728cb046c5_train_data.json type: field_input: properties field_instruction: molecule field_output: caption format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 16 gradient_checkpointing: false group_by_length: true hub_model_id: dabrown/83c9d0d6-e162-49e1-9c59-e6784410cbc9 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: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: false lora_inference_mode: true lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 1500 micro_batch_size: 2 mlflow_experiment_name: /tmp/e0b267728cb046c5_train_data.json model_type: AutoModelForCausalLM modules_to_save: lm_head num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true peft_use_rslora: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 1024 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: 466383e9-dda5-45c3-9b3a-fe34bcfea887 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 466383e9-dda5-45c3-9b3a-fe34bcfea887 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 83c9d0d6-e162-49e1-9c59-e6784410cbc9 This model is a fine-tuned version of [Xenova/tiny-random-Phi3ForCausalLM](https://huggingface.co/Xenova/tiny-random-Phi3ForCausalLM) on the None dataset. It achieves the following results on the evaluation set: - Loss: 10.0180 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 1500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 10.3727 | 0.0001 | 1 | 10.3781 | | 10.1395 | 0.0394 | 375 | 10.0757 | | 9.9922 | 0.0787 | 750 | 10.0363 | | 10.0856 | 0.1181 | 1125 | 10.0204 | | 10.085 | 0.1574 | 1500 | 10.0180 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.3.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3
AMindToThink/PYTHIA-70M-DEDUPED-FT-ORPO-ISAERFT_run-pythia-70m-deduped-20250228-0442
AMindToThink
2025-02-28T05:35:17Z
0
0
transformers
[ "transformers", "generated_from_trainer", "smol-course", "module_1", "isaerft", "arxiv:2403.07691", "base_model:EleutherAI/pythia-70m-deduped", "base_model:finetune:EleutherAI/pythia-70m-deduped", "endpoints_compatible", "region:us" ]
null
2025-02-28T05:35:16Z
--- base_model: EleutherAI/pythia-70m-deduped library_name: transformers model_name: PYTHIA-70M-DEDUPED-FT-ORPO-ISAERFT_run-pythia-70m-deduped-20250228-0442 tags: - generated_from_trainer - smol-course - module_1 - isaerft licence: license --- # Model Card for PYTHIA-70M-DEDUPED-FT-ORPO-ISAERFT_run-pythia-70m-deduped-20250228-0442 This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped). 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="AMindToThink/PYTHIA-70M-DEDUPED-FT-ORPO-ISAERFT_run-pythia-70m-deduped-20250228-0442", 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 ORPO, a method introduced in [ORPO: Monolithic Preference Optimization without Reference Model](https://huggingface.co/papers/2403.07691). ### Framework versions - TRL: 0.15.1 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 2.21.0 - Tokenizers: 0.21.0 ## Citations Cite ORPO as: ```bibtex @article{hong2024orpo, title = {{ORPO: Monolithic Preference Optimization without Reference Model}}, author = {Jiwoo Hong and Noah Lee and James Thorne}, year = 2024, eprint = {arXiv:2403.07691} } ``` 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}} } ```
RichardErkhov/Zekunli_-_qwen2.5-1.5b-lora-w-cot-w-cor-awq
RichardErkhov
2025-02-28T05:34:51Z
0
0
null
[ "safetensors", "qwen2", "arxiv:1910.09700", "4-bit", "awq", "region:us" ]
null
2025-02-28T05:33:51Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) qwen2.5-1.5b-lora-w-cot-w-cor - AWQ - Model creator: https://huggingface.co/Zekunli/ - Original model: https://huggingface.co/Zekunli/qwen2.5-1.5b-lora-w-cot-w-cor/ 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]
RichardErkhov/mu0gum_-_AIFT-42dot_LLM-SFT-1.3B-ao-instruct-all-v1.1-8bits
RichardErkhov
2025-02-28T05:34:42Z
0
0
null
[ "safetensors", "llama", "8-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:33:40Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) AIFT-42dot_LLM-SFT-1.3B-ao-instruct-all-v1.1 - bnb 8bits - Model creator: https://huggingface.co/mu0gum/ - Original model: https://huggingface.co/mu0gum/AIFT-42dot_LLM-SFT-1.3B-ao-instruct-all-v1.1/ Original model description: --- license: cc-by-nc-4.0 --- # AIFT-42dot-LLM-PLM-1.3B-ao-instruct-all-v1.11 베이스 모델 : 42dot/42dot_LLM-SFT-1.3B 학습 데이터 : 자체 제작한 Open Orca 스타일 데이터셋 약 48,000건 (중복 제거 및 데이터 분포 조정) 학습 방법 : Full finetuning epoch : 3 ## ko-lm-evaluation-harness(5-shot) |kobest_boolq|kobest_copa|kobest_hellaswag|pawsx_ko| |--|--|--|--| |0.52065527065527|0.721|0.466|0.5475| ## Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.0.0 - Tokenizers 0.15.0
RichardErkhov/mjalg_-_sc2-function-code-8bits
RichardErkhov
2025-02-28T05:34:13Z
0
0
null
[ "safetensors", "starcoder2", "arxiv:1910.09700", "8-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:32:15Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) sc2-function-code - bnb 8bits - Model creator: https://huggingface.co/mjalg/ - Original model: https://huggingface.co/mjalg/sc2-function-code/ Original model description: --- library_name: transformers tags: - llama-factory --- # 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/onnx-community_-_moondream2.text_model-ONNX-4bits
RichardErkhov
2025-02-28T05:33:59Z
0
0
null
[ "safetensors", "phi", "4-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:33:15Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) moondream2.text_model-ONNX - bnb 4bits - Model creator: https://huggingface.co/onnx-community/ - Original model: https://huggingface.co/onnx-community/moondream2.text_model-ONNX/ Original model description: --- library_name: transformers.js tags: - transformers --- The language model used by https://huggingface.co/vikhyatk/moondream2 (2024-08-26). ## Usage (Transformers.js) If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using: ```bash npm i @huggingface/transformers ``` **Example:** Text generation with `onnx-community/moondream2.text_model-ONNX`. ```js import { pipeline } from "@huggingface/transformers"; // Create a text generation pipeline const generator = await pipeline( "text-generation", "onnx-community/moondream2.text_model-ONNX", { dtype: "q4" }, ); // Generate a response const text = "Once upon a time"; const output = await generator(text, { max_new_tokens: 128 }); console.log(output[0].generated_text); ``` <details> <summary>Example output</summary> ``` Once upon a time, there was a young girl named Lily who loved to read books. She would spend hours lost in the pages of her favorite stories, imagining herself as the main characters. One day, while reading a book about a magical forest, Lily had an idea. She wanted to create her own magical forest, filled with all the wonders and adventures she had read about in her books. Lily knew that she needed to gather the right materials to create her magical forest. She decided to use her imagination and creativity to make the forest come to life. She gathered her friends and family to help her, and together they began to build the forest ``` </details> --- Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
mradermacher/ape-fiction-instruct-i1-GGUF
mradermacher
2025-02-28T05:33:34Z
0
0
transformers
[ "transformers", "gguf", "unsloth", "trl", "sft", "en", "dataset:leftyfeep/fiction-chapters-24kmax", "base_model:leftyfeep/ape-fiction-instruct", "base_model:quantized:leftyfeep/ape-fiction-instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]
null
2025-02-28T03:59:00Z
--- base_model: leftyfeep/ape-fiction-instruct datasets: - leftyfeep/fiction-chapters-24kmax language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - unsloth - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/leftyfeep/ape-fiction-instruct <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/ape-fiction-instruct-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/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-IQ2_S.gguf) | i1-IQ2_S | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.6 | very low quality | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.7 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.8 | | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-Q4_0.gguf) | i1-Q4_0 | 7.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.2 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-Q4_1.gguf) | i1-Q4_1 | 7.9 | | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.6 | | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.8 | | | [GGUF](https://huggingface.co/mradermacher/ape-fiction-instruct-i1-GGUF/resolve/main/ape-fiction-instruct.i1-Q6_K.gguf) | i1-Q6_K | 10.2 | 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 -->
PrunaAI/HuggingFaceTB-SmolLM-135M-GGUF-smashed
PrunaAI
2025-02-28T05:32:44Z
0
0
null
[ "gguf", "pruna-ai", "base_model:HuggingFaceTB/SmolLM-135M", "base_model:quantized:HuggingFaceTB/SmolLM-135M", "endpoints_compatible", "region:us" ]
null
2025-02-28T05:30:41Z
--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: HuggingFaceTB/SmolLM-135M 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 HuggingFaceTB/SmolLM-135M 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: HuggingFaceTB-SmolLM-135M-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 HuggingFaceTB-SmolLM-135M-GGUF-smashed SmolLM-135M.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 HuggingFaceTB-SmolLM-135M-GGUF-smashed --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` 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 HuggingFaceTB-SmolLM-135M-GGUF-smashed SmolLM-135M.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 SmolLM-135M.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="./SmolLM-135M.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="./SmolLM-135M.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).
RichardErkhov/mu0gum_-_AIFT-42dot_LLM-SFT-1.3B-ao-instruct-all-v1.1-4bits
RichardErkhov
2025-02-28T05:32:13Z
0
0
null
[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:31:30Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) AIFT-42dot_LLM-SFT-1.3B-ao-instruct-all-v1.1 - bnb 4bits - Model creator: https://huggingface.co/mu0gum/ - Original model: https://huggingface.co/mu0gum/AIFT-42dot_LLM-SFT-1.3B-ao-instruct-all-v1.1/ Original model description: --- license: cc-by-nc-4.0 --- # AIFT-42dot-LLM-PLM-1.3B-ao-instruct-all-v1.11 베이스 모델 : 42dot/42dot_LLM-SFT-1.3B 학습 데이터 : 자체 제작한 Open Orca 스타일 데이터셋 약 48,000건 (중복 제거 및 데이터 분포 조정) 학습 방법 : Full finetuning epoch : 3 ## ko-lm-evaluation-harness(5-shot) |kobest_boolq|kobest_copa|kobest_hellaswag|pawsx_ko| |--|--|--|--| |0.52065527065527|0.721|0.466|0.5475| ## Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.0.0 - Tokenizers 0.15.0
dabrown/aadd84ed-8550-44a7-a3e0-eaf3a08fb1ee
dabrown
2025-02-28T05:31:28Z
0
0
peft
[ "peft", "safetensors", "starcoder2", "axolotl", "generated_from_trainer", "base_model:bigcode/starcoder2-3b", "base_model:adapter:bigcode/starcoder2-3b", "license:bigcode-openrail-m", "region:us" ]
null
2025-02-28T05:27:58Z
--- library_name: peft license: bigcode-openrail-m base_model: bigcode/starcoder2-3b tags: - axolotl - generated_from_trainer model-index: - name: aadd84ed-8550-44a7-a3e0-eaf3a08fb1ee results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: bigcode/starcoder2-3b 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 early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: true hub_model_id: dabrown/aadd84ed-8550-44a7-a3e0-eaf3a08fb1ee 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: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: false lora_inference_mode: true lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 1500 micro_batch_size: 2 mlflow_experiment_name: /tmp/012ab4813cc99fb8_train_data.json model_type: AutoModelForCausalLM modules_to_save: lm_head num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true peft_use_rslora: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 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: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # aadd84ed-8550-44a7-a3e0-eaf3a08fb1ee This model is a fine-tuned version of [bigcode/starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3062 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 198 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 10.9565 | 0.0051 | 1 | 0.9137 | | 2.7123 | 0.2525 | 50 | 0.4196 | | 2.3208 | 0.5051 | 100 | 0.3391 | | 1.8126 | 0.7576 | 150 | 0.3062 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
TobiGeth/tg_user_2134108077_lora_1740719960
TobiGeth
2025-02-28T05:30:42Z
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
2025-02-28T05:30:40Z
--- 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: USER_2134108077_1740719960 --- # Tg_User_2134108077_Lora_1740719960 <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `USER_2134108077_1740719960` to trigger the image generation. ## 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('TobiGeth/tg_user_2134108077_lora_1740719960', weight_name='lora.safetensors') image = pipeline('your prompt').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)
mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF
mradermacher
2025-02-28T05:30:15Z
0
0
transformers
[ "transformers", "gguf", "en", "dataset:danielpark/gorani-100k-llama2-13b-instruct", "base_model:danielpark/gorani-100k-llama2-13b-instruct", "base_model:quantized:danielpark/gorani-100k-llama2-13b-instruct", "endpoints_compatible", "region:us", "imatrix" ]
null
2025-02-27T22:32:29Z
--- base_model: danielpark/gorani-100k-llama2-13b-instruct datasets: - danielpark/gorani-100k-llama2-13b-instruct language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/danielpark/gorani-100k-llama2-13b-instruct <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-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/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-IQ1_S.gguf) | i1-IQ1_S | 3.0 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-IQ1_M.gguf) | i1-IQ1_M | 3.2 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-IQ2_S.gguf) | i1-IQ2_S | 4.3 | | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.5 | very low quality | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-IQ2_M.gguf) | i1-IQ2_M | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-Q2_K.gguf) | i1-Q2_K | 5.0 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-IQ3_S.gguf) | i1-IQ3_S | 5.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-IQ3_M.gguf) | i1-IQ3_M | 6.1 | | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-Q3_K_L.gguf) | i1-Q3_K_L | 7.0 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-IQ4_XS.gguf) | i1-IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.5 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-Q4_0.gguf) | i1-Q4_0 | 7.5 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.5 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-Q4_K_M.gguf) | i1-Q4_K_M | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-Q4_1.gguf) | i1-Q4_1 | 8.3 | | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-Q5_K_S.gguf) | i1-Q5_K_S | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-Q5_K_M.gguf) | i1-Q5_K_M | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/gorani-100k-llama2-13b-instruct-i1-GGUF/resolve/main/gorani-100k-llama2-13b-instruct.i1-Q6_K.gguf) | i1-Q6_K | 10.8 | 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 -->
PrunaAI/stabilityai-stablecode-completion-alpha-3b-4k-AWQ-4bit-smashed
PrunaAI
2025-02-28T05:30:15Z
4
0
null
[ "safetensors", "gpt_neox", "pruna-ai", "4-bit", "awq", "region:us" ]
null
2025-02-24T19:38:02Z
--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: ORIGINAL_REPO_NAME 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.gg/rskEr4BZJx) # 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/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with awq. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - ***What is the model format?*** We use safetensors. - ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data. - ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - ***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). - ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo ORIGINAL_REPO_NAME installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install autoawq ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from awq import AutoAWQForCausalLM model = AutoAWQForCausalLM.from_quantized("PrunaAI/stabilityai-stablecode-completion-alpha-3b-4k-AWQ-4bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("ORIGINAL_REPO_NAME") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## 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 ORIGINAL_REPO_NAME 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).
mariadhakal/gemma-2-2B-it-thinking-function_calling-V0
mariadhakal
2025-02-28T05:28:53Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-2-2b-it", "base_model:finetune:google/gemma-2-2b-it", "endpoints_compatible", "region:us" ]
null
2025-02-28T05:27:34Z
--- base_model: google/gemma-2-2b-it library_name: transformers model_name: gemma-2-2B-it-thinking-function_calling-V0 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-2-2B-it-thinking-function_calling-V0 This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it). 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="mariadhakal/gemma-2-2B-it-thinking-function_calling-V0", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.49.0 - Pytorch: 2.1.0a0+gita8e7c98 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
RichardErkhov/mu0gum_-_AIFT-42dot_LLM-PLM-1.3B-ao-instruct-all-v1.0-awq
RichardErkhov
2025-02-28T05:27:53Z
0
0
null
[ "safetensors", "llama", "4-bit", "awq", "region:us" ]
null
2025-02-28T05:26:59Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) AIFT-42dot_LLM-PLM-1.3B-ao-instruct-all-v1.0 - AWQ - Model creator: https://huggingface.co/mu0gum/ - Original model: https://huggingface.co/mu0gum/AIFT-42dot_LLM-PLM-1.3B-ao-instruct-all-v1.0/ Original model description: --- license: cc-by-nc-4.0 --- # AIFT-42dot_LLM-PLM-1.3B-ao-instruct-all-v1.0 베이스 모델 : 42dot/42dot_LLM-PLM-1.3B 학습 데이터 : 자체 제작한 Open Orca 스타일 데이터셋 약 48,000건 (중복 제거 및 데이터 분포 조정) 학습 방법 : Full finetuning epoch : 3 ## ko-lm-evaluation-harness(5-shot) |kobest_boolq|kobest_copa|kobest_hellaswag|pawsx_ko| |--|--|--|--| |0.5220797720797721|0.72|0.458|0.563| ## Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.0.0 - Tokenizers 0.15.0
RichardErkhov/WYNN747_-_Burmese-GPT-qa_sys6_main_2-8bits
RichardErkhov
2025-02-28T05:27:52Z
0
0
null
[ "safetensors", "gpt2", "arxiv:1910.09700", "8-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:26:47Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Burmese-GPT-qa_sys6_main_2 - bnb 8bits - Model creator: https://huggingface.co/WYNN747/ - Original model: https://huggingface.co/WYNN747/Burmese-GPT-qa_sys6_main_2/ 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]
yyunxg/lora-trained-xl
yyunxg
2025-02-28T05:27:05Z
0
0
diffusers
[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]
text-to-image
2025-02-28T04:33:45Z
--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a photo of a man widget: - text: A photo of a man holding flowers output: url: image_0.png - text: A photo of a man holding flowers output: url: image_1.png - text: A photo of a man holding flowers output: url: image_2.png - text: A photo of a man holding flowers output: url: image_3.png tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - yyunxg/lora-trained-xl <Gallery /> ## Model description These are yyunxg/lora-trained-xl LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of a man to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](yyunxg/lora-trained-xl/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
RichardErkhov/WYNN747_-_Burmese-GPT-qa_sys6_main_2-4bits
RichardErkhov
2025-02-28T05:26:07Z
0
0
null
[ "safetensors", "gpt2", "arxiv:1910.09700", "4-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:25:25Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Burmese-GPT-qa_sys6_main_2 - bnb 4bits - Model creator: https://huggingface.co/WYNN747/ - Original model: https://huggingface.co/WYNN747/Burmese-GPT-qa_sys6_main_2/ 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]
kallilikhitha123/finetuned_llama_8b_matching_300_28-02-2025
kallilikhitha123
2025-02-28T05:26:03Z
0
0
transformers
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2025-02-28T05:20:16Z
--- 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]
coffiee/moo3
coffiee
2025-02-28T05:24:32Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-02-28T05:23:35Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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/onekq-ai_-_starcoder2-3b-instruct-v0.1-4bits
RichardErkhov
2025-02-28T05:24:26Z
0
0
null
[ "safetensors", "starcoder2", "4-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:23:15Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) starcoder2-3b-instruct-v0.1 - bnb 4bits - Model creator: https://huggingface.co/onekq-ai/ - Original model: https://huggingface.co/onekq-ai/starcoder2-3b-instruct-v0.1/ Original model description: --- license: bigcode-openrail-m datasets: - bigcode/self-oss-instruct-sc2-exec-filter-50k base_model: - bigcode/starcoder2-3b library_name: transformers --- Starcoder2-3b finetuned the same way as https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1 using https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k * Epochs: 1 * Learning Rate: 0.0001 * Lora Rank: 8 * Batch Size: 16 * Evaluation Split: 0
mradermacher/Nova-14b-sce-GGUF
mradermacher
2025-02-28T05:23:06Z
0
0
transformers
[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "en", "base_model:Quazim0t0/Nova-14b-sce", "base_model:quantized:Quazim0t0/Nova-14b-sce", "endpoints_compatible", "region:us", "conversational" ]
null
2025-02-28T00:09:30Z
--- base_model: Quazim0t0/Nova-14b-sce language: - en library_name: transformers quantized_by: mradermacher tags: - merge - mergekit - lazymergekit --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Quazim0t0/Nova-14b-sce <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Nova-14b-sce-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/Nova-14b-sce-GGUF/resolve/main/Nova-14b-sce.Q2_K.gguf) | Q2_K | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Nova-14b-sce-GGUF/resolve/main/Nova-14b-sce.Q3_K_S.gguf) | Q3_K_S | 6.6 | | | [GGUF](https://huggingface.co/mradermacher/Nova-14b-sce-GGUF/resolve/main/Nova-14b-sce.Q3_K_M.gguf) | Q3_K_M | 7.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Nova-14b-sce-GGUF/resolve/main/Nova-14b-sce.Q3_K_L.gguf) | Q3_K_L | 7.9 | | | [GGUF](https://huggingface.co/mradermacher/Nova-14b-sce-GGUF/resolve/main/Nova-14b-sce.IQ4_XS.gguf) | IQ4_XS | 8.2 | | | [GGUF](https://huggingface.co/mradermacher/Nova-14b-sce-GGUF/resolve/main/Nova-14b-sce.Q4_K_S.gguf) | Q4_K_S | 8.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Nova-14b-sce-GGUF/resolve/main/Nova-14b-sce.Q4_K_M.gguf) | Q4_K_M | 9.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Nova-14b-sce-GGUF/resolve/main/Nova-14b-sce.Q5_K_S.gguf) | Q5_K_S | 10.3 | | | [GGUF](https://huggingface.co/mradermacher/Nova-14b-sce-GGUF/resolve/main/Nova-14b-sce.Q5_K_M.gguf) | Q5_K_M | 10.5 | | | [GGUF](https://huggingface.co/mradermacher/Nova-14b-sce-GGUF/resolve/main/Nova-14b-sce.Q6_K.gguf) | Q6_K | 12.1 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Nova-14b-sce-GGUF/resolve/main/Nova-14b-sce.Q8_0.gguf) | Q8_0 | 15.7 | 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 -->
lesso17/571d9e9e-4be1-4797-a5da-cf93f41677d8
lesso17
2025-02-28T05:22:22Z
0
0
peft
[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:dltjdgh0928/test_instruction", "base_model:adapter:dltjdgh0928/test_instruction", "license:apache-2.0", "region:us" ]
null
2025-02-28T04:33:02Z
--- library_name: peft license: apache-2.0 base_model: dltjdgh0928/test_instruction tags: - axolotl - generated_from_trainer model-index: - name: 571d9e9e-4be1-4797-a5da-cf93f41677d8 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora auto_find_batch_size: true base_model: dltjdgh0928/test_instruction bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 943efde6ed250892_train_data.json ds_type: json format: custom path: /workspace/input_data/943efde6ed250892_train_data.json type: field_input: conversation field_instruction: note field_output: summary format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 50 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: true hub_model_id: lesso17/571d9e9e-4be1-4797-a5da-cf93f41677d8 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000217 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/943efde6ed250892_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null seed: 170 sequence_len: 512 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: online wandb_name: 718e1434-c66b-47ac-a585-5409ff9009c4 wandb_project: 17a wandb_run: your_name wandb_runid: 718e1434-c66b-47ac-a585-5409ff9009c4 warmup_steps: 50 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 571d9e9e-4be1-4797-a5da-cf93f41677d8 This model is a fine-tuned version of [dltjdgh0928/test_instruction](https://huggingface.co/dltjdgh0928/test_instruction) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0838 ## 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: 0.000217 - train_batch_size: 4 - eval_batch_size: 4 - seed: 170 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0003 | 1 | 0.9501 | | 0.226 | 0.0142 | 50 | 0.1125 | | 0.2299 | 0.0284 | 100 | 0.1081 | | 0.2113 | 0.0425 | 150 | 0.1046 | | 0.202 | 0.0567 | 200 | 0.0998 | | 0.1946 | 0.0709 | 250 | 0.0952 | | 0.1814 | 0.0851 | 300 | 0.0919 | | 0.178 | 0.0992 | 350 | 0.0880 | | 0.1631 | 0.1134 | 400 | 0.0858 | | 0.1702 | 0.1276 | 450 | 0.0841 | | 0.186 | 0.1418 | 500 | 0.0838 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
RichardErkhov/WYNN747_-_Burmese-GPT-main-v7-1k-no-ovr-4bits
RichardErkhov
2025-02-28T05:20:07Z
0
0
null
[ "safetensors", "gpt2", "arxiv:1910.09700", "4-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:19:23Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Burmese-GPT-main-v7-1k-no-ovr - bnb 4bits - Model creator: https://huggingface.co/WYNN747/ - Original model: https://huggingface.co/WYNN747/Burmese-GPT-main-v7-1k-no-ovr/ 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]
prithivMLmods/Porpoise-Opus-14B-Exp
prithivMLmods
2025-02-28T05:19:01Z
0
11
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "trl", "sft", "conversational", "en", "zh", "base_model:prithivMLmods/Sombrero-Opus-14B-Elite5", "base_model:finetune:prithivMLmods/Sombrero-Opus-14B-Elite5", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2025-02-26T19:15:10Z
--- license: apache-2.0 language: - en - zh base_model: - prithivMLmods/Sombrero-Opus-14B-Elite5 pipeline_tag: text-generation library_name: transformers tags: - text-generation-inference - trl - sft model-index: - name: Porpoise-Opus-14B-Exp results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: wis-k/instruction-following-eval split: train args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 70.98 name: averaged accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FPorpoise-Opus-14B-Exp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: SaylorTwift/bbh split: test args: num_few_shot: 3 metrics: - type: acc_norm value: 49.95 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FPorpoise-Opus-14B-Exp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: lighteval/MATH-Hard split: test args: num_few_shot: 4 metrics: - type: exact_match value: 40.41 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FPorpoise-Opus-14B-Exp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa split: train args: num_few_shot: 0 metrics: - type: acc_norm value: 19.13 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FPorpoise-Opus-14B-Exp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 21.3 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FPorpoise-Opus-14B-Exp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 48.85 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FPorpoise-Opus-14B-Exp name: Open LLM Leaderboard --- ![xszfvsxdvsdf.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/LCPddzlEU2sA9xet5b3QN.png) # **Porpoise-Opus-14B-Exp** Porpoise-Opus-14B-Exp is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. This model is optimized for general-purpose reasoning and answering, excelling in contextual understanding, logical deduction, and multi-step problem-solving. It has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets to improve comprehension, structured responses, and conversational intelligence. ## **Key Improvements** 1. **Enhanced General Knowledge**: The model provides broad knowledge across various domains, improving capabilities in answering questions accurately and generating coherent responses. 2. **Improved Instruction Following**: Significant advancements in understanding and following complex instructions, generating structured responses, and maintaining coherence over extended interactions. 3. **Versatile Adaptability**: More resilient to diverse prompts, enhancing its ability to handle a wide range of topics and conversation styles, including open-ended and structured inquiries. 4. **Long-Context Support**: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed responses. 5. **Multilingual Proficiency**: Supports over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. ## **Quickstart with transformers** Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "prithivMLmods/Porpoise-Opus-14B-Exp" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "What are the key principles of general-purpose AI?" messages = [ {"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## **Intended Use** 1. **General-Purpose Reasoning**: Designed for broad applicability, assisting with logical reasoning, answering diverse questions, and solving general knowledge problems. 2. **Educational and Informational Assistance**: Suitable for providing explanations, summaries, and research-based responses for students, educators, and general users. 3. **Conversational AI and Chatbots**: Ideal for building intelligent conversational agents that require contextual understanding and dynamic response generation. 4. **Multilingual Applications**: Supports global communication, translations, and multilingual content generation. 5. **Structured Data Processing**: Capable of analyzing and generating structured outputs, such as tables and JSON, useful for data science and automation. 6. **Long-Form Content Generation**: Can generate extended responses, including articles, reports, and guides, maintaining coherence over large text outputs. ## **Limitations** 1. **Hardware Requirements**: Requires high-memory GPUs or TPUs due to its large parameter size and long-context support. 2. **Potential Bias in Responses**: While designed to be neutral, outputs may still reflect biases present in training data. 3. **Inconsistent Outputs in Creative Tasks**: May produce variable results in storytelling and highly subjective topics. 4. **Limited Real-World Awareness**: Does not have access to real-time events beyond its training cutoff. 5. **Error Propagation in Extended Outputs**: Minor errors in early responses may affect overall coherence in long-form outputs. 6. **Prompt Sensitivity**: The effectiveness of responses may depend on how well the input prompt is structured. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__Porpoise-Opus-14B-Exp-details)! Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FPorpoise-Opus-14B-Exp&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)! | Metric |Value (%)| |-------------------|--------:| |**Average** | 41.77| |IFEval (0-Shot) | 70.98| |BBH (3-Shot) | 49.95| |MATH Lvl 5 (4-Shot)| 40.41| |GPQA (0-shot) | 19.13| |MuSR (0-shot) | 21.30| |MMLU-PRO (5-shot) | 48.85|
prithivMLmods/Lacerta-Opus-14B-Elite8
prithivMLmods
2025-02-28T05:18:11Z
0
10
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "trl", "sft", "elite8", "conversational", "en", "zh", "base_model:prithivMLmods/Messier-Opus-14B-Elite7", "base_model:finetune:prithivMLmods/Messier-Opus-14B-Elite7", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2025-02-27T10:24:59Z
--- license: apache-2.0 language: - en - zh base_model: - prithivMLmods/Messier-Opus-14B-Elite7 pipeline_tag: text-generation library_name: transformers tags: - text-generation-inference - trl - sft - elite8 model-index: - name: Lacerta-Opus-14B-Elite8 results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: wis-k/instruction-following-eval split: train args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 61.41 name: averaged accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FLacerta-Opus-14B-Elite8 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: SaylorTwift/bbh split: test args: num_few_shot: 3 metrics: - type: acc_norm value: 48.18 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FLacerta-Opus-14B-Elite8 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: lighteval/MATH-Hard split: test args: num_few_shot: 4 metrics: - type: exact_match value: 36.48 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FLacerta-Opus-14B-Elite8 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa split: train args: num_few_shot: 0 metrics: - type: acc_norm value: 17.11 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FLacerta-Opus-14B-Elite8 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 17.21 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FLacerta-Opus-14B-Elite8 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 48.02 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FLacerta-Opus-14B-Elite8 name: Open LLM Leaderboard --- ![2.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Fx23p-XW8GjKTOqEP6LwQ.png) # **Lacerta-Opus-14B-Elite8** Lacerta-Opus-14B-Elite8 is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. This model is optimized for general-purpose reasoning and answering, excelling in contextual understanding, logical deduction, and multi-step problem-solving. It has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets to improve comprehension, structured responses, and conversational intelligence. ## **Key Improvements** 1. **Enhanced General Knowledge**: The model provides broad knowledge across various domains, improving capabilities in answering questions accurately and generating coherent responses. 2. **Improved Instruction Following**: Significant advancements in understanding and following complex instructions, generating structured responses, and maintaining coherence over extended interactions. 3. **Versatile Adaptability**: More resilient to diverse prompts, enhancing its ability to handle a wide range of topics and conversation styles, including open-ended and structured inquiries. 4. **Long-Context Support**: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed responses. 5. **Multilingual Proficiency**: Supports over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. ## **Quickstart with transformers** Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "prithivMLmods/Lacerta-Opus-14B-Elite8" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "What are the key principles of general-purpose AI?" messages = [ {"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## **Intended Use** 1. **General-Purpose Reasoning**: Designed for broad applicability, assisting with logical reasoning, answering diverse questions, and solving general knowledge problems. 2. **Educational and Informational Assistance**: Suitable for providing explanations, summaries, and research-based responses for students, educators, and general users. 3. **Conversational AI and Chatbots**: Ideal for building intelligent conversational agents that require contextual understanding and dynamic response generation. 4. **Multilingual Applications**: Supports global communication, translations, and multilingual content generation. 5. **Structured Data Processing**: Capable of analyzing and generating structured outputs, such as tables and JSON, useful for data science and automation. 6. **Long-Form Content Generation**: Can generate extended responses, including articles, reports, and guides, maintaining coherence over large text outputs. ## **Limitations** 1. **Hardware Requirements**: Requires high-memory GPUs or TPUs due to its large parameter size and long-context support. 2. **Potential Bias in Responses**: While designed to be neutral, outputs may still reflect biases present in training data. 3. **Inconsistent Outputs in Creative Tasks**: May produce variable results in storytelling and highly subjective topics. 4. **Limited Real-World Awareness**: Does not have access to real-time events beyond its training cutoff. 5. **Error Propagation in Extended Outputs**: Minor errors in early responses may affect overall coherence in long-form outputs. 6. **Prompt Sensitivity**: The effectiveness of responses may depend on how well the input prompt is structured. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__Lacerta-Opus-14B-Elite8-details)! Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FLacerta-Opus-14B-Elite8&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)! | Metric |Value (%)| |-------------------|--------:| |**Average** | 38.07| |IFEval (0-Shot) | 61.41| |BBH (3-Shot) | 48.18| |MATH Lvl 5 (4-Shot)| 36.48| |GPQA (0-shot) | 17.11| |MuSR (0-shot) | 17.21| |MMLU-PRO (5-shot) | 48.02|
RichardErkhov/ylalain_-_ECE-PRYMMAL-YL-1B-SLERP-V7-4bits
RichardErkhov
2025-02-28T05:18:07Z
0
0
null
[ "safetensors", "qwen2", "arxiv:1910.09700", "4-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:16:45Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) ECE-PRYMMAL-YL-1B-SLERP-V7 - bnb 4bits - Model creator: https://huggingface.co/ylalain/ - Original model: https://huggingface.co/ylalain/ECE-PRYMMAL-YL-1B-SLERP-V7/ Original model description: --- library_name: transformers license: apache-2.0 --- # 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/WYNN747_-_Burmese-GPT-main-v7-1k-8bits
RichardErkhov
2025-02-28T05:17:15Z
0
0
null
[ "safetensors", "gpt2", "arxiv:1910.09700", "8-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:16:18Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Burmese-GPT-main-v7-1k - bnb 8bits - Model creator: https://huggingface.co/WYNN747/ - Original model: https://huggingface.co/WYNN747/Burmese-GPT-main-v7-1k/ 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]
RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf
RichardErkhov
2025-02-28T05:16:18Z
0
0
null
[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2025-02-28T04:22:30Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) shwartz2_normal_merged - GGUF - Model creator: https://huggingface.co/AvniMittal13/ - Original model: https://huggingface.co/AvniMittal13/shwartz2_normal_merged/ | Name | Quant method | Size | | ---- | ---- | ---- | | [shwartz2_normal_merged.Q2_K.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.Q2_K.gguf) | Q2_K | 1.03GB | | [shwartz2_normal_merged.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.IQ3_XS.gguf) | IQ3_XS | 1.12GB | | [shwartz2_normal_merged.IQ3_S.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.IQ3_S.gguf) | IQ3_S | 1.16GB | | [shwartz2_normal_merged.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.Q3_K_S.gguf) | Q3_K_S | 1.16GB | | [shwartz2_normal_merged.IQ3_M.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.IQ3_M.gguf) | IQ3_M | 1.23GB | | [shwartz2_normal_merged.Q3_K.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.Q3_K.gguf) | Q3_K | 1.33GB | | [shwartz2_normal_merged.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.Q3_K_M.gguf) | Q3_K_M | 1.33GB | | [shwartz2_normal_merged.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.Q3_K_L.gguf) | Q3_K_L | 1.46GB | | [shwartz2_normal_merged.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.IQ4_XS.gguf) | IQ4_XS | 1.43GB | | [shwartz2_normal_merged.Q4_0.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.Q4_0.gguf) | Q4_0 | 1.49GB | | [shwartz2_normal_merged.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.IQ4_NL.gguf) | IQ4_NL | 1.5GB | | [shwartz2_normal_merged.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.Q4_K_S.gguf) | Q4_K_S | 1.5GB | | [shwartz2_normal_merged.Q4_K.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.Q4_K.gguf) | Q4_K | 1.62GB | | [shwartz2_normal_merged.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.Q4_K_M.gguf) | Q4_K_M | 1.62GB | | [shwartz2_normal_merged.Q4_1.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.Q4_1.gguf) | Q4_1 | 1.64GB | | [shwartz2_normal_merged.Q5_0.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.Q5_0.gguf) | Q5_0 | 1.8GB | | [shwartz2_normal_merged.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.Q5_K_S.gguf) | Q5_K_S | 1.8GB | | [shwartz2_normal_merged.Q5_K.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.Q5_K.gguf) | Q5_K | 1.86GB | | [shwartz2_normal_merged.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.Q5_K_M.gguf) | Q5_K_M | 1.86GB | | [shwartz2_normal_merged.Q5_1.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.Q5_1.gguf) | Q5_1 | 1.95GB | | [shwartz2_normal_merged.Q6_K.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.Q6_K.gguf) | Q6_K | 2.12GB | | [shwartz2_normal_merged.Q8_0.gguf](https://huggingface.co/RichardErkhov/AvniMittal13_-_shwartz2_normal_merged-gguf/blob/main/shwartz2_normal_merged.Q8_0.gguf) | Q8_0 | 2.75GB | 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]
zt-ai/DeepSeekFakeNews-LLM-7B-Chat
zt-ai
2025-02-28T05:15:30Z
12
2
null
[ "pytorch", "safetensors", "llama", "zh", "base_model:deepseek-ai/deepseek-llm-7b-chat", "base_model:finetune:deepseek-ai/deepseek-llm-7b-chat", "doi:10.57967/hf/4631", "license:mit", "region:us" ]
null
2025-02-10T02:44:54Z
--- license: mit language: - zh base_model: - deepseek-ai/deepseek-llm-7b-chat --- # Deep **SEEK-FAKE-NEWS** LLM <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <!-- markdownlint-disable no-duplicate-header --> <div align="center"> <img src="figures/logo_v1.0.png" width="100%" height="50%" alt="DeepSeekFakeNews-LLM" /> </div> <p align="center"> <a href="https://github.com/zoltol/DeepSeekFakeNews-LLM-7B-Chat"><b>Project Link</b>👁️</a> <a href="http://faculty.neu.edu.cn/tanzhenhua/zh_CN/zdylm/228486/list/index.htm"><b>Lab Link</b>👁️</a> </p> ### 1. Introduction of Deep **SEEK-FAKE-NEWS** LLM The reasoning model Deep SEEK-FAKE-NEWS LLM was fine-tuned on the DeepSeek-LLM-7B-Chat base model, utilizing a newly constructed fake news instruction dataset built upon the MCFEND data. Compared to other reasoning models such as DeepSeek-LLM-7B-Chat, this reasoning model not only achieves significant improvements in F1 and accuracy (Acc) metrics but also excels in reasoning analysis, providing rationales that serve as well-founded supports for its conclusions. This work provides an adapter for LLMs, contributing significantly to the advancement of Artificial General Intelligence (AGI). The adapter enhances the capabilities of LLMs, enabling it to perform more effectively in the specific domain of fake news detection. ### 2. Model Summary `deepseekfakenews-llm-7b-chat` is a 7B parameter model initialized from `deepseek-llm-7b-chat` and fine-tuned on the [fake news instruction dataset](https://drive.google.com/drive/folders/1PkOX11062v6bN7sjSw_qNJRrXE6um61o?usp=sharing) that we constructed based on the [MCFEND](https://github.com/TrustworthyComp) dataset. - **Github Page:** [zoltol/DeepSeekFakeNews-LLM-7B-Chat](https://github.com/zoltol/DeepSeekFakeNews-LLM-7B-Chat) - **Huggingface Repository:** [zt-ai/DeepSeekFakeNews-LLM-7B-Chat](https://huggingface.co/zt-ai/DeepSeekFakeNews-LLM-7B-Chat) - **Demo of Chatting With DeepSeekFakeNews-LLM: <div align="center"> <img src="figures/demo.PNG" width="100%" alt="Chat with DeepSeekFakeNews-LLM" /> </div> ### 3. How to Use Here are some examples of how to use our model. ```python import torch from peft import PeftModel from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig model_name = "zt-ai/DeepSeekFakeNews-llm-7b-chat" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) model.generation_config = GenerationConfig.from_pretrained(model_name) model.generation_config.pad_token_id = model.generation_config.eos_token_id model = PeftModel.from_pretrained(model, model_name) messages = [ { "role": "user", "content": """假新闻的表现可以总结为以下几个方面:1. 逻辑和事实矛盾。2.断章取义和误导性信息。3.夸张标题和吸引眼球的内容。4.情绪化和极端语言。5.偏见和单一立场。请从这几个方面分析新闻的真实性(真新闻或假新闻): 发布时间: 新闻标题: 新闻内容: """} ] input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt") outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100) result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True) print(result) ``` Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input. ``` User: {messages[0]['content']} Assistant: ``` **Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<|begin▁of▁sentence|>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input. ### 4. Dataset The SEEK-FAKE-NEWS LLM is post-trained on the [fake news instruction dataset](https://drive.google.com/drive/folders/1PkOX11062v6bN7sjSw_qNJRrXE6um61o?usp=sharing) that we constructed based on the [MCFEND](https://github.com/TrustworthyComp) dataset. ### 5. Evaluation Results | LLMs | F1. | Acc. | |--------------------------|-------|-------| | DeepSeek-LLM-7B-Chat | 64.74 | 63.58 | | DeepSeekFakeNews-LLM-7B-Chat | 84.17 | 84.48 | ## 6. Citation ``` @misc {tao_zhang_2025, author = { {Tao Zhang} }, title = { DeepSeekFakeNews-LLM-7B-Chat (Revision ccc44ec) }, year = 2025, url = { https://huggingface.co/zt-ai/DeepSeekFakeNews-LLM-7B-Chat }, doi = { 10.57967/hf/4631 }, publisher = { Hugging Face } } ``` ### 7. License This code repository is licensed under the MIT License. The use of DeepSeekFakeNews-LLM models is subject to the Model License. DeepSeekFakeNews-LLM supports commercial use. <!-- See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details. --> ### 8. Contact If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]) or [[email protected]](mailto:[email protected]).
RichardErkhov/mesolitica_-_mallam-3b-20k-instructions-8bits
RichardErkhov
2025-02-28T05:15:18Z
0
0
null
[ "safetensors", "mistral", "8-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:13:27Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mallam-3b-20k-instructions - bnb 8bits - Model creator: https://huggingface.co/mesolitica/ - Original model: https://huggingface.co/mesolitica/mallam-3b-20k-instructions/ Original model description: --- language: - ms --- # Full Parameter Finetuning MaLLaM 🌙 3B 20480 context length on Malaysian instructions dataset README at https://github.com/mesolitica/malaya/tree/5.1/session/mistral#mallam-3b We use exact Mistral Instruct chat template. WandB, https://wandb.ai/mesolitica/fpf-mallam-3b-instructions-16k?workspace=user-husein-mesolitica WandB report, https://wandb.ai/mesolitica/fpf-mallam-5b-instructions-16k/reports/Instruction-finetuning--Vmlldzo2MjE5Njg2 ## Dataset Dataset gathered at https://huggingface.co/collections/mesolitica/malaysian-synthetic-dataset-656c2673fe7fe0b1e9e25fe2 Notebook to prepare dataset at https://github.com/mesolitica/malaysian-dataset/blob/master/llm-instruction/combine-malay-no-alignment-multitasks-partial-ultrachat-v2.ipynb ## Limitations This model is a quick demonstration that the base model can be easily fine-tuned to achieve some performance. It does have minimal moderation mechanisms. ## how-to ```python from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig import torch import json def parse_mistral_chat(messages, function_call = None): user_query = messages[-1]['content'] users, assistants = [], [] for q in messages[:-1]: if q['role'] == 'user': users.append(q['content']) elif q['role'] == 'assistant': assistants.append(q['content']) texts = ['<s>'] if function_call: fs = [] for f in function_call: f = json.dumps(f, indent=4) fs.append(f) fs = '\n\n'.join(fs) texts.append(f'\n[FUNCTIONCALL]\n{fs}\n') for u, a in zip(users, assistants): texts.append(f'[INST] {u.strip()} [/INST] {a.strip()}</s>') texts.append(f'[INST] {user_query.strip()} [/INST]') prompt = ''.join(texts).strip() return prompt TORCH_DTYPE = 'bfloat16' nf4_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type='nf4', bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=getattr(torch, TORCH_DTYPE) ) tokenizer = AutoTokenizer.from_pretrained('mesolitica/mallam-3b-20k-instructions') model = AutoModelForCausalLM.from_pretrained( 'mesolitica/mallam-3b-20k-instructions', use_flash_attention_2 = True, quantization_config = nf4_config ) messages = [ {'role': 'user', 'content': 'kwsp tu apa'} ] prompt = parse_mistral_chat(messages) inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda') generate_kwargs = dict( inputs, max_new_tokens=1024, top_p=0.95, top_k=50, temperature=0.9, do_sample=True, num_beams=1, ) r = model.generate(**generate_kwargs) tokenizer.decode(r[0]) ``` ```text <s> [INST] kwsp tu apa [/INST]KWSP bermaksud Kumpulan Wang Simpanan Pekerja. Ia adalah sebuah institusi simpanan persaraan yang ditubuhkan oleh Kementerian Kewangan Malaysia untuk tujuan mengumpul simpanan ahli untuk dibayar pada umur persaraan, penuh atau penuh persaraan penuh. KWSP ditubuhkan pada tahun 1951 dan mula beroperasi pada tahun 1952. KWSP adalah salah satu institusi simpanan persaraan terbesar di dunia, dengan pangkalan ahli sekitar 14 juta ahli.</s> ```
TobiGeth/tg_user_416463659_lora_1740718923
TobiGeth
2025-02-28T05:13:35Z
0
0
diffusers
[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]
text-to-image
2025-02-28T05:13:34Z
--- 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: USER_416463659_1740718923 --- # Tg_User_416463659_Lora_1740718923 <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `USER_416463659_1740718923` to trigger the image generation. ## 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('TobiGeth/tg_user_416463659_lora_1740718923', weight_name='lora.safetensors') image = pipeline('your prompt').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)
vag12345678/ruchika1
vag12345678
2025-02-28T05:13:12Z
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
2025-02-28T04:39:55Z
--- 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: Sidf --- # Ruchika1 <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Sidf` to trigger the image generation. ## 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('vag12345678/ruchika1', weight_name='lora.safetensors') image = pipeline('your prompt').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)
RichardErkhov/WYNN747_-_Burmese-GPT-main-sentence-8bits
RichardErkhov
2025-02-28T05:12:28Z
0
0
null
[ "safetensors", "gpt2", "arxiv:1910.09700", "8-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:11:28Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Burmese-GPT-main-sentence - bnb 8bits - Model creator: https://huggingface.co/WYNN747/ - Original model: https://huggingface.co/WYNN747/Burmese-GPT-main-sentence/ 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]
coffiee/moo2
coffiee
2025-02-28T05:11:28Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-02-28T05:10:36Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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/gohsyi_-_Llama-3.2-3B-8bits
RichardErkhov
2025-02-28T05:11:27Z
0
0
null
[ "safetensors", "llama", "arxiv:2204.05149", "8-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:08:29Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Llama-3.2-3B - bnb 8bits - Model creator: https://huggingface.co/gohsyi/ - Original model: https://huggingface.co/gohsyi/Llama-3.2-3B/ Original model description: --- language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3.2 extra_gated_prompt: >- ### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT Llama 3.2 Version Release Date: September 25, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “Llama 3.2” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://www.llama.com/llama-downloads. “Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion thereof) made available under this Agreement. “Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Llama 3.2 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use Policy (“**Policy**”). The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy). #### Prohibited Uses We want everyone to use Llama 3.2 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. 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Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.2 related to the following: 8. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997 9. Guns and illegal weapons (including weapon development) 10. Illegal drugs and regulated/controlled substances 11. Operation of critical infrastructure, transportation technologies, or heavy machinery 12. Self-harm or harm to others, including suicide, cutting, and eating disorders 13. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Llama 3.2 related to the following: 14. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 15. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 16. Generating, promoting, or further distributing spam 17. Impersonating another individual without consent, authorization, or legal right 18. Representing that the use of Llama 3.2 or outputs are human-generated 19. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement  4. Fail to appropriately disclose to end users any known dangers of your AI system 5. Interact with third party tools, models, or software designed to generate unlawful content or engage in unlawful or harmful conduct and/or represent that the outputs of such tools, models, or software are associated with Meta or Llama 3.2 With respect to any multimodal models included in Llama 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union. This restriction does not apply to end users of a product or service that incorporates any such multimodal models. Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Information The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. **Model Developer:** Meta **Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. | | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff | | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | | Llama 3.2 (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 128k | Yes | Yes | Up to 9T tokens | December 2023 | | | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | | **Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. **Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date:** Sept 25, 2024 **Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. **License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). **Feedback:** Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama-models/tree/main/models/llama3_2). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. **Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## How to use This repository contains two versions of Llama-3.2-3B, for use with transformers and with the original `llama` codebase. ### Use with transformers Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function. Make sure to update your transformers installation via pip install --upgrade transformers. ```python import torch from transformers import pipeline model_id = "meta-llama/Llama-3.2-3B" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto" ) pipe("The key to life is") ``` ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama). To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Llama-3.2-3B --include "original/*" --local-dir Llama-3.2-3B ``` ## Hardware and Software **Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure. **Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. ## **Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. | | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) | | :---- | :---: | ----- | :---: | :---: | :---: | | Llama 3.2 1B | 370k | \- | 700 | 107 | 0 | | Llama 3.2 3B | 460k | \- | 700 | 133 | 0 | | Total | 830k | 86k | | 240 | 0 | The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data **Overview:** Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). **Data Freshness:** The pretraining data has a cutoff of December 2023\. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models | Category | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B | | ----- | ----- | :---: | :---: | :---: | :---: | :---: | | General | MMLU | 5 | macro\_avg/acc\_char | 32.2 | 58 | 66.7 | | | AGIEval English | 3-5 | average/acc\_char | 23.3 | 39.2 | 47.8 | | | ARC-Challenge | 25 | acc\_char | 32.8 | 69.1 | 79.7 | | Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 | | | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 | | | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 | | Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 | ### Instruction Tuned Models | Capability | | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B | | :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: | | General | | MMLU | 5 | macro\_avg/acc | 49.3 | 63.4 | 69.4 | | Re-writing | | Open-rewrite eval | 0 | micro\_avg/rougeL | 41.6 | 40.1 | 40.9 | | Summarization | | TLDR9+ (test) | 1 | rougeL | 16.8 | 19.0 | 17.2 | | Instruction following | | IFEval | 0 | avg(prompt/instruction acc loose/strict) | 59.5 | 77.4 | 80.4 | | Math | | GSM8K (CoT) | 8 | em\_maj1@1 | 44.4 | 77.7 | 84.5 | | | | MATH (CoT) | 0 | final\_em | 30.6 | 47.3 | 51.9 | | Reasoning | | ARC-C | 0 | acc | 59.4 | 78.6 | 83.4 | | | | GPQA | 0 | acc | 27.2 | 32.8 | 32.8 | | | | Hellaswag | 0 | acc | 41.2 | 69.8 | 78.7 | | Tool Use | | BFCL V2 | 0 | acc | 25.7 | 67.0 | 70.9 | | | | Nexus | 0 | macro\_avg/acc | 13.5 | 34.3 | 38.5 | | Long Context | | InfiniteBench/En.QA | 0 | longbook\_qa/f1 | 20.3 | 19.8 | 27.3 | | | | InfiniteBench/En.MC | 0 | longbook\_choice/acc | 38.0 | 63.3 | 72.2 | | | | NIH/Multi-needle | 0 | recall | 75.0 | 84.7 | 98.8 | | Multilingual | | MGSM (CoT) | 0 | em | 24.5 | 58.2 | 68.9 | ### Multilingual Benchmarks | Category | Benchmark | Language | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B | | :---: | :---: | :---: | :---: | :---: | :---: | | General | MMLU (5-shot, macro\_avg/acc) | Portuguese | 39.82 | 54.48 | 62.12 | | | | Spanish | 41.5 | 55.1 | 62.5 | | | | Italian | 39.8 | 53.8 | 61.6 | | | | German | 39.2 | 53.3 | 60.6 | | | | French | 40.5 | 54.6 | 62.3 | | | | Hindi | 33.5 | 43.3 | 50.9 | | | | Thai | 34.7 | 44.5 | 50.3 | ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment **Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct **Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). **Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. **Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems **Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases **Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. **Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations **Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. **Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: **1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. **2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. **3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community **Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). **Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). **Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations **Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. **Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
RichardErkhov/Youlln_-_ECE-PRYMMAL-YL-1B-SLERP-V2-8bits
RichardErkhov
2025-02-28T05:11:09Z
0
0
null
[ "safetensors", "qwen2", "arxiv:1910.09700", "8-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:09:32Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) ECE-PRYMMAL-YL-1B-SLERP-V2 - bnb 8bits - Model creator: https://huggingface.co/Youlln/ - Original model: https://huggingface.co/Youlln/ECE-PRYMMAL-YL-1B-SLERP-V2/ Original model description: --- library_name: transformers license: apache-2.0 --- # 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]
Tapan101/QwenMath
Tapan101
2025-02-28T05:11:04Z
0
0
transformers
[ "transformers", "pytorch", "qwen2", "text-generation", "text-generation-inference", "trl", "grpo", "QLORA", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2025-02-27T20:10:59Z
--- base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - qwen2 - trl - grpo - QLORA license: apache-2.0 language: - en --- --- This is a resoning model for solving high school level Math. --- # Uploaded model - **Developed by:** Tapan101 - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
RichardErkhov/Zekunli_-_qwen2.5-1.5b-lora-w-cot-w-cor-8bits
RichardErkhov
2025-02-28T05:11:02Z
0
0
null
[ "safetensors", "qwen2", "arxiv:1910.09700", "8-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:09:52Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) qwen2.5-1.5b-lora-w-cot-w-cor - bnb 8bits - Model creator: https://huggingface.co/Zekunli/ - Original model: https://huggingface.co/Zekunli/qwen2.5-1.5b-lora-w-cot-w-cor/ 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]
featherless-ai-quants/Qwen-Qwen2.5-14B-Instruct-1M-GGUF
featherless-ai-quants
2025-02-28T05:10:40Z
0
0
null
[ "gguf", "text-generation", "base_model:Qwen/Qwen2.5-14B-Instruct-1M", "base_model:quantized:Qwen/Qwen2.5-14B-Instruct-1M", "endpoints_compatible", "region:us", "conversational" ]
text-generation
2025-02-28T04:55:55Z
--- base_model: Qwen/Qwen2.5-14B-Instruct-1M pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # Qwen/Qwen2.5-14B-Instruct-1M GGUF Quantizations 🚀 ![Featherless AI Quants](./featherless-quants.png) *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | IQ4_XS | [Qwen-Qwen2.5-14B-Instruct-1M-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/Qwen-Qwen2.5-14B-Instruct-1M-GGUF/blob/main/Qwen-Qwen2.5-14B-Instruct-1M-IQ4_XS.gguf) | 7806.96 MB | | Q2_K | [Qwen-Qwen2.5-14B-Instruct-1M-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/Qwen-Qwen2.5-14B-Instruct-1M-GGUF/blob/main/Qwen-Qwen2.5-14B-Instruct-1M-Q2_K.gguf) | 5503.18 MB | | Q3_K_L | [Qwen-Qwen2.5-14B-Instruct-1M-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/Qwen-Qwen2.5-14B-Instruct-1M-GGUF/blob/main/Qwen-Qwen2.5-14B-Instruct-1M-Q3_K_L.gguf) | 7557.65 MB | | Q3_K_M | [Qwen-Qwen2.5-14B-Instruct-1M-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/Qwen-Qwen2.5-14B-Instruct-1M-GGUF/blob/main/Qwen-Qwen2.5-14B-Instruct-1M-Q3_K_M.gguf) | 6999.21 MB | | Q3_K_S | [Qwen-Qwen2.5-14B-Instruct-1M-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/Qwen-Qwen2.5-14B-Instruct-1M-GGUF/blob/main/Qwen-Qwen2.5-14B-Instruct-1M-Q3_K_S.gguf) | 6351.09 MB | | Q4_K_M | [Qwen-Qwen2.5-14B-Instruct-1M-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/Qwen-Qwen2.5-14B-Instruct-1M-GGUF/blob/main/Qwen-Qwen2.5-14B-Instruct-1M-Q4_K_M.gguf) | 8571.73 MB | | Q4_K_S | [Qwen-Qwen2.5-14B-Instruct-1M-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/Qwen-Qwen2.5-14B-Instruct-1M-GGUF/blob/main/Qwen-Qwen2.5-14B-Instruct-1M-Q4_K_S.gguf) | 8176.26 MB | | Q5_K_M | [Qwen-Qwen2.5-14B-Instruct-1M-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/Qwen-Qwen2.5-14B-Instruct-1M-GGUF/blob/main/Qwen-Qwen2.5-14B-Instruct-1M-Q5_K_M.gguf) | 10022.04 MB | | Q5_K_S | [Qwen-Qwen2.5-14B-Instruct-1M-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/Qwen-Qwen2.5-14B-Instruct-1M-GGUF/blob/main/Qwen-Qwen2.5-14B-Instruct-1M-Q5_K_S.gguf) | 9790.95 MB | | Q6_K | [Qwen-Qwen2.5-14B-Instruct-1M-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/Qwen-Qwen2.5-14B-Instruct-1M-GGUF/blob/main/Qwen-Qwen2.5-14B-Instruct-1M-Q6_K.gguf) | 11563.00 MB | | Q8_0 | [Qwen-Qwen2.5-14B-Instruct-1M-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/Qwen-Qwen2.5-14B-Instruct-1M-GGUF/blob/main/Qwen-Qwen2.5-14B-Instruct-1M-Q8_0.gguf) | 14974.21 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
RichardErkhov/Xelvis_-_finetuned-phi-medical-model-8bits
RichardErkhov
2025-02-28T05:10:35Z
0
0
null
[ "safetensors", "phi", "arxiv:1910.09700", "8-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:09:35Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) finetuned-phi-medical-model - bnb 8bits - Model creator: https://huggingface.co/Xelvis/ - Original model: https://huggingface.co/Xelvis/finetuned-phi-medical-model/ 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]
RichardErkhov/Zekunli_-_qwen2.5-1.5b-lora-w-cot-w-cor-4bits
RichardErkhov
2025-02-28T05:09:15Z
0
0
null
[ "safetensors", "qwen2", "arxiv:1910.09700", "4-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:08:28Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) qwen2.5-1.5b-lora-w-cot-w-cor - bnb 4bits - Model creator: https://huggingface.co/Zekunli/ - Original model: https://huggingface.co/Zekunli/qwen2.5-1.5b-lora-w-cot-w-cor/ 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]
RichardErkhov/Xelvis_-_finetuned-phi-medical-model-4bits
RichardErkhov
2025-02-28T05:08:56Z
0
0
null
[ "safetensors", "phi", "arxiv:1910.09700", "4-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:08:07Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) finetuned-phi-medical-model - bnb 4bits - Model creator: https://huggingface.co/Xelvis/ - Original model: https://huggingface.co/Xelvis/finetuned-phi-medical-model/ 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]
priayu/_WASTE
priayu
2025-02-28T05:08:16Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2025-02-28T05:08:16Z
--- license: apache-2.0 ---
mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF
mradermacher
2025-02-28T05:08:14Z
0
0
transformers
[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Nexesenex/Llama_3.1_8b_Smarteaz_0.11a", "base_model:quantized:Nexesenex/Llama_3.1_8b_Smarteaz_0.11a", "endpoints_compatible", "region:us", "imatrix", "conversational" ]
null
2025-02-28T00:26:02Z
--- base_model: Nexesenex/Llama_3.1_8b_Smarteaz_0.11a 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/Nexesenex/Llama_3.1_8b_Smarteaz_0.11a <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-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/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.1 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.8 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-Q4_1.gguf) | i1-Q4_1 | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Smarteaz_0.11a-i1-GGUF/resolve/main/Llama_3.1_8b_Smarteaz_0.11a.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | 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 -->
RichardErkhov/besimray_-_miner_id_3_53482eaf-613d-40b1-a3b3-eb83714b00c8_1729803098-8bits
RichardErkhov
2025-02-28T05:06:14Z
0
0
null
[ "safetensors", "llama", "8-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:04:11Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) miner_id_3_53482eaf-613d-40b1-a3b3-eb83714b00c8_1729803098 - bnb 8bits - Model creator: https://huggingface.co/besimray/ - Original model: https://huggingface.co/besimray/miner_id_3_53482eaf-613d-40b1-a3b3-eb83714b00c8_1729803098/ Original model description: --- base_model: meta-llama/Llama-3.2-3B language: - en library_name: transformers license: llama3.2 tags: - llama-3 - llama - meta - facebook - unsloth - transformers --- # Finetune Llama 3.2, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth! We have a free Google Colab Tesla T4 notebook for Llama 3.2 (3B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/unsloth) [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) # Llama-3.2-3B For more details on the model, please go to Meta's original [model card](https://huggingface.co/meta-llama/Llama-3.2-3B) ## ✨ Finetune for Free All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. | Unsloth supports | Free Notebooks | Performance | Memory use | |-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------| | **Llama-3.2 (3B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) | 2.4x faster | 58% less | | **Llama-3.1 (11B vision)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) | 2.4x faster | 58% less | | **Llama-3.1 (8B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) | 2.4x faster | 58% less | | **Phi-3.5 (mini)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing) | 2x faster | 50% less | | **Gemma 2 (9B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing) | 2.4x faster | 58% less | | **Mistral (7B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less | | **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less | - This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates. - This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster. ## Special Thanks A huge thank you to the Meta and Llama team for creating and releasing these models. ## Model Information The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. **Model developer**: Meta **Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. **Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. **Llama 3.2 family of models** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date:** Sept 25, 2024 **Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. **License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
RichardErkhov/its5Q_-_rugpt3large_mailqa-8bits
RichardErkhov
2025-02-28T05:05:18Z
0
0
null
[ "safetensors", "gpt2", "8-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:04:46Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) rugpt3large_mailqa - bnb 8bits - Model creator: https://huggingface.co/its5Q/ - Original model: https://huggingface.co/its5Q/rugpt3large_mailqa/ Original model description: --- language: - ru tags: - PyTorch - Transformers --- # rugpt3large\_mailqa Model was finetuned with sequence length 1024 for 516000 steps on a dataset of otvet.mail.ru questions and answers. The raw dataset can be found [here](https://www.kaggle.com/datasets/atleast6characterss/otvetmailru-full). Beware that the data contains a good portion of toxic language, so the answers can be unpredictable. Jupyter notebook with an example of how to inference this model can be found in the [repository](https://github.com/NeuralPushkin/MailRu_Q-A)
RichardErkhov/mu0gum_-_AIFT-42dot_LLM-PLM-1.3B-ao-instruct-all-v1.0-8bits
RichardErkhov
2025-02-28T05:04:39Z
0
0
null
[ "safetensors", "llama", "8-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:03:42Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) AIFT-42dot_LLM-PLM-1.3B-ao-instruct-all-v1.0 - bnb 8bits - Model creator: https://huggingface.co/mu0gum/ - Original model: https://huggingface.co/mu0gum/AIFT-42dot_LLM-PLM-1.3B-ao-instruct-all-v1.0/ Original model description: --- license: cc-by-nc-4.0 --- # AIFT-42dot_LLM-PLM-1.3B-ao-instruct-all-v1.0 베이스 모델 : 42dot/42dot_LLM-PLM-1.3B 학습 데이터 : 자체 제작한 Open Orca 스타일 데이터셋 약 48,000건 (중복 제거 및 데이터 분포 조정) 학습 방법 : Full finetuning epoch : 3 ## ko-lm-evaluation-harness(5-shot) |kobest_boolq|kobest_copa|kobest_hellaswag|pawsx_ko| |--|--|--|--| |0.5220797720797721|0.72|0.458|0.563| ## Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.0.0 - Tokenizers 0.15.0
RichardErkhov/Stormbreakerr20_-_moondream-ft-2-men-tshirts-8bits
RichardErkhov
2025-02-28T05:04:18Z
0
0
null
[ "safetensors", "phi", "arxiv:1910.09700", "8-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:03:11Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) moondream-ft-2-men-tshirts - bnb 8bits - Model creator: https://huggingface.co/Stormbreakerr20/ - Original model: https://huggingface.co/Stormbreakerr20/moondream-ft-2-men-tshirts/ 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]
RichardErkhov/mesolitica_-_mallam-3B-4096-4bits
RichardErkhov
2025-02-28T05:03:56Z
0
0
null
[ "safetensors", "mistral", "4-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:02:42Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mallam-3B-4096 - bnb 4bits - Model creator: https://huggingface.co/mesolitica/ - Original model: https://huggingface.co/mesolitica/mallam-3B-4096/ Original model description: --- language: - ms --- # MaLLaM 🌙 3B (Malaysia Large Language Model), Pretrain 3B 4096 context length on Malaysian text Pretrain from scratch 3B parameters using Mistral architecture on 90B Malaysian text tokens. README at https://github.com/mesolitica/malaya/tree/5.1/pretrained-model/mistral - Trained on 90B tokens, gathered at https://github.com/malaysia-ai/dedup-text-dataset/tree/main/pretrain-llm - We use Ray cluster to train on 5 nodes of 4x A100 80GB, https://github.com/malaysia-ai/jupyter-gpu/tree/main/ray WandB, https://wandb.ai/mesolitica/pretrain-mistral-3b?workspace=user-husein-mesolitica WandB report, https://wandb.ai/mesolitica/pretrain-mistral-3b/reports/Pretrain-Larger-Malaysian-Mistral--Vmlldzo2MDkyOTgz Technical report, https://github.com/mesolitica/malaya/wiki/MaLLaM-%F0%9F%8C%99-Malaysia-Large-Language-Model ## how-to ```python from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig import torch TORCH_DTYPE = 'bfloat16' nf4_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type='nf4', bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=getattr(torch, TORCH_DTYPE) ) tokenizer = AutoTokenizer.from_pretrained('mesolitica/mallam-3B-4096') model = AutoModelForCausalLM.from_pretrained( 'mesolitica/mallam-3B-4096', use_flash_attention_2 = True, quantization_config = nf4_config ) prompt = '<s>nama saya' inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda') generate_kwargs = dict( inputs, max_new_tokens=512, top_p=0.95, top_k=50, temperature=0.9, do_sample=True, num_beams=1, repetition_penalty=1.05, ) r = model.generate(**generate_kwargs) ```
RichardErkhov/Zekunli_-_qwen2.5-1.5b-lora-wo-cot-wo-cor-8bits
RichardErkhov
2025-02-28T05:03:30Z
0
0
null
[ "safetensors", "qwen2", "arxiv:1910.09700", "8-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:02:24Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) qwen2.5-1.5b-lora-wo-cot-wo-cor - bnb 8bits - Model creator: https://huggingface.co/Zekunli/ - Original model: https://huggingface.co/Zekunli/qwen2.5-1.5b-lora-wo-cot-wo-cor/ 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]
aadhibest/smolvlm-instruct-28-02-2025
aadhibest
2025-02-28T05:03:25Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:HuggingFaceTB/SmolVLM-Instruct", "base_model:finetune:HuggingFaceTB/SmolVLM-Instruct", "endpoints_compatible", "region:us" ]
null
2025-02-28T00:14:20Z
--- base_model: HuggingFaceTB/SmolVLM-Instruct library_name: transformers model_name: smolvlm-instruct-28-02-2025 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for smolvlm-instruct-28-02-2025 This model is a fine-tuned version of [HuggingFaceTB/SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-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="aadhibest/smolvlm-instruct-28-02-2025", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.0 - Transformers: 4.49.0 - Pytorch: 2.6.0+cu118 - Datasets: 3.3.1 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
aleegis12/c4b0231e-c4ce-45ca-acc6-2b558aff3187
aleegis12
2025-02-28T05:03:18Z
0
0
peft
[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-0.5B", "base_model:adapter:Qwen/Qwen2-0.5B", "license:apache-2.0", "region:us" ]
null
2025-02-28T04:35:11Z
--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-0.5B tags: - axolotl - generated_from_trainer model-index: - name: c4b0231e-c4ce-45ca-acc6-2b558aff3187 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2-0.5B bf16: true chat_template: llama3 dataloader_num_workers: 12 dataset_prepared_path: null datasets: - data_files: - 5513b998661e6cb3_train_data.json ds_type: json format: custom path: /workspace/input_data/5513b998661e6cb3_train_data.json type: field_input: old_contents field_instruction: instruction field_output: content 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: 300 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: true hub_model_id: aleegis12/c4b0231e-c4ce-45ca-acc6-2b558aff3187 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: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 6000 micro_batch_size: 4 mlflow_experiment_name: /tmp/5513b998661e6cb3_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optim_args: adam_beta1: 0.9 adam_beta2: 0.999 adam_epsilon: 1e-8 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 300 saves_per_epoch: null sequence_len: 1024 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: online wandb_name: cdec897c-7948-4e87-9bae-779bd9138cdd wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: cdec897c-7948-4e87-9bae-779bd9138cdd warmup_steps: 100 weight_decay: 0.1 xformers_attention: null ``` </details><br> # c4b0231e-c4ce-45ca-acc6-2b558aff3187 This model is a fine-tuned version of [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1922 ## 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: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.999,adam_epsilon=1e-8 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 6000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0008 | 1 | 0.4837 | | 0.3095 | 0.2265 | 300 | 0.1902 | | 0.3482 | 0.4531 | 600 | 0.1907 | | 0.2556 | 0.6796 | 900 | 0.1902 | | 0.286 | 0.9062 | 1200 | 0.1882 | | 0.1485 | 1.1327 | 1500 | 0.1917 | | 0.1464 | 1.3593 | 1800 | 0.1934 | | 0.1351 | 1.5858 | 2100 | 0.1922 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
balter4/benny
balter4
2025-02-28T05:03:05Z
0
0
diffusers
[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]
text-to-image
2025-02-28T04:32:33Z
--- 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: benny --- # Benny <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `benny` to trigger the image generation. ## 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('balter4/benny', weight_name='lora.safetensors') image = pipeline('your prompt').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)
RichardErkhov/Stormbreakerr20_-_moondream-ft-2-men-tshirts-4bits
RichardErkhov
2025-02-28T05:02:41Z
0
0
null
[ "safetensors", "phi", "arxiv:1910.09700", "4-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:01:53Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) moondream-ft-2-men-tshirts - bnb 4bits - Model creator: https://huggingface.co/Stormbreakerr20/ - Original model: https://huggingface.co/Stormbreakerr20/moondream-ft-2-men-tshirts/ 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]
RichardErkhov/steeldream_-_letov-4bits
RichardErkhov
2025-02-28T05:02:06Z
0
0
null
[ "safetensors", "gpt2", "4-bit", "bitsandbytes", "region:us" ]
null
2025-02-28T05:01:37Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) letov - bnb 4bits - Model creator: https://huggingface.co/steeldream/ - Original model: https://huggingface.co/steeldream/letov/ Original model description: --- license: cc-by-4.0 ---
mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF
mradermacher
2025-02-28T05:01:55Z
278
0
transformers
[ "transformers", "gguf", "en", "base_model:pr0j3ct-m1ndb0t-2045/M1NDB0T-SkyN0VA-32", "base_model:quantized:pr0j3ct-m1ndb0t-2045/M1NDB0T-SkyN0VA-32", "endpoints_compatible", "region:us", "imatrix", "conversational" ]
null
2025-02-26T21:43:21Z
--- base_model: pr0j3ct-m1ndb0t-2045/M1NDB0T-SkyN0VA-32 language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/pr0j3ct-m1ndb0t-2045/M1NDB0T-SkyN0VA-32 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-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/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-IQ1_M.gguf) | i1-IQ1_M | 8.0 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 | | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-IQ2_S.gguf) | i1-IQ2_S | 10.5 | | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-IQ2_M.gguf) | i1-IQ2_M | 11.4 | | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-Q2_K_S.gguf) | i1-Q2_K_S | 11.6 | very low quality | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-Q2_K.gguf) | i1-Q2_K | 12.4 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.8 | | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.5 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-IQ3_S.gguf) | i1-IQ3_S | 14.5 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-IQ3_M.gguf) | i1-IQ3_M | 14.9 | | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.0 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.3 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.8 | | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-Q4_0.gguf) | i1-Q4_0 | 18.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.9 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-Q4_1.gguf) | i1-Q4_1 | 20.7 | | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.7 | | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.4 | | | [GGUF](https://huggingface.co/mradermacher/M1NDB0T-SkyN0VA-32-i1-GGUF/resolve/main/M1NDB0T-SkyN0VA-32.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 -->
lesso11/67924526-fb84-4ddf-863b-6935dbc1a3b9
lesso11
2025-02-28T05:01:39Z
0
0
peft
[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-14B-Instruct", "base_model:adapter:unsloth/Qwen2.5-14B-Instruct", "license:apache-2.0", "region:us" ]
null
2025-02-28T01:39:25Z
--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-14B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 67924526-fb84-4ddf-863b-6935dbc1a3b9 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora auto_find_batch_size: true base_model: unsloth/Qwen2.5-14B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 3f057bb44ca73b8c_train_data.json ds_type: json format: custom path: /workspace/input_data/3f057bb44ca73b8c_train_data.json type: field_input: audience field_instruction: prompt field_output: text format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 50 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: true hub_model_id: lesso11/67924526-fb84-4ddf-863b-6935dbc1a3b9 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000211 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/3f057bb44ca73b8c_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null seed: 110 sequence_len: 512 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: online wandb_name: 60f6c6b0-a69a-40f6-bfca-70d6c4381dbe wandb_project: 11a wandb_run: your_name wandb_runid: 60f6c6b0-a69a-40f6-bfca-70d6c4381dbe warmup_steps: 50 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 67924526-fb84-4ddf-863b-6935dbc1a3b9 This model is a fine-tuned version of [unsloth/Qwen2.5-14B-Instruct](https://huggingface.co/unsloth/Qwen2.5-14B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0155 ## 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: 0.000211 - train_batch_size: 4 - eval_batch_size: 4 - seed: 110 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0000 | 1 | 1.2357 | | 1.0209 | 0.0021 | 50 | 1.0494 | | 1.0478 | 0.0042 | 100 | 1.0414 | | 1.0511 | 0.0063 | 150 | 1.0331 | | 1.0347 | 0.0084 | 200 | 1.0311 | | 1.0462 | 0.0106 | 250 | 1.0249 | | 1.0886 | 0.0127 | 300 | 1.0217 | | 1.0167 | 0.0148 | 350 | 1.0186 | | 1.0934 | 0.0169 | 400 | 1.0166 | | 0.966 | 0.0190 | 450 | 1.0158 | | 1.1311 | 0.0211 | 500 | 1.0155 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lesso07/3468a1e5-55db-4b37-abde-5aaa56c04b9b
lesso07
2025-02-28T05:00:54Z
0
0
peft
[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-14B-Instruct", "base_model:adapter:unsloth/Qwen2.5-14B-Instruct", "license:apache-2.0", "region:us" ]
null
2025-02-28T01:39:25Z
--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-14B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 3468a1e5-55db-4b37-abde-5aaa56c04b9b results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora auto_find_batch_size: true base_model: unsloth/Qwen2.5-14B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 3f057bb44ca73b8c_train_data.json ds_type: json format: custom path: /workspace/input_data/3f057bb44ca73b8c_train_data.json type: field_input: audience field_instruction: prompt field_output: text format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 50 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: true hub_model_id: lesso07/3468a1e5-55db-4b37-abde-5aaa56c04b9b hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000207 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/3f057bb44ca73b8c_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null seed: 70 sequence_len: 512 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: online wandb_name: 60f6c6b0-a69a-40f6-bfca-70d6c4381dbe wandb_project: 07a wandb_run: your_name wandb_runid: 60f6c6b0-a69a-40f6-bfca-70d6c4381dbe warmup_steps: 50 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 3468a1e5-55db-4b37-abde-5aaa56c04b9b This model is a fine-tuned version of [unsloth/Qwen2.5-14B-Instruct](https://huggingface.co/unsloth/Qwen2.5-14B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0152 ## 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: 0.000207 - train_batch_size: 4 - eval_batch_size: 4 - seed: 70 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0000 | 1 | 1.2358 | | 0.9893 | 0.0021 | 50 | 1.0476 | | 1.1448 | 0.0042 | 100 | 1.0379 | | 1.0595 | 0.0063 | 150 | 1.0323 | | 1.08 | 0.0084 | 200 | 1.0315 | | 1.0226 | 0.0106 | 250 | 1.0245 | | 1.0548 | 0.0127 | 300 | 1.0214 | | 0.999 | 0.0148 | 350 | 1.0186 | | 1.0924 | 0.0169 | 400 | 1.0166 | | 1.0206 | 0.0190 | 450 | 1.0157 | | 1.0201 | 0.0211 | 500 | 1.0152 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
JacksonBrune/316431fc-7d3d-4931-9641-d0714e80c602
JacksonBrune
2025-02-28T05:00:40Z
0
0
peft
[ "peft", "generated_from_trainer", "base_model:Qwen/Qwen2-0.5B", "base_model:adapter:Qwen/Qwen2-0.5B", "region:us" ]
null
2025-02-28T05:00:34Z
--- library_name: peft tags: - generated_from_trainer base_model: Qwen/Qwen2-0.5B model-index: - name: JacksonBrune/316431fc-7d3d-4931-9641-d0714e80c602 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. --> # JacksonBrune/316431fc-7d3d-4931-9641-d0714e80c602 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1951 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
AngelaAyuda/ppo-Huggy
AngelaAyuda
2025-02-28T05:00:38Z
0
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2025-02-28T05:00:32Z
--- 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: AngelaAyuda/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
divij30/legal-llama3.2-3B-app
divij30
2025-02-28T04:59:40Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-02-28T04:57:39Z
--- 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]
lesso14/03099ad5-4dc6-406e-8226-2fd00389d319
lesso14
2025-02-28T04:58:40Z
0
0
peft
[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer", "base_model:adapter:NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer", "license:other", "region:us" ]
null
2025-02-27T22:53:10Z
--- library_name: peft license: other base_model: NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer tags: - axolotl - generated_from_trainer model-index: - name: 03099ad5-4dc6-406e-8226-2fd00389d319 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora auto_find_batch_size: true base_model: NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 67a64d4e8799f348_train_data.json ds_type: json format: custom path: /workspace/input_data/67a64d4e8799f348_train_data.json type: field_instruction: instruction field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 50 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: true hub_model_id: lesso14/03099ad5-4dc6-406e-8226-2fd00389d319 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000214 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/67a64d4e8799f348_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null seed: 140 sequence_len: 512 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: online wandb_name: b63b2f41-5360-44a3-bf07-b59ccbe2f2f3 wandb_project: 14a wandb_run: your_name wandb_runid: b63b2f41-5360-44a3-bf07-b59ccbe2f2f3 warmup_steps: 50 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 03099ad5-4dc6-406e-8226-2fd00389d319 This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2431 ## 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: 0.000214 - train_batch_size: 4 - eval_batch_size: 4 - seed: 140 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0000 | 1 | 1.9750 | | 1.4603 | 0.0008 | 50 | 1.3611 | | 1.7302 | 0.0015 | 100 | 1.3578 | | 1.8879 | 0.0023 | 150 | 1.3032 | | 1.6429 | 0.0030 | 200 | 1.2977 | | 1.6246 | 0.0038 | 250 | 1.2870 | | 1.3551 | 0.0045 | 300 | 1.2701 | | 1.7154 | 0.0053 | 350 | 1.2569 | | 1.7392 | 0.0060 | 400 | 1.2489 | | 1.4551 | 0.0068 | 450 | 1.2462 | | 1.5776 | 0.0075 | 500 | 1.2431 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
nathunt1996/379ba3b8-4c1c-40ff-a60e-785287adf4ca
nathunt1996
2025-02-28T04:57:05Z
0
0
peft
[ "peft", "generated_from_trainer", "base_model:Qwen/Qwen2-0.5B", "base_model:adapter:Qwen/Qwen2-0.5B", "region:us" ]
null
2025-02-28T04:56:59Z
--- library_name: peft tags: - generated_from_trainer base_model: Qwen/Qwen2-0.5B model-index: - name: nathunt1996/379ba3b8-4c1c-40ff-a60e-785287adf4ca 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. --> # nathunt1996/379ba3b8-4c1c-40ff-a60e-785287adf4ca This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1820 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
baby-dev/e7da02b0-f056-4843-97eb-54d45c86b9b9
baby-dev
2025-02-28T04:56:26Z
0
0
peft
[ "peft", "generated_from_trainer", "base_model:Qwen/Qwen2-0.5B", "base_model:adapter:Qwen/Qwen2-0.5B", "region:us" ]
null
2025-02-28T04:56:16Z
--- library_name: peft tags: - generated_from_trainer base_model: Qwen/Qwen2-0.5B model-index: - name: baby-dev/e7da02b0-f056-4843-97eb-54d45c86b9b9 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. --> # baby-dev/e7da02b0-f056-4843-97eb-54d45c86b9b9 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1816 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
TobiGeth/tg_user_744945473_lora_1740717785
TobiGeth
2025-02-28T04:54:36Z
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
2025-02-28T04:54:35Z
--- 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: USER_744945473_1740717785 --- # Tg_User_744945473_Lora_1740717785 <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `USER_744945473_1740717785` to trigger the image generation. ## 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('TobiGeth/tg_user_744945473_lora_1740717785', weight_name='lora.safetensors') image = pipeline('your prompt').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)
jiinking/1first_GQA4_llama3B_model
jiinking
2025-02-28T04:52:47Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-02-28T02:59:25Z
--- 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]
TOMFORD79/VOLVO_X3
TOMFORD79
2025-02-28T04:51:16Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-02-28T04:38:10Z
--- 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]
TienAnh/finetune-Qwen2.5-0.5B-Instruct-lora
TienAnh
2025-02-28T04:48:56Z
0
0
transformers
[ "transformers", "pytorch", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2025-02-28T04:48:12Z
--- base_model: unsloth/Qwen2.5-0.5B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** TienAnh - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2.5-0.5B-Instruct 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)
byates4/convnext-large-384-22k-1k-finetuned-eurosat-albumentations
byates4
2025-02-28T04:46:48Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "convnext", "image-classification", "generated_from_trainer", "base_model:facebook/convnext-large-384-22k-1k", "base_model:finetune:facebook/convnext-large-384-22k-1k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2025-02-28T03:50:19Z
--- library_name: transformers license: apache-2.0 base_model: facebook/convnext-large-384-22k-1k tags: - generated_from_trainer model-index: - name: convnext-large-384-22k-1k-finetuned-eurosat-albumentations 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. --> # convnext-large-384-22k-1k-finetuned-eurosat-albumentations This model is a fine-tuned version of [facebook/convnext-large-384-22k-1k](https://huggingface.co/facebook/convnext-large-384-22k-1k) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.4469 - eval_accuracy: 0.8527 - eval_runtime: 6.1106 - eval_samples_per_second: 47.786 - eval_steps_per_second: 3.109 - epoch: 9.3836 - step: 178 ## 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: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Framework versions - Transformers 4.48.3 - Pytorch 2.5.1+cu124 - Datasets 3.3.2 - Tokenizers 0.21.0
lesso02/2bae7a83-ace2-4cb2-9817-189a90d40ac0
lesso02
2025-02-28T04:45:24Z
0
0
peft
[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-0.5B", "base_model:adapter:Qwen/Qwen2-0.5B", "license:apache-2.0", "region:us" ]
null
2025-02-28T04:35:39Z
--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-0.5B tags: - axolotl - generated_from_trainer model-index: - name: 2bae7a83-ace2-4cb2-9817-189a90d40ac0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora auto_find_batch_size: true base_model: Qwen/Qwen2-0.5B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 5513b998661e6cb3_train_data.json ds_type: json format: custom path: /workspace/input_data/5513b998661e6cb3_train_data.json type: field_input: old_contents field_instruction: instruction field_output: content format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 50 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: true hub_model_id: lesso02/2bae7a83-ace2-4cb2-9817-189a90d40ac0 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000202 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/5513b998661e6cb3_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null seed: 20 sequence_len: 512 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: online wandb_name: cdec897c-7948-4e87-9bae-779bd9138cdd wandb_project: 02a wandb_run: your_name wandb_runid: cdec897c-7948-4e87-9bae-779bd9138cdd warmup_steps: 50 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 2bae7a83-ace2-4cb2-9817-189a90d40ac0 This model is a fine-tuned version of [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1771 ## 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: 0.000202 - train_batch_size: 4 - eval_batch_size: 4 - seed: 20 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0004 | 1 | 0.4801 | | 0.3136 | 0.0189 | 50 | 0.1951 | | 0.3894 | 0.0378 | 100 | 0.1854 | | 0.3781 | 0.0566 | 150 | 0.1827 | | 0.2819 | 0.0755 | 200 | 0.1806 | | 0.2482 | 0.0944 | 250 | 0.1787 | | 0.2793 | 0.1133 | 300 | 0.1779 | | 0.2982 | 0.1322 | 350 | 0.1775 | | 0.3013 | 0.1510 | 400 | 0.1772 | | 0.2857 | 0.1699 | 450 | 0.1765 | | 0.337 | 0.1888 | 500 | 0.1771 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
PrunaAI/Qwen-Qwen2.5-0.5B-GGUF-smashed
PrunaAI
2025-02-28T04:45:15Z
0
0
null
[ "gguf", "pruna-ai", "base_model:Qwen/Qwen2.5-0.5B", "base_model:quantized:Qwen/Qwen2.5-0.5B", "endpoints_compatible", "region:us", "conversational" ]
null
2025-02-28T04:38:54Z
--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: Qwen/Qwen2.5-0.5B 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 Qwen/Qwen2.5-0.5B 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: Qwen-Qwen2.5-0.5B-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 Qwen-Qwen2.5-0.5B-GGUF-smashed Qwen2.5-0.5B.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 Qwen-Qwen2.5-0.5B-GGUF-smashed --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` 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 Qwen-Qwen2.5-0.5B-GGUF-smashed Qwen2.5-0.5B.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 Qwen2.5-0.5B.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="./Qwen2.5-0.5B.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="./Qwen2.5-0.5B.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).
coffiee/moo1
coffiee
2025-02-28T04:44:42Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-02-28T04:43:50Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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. 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]
visdata/wld9
visdata
2025-02-28T04:41:56Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-02-28T04:36:28Z
--- 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. 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]
visdata/wld5
visdata
2025-02-28T04:41:55Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-02-28T04:35:31Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
visdata/wld4
visdata
2025-02-28T04:36:59Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-02-28T04:31:24Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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visdata/wld6
visdata
2025-02-28T04:36:34Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-02-28T04:31:18Z
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visdata/wld3
visdata
2025-02-28T04:36:28Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-02-28T04:30:32Z
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HafijulHoquenabid2/phi-02-finetuned-1
HafijulHoquenabid2
2025-02-28T04:36:23Z
0
0
transformers
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2025-02-28T04:36:17Z
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visdata/wld2
visdata
2025-02-28T04:33:18Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-02-28T04:27:42Z
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visdata/wld7
visdata
2025-02-28T04:30:27Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-02-28T04:24:25Z
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divij30/legal-llama3.2-3B-random
divij30
2025-02-28T04:24:42Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-02-28T04:22:25Z
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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]
mohammadsa92/tinyzebra-merged
mohammadsa92
2025-02-28T04:24:07Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-02-28T04:21:16Z
--- 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]
oddadmix/unsloth-Qwen2-VL-7B-Instruct-arabic
oddadmix
2025-02-28T04:23:34Z
0
0
transformers
[ "transformers", "safetensors", "qwen2_vl", "image-text-to-text", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]
image-text-to-text
2025-02-28T04:19:37Z
--- base_model: unsloth/qwen2-vl-7b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_vl license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** oddadmix - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen2-vl-7b-instruct-unsloth-bnb-4bit This qwen2_vl model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Zack-Z/llama31_8bi_CoTsft_rs3407_1_e1
Zack-Z
2025-02-28T04:22:39Z
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
2025-02-28T04:00:19Z
--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft --- # Uploaded model - **Developed by:** Zack-Z - **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)
PrunaAI/NousResearch-DeepHermes-3-Llama-3-8B-Preview-HQQ-8bit-smashed
PrunaAI
2025-02-28T04:22:34Z
2
0
null
[ "llama", "pruna-ai", "hqq", "region:us" ]
null
2025-02-24T19:14:46Z
--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: ORIGINAL_REPO_NAME 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.gg/rskEr4BZJx) # 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/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with hqq. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - ***What is the model format?*** We use safetensors. - ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data. - ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - ***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). - ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo ORIGINAL_REPO_NAME installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install hqq ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from hqq.engine.hf import HQQModelForCausalLM from hqq.models.hf.base import AutoHQQHFModel try: model = HQQModelForCausalLM.from_quantized("PrunaAI/NousResearch-DeepHermes-3-Llama-3-8B-Preview-HQQ-8bit-smashed", device_map='auto') except: model = AutoHQQHFModel.from_quantized("PrunaAI/NousResearch-DeepHermes-3-Llama-3-8B-Preview-HQQ-8bit-smashed") tokenizer = AutoTokenizer.from_pretrained("ORIGINAL_REPO_NAME") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## 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 ORIGINAL_REPO_NAME 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).
musketshugging/Llama-3.3-70B-Instruct-AWQ-4bit
musketshugging
2025-02-28T04:22:04Z
0
0
null
[ "safetensors", "llama", "en", "base_model:meta-llama/Llama-3.3-70B-Instruct", "base_model:quantized:meta-llama/Llama-3.3-70B-Instruct", "license:apache-2.0", "4-bit", "awq", "region:us" ]
null
2025-02-27T23:53:38Z
--- license: apache-2.0 language: - en base_model: - meta-llama/Llama-3.3-70B-Instruct ---
huCozec2013/w2v-bert-2.0-mongolian-colab-CV16.0
huCozec2013
2025-02-28T04:19:07Z
0
0
transformers
[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2025-02-28T00:16:16Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
mohammadsa92/tinyzebra-lora
mohammadsa92
2025-02-28T04:15:58Z
0
0
transformers
[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:unsloth/DeepSeek-R1-Distill-Qwen-1.5B", "base_model:finetune:unsloth/DeepSeek-R1-Distill-Qwen-1.5B", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-02-28T04:15:52Z
--- base_model: unsloth/DeepSeek-R1-Distill-Qwen-1.5B tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** mohammadsa92 - **License:** apache-2.0 - **Finetuned from model :** unsloth/DeepSeek-R1-Distill-Qwen-1.5B 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)
aircrypto/video_rag_v1
aircrypto
2025-02-28T04:15:14Z
0
0
null
[ "pytorch", "region:us" ]
null
2025-02-28T04:12:12Z
# My CLIP Video-Text Model This model was trained on the MSR-VTT dataset using a custom CLIP-based architecture. self.video_proj = nn.Sequential( nn.Linear(512, 2048), nn.ReLU(), nn.Linear(2048, 2048), nn.ReLU(), nn.Linear(2048, 512) )
salsina/DeepSeek_R1_full
salsina
2025-02-28T04:14:25Z
0
0
transformers
[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-02-28T04:13:56Z
--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** salsina - **License:** apache-2.0 - **Finetuned from model :** unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
nguyenthetuyen/Qwen2.5-1.5B-text2SQL
nguyenthetuyen
2025-02-28T04:12:59Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-02-28T04:10:26Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Harveenchadha/Qwen-2.5-7B-Simple-RL
Harveenchadha
2025-02-28T04:12:54Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-02-27T07:01:45Z
--- base_model: Qwen/Qwen2.5-Math-7B library_name: transformers model_name: Qwen-2.5-7B-Simple-RL tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-Simple-RL This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B). 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="Harveenchadha/Qwen-2.5-7B-Simple-RL", 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/rv2008/huggingface/runs/0myp4aqp) 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: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.3.2 - 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}} } ```
mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF
mradermacher
2025-02-28T04:12:32Z
0
0
transformers
[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Nexesenex/Llama_3.1_8b_Dolermed_V1.01", "base_model:quantized:Nexesenex/Llama_3.1_8b_Dolermed_V1.01", "license:llama3.1", "endpoints_compatible", "region:us", "imatrix", "conversational" ]
null
2025-02-28T02:56:56Z
--- base_model: Nexesenex/Llama_3.1_8b_Dolermed_V1.01 language: - en library_name: transformers license: llama3.1 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/Nexesenex/Llama_3.1_8b_Dolermed_V1.01 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-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/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.1 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.8 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-Q4_1.gguf) | i1-Q4_1 | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_Dolermed_V1.01-i1-GGUF/resolve/main/Llama_3.1_8b_Dolermed_V1.01.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | 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 -->
svsaurav95/Action-Detection-Vit-LSTM
svsaurav95
2025-02-28T04:12:06Z
0
0
null
[ "Action", "Vit", "Vit-LSTM", "video", "classification", "sportlabels", "pytorch", "LSTM", "video-classification", "en", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:mit", "region:us" ]
video-classification
2025-02-27T17:13:19Z
--- license: mit language: - en base_model: - google/vit-base-patch16-224 pipeline_tag: video-classification tags: - Action - Vit - Vit-LSTM - video - classification - sportlabels - pytorch - LSTM --- **ViT-LSTM Action Recognition** Overview This project implements an Action Recognition Model using a ViT-LSTM architecture. It takes a short video as input and predicts the action performed in the video. The model extracts frame-wise ViT features and processes them using an LSTM to capture temporal dependencies. **Model Details** Base Model: ViT-Base-Patch16-224 Architecture: ViT (Feature Extractor) + LSTM (Temporal Modeling) Number of Classes: 5 Dataset: Custom dataset with the following action categories: BaseballPitch Basketball BenchPress Biking Billiards **Working** Extract Frames – The model extracts up to 16 frames from the uploaded video. Feature Extraction – Each frame is passed through ViT, and feature vectors are obtained. Temporal Processing – The LSTM processes these features to capture motion information. Prediction – The final output is classified into one of the 5 action categories. Model Training Details Feature Dimension: 768 LSTM Hidden Dimension: 512 Number of LSTM Layers: 2 (Bidirectional) Dropout: 0.3 Optimizer: Adam Loss Function: Cross-Entropy Loss Example Usage (Code Snippet) If you want to use this model locally: ```` import torch from transformers import ViTImageProcessor, ViTModel from PIL import Image import cv2 # Load Pretrained ViT vit_processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224") vit_model = ViTModel.from_pretrained("google/vit-base-patch16-224") # Load Custom ViT-LSTM Model model = torch.load("Vit-LSTM.pth") model.eval() # Process an Example Video video_path = "example.mp4" cap = cv2.VideoCapture(video_path) frames = [] while cap.isOpened(): ret, frame = cap.read() if not ret: break frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frames.append(Image.fromarray(frame)) cap.release() # Extract Features inputs = vit_processor(images=frames, return_tensors="pt")["pixel_values"] features = vit_model(inputs).last_hidden_state.mean(dim=1) # Predict features = features.unsqueeze(0) # Add batch dimension output = model(features) predicted_class = torch.argmax(output, dim=1).item() LABELS = ["BaseballPitch", "Basketball", "BenchPress", "Biking", "Billiards"] print("Predicted Action:", LABELS[predicted_class]) ```` **Contributors** Saurav Dhiani – Model Development & Deployment ViT & LSTM – Core ML Architecture
Tingli/deepseek-coder-1.3b-unsloth-1
Tingli
2025-02-28T04:11:15Z
0
0
transformers
[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:deepseek-ai/deepseek-coder-1.3b-base", "base_model:finetune:deepseek-ai/deepseek-coder-1.3b-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-02-28T04:11:09Z
--- base_model: deepseek-ai/deepseek-coder-1.3b-base tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** Tingli - **License:** apache-2.0 - **Finetuned from model :** deepseek-ai/deepseek-coder-1.3b-base 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)
DanierlEohnson/PrimeBiome
DanierlEohnson
2025-02-28T04:10:20Z
0
0
null
[ "region:us" ]
null
2025-02-28T04:06:27Z
Official Website:- https://supplementcarts.com/prime-biome-official/ Before diving into the specifics of Prime Biome, it’s essential to understand why gut health matters. The digestive system is home to trillions of bacteria, both good and bad. This collection of microorganisms, known as the gut microbiome, plays a crucial role in digestion, immunity, and even mental health.
shivanisk/llm_cat_mistral_sft
shivanisk
2025-02-28T04:08:03Z
0
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]
text-generation
2025-02-27T13:24:44Z
--- 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. 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]