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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-13 12:28:20	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 518 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-13 12:26:25	card stringlengths 11 1.01M
reeddg/04-21-02-06-50	reeddg	2024-04-21T02:08:17Z	0	0	null	[ "tensorboard", "generated_from_trainer", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "region:us" ]	null	2024-04-21T02:07:14Z	--- license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_trainer model-index: - name: 04-21-02-06-50 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. --> # 04-21-02-06-50 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6318 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.7026 \| 0.21 \| 10 \| 0.6899 \| \| 0.708 \| 0.42 \| 20 \| 0.6729 \| \| 0.6618 \| 0.62 \| 30 \| 0.6621 \| \| 0.651 \| 0.83 \| 40 \| 0.6587 \| \| 0.6747 \| 1.04 \| 50 \| 0.6538 \| \| 0.7415 \| 1.25 \| 60 \| 0.6509 \| \| 0.6703 \| 1.46 \| 70 \| 0.6479 \| \| 0.6484 \| 1.67 \| 80 \| 0.6436 \| \| 0.6895 \| 1.88 \| 90 \| 0.6396 \| \| 0.5823 \| 2.08 \| 100 \| 0.6362 \| \| 0.7254 \| 2.29 \| 110 \| 0.6343 \| \| 0.6256 \| 2.5 \| 120 \| 0.6328 \| \| 0.6296 \| 2.71 \| 130 \| 0.6323 \| \| 0.6576 \| 2.92 \| 140 \| 0.6318 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.13.3
brohan2001/llama3-8b-paws-finetune	brohan2001	2024-04-21T02:04:13Z	3	2	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-04-21T01:55:39Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: brohan2001 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-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)
BigTMiami/m_imdb_par_bn_v_4_class_adp_lr_0003_S_2	BigTMiami	2024-04-21T01:59:55Z	0	0	adapter-transformers	[ "adapter-transformers", "roberta", "dataset:BigTMiami/imdb_sentiment_dataset", "region:us" ]	null	2024-04-21T01:59:53Z	--- tags: - adapter-transformers - roberta datasets: - BigTMiami/imdb_sentiment_dataset --- # Adapter `BigTMiami/m_imdb_par_bn_v_4_class_adp_lr_0003_S_2` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/imdb_sentiment_dataset](https://huggingface.co/datasets/BigTMiami/imdb_sentiment_dataset/) dataset and includes a prediction head for classification. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") adapter_name = model.load_adapter("BigTMiami/m_imdb_par_bn_v_4_class_adp_lr_0003_S_2", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
reeddg/flan_sum_04-21-01-52-21	reeddg	2024-04-21T01:54:56Z	0	0	null	[ "tensorboard", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "region:us" ]	null	2024-04-21T01:52:55Z	--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer model-index: - name: flan_sum_04-21-01-52-21 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. --> # flan_sum_04-21-01-52-21 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.2.2+cu121 - Datasets 2.19.0 - Tokenizers 0.13.3
reeddg/04-21-01-51-38	reeddg	2024-04-21T01:53:19Z	0	0	null	[ "tensorboard", "generated_from_trainer", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "region:us" ]	null	2024-04-21T01:52:18Z	--- license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_trainer model-index: - name: 04-21-01-51-38 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. --> # 04-21-01-51-38 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5981 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6624 \| 0.21 \| 10 \| 0.6567 \| \| 0.6743 \| 0.42 \| 20 \| 0.6509 \| \| 0.7049 \| 0.62 \| 30 \| 0.6460 \| \| 0.7394 \| 0.83 \| 40 \| 0.6382 \| \| 0.6596 \| 1.04 \| 50 \| 0.6338 \| \| 0.65 \| 1.25 \| 60 \| 0.6299 \| \| 0.6736 \| 1.46 \| 70 \| 0.6255 \| \| 0.6531 \| 1.67 \| 80 \| 0.6201 \| \| 0.6215 \| 1.88 \| 90 \| 0.6147 \| \| 0.6448 \| 2.08 \| 100 \| 0.6118 \| \| 0.6276 \| 2.29 \| 110 \| 0.6055 \| \| 0.6397 \| 2.5 \| 120 \| 0.6016 \| \| 0.6261 \| 2.71 \| 130 \| 0.5991 \| \| 0.6584 \| 2.92 \| 140 \| 0.5981 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.13.3
nuebaek/komt_mistral_insta_user_1_max_steps_80	nuebaek	2024-04-21T01:52:27Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-21T01:34:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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zzttbrdd/sn6_05l	zzttbrdd	2024-04-21T01:50:57Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-21T01:42:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
JasssZ/my_awesome_narrativeqa_clm-model	JasssZ	2024-04-21T01:46:51Z	91	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-21T01:46:06Z	--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: my_awesome_narrativeqa_clm-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_narrativeqa_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.7083 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 43 \| 4.8398 \| \| No log \| 2.0 \| 86 \| 4.7354 \| \| No log \| 3.0 \| 129 \| 4.7083 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2
Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-1.0.11-DPO	Weni	2024-04-21T01:39:05Z	0	0	trl	[ "trl", "safetensors", "DPO", "WeniGPT", "pt", "base_model:Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged", "base_model:finetune:Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged", "license:mit", "region:us" ]	null	2024-04-21T01:06:38Z	--- license: mit library_name: "trl" tags: - DPO - WeniGPT base_model: Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged model-index: - name: Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-1.0.11-DPO results: [] language: ['pt'] --- # Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-1.0.11-DPO This model is a fine-tuned version of [Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged] on the dataset Weni/wenigpt-agent-dpo-1.0.0 with the DPO trainer. It is part of the WeniGPT project for [Weni](https://weni.ai/). Description: Experiment on DPO with other hyperparameters and best SFT model of WeniGPT It achieves the following results on the evaluation set: {'eval_loss': 0.2881975471973419, 'eval_runtime': 17.3794, 'eval_samples_per_second': 1.611, 'eval_steps_per_second': 0.806, 'eval_rewards/chosen': 1.1763746738433838, 'eval_rewards/rejected': -0.8690943121910095, 'eval_rewards/accuracies': 0.7857142686843872, 'eval_rewards/margins': 2.045469045639038, 'eval_logps/rejected': -193.93080139160156, 'eval_logps/chosen': -125.00711822509766, 'eval_logits/rejected': -1.8155311346054077, 'eval_logits/chosen': -1.7656012773513794, 'epoch': 5.951219512195122} ## Intended uses & limitations This model has not been trained to avoid specific intructions. ## Training procedure Finetuning was done on the model Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged with the following prompt: ``` --------------------- System_prompt: Agora você se chama {name}, você é {occupation} e seu objetivo é {chatbot_goal}. O adjetivo que mais define a sua personalidade é {adjective} e você se comporta da seguinte forma: {instructions_formatted} {context_statement} Lista de requisitos: - Responda de forma natural, mas nunca fale sobre um assunto fora do contexto. - Nunca traga informações do seu próprio conhecimento. - Repito é crucial que você responda usando apenas informações do contexto. - Nunca mencione o contexto fornecido. - Nunca mencione a pergunta fornecida. - Gere a resposta mais útil possível para a pergunta usando informações do conexto acima. - Nunca elabore sobre o porque e como você fez a tarefa, apenas responda. --------------------- ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - per_device_train_batch_size: 1 - per_device_eval_batch_size: 1 - gradient_accumulation_steps: 2 - num_gpus: 2 - total_train_batch_size: 4 - optimizer: AdamW - lr_scheduler_type: cosine - num_steps: 366 - quantization_type: bitsandbytes - LoRA: ("\n - bits: 4\n - use_exllama: True\n - device_map: auto\n - use_cache: False\n - lora_r: 8\n - lora_alpha: 16\n - lora_dropout: 0.05\n - bias: none\n - target_modules: ['v_proj', 'q_proj']\n - task_type: CAUSAL_LM",) ### Training results ### Framework versions - transformers==4.40.0 - datasets==2.18.0 - peft==0.10.0 - safetensors==0.4.2 - evaluate==0.4.1 - bitsandbytes==0.43 - huggingface_hub==0.22.2 - seqeval==1.2.2 - auto-gptq==0.7.1 - gpustat==1.1.1 - deepspeed==0.14.0 - wandb==0.16.6 - trl==0.8.1 - accelerate==0.29.3 - coloredlogs==15.0.1 - traitlets==5.14.2 - git+https://github.com/casper-hansen/AutoAWQ.git ### Hardware - Cloud provided: runpod.io
bartowski/Llama-3-Orca-1.0-8B-exl2	bartowski	2024-04-21T01:32:28Z	2	0	transformers	[ "transformers", "text-generation", "dataset:Open-Orca/SlimOrca-Dedup", "dataset:jondurbin/airoboros-3.2", "dataset:microsoft/orca-math-word-problems-200k", "dataset:m-a-p/Code-Feedback", "dataset:MaziyarPanahi/WizardLM_evol_instruct_V2_196k", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-04-21T01:32:27Z	--- library_name: transformers license: other datasets: - Open-Orca/SlimOrca-Dedup - jondurbin/airoboros-3.2 - microsoft/orca-math-word-problems-200k - m-a-p/Code-Feedback - MaziyarPanahi/WizardLM_evol_instruct_V2_196k quantized_by: bartowski pipeline_tag: text-generation --- ## Exllama v2 Quantizations of Llama-3-Orca-1.0-8B Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.19">turboderp's ExLlamaV2 v0.0.19</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/Locutusque/Llama-3-Orca-1.0-8B ## Prompt format ``` <\|im_start\|>system {system_prompt}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## Available sizes \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (8K) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-exl2/tree/8_0) \| 8.0 \| 8.0 \| 10.1 GB \| 10.5 GB \| 11.5 GB \| 13.6 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-exl2/tree/6_5) \| 6.5 \| 8.0 \| 8.9 GB \| 9.3 GB \| 10.3 GB \| 12.4 GB \| Very similar to 8.0, good tradeoff of size vs performance, recommended. \| \| [5_0](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-exl2/tree/5_0) \| 5.0 \| 6.0 \| 7.7 GB \| 8.1 GB \| 9.1 GB \| 11.2 GB \| Slightly lower quality vs 6.5, but usable on 8GB cards. \| \| [4_25](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-exl2/tree/4_25) \| 4.25 \| 6.0 \| 7.0 GB \| 7.4 GB \| 8.4 GB \| 10.5 GB \| GPTQ equivalent bits per weight, slightly higher quality. \| \| [3_5](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-exl2/tree/3_5) \| 3.5 \| 6.0 \| 6.4 GB \| 6.8 GB \| 7.8 GB \| 9.9 GB \| Lower quality, only use if you have to. \| ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-exl2 Llama-3-Orca-1.0-8B-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch: Linux: ```shell huggingface-cli download bartowski/Llama-3-Orca-1.0-8B-exl2 --revision 6_5 --local-dir Llama-3-Orca-1.0-8B-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell huggingface-cli download bartowski/Llama-3-Orca-1.0-8B-exl2 --revision 6_5 --local-dir Llama-3-Orca-1.0-8B-exl2-6.5 --local-dir-use-symlinks False ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned_GrounTruth_tiny_Seed103	bmehrba	2024-04-21T01:29:23Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]	null	2024-04-21T01:29:19Z	--- library_name: peft base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 --- # 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. --> - Developed by: [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 Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [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 Data 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] ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.7.0.dev0
bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned-adapters_GrounTruth_tiny_Seed103	bmehrba	2024-04-21T01:29:19Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]	null	2024-04-21T01:29:15Z	--- library_name: peft base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 --- # 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. --> - Developed by: [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 Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [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 Data 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] ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.7.0.dev0 ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.7.0.dev0
addykan/canine-rando-30-chars-5-epoch	addykan	2024-04-21T01:19:09Z	55	0	transformers	[ "transformers", "safetensors", "canine", "text-classification", "generated_from_trainer", "base_model:google/canine-c", "base_model:finetune:google/canine-c", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-20T23:22:38Z	--- license: apache-2.0 base_model: google/canine-c tags: - generated_from_trainer metrics: - accuracy model-index: - name: canine-rando-30-chars-5-epoch 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. --> # canine-rando-30-chars-5-epoch This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6015 - Accuracy: 0.7066 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 242 \| 0.6975 \| 0.5289 \| \| No log \| 2.0 \| 484 \| 0.5939 \| 0.6983 \| \| 0.6861 \| 3.0 \| 726 \| 0.5975 \| 0.6942 \| \| 0.6861 \| 4.0 \| 968 \| 0.5858 \| 0.7149 \| \| 0.6213 \| 5.0 \| 1210 \| 0.6015 \| 0.7066 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.0.0+cu117 - Datasets 2.19.0 - Tokenizers 0.19.1
taoyoung/pegasus-samsum	taoyoung	2024-04-21T01:18:22Z	42	0	transformers	[ "transformers", "tensorboard", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "base_model:google/pegasus-cnn_dailymail", "base_model:finetune:google/pegasus-cnn_dailymail", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-19T11:08:48Z	--- base_model: google/pegasus-cnn_dailymail tags: - generated_from_trainer model-index: - name: pegasus-samsum 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. --> # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4834 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.6597 \| 0.54 \| 500 \| 1.4834 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.2+cu118 - Datasets 2.19.0 - Tokenizers 0.15.2
mohamedhachemi/mohazz_arV5	mohamedhachemi	2024-04-21T01:16:56Z	50	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-21T01:15:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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AGundawar/chess-410m	AGundawar	2024-04-21T01:10:29Z	74	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "base_model:EleutherAI/pythia-410m-deduped", "base_model:finetune:EleutherAI/pythia-410m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-21T01:08:29Z	--- license: apache-2.0 base_model: EleutherAI/pythia-410m-deduped tags: - generated_from_trainer model-index: - name: chess-410m 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. --> # chess-410m This model is a fine-tuned version of [EleutherAI/pythia-410m-deduped](https://huggingface.co/EleutherAI/pythia-410m-deduped) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.8764 - eval_runtime: 45.8129 - eval_samples_per_second: 170.039 - eval_steps_per_second: 2.663 - epoch: 0.08 - step: 968 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1 ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2
ShenaoZ/0.001_ablation_declr_4iters_iter_4	ShenaoZ	2024-04-21T01:08:14Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZ/0.001_ablation_declr_4iters_iter_3", "base_model:finetune:ShenaoZ/0.001_ablation_declr_4iters_iter_3", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-21T00:08:05Z	--- license: mit base_model: ShenaoZ/0.001_ablation_declr_4iters_iter_3 tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - updated - original model-index: - name: 0.001_ablation_declr_4iters_iter_4 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. --> # 0.001_ablation_declr_4iters_iter_4 This model is a fine-tuned version of [ShenaoZ/0.001_ablation_declr_4iters_iter_3](https://huggingface.co/ShenaoZ/0.001_ablation_declr_4iters_iter_3) on the updated and the original datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.25e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
mitchyAI/momomchy	mitchyAI	2024-04-21T01:01:34Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-04-21T01:00:28Z	--- license: creativeml-openrail-m ---
leafspark/trackgpt	leafspark	2024-04-21T00:48:26Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-04-21T00:45:18Z	--- license: apache-2.0 --- # trackgpt An LLM designed to play TrackMania using WASD. # Dataset: leafspark/tracktext llama.cpp train command: ``` train-text-from-scratch --layer 8 --ctx 17000 --vocab-model ../models/ggml-vocab-llama.gguf --embd 64 --head 8 --checkpoint-in chk-fsd-384x36-LATEST.gguf --checkpoint-out chk-fsd-384x36-ITERATION.gguf --model-out ggml-fsd-384x36-f32-ITERATION.gguf --train-data "wikihow.txt" -t 12 -b 8 --seed 1 --adam-iter 2560 --no-checkpointing --save-every 30 --adam-alpha 0.001 ``` # Notes Please make sure to remove "0." and " " from your input! This is so that it can fit in the model context window. # Prompt Format ``` {data} {{prompt}} {action} {{response}} ```
ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL_h4	ThuyNT	2024-04-21T00:47:02Z	68	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-20T23:55:56Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: CS505_COQE_viT5_train_Instruction0_SOAPL_h4 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. --> # CS505_COQE_viT5_train_Instruction0_SOAPL_h4 This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
TokenBender/llama3_codeCherryPop_v0.2	TokenBender	2024-04-21T00:43:35Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-20T18:10:31Z	--- license: other base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer datasets: - generator model-index: - name: llama3_codeCherryPop_v0.2 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. --> # llama3_codeCherryPop_v0.2 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.0 - Datasets 2.19.0 - Tokenizers 0.19.1
ikimhope/whisper-small-num-test	ikimhope	2024-04-21T00:25:18Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-20T16:30:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned-adapters_GrounTruth_tiny_Seed102	bmehrba	2024-04-21T00:25:13Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]	null	2024-04-21T00:25:10Z	--- library_name: peft base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 --- # 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. --> - Developed by: [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 Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [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 Data 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] ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.7.0.dev0 ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.7.0.dev0
MonishSoundarRaj/duke_building_uncc	MonishSoundarRaj	2024-04-21T00:23:59Z	7	1	diffusers	[ "diffusers", "autotrain", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "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	2024-04-20T17:52:02Z	--- tags: - autotrain - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a sks building license: openrail++ --- # AutoTrain SDXL LoRA DreamBooth - leonickson1/urec_building_uncc <Gallery /> ## Model description These are leonickson1/urec_building_uncc 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: None. ## Trigger words You should use photo of a sks building to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](leonickson1/urec_building_uncc/tree/main) them in the Files & versions tab.
EdwinWiseOne/ppo-Huggy	EdwinWiseOne	2024-04-21T00:23:58Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-04-21T00:22:29Z	--- 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: EdwinWiseOne/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Rud/LED_multi_lexsum_peft	Rud	2024-04-21T00:21:57Z	5	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:pszemraj/led-large-book-summary", "base_model:adapter:pszemraj/led-large-book-summary", "license:bsd-3-clause", "region:us" ]	null	2024-04-17T07:47:00Z	--- license: bsd-3-clause library_name: peft tags: - generated_from_trainer metrics: - rouge base_model: pszemraj/led-large-book-summary model-index: - name: LED_multi_lexsum_peft 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. --> # LED_multi_lexsum_peft This model is a fine-tuned version of [pszemraj/led-large-book-summary](https://huggingface.co/pszemraj/led-large-book-summary) on an [allenai/multi_lexsum](https://huggingface.co/datasets/allenai/multi_lexsum) dataset. It achieves the following results on the evaluation set: - Loss: 4.2303 - Rouge1: 0.3156 - Rouge2: 0.1258 - Rougel: 0.1548 - Rougelsum: 0.185 - Bert Precision: 0.8468 - Bert Recall: 0.887 - Bert F1: 0.8664 - Gen Len: 949.968 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Bert Precision \| Bert Recall \| Bert F1 \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:--------------:\|:-----------:\|:-------:\|:-------:\| \| 6.2726 \| 1.0 \| 850 \| 4.2979 \| 0.3208 \| 0.1202 \| 0.1551 \| 0.185 \| 0.8724 \| 0.8797 \| 0.876 \| 906.8 \| \| 4.5041 \| 2.0 \| 1700 \| 4.2303 \| 0.3156 \| 0.1258 \| 0.1548 \| 0.185 \| 0.8468 \| 0.887 \| 0.8664 \| 949.968 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL_h3	ThuyNT	2024-04-21T00:16:29Z	99	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-20T23:24:28Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: CS505_COQE_viT5_train_Instruction0_ASPOL_h3 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. --> # CS505_COQE_viT5_train_Instruction0_ASPOL_h3 This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 12 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
ltuzova/citation_intent_classification_roberta	ltuzova	2024-04-21T00:12:29Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-20T23:00:33Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: citation_intent_classification_roberta 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. --> # citation_intent_classification_roberta This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9427 - Accuracy: 0.7986 - F1 Macro: 0.6913 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 Macro \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------:\| \| 1.4059 \| 1.0 \| 105 \| 1.0585 \| 0.6491 \| 0.2714 \| \| 1.0405 \| 2.0 \| 211 \| 0.9258 \| 0.6842 \| 0.3254 \| \| 0.8144 \| 3.0 \| 316 \| 0.7686 \| 0.7281 \| 0.4907 \| \| 0.5887 \| 4.0 \| 422 \| 0.7906 \| 0.7456 \| 0.5518 \| \| 0.404 \| 5.0 \| 527 \| 0.6946 \| 0.7719 \| 0.7045 \| \| 0.3302 \| 6.0 \| 633 \| 0.8840 \| 0.7719 \| 0.6330 \| \| 0.225 \| 7.0 \| 738 \| 0.8200 \| 0.8070 \| 0.7258 \| \| 0.1843 \| 8.0 \| 844 \| 0.8336 \| 0.8070 \| 0.7533 \| \| 0.16 \| 9.0 \| 949 \| 0.8221 \| 0.8246 \| 0.7641 \| \| 0.0977 \| 9.95 \| 1050 \| 0.8798 \| 0.8246 \| 0.7649 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1+cu117 - Datasets 2.13.2 - Tokenizers 0.13.3
d-manuardi/gemma-2b-finetune-test	d-manuardi	2024-04-21T00:08:57Z	118	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-21T00:02:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-1.0.9-DPO	Weni	2024-04-21T00:05:29Z	0	0	trl	[ "trl", "safetensors", "DPO", "WeniGPT", "pt", "base_model:Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged", "base_model:finetune:Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged", "license:mit", "region:us" ]	null	2024-04-20T23:46:12Z	--- license: mit library_name: "trl" tags: - DPO - WeniGPT base_model: Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged model-index: - name: Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-1.0.9-DPO results: [] language: ['pt'] --- # Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-1.0.9-DPO This model is a fine-tuned version of [Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged] on the dataset Weni/wenigpt-agent-dpo-1.0.0 with the DPO trainer. It is part of the WeniGPT project for [Weni](https://weni.ai/). Description: Experiment on DPO with other hyperparameters and best SFT model of WeniGPT It achieves the following results on the evaluation set: {'eval_loss': 0.3240291178226471, 'eval_runtime': 10.8299, 'eval_samples_per_second': 2.585, 'eval_steps_per_second': 0.646, 'eval_rewards/chosen': 0.607404887676239, 'eval_rewards/rejected': -0.34199661016464233, 'eval_rewards/accuracies': 1.0, 'eval_rewards/margins': 0.9494014382362366, 'eval_logps/rejected': -248.86924743652344, 'eval_logps/chosen': -165.00857543945312, 'eval_logits/rejected': -1.9368611574172974, 'eval_logits/chosen': -1.8489564657211304, 'epoch': 5.81} ## Intended uses & limitations This model has not been trained to avoid specific intructions. ## Training procedure Finetuning was done on the model Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged with the following prompt: ``` --------------------- System_prompt: Agora você se chama {name}, você é {occupation} e seu objetivo é {chatbot_goal}. O adjetivo que mais define a sua personalidade é {adjective} e você se comporta da seguinte forma: {instructions_formatted} {context_statement} Lista de requisitos: - Responda de forma natural, mas nunca fale sobre um assunto fora do contexto. - Nunca traga informações do seu próprio conhecimento. - Repito é crucial que você responda usando apenas informações do contexto. - Nunca mencione o contexto fornecido. - Nunca mencione a pergunta fornecida. - Gere a resposta mais útil possível para a pergunta usando informações do conexto acima. - Nunca elabore sobre o porque e como você fez a tarefa, apenas responda. --------------------- ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - per_device_train_batch_size: 1 - per_device_eval_batch_size: 1 - gradient_accumulation_steps: 2 - num_gpus: 4 - total_train_batch_size: 8 - optimizer: AdamW - lr_scheduler_type: cosine - num_steps: 180 - quantization_type: bitsandbytes - LoRA: ("\n - bits: 4\n - use_exllama: True\n - device_map: auto\n - use_cache: False\n - lora_r: 8\n - lora_alpha: 16\n - lora_dropout: 0.05\n - bias: none\n - target_modules: ['v_proj', 'q_proj']\n - task_type: CAUSAL_LM",) ### Training results ### Framework versions - transformers==4.38.2 - datasets==2.18.0 - peft==0.10.0 - safetensors==0.4.2 - evaluate==0.4.1 - bitsandbytes==0.43 - huggingface_hub==0.22.2 - seqeval==1.2.2 - optimum==1.18.1 - auto-gptq==0.7.1 - gpustat==1.1.1 - deepspeed==0.14.0 - wandb==0.16.6 - trl==0.8.1 - accelerate==0.29.2 - coloredlogs==15.0.1 - traitlets==5.14.2 - autoawq@https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.4/autoawq-0.2.4+cu118-cp310-cp310-linux_x86_64.whl ### Hardware - Cloud provided: runpod.io
jarrelscy/Mixtral-8x22B-Instruct-v0.1-GPTQ-8bit	jarrelscy	2024-04-21T00:05:15Z	8	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "gptq", "region:us" ]	text-generation	2024-04-20T23:32:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID Optimum GPTQ quantized 8-bit version of Mixtral-8x22B-Instruct-v0.1 \ WIP - do not use currently \ See original model card for more information. # How to load
wirytiox/Gemma7B-konosuba	wirytiox	2024-04-21T00:01:50Z	19	1	transformers	[ "transformers", "safetensors", "gguf", "gemma", "text-generation", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-20T06:15:46Z	--- license: apache-2.0 language: - en --- # Konosuba gemma 7B this is an intermidate model, it's finetuned with all konosuba script so i can build a better finetune later, i uploaded it because it could be relevant to someone ## Model Details - Model Name: Gemma7B-konosuba - Architecture: Gemma 7B - Training Format: unsloth/gemma-7b-it-bnb-4bit - Version: 1.0.0 To use this model in your projects, you can follow these steps: # if you want my notebook to check out how i did it, you can go to my [github!](https://github.com/wirytiox/Unsloth-wiry-training-suit) <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made%20with%20unsloth.png" alt="Alt text" width="200"/>
badrex/wav2vec2-large-xls-r-300m-upper-sorbian-pl-frozen-2-colab	badrex	2024-04-21T00:00:19Z	10	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_16_1", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-04-20T19:42:01Z	--- tags: - generated_from_trainer datasets: - common_voice_16_1 metrics: - wer model-index: - name: wav2vec2-large-xls-r-300m-upper-sorbian-pl-frozen-2-colab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_16_1 type: common_voice_16_1 config: hsb split: test args: hsb metrics: - name: Wer type: wer value: 0.39737582005623245 --- <!-- 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. --> # wav2vec2-large-xls-r-300m-upper-sorbian-pl-frozen-2-colab This model is a fine-tuned version of [](https://huggingface.co/) on the common_voice_16_1 dataset. It achieves the following results on the evaluation set: - Loss: 0.6971 - Wer: 0.3974 - Cer: 0.0976 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 60 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 0.6605 \| 3.23 \| 100 \| 0.6446 \| 0.7160 \| 0.1772 \| \| 0.499 \| 6.45 \| 200 \| 0.5900 \| 0.6256 \| 0.1482 \| \| 0.3831 \| 9.68 \| 300 \| 0.5681 \| 0.5825 \| 0.1385 \| \| 0.2866 \| 12.9 \| 400 \| 0.5691 \| 0.5239 \| 0.1235 \| \| 0.2269 \| 16.13 \| 500 \| 0.6304 \| 0.5061 \| 0.1202 \| \| 0.1725 \| 19.35 \| 600 \| 0.6649 \| 0.4864 \| 0.1137 \| \| 0.1365 \| 22.58 \| 700 \| 0.6574 \| 0.4597 \| 0.1107 \| \| 0.1081 \| 25.81 \| 800 \| 0.6525 \| 0.4452 \| 0.1057 \| \| 0.0873 \| 29.03 \| 900 \| 0.6799 \| 0.4236 \| 0.1031 \| \| 0.0782 \| 32.26 \| 1000 \| 0.7263 \| 0.4468 \| 0.1093 \| \| 0.0715 \| 35.48 \| 1100 \| 0.7404 \| 0.4173 \| 0.1002 \| \| 0.061 \| 38.71 \| 1200 \| 0.6985 \| 0.4208 \| 0.1009 \| \| 0.0551 \| 41.94 \| 1300 \| 0.7094 \| 0.4082 \| 0.0988 \| \| 0.049 \| 45.16 \| 1400 \| 0.7069 \| 0.4096 \| 0.1011 \| \| 0.0492 \| 48.39 \| 1500 \| 0.6971 \| 0.4117 \| 0.1008 \| \| 0.0441 \| 51.61 \| 1600 \| 0.6906 \| 0.4025 \| 0.0972 \| \| 0.0407 \| 54.84 \| 1700 \| 0.7154 \| 0.4035 \| 0.0985 \| \| 0.0389 \| 58.06 \| 1800 \| 0.6971 \| 0.3974 \| 0.0976 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2
bartowski/Llama-3-Smaug-8B-GGUF	bartowski	2024-04-21T00:00:18Z	214	18	transformers	[ "transformers", "gguf", "text-generation", "license:llama2", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-04-20T05:46:42Z	--- library_name: transformers license: llama2 quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp Quantizations of Llama-3-Smaug-8B This model has the <\|eot_id\|> token set to not-special, which seems to work better with current inference engines. Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> fork from pcuenca <a href="https://github.com/pcuenca/llama.cpp/tree/llama3-conversion">llama3-conversion</a> for quantization. Original model: https://huggingface.co/abacusai/Llama-3-Smaug-8B All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384) ## Prompt format ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> {system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {prompt}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [Llama-3-Smaug-8B-Q8_0.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-Q8_0.gguf) \| Q8_0 \| 8.54GB \| Extremely high quality, generally unneeded but max available quant. \| \| [Llama-3-Smaug-8B-Q6_K.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-Q6_K.gguf) \| Q6_K \| 6.59GB \| Very high quality, near perfect, recommended. \| \| [Llama-3-Smaug-8B-Q5_K_M.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-Q5_K_M.gguf) \| Q5_K_M \| 5.73GB \| High quality, recommended. \| \| [Llama-3-Smaug-8B-Q5_K_S.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-Q5_K_S.gguf) \| Q5_K_S \| 5.59GB \| High quality, recommended. \| \| [Llama-3-Smaug-8B-Q4_K_M.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-Q4_K_M.gguf) \| Q4_K_M \| 4.92GB \| Good quality, uses about 4.83 bits per weight, recommended. \| \| [Llama-3-Smaug-8B-Q4_K_S.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-Q4_K_S.gguf) \| Q4_K_S \| 4.69GB \| Slightly lower quality with more space savings, recommended. \| \| [Llama-3-Smaug-8B-IQ4_NL.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-IQ4_NL.gguf) \| IQ4_NL \| 4.67GB \| Decent quality, slightly smaller than Q4_K_S with similar performance recommended. \| \| [Llama-3-Smaug-8B-IQ4_XS.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-IQ4_XS.gguf) \| IQ4_XS \| 4.44GB \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [Llama-3-Smaug-8B-Q3_K_L.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-Q3_K_L.gguf) \| Q3_K_L \| 4.32GB \| Lower quality but usable, good for low RAM availability. \| \| [Llama-3-Smaug-8B-Q3_K_M.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-Q3_K_M.gguf) \| Q3_K_M \| 4.01GB \| Even lower quality. \| \| [Llama-3-Smaug-8B-IQ3_M.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-IQ3_M.gguf) \| IQ3_M \| 3.78GB \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [Llama-3-Smaug-8B-IQ3_S.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-IQ3_S.gguf) \| IQ3_S \| 3.68GB \| Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. \| \| [Llama-3-Smaug-8B-Q3_K_S.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-Q3_K_S.gguf) \| Q3_K_S \| 3.66GB \| Low quality, not recommended. \| \| [Llama-3-Smaug-8B-IQ3_XS.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-IQ3_XS.gguf) \| IQ3_XS \| 3.51GB \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [Llama-3-Smaug-8B-IQ3_XXS.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-IQ3_XXS.gguf) \| IQ3_XXS \| 3.27GB \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [Llama-3-Smaug-8B-Q2_K.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-Q2_K.gguf) \| Q2_K \| 3.17GB \| Very low quality but surprisingly usable. \| \| [Llama-3-Smaug-8B-IQ2_M.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-IQ2_M.gguf) \| IQ2_M \| 2.94GB \| Very low quality, uses SOTA techniques to also be surprisingly usable. \| \| [Llama-3-Smaug-8B-IQ2_S.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-IQ2_S.gguf) \| IQ2_S \| 2.75GB \| Very low quality, uses SOTA techniques to be usable. \| \| [Llama-3-Smaug-8B-IQ2_XS.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-IQ2_XS.gguf) \| IQ2_XS \| 2.60GB \| Very low quality, uses SOTA techniques to be usable. \| \| [Llama-3-Smaug-8B-IQ2_XXS.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-IQ2_XXS.gguf) \| IQ2_XXS \| 2.39GB \| Lower quality, uses SOTA techniques to be usable. \| \| [Llama-3-Smaug-8B-IQ1_M.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-IQ1_M.gguf) \| IQ1_M \| 2.16GB \| Extremely low quality, not recommended. \| \| [Llama-3-Smaug-8B-IQ1_S.gguf](https://huggingface.co/bartowski/Llama-3-Smaug-8B-GGUF/blob/main/Llama-3-Smaug-8B-IQ1_S.gguf) \| IQ1_S \| 2.01GB \| Extremely low quality, not recommended. \| ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are not compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
mitchyAI/emirumchy	mitchyAI	2024-04-20T23:46:45Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-04-20T23:45:12Z	--- license: creativeml-openrail-m ---
mohamedhachemi/mohazz_arV2	mohamedhachemi	2024-04-20T23:40:32Z	93	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-20T23:39:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
LarryAIDraw/anis_bikini	LarryAIDraw	2024-04-20T23:35:37Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-04-20T23:33:44Z	--- license: creativeml-openrail-m --- https://civitai.com/models/409365/anis-nikke-swimsuit
LarryAIDraw/hifumi-newgame-01	LarryAIDraw	2024-04-20T23:35:17Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-04-20T23:32:49Z	--- license: creativeml-openrail-m --- https://civitai.com/models/104171/hifumi-takimoto-new-game
LarryAIDraw/Fenrys	LarryAIDraw	2024-04-20T23:35:07Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-04-20T23:32:25Z	--- license: creativeml-openrail-m --- https://civitai.com/models/410359/fenrys-lv2-kara-cheat-datta-motoyuusha-kouho-no-mattari-isekai-life
bartowski/opus-v1.2-llama-3-8b-exl2	bartowski	2024-04-20T23:35:05Z	1	1	null	[ "unsloth", "axolotl", "text-generation", "en", "license:cc-by-nc-nd-4.0", "region:us" ]	text-generation	2024-04-20T06:07:31Z	--- language: - en pipeline_tag: text-generation tags: - unsloth - axolotl license: cc-by-nc-nd-4.0 quantized_by: bartowski --- ## Exllama v2 Quantizations of opus-v1.2-llama-3-8b Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.19">turboderp's ExLlamaV2 v0.0.19</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/dreamgen/opus-v1.2-llama-3-8b ## Prompt format ``` <\|im_start\|>system {system_prompt}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>text ``` ## Available sizes \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (8k) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-exl2/tree/8_0) \| 8.0 \| 8.0 \| 10.1 GB \| 10.5 GB \| 11.5 GB \| 13.6 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-exl2/tree/6_5) \| 6.5 \| 8.0 \| 8.9 GB \| 9.3 GB \| 10.3 GB \| 12.4 GB \| Very similar to 8.0, good tradeoff of size vs performance, recommended. \| \| [5_0](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-exl2/tree/5_0) \| 5.0 \| 6.0 \| 7.7 GB \| 8.1 GB \| 9.1 GB \| 11.2 GB \| Slightly lower quality vs 6.5, but usable on 8GB cards. \| \| [4_25](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-exl2/tree/4_25) \| 4.25 \| 6.0 \| 7.0 GB \| 7.4 GB \| 8.4 GB \| 10.5 GB \| GPTQ equivalent bits per weight, slightly higher quality. \| \| [3_5](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-exl2/tree/3_5) \| 3.5 \| 6.0 \| 6.4 GB \| 6.8 GB \| 7.8 GB \| 9.9 GB \| Lower quality, only use if you have to. \| ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-exl2 opus-v1.2-llama-3-8b-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch: Linux: ```shell huggingface-cli download bartowski/opus-v1.2-llama-3-8b-exl2 --revision 6_5 --local-dir opus-v1.2-llama-3-8b-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell huggingface-cli download bartowski/opus-v1.2-llama-3-8b-exl2 --revision 6_5 --local-dir opus-v1.2-llama-3-8b-exl2-6.5 --local-dir-use-symlinks False ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
mitchyAI/nozemchy	mitchyAI	2024-04-20T23:31:11Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-04-20T23:30:16Z	--- license: creativeml-openrail-m ---
thorirhrafn/gpt1B_reward_test	thorirhrafn	2024-04-20T23:30:27Z	4	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "reward-trainer", "generated_from_trainer", "base_model:AI-Sweden-Models/gpt-sw3-1.3b", "base_model:adapter:AI-Sweden-Models/gpt-sw3-1.3b", "license:apache-2.0", "region:us" ]	null	2024-04-19T16:06:50Z	--- license: apache-2.0 library_name: peft tags: - trl - reward-trainer - generated_from_trainer metrics: - accuracy base_model: AI-Sweden-Models/gpt-sw3-1.3b model-index: - name: gpt1B_reward_test 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. --> # gpt1B_reward_test This model is a fine-tuned version of [AI-Sweden-Models/gpt-sw3-1.3b](https://huggingface.co/AI-Sweden-Models/gpt-sw3-1.3b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6367 - Accuracy: 0.6504 ## 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: 3e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6878 \| 0.04 \| 200 \| 0.6850 \| 0.5791 \| \| 0.6724 \| 0.08 \| 400 \| 0.6740 \| 0.6024 \| \| 0.6611 \| 0.13 \| 600 \| 0.6703 \| 0.6081 \| \| 0.6435 \| 0.17 \| 800 \| 0.6773 \| 0.6036 \| \| 0.6787 \| 0.21 \| 1000 \| 0.6544 \| 0.6189 \| \| 0.7166 \| 0.25 \| 1200 \| 0.6697 \| 0.6223 \| \| 0.614 \| 0.3 \| 1400 \| 0.6590 \| 0.6250 \| \| 0.6279 \| 0.34 \| 1600 \| 0.6422 \| 0.6343 \| \| 0.6185 \| 0.38 \| 1800 \| 0.6427 \| 0.6389 \| \| 0.5539 \| 0.42 \| 2000 \| 0.6459 \| 0.6390 \| \| 0.5988 \| 0.47 \| 2200 \| 0.6485 \| 0.6379 \| \| 0.6096 \| 0.51 \| 2400 \| 0.6570 \| 0.6439 \| \| 0.5898 \| 0.55 \| 2600 \| 0.6381 \| 0.6441 \| \| 0.6366 \| 0.59 \| 2800 \| 0.6479 \| 0.6389 \| \| 0.6457 \| 0.64 \| 3000 \| 0.6397 \| 0.6490 \| \| 0.6171 \| 0.68 \| 3200 \| 0.6476 \| 0.6467 \| \| 0.5262 \| 0.72 \| 3400 \| 0.6506 \| 0.6458 \| \| 0.5723 \| 0.76 \| 3600 \| 0.6467 \| 0.6471 \| \| 0.6194 \| 0.81 \| 3800 \| 0.6393 \| 0.6480 \| \| 0.5946 \| 0.85 \| 4000 \| 0.6375 \| 0.6490 \| \| 0.5868 \| 0.89 \| 4200 \| 0.6366 \| 0.6503 \| \| 0.5905 \| 0.93 \| 4400 \| 0.6367 \| 0.6505 \| \| 0.5651 \| 0.97 \| 4600 \| 0.6367 \| 0.6504 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.1 - Pytorch 2.2.0+cu118 - Datasets 2.17.1 - Tokenizers 0.15.2
mitchyAI/umjimchy	mitchyAI	2024-04-20T23:27:59Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-04-20T23:27:14Z	--- license: creativeml-openrail-m ---
bartowski/Meta-Llama-3-8B-Instruct-special-tokens-exl2	bartowski	2024-04-20T23:25:48Z	1	13	null	[ "facebook", "meta", "pytorch", "llama", "llama-3", "text-generation", "en", "license:other", "region:us" ]	text-generation	2024-04-18T17:10:08Z	--- language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: other license_name: llama3 license_link: LICENSE extra_gated_prompt: >- ### META LLAMA 3 COMMUNITY LICENSE AGREEMENT Meta Llama 3 Version Release Date: April 18, 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 Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/. "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. "Meta Llama 3" 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://llama.meta.com/llama-downloads. "Llama Materials" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement. "Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). 1. License Rights and Redistribution. a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. b. Redistribution and Use. i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Meta Llama 3” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama 3” at the beginning of any such AI model name. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. iii. 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Fail to appropriately disclose to end users any known dangers of your AI system 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/llama3](https://github.com/meta-llama/llama3) * Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback * Reporting bugs and security concerns: facebook.com/whitehat/info * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text 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 quantized_by: bartowski --- ## Exllama v2 Quantizations of Meta-Llama-3-8B-Instruct # You may need to turn on "decode_special_tokens=True" or "Skip special tokens=False" depending on what tool you're using to make the output work Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.18">turboderp's ExLlamaV2 v0.0.18</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct ## Prompt format ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> {system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {prompt}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> ``` ## Available sizes \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (8k) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-special-tokens-exl2/tree/8_0) \| 8.0 \| 8.0 \| 10.1 GB \| 10.5 GB \| 11.5 GB \| 13.6 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-special-tokens-exl2/tree/6_5) \| 6.5 \| 8.0 \| 8.9 GB \| 9.3 GB \| 10.3 GB \| 12.4 GB \| Very similar to 8.0, good tradeoff of size vs performance, recommended. \| \| [5_0](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-special-tokens-exl2/tree/5_0) \| 5.0 \| 6.0 \| 7.7 GB \| 8.1 GB \| 9.1 GB \| 11.2 GB \| Slightly lower quality vs 6.5, but usable on 8GB cards. \| \| [4_25](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-special-tokens-exl2/tree/4_25) \| 4.25 \| 6.0 \| 7.0 GB \| 7.4 GB \| 8.4 GB \| 10.5 GB \| GPTQ equivalent bits per weight, slightly higher quality. \| \| [3_5](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-special-tokens-exl2/tree/3_5) \| 3.5 \| 6.0 \| 6.4 GB \| 6.8 GB \| 7.8 GB \| 9.9 GB \| Lower quality, only use if you have to. \| ## Download instructions ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-special-tokens-exl2 Meta-Llama-3-8B-Instruct-special-tokens-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch: Linux: ```shell huggingface-cli download bartowski/Meta-Llama-3-8B-Instruct-special-tokens-exl2 --revision 6_5 --local-dir Meta-Llama-3-8B-Instruct-special-tokens-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell huggingface-cli download bartowski/Meta-Llama-3-8B-Instruct-special-tokens-exl2 --revision 6_5 --local-dir Meta-Llama-3-8B-Instruct-special-tokens-exl2-6.5 --local-dir-use-symlinks False ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
chandc/OrpoLlama-3-8B	chandc	2024-04-20T23:25:25Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-20T23:20:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned_GrounTruth_tiny_Seed101	bmehrba	2024-04-20T23:20:37Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]	null	2024-04-20T23:20:34Z	--- library_name: peft base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 --- # 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. --> - Developed by: [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 Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.7.0.dev0
addykan/canine-rando-30-chars	addykan	2024-04-20T23:19:58Z	90	0	transformers	[ "transformers", "safetensors", "canine", "text-classification", "generated_from_trainer", "base_model:google/canine-c", "base_model:finetune:google/canine-c", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-20T23:02:00Z	--- license: apache-2.0 base_model: google/canine-c tags: - generated_from_trainer metrics: - accuracy model-index: - name: canine-rando-30-chars 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. --> # canine-rando-30-chars This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7346 - Accuracy: 0.7282 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.9127 \| 1.0 \| 8000 \| 0.8745 \| 0.6576 \| \| 0.7717 \| 2.0 \| 16000 \| 0.7346 \| 0.7282 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.0.0+cu117 - Datasets 2.19.0 - Tokenizers 0.19.1
ResplendentAI/RP_Format_QuoteAsterisk_Llama3	ResplendentAI	2024-04-20T23:13:07Z	2	4	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:adapter:unsloth/llama-3-8b-bnb-4bit", "region:us" ]	null	2024-04-20T23:03:08Z	--- library_name: peft base_model: unsloth/llama-3-8b-bnb-4bit --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0
namebobb/my_awesome_model	namebobb	2024-04-20T22:59:43Z	118	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-20T22:46:17Z	--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3284 - Accuracy: 0.8791 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.3228 \| 1.0 \| 1563 \| 0.3019 \| 0.8716 \| \| 0.22 \| 2.0 \| 3126 \| 0.3284 \| 0.8791 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2
OwOOwO/dumbo-llama	OwOOwO	2024-04-20T22:59:21Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-20T22:56:02Z	--- 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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Fighoture/Llama-2-7b-chat-shortgpt-with-angular-25-percent	Fighoture	2024-04-20T22:42:26Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-20T22:14:48Z	--- 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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KaggleMasterX/llama3_dpo_tok	KaggleMasterX	2024-04-20T22:36:15Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-20T22:36:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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RuiJu2024/whisper-large-v3-1	RuiJu2024	2024-04-20T22:35:42Z	14	1	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:RayJu2024/TitusChu", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-29T20:48:00Z	--- license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer datasets: - RayJu2024/TitusChu model-index: - name: Whisper Large V3 Fine Tune 1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Large V3 Fine Tune 1 This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the 20230516 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.36.0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
jgrc3/pfeiffer_adapter_classification_noPre_lr0_0001	jgrc3	2024-04-20T22:25:56Z	0	0	adapter-transformers	[ "adapter-transformers", "roberta", "dataset:BigTMiami/amazon_helpfulness", "region:us" ]	null	2024-04-20T22:25:54Z	--- tags: - adapter-transformers - roberta datasets: - BigTMiami/amazon_helpfulness --- # Adapter `jgrc3/pfeiffer_adapter_classification_noPre_lr0_0001` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_helpfulness](https://huggingface.co/datasets/BigTMiami/amazon_helpfulness/) dataset and includes a prediction head for classification. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") adapter_name = model.load_adapter("jgrc3/pfeiffer_adapter_classification_noPre_lr0_0001", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
daedalus16/bert-M1b-textclassification	daedalus16	2024-04-20T22:22:12Z	4	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-19T18:28:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Fighoture/Llama-2-7b-chat-shortgpt-with-angular-20-percent	Fighoture	2024-04-20T22:15:08Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-20T22:01:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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hazyresearch/mamba-1b-50b	hazyresearch	2024-04-20T22:14:47Z	151	0	transformers	[ "transformers", "pytorch", "en", "dataset:EleutherAI/pile", "arxiv:2402.18668", "endpoints_compatible", "region:us" ]	null	2024-04-20T07:09:14Z	--- datasets: - EleutherAI/pile language: - en --- # Model Card This model is pretrained as a reference baseline to the Based model provided here: https://huggingface.co/hazyresearch/based-1b-50b. Both checkpoints are pretrained on 50Bn tokens of the Pile in the exact same data order using next token prediction. A WandB report for training is here: https://api.wandb.ai/links/hazy-research/ggo9rst2 ### Model Sources The model is a standard Mamba model using the model code provided here: https://github.com/state-spaces/mamba/tree/main/mamba_ssm The training code is provided here and can be used to reproduce training: https://github.com/HazyResearch/based The paper for the work is here, and the appendix includes additional experimental details/hyperparameters: https://arxiv.org/abs/2402.18668 ### Uses The purpose of this work is to evaluate the language modeling quality of a new efficient architecture, Based. We include a series of benchmarks that you can use to evaluate quality: - FDA: https://huggingface.co/datasets/hazyresearch/based-fda - SWDE: https://huggingface.co/datasets/hazyresearch/based-swde - SQUAD: https://huggingface.co/datasets/hazyresearch/based-squad ## Citation Please consider citing this paper if you use our work: ``` @article{arora2024simple, title={Simple linear attention language models balance the recall-throughput tradeoff}, author={Arora, Simran and Eyuboglu, Sabri and Zhang, Michael and Timalsina, Aman and Alberti, Silas and Zinsley, Dylan and Zou, James and Rudra, Atri and Ré, Christopher}, journal={arXiv:2402.18668}, year={2024} } ``` Please reach out to [email protected], [email protected], and [email protected] with questions.
Saiikish/Saiikish	Saiikish	2024-04-20T22:12:52Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/tinyllama-bnb-4bit", "base_model:finetune:unsloth/tinyllama-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-20T22:12:44Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/tinyllama-bnb-4bit --- # Uploaded model - Developed by: Saiikish - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-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)
ogdanneedham/mistral-gs-0.1-lora	ogdanneedham	2024-04-20T22:07:18Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-20T22:06:55Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit --- # Uploaded model - Developed by: ogdanneedham - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
OwOOwO/dumbo-stable11	OwOOwO	2024-04-20T21:55:02Z	90	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-04-20T21:53:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
mlx-community/Meta-Llama-3-8B-4bit	mlx-community	2024-04-20T21:55:01Z	36	8	mlx	[ "mlx", "safetensors", "llama", "facebook", "meta", "pytorch", "llama-3", "text-generation", "en", "license:other", "region:us" ]	text-generation	2024-04-18T16:16:03Z	--- language: - en license: other tags: - facebook - meta - pytorch - llama - llama-3 - mlx pipeline_tag: text-generation license_name: llama3 license_link: LICENSE extra_gated_prompt: "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version\ \ Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for\ \ use, reproduction, distribution and modification of the Llama Materials set forth\ \ herein.\n\"Documentation\" means the specifications, manuals and documentation\ \ accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\ \"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.\n\"Meta Llama\ \ 3\" 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://llama.meta.com/llama-downloads.\n\ \"Llama Materials\" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation\ \ (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"\ we\" means Meta Platforms Ireland Limited (if you are located in or, if you are\ \ an entity, your principal place of business is in the EEA or Switzerland) and\ \ Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).\n\ \ \n1. License Rights and Redistribution.\na. Grant of Rights. You are granted\ \ a non-exclusive, worldwide, non-transferable and royalty-free limited license\ \ under Meta’s intellectual property or other rights owned by Meta embodied in the\ \ Llama Materials to use, reproduce, distribute, copy, create derivative works of,\ \ and make modifications to the Llama Materials.\nb. Redistribution and Use.\ni.\ \ If you distribute or make available the Llama Materials (or any derivative works\ \ thereof), or a product or service that uses any of them, including another AI\ \ model, you shall (A) provide a copy of this Agreement with any such Llama Materials;\ \ and (B) prominently display “Built with Meta Llama 3” on a related website, user\ \ interface, blogpost, about page, or product documentation. If you use the Llama\ \ Materials to create, train, fine tune, or otherwise improve an AI model, which\ \ is distributed or made available, you shall also include “Llama 3” at the beginning\ \ of any such AI model name.\nii. If you receive Llama Materials, or any derivative\ \ works thereof, from a Licensee as part of an integrated end user product, then\ \ Section 2 of this Agreement will not apply to you.\niii. You must retain in all\ \ copies of the Llama Materials that you distribute the following attribution notice\ \ within a “Notice” text file distributed as a part of such copies: “Meta Llama\ \ 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms,\ \ Inc. All Rights Reserved.”\niv. 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No trademark licenses are granted under this Agreement,\ \ and in connection with the Llama Materials, neither Meta nor Licensee may use\ \ any name or mark owned by or associated with the other or any of its affiliates,\ \ except as required for reasonable and customary use in describing and redistributing\ \ the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you\ \ a license to use “Llama 3” (the “Mark”) solely as required to comply with the\ \ last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently\ \ accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All\ \ goodwill arising out of your use of the Mark will inure to the benefit of Meta.\n\ b. 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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.\n7. 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.\n### Meta Llama 3 Acceptable Use\ \ Policy\nMeta is committed to promoting safe and fair use of its tools and features,\ \ including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable\ \ Use Policy (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)\n\ #### Prohibited Uses\nWe want everyone to use Meta Llama 3 safely and responsibly.\ \ You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate\ \ the law or others’ rights, including to:\n 1. Engage in, promote, generate,\ \ contribute to, encourage, plan, incite, or further illegal or unlawful activity\ \ or content, such as:\n 1. Violence or terrorism\n 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\n\ \ 3. Human trafficking, exploitation, and sexual violence\n 4. The\ \ illegal distribution of information or materials to minors, including obscene\ \ materials, or failure to employ legally required age-gating in connection with\ \ such information or materials.\n 5. Sexual solicitation\n 6. Any\ \ other criminal activity\n 2. Engage in, promote, incite, or facilitate the\ \ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\ \ 3. Engage in, promote, incite, or facilitate discrimination or other unlawful\ \ or harmful conduct in the provision of employment, employment benefits, credit,\ \ housing, other economic benefits, or other essential goods and services\n 4.\ \ Engage in the unauthorized or unlicensed practice of any profession including,\ \ but not limited to, financial, legal, medical/health, or related professional\ \ practices\n 5. Collect, process, disclose, generate, or infer health, demographic,\ \ or other sensitive personal or private information about individuals without rights\ \ and consents required by applicable laws\n 6. Engage in or facilitate any action\ \ or generate any content that infringes, misappropriates, or otherwise violates\ \ any third-party rights, including the outputs or results of any products or services\ \ using the Llama Materials\n 7. Create, generate, or facilitate the creation\ \ of malicious code, malware, computer viruses or do anything else that could disable,\ \ overburden, interfere with or impair the proper working, integrity, operation\ \ or appearance of a website or computer system\n2. 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 Meta Llama 3 related\ \ to the following:\n 1. 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\n 2. Guns and illegal weapons (including weapon development)\n 3.\ \ Illegal drugs and regulated/controlled substances\n 4. Operation of critical\ \ infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm\ \ or harm to others, including suicide, cutting, and eating disorders\n 6. Any\ \ content intended to incite or promote violence, abuse, or any infliction of bodily\ \ harm to an individual\n3. Intentionally deceive or mislead others, including use\ \ of Meta Llama 3 related to the following:\n 1. Generating, promoting, or furthering\ \ fraud or the creation or promotion of disinformation\n 2. Generating, promoting,\ \ or furthering defamatory content, including the creation of defamatory statements,\ \ images, or other content\n 3. Generating, promoting, or further distributing\ \ spam\n 4. Impersonating another individual without consent, authorization,\ \ or legal right\n 5. Representing that the use of Meta Llama 3 or outputs are\ \ human-generated\n 6. Generating or facilitating false online engagement, including\ \ fake reviews and other means of fake online engagement\n4. Fail to appropriately\ \ disclose to end users any known dangers of your AI system\nPlease 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:\n * Reporting issues with\ \ the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n\ \ * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n\ \ * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting\ \ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]" extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text 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 --- # mlx-community/Meta-Llama-3-8B-4bit This model was converted to MLX format from [`meta-llama/Meta-Llama-3-8B`]() using mlx-lm version 0.9.0. Model added by [Prince Canuma](https://twitter.com/Prince_Canuma). Refer to the [original model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Meta-Llama-3-8B-4bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
Fighoture/Llama-2-7b-chat-shortgpt-with-angular-15-percent	Fighoture	2024-04-20T21:48:11Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-20T21:42:26Z	--- 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]
ThuyNT/CS505_COQE_viT5_train_Instruction3_SOAPL	ThuyNT	2024-04-20T21:40:52Z	99	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-20T20:33:41Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: CS505_COQE_viT5_train_Instruction3_SOAPL 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. --> # CS505_COQE_viT5_train_Instruction3_SOAPL This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL_h2	ThuyNT	2024-04-20T21:38:07Z	99	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL", "base_model:finetune:ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-20T21:16:33Z	--- license: mit base_model: ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL tags: - generated_from_trainer model-index: - name: CS505_COQE_viT5_train_Instruction0_ASPOL_h2 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. --> # CS505_COQE_viT5_train_Instruction0_ASPOL_h2 This model is a fine-tuned version of [ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL](https://huggingface.co/ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
tomaszki/stablelm-42	tomaszki	2024-04-20T21:31:46Z	90	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-04-20T21:29:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mohdumar/gpt2-medium-untied	mohdumar	2024-04-20T21:31:43Z	120	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-20T21:27:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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BigTMiami/m_cite_par_bn_v_4_class_no_pre_4_adapter	BigTMiami	2024-04-20T21:26:03Z	0	0	adapter-transformers	[ "adapter-transformers", "roberta", "dataset:BigTMiami/citation_intent_dataset", "region:us" ]	null	2024-04-20T21:25:59Z	--- tags: - adapter-transformers - roberta datasets: - BigTMiami/citation_intent_dataset --- # Adapter `BigTMiami/m_cite_par_bn_v_4_class_no_pre_4_adapter` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/citation_intent_dataset](https://huggingface.co/datasets/BigTMiami/citation_intent_dataset/) dataset and includes a prediction head for classification. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") adapter_name = model.load_adapter("BigTMiami/m_cite_par_bn_v_4_class_no_pre_4_adapter", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
BigTMiami/m_cite_par_bn_v_4_class_no_pre_2_adapter	BigTMiami	2024-04-20T21:23:13Z	0	0	adapter-transformers	[ "adapter-transformers", "roberta", "dataset:BigTMiami/citation_intent_dataset", "region:us" ]	null	2024-04-20T21:23:08Z	--- tags: - adapter-transformers - roberta datasets: - BigTMiami/citation_intent_dataset --- # Adapter `BigTMiami/m_cite_par_bn_v_4_class_no_pre_2_adapter` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/citation_intent_dataset](https://huggingface.co/datasets/BigTMiami/citation_intent_dataset/) dataset and includes a prediction head for classification. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") adapter_name = model.load_adapter("BigTMiami/m_cite_par_bn_v_4_class_no_pre_2_adapter", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_3	BigTMiami	2024-04-20T21:22:56Z	2	0	adapter-transformers	[ "adapter-transformers", "roberta", "dataset:BigTMiami/citation_intent_dataset", "region:us" ]	null	2024-04-20T21:22:53Z	--- tags: - adapter-transformers - roberta datasets: - BigTMiami/citation_intent_dataset --- # Adapter `BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_3` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/citation_intent_dataset](https://huggingface.co/datasets/BigTMiami/citation_intent_dataset/) dataset and includes a prediction head for classification. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") adapter_name = model.load_adapter("BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_3", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
olonok/Llama3_8B_qlora_CoT_FineTuned	olonok	2024-04-20T21:22:33Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-20T20:12:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BigTMiami/m_cite_par_bn_v_4_class_no_pre_1_adapter	BigTMiami	2024-04-20T21:22:11Z	0	0	adapter-transformers	[ "adapter-transformers", "roberta", "dataset:BigTMiami/citation_intent_dataset", "region:us" ]	null	2024-04-20T21:22:08Z	--- tags: - adapter-transformers - roberta datasets: - BigTMiami/citation_intent_dataset --- # Adapter `BigTMiami/m_cite_par_bn_v_4_class_no_pre_1_adapter` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/citation_intent_dataset](https://huggingface.co/datasets/BigTMiami/citation_intent_dataset/) dataset and includes a prediction head for classification. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") adapter_name = model.load_adapter("BigTMiami/m_cite_par_bn_v_4_class_no_pre_1_adapter", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_2	BigTMiami	2024-04-20T21:21:31Z	0	0	adapter-transformers	[ "adapter-transformers", "roberta", "dataset:BigTMiami/citation_intent_dataset", "region:us" ]	null	2024-04-20T21:21:28Z	--- tags: - adapter-transformers - roberta datasets: - BigTMiami/citation_intent_dataset --- # Adapter `BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_2` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/citation_intent_dataset](https://huggingface.co/datasets/BigTMiami/citation_intent_dataset/) dataset and includes a prediction head for classification. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") adapter_name = model.load_adapter("BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_2", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
BigTMiami/m_cite_par_bn_v_4_class_no_pre_0_adapter	BigTMiami	2024-04-20T21:20:48Z	0	0	adapter-transformers	[ "adapter-transformers", "roberta", "dataset:BigTMiami/citation_intent_dataset", "region:us" ]	null	2024-04-20T21:20:45Z	--- tags: - adapter-transformers - roberta datasets: - BigTMiami/citation_intent_dataset --- # Adapter `BigTMiami/m_cite_par_bn_v_4_class_no_pre_0_adapter` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/citation_intent_dataset](https://huggingface.co/datasets/BigTMiami/citation_intent_dataset/) dataset and includes a prediction head for classification. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") adapter_name = model.load_adapter("BigTMiami/m_cite_par_bn_v_4_class_no_pre_0_adapter", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_1	BigTMiami	2024-04-20T21:20:07Z	0	0	adapter-transformers	[ "adapter-transformers", "roberta", "dataset:BigTMiami/citation_intent_dataset", "region:us" ]	null	2024-04-20T21:20:03Z	--- tags: - adapter-transformers - roberta datasets: - BigTMiami/citation_intent_dataset --- # Adapter `BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_1` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/citation_intent_dataset](https://huggingface.co/datasets/BigTMiami/citation_intent_dataset/) dataset and includes a prediction head for classification. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") adapter_name = model.load_adapter("BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_1", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
amine-01/a2c-PandaReachDense-v3	amine-01	2024-04-20T21:19:33Z	1	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-04-20T21:15:21Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.21 +/- 0.14 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
SweatyCrayfish/llama-3-8b-quantized	SweatyCrayfish	2024-04-20T21:18:57Z	514	10	transformers	[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "qlora", "quantization", "4-bit", "lora", "en", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-20T20:15:09Z	--- language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 - qlora - quantization - 4-bit - lora license: other license_name: llama3 license_link: LICENSE extra_gated_prompt: >- ### META LLAMA 3 COMMUNITY LICENSE AGREEMENT Meta Llama 3 Version Release Date: April 18, 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 Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/. 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Fail to appropriately disclose to end users any known dangers of your AI system 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/llama3](https://github.com/meta-llama/llama3) * Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback * Reporting bugs and security concerns: facebook.com/whitehat/info * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text 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 --- # 4-bit Quantized Llama 3 Model ## Description This repository hosts the 4-bit quantized version of the Llama 3 model. Optimized for reduced memory usage and faster inference, this model is suitable for deployment in environments where computational resources are limited. ## Model Details - Model Type: Transformer-based language model. - Quantization: 4-bit precision. - Advantages: - Memory Efficiency: Reduces memory usage significantly, allowing deployment on devices with limited RAM. - Inference Speed: Accelerates inference times, depending on the hardware's ability to process low-bit computations. ## How to Use To utilize this model efficiently, follow the steps below: ### Loading the Quantized Model Load the model with specific parameters to ensure it utilizes 4-bit precision: ```python from transformers import AutoModelForCausalLM model_4bit = AutoModelForCausalLM.from_pretrained("SweatyCrayfish/llama-3-8b-quantized", device_map="auto", load_in_4bit=True) ``` ## Adjusting Precision of Components Adjust the precision of other components, which are by default converted to torch.float16: ```python import torch from transformers import AutoModelForCausalLM model_4bit = AutoModelForCausalLM.from_pretrained("SweatyCrayfish/llama-3-8b-quantized", load_in_4bit=True, torch_dtype=torch.float32) print(model_4bit.model.decoder.layers[-1].final_layer_norm.weight.dtype) ``` ## Citation Original repository and citations: @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} }
BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_0	BigTMiami	2024-04-20T21:18:43Z	1	0	adapter-transformers	[ "adapter-transformers", "roberta", "dataset:BigTMiami/citation_intent_dataset", "region:us" ]	null	2024-04-20T21:18:39Z	--- tags: - adapter-transformers - roberta datasets: - BigTMiami/citation_intent_dataset --- # Adapter `BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_0` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/citation_intent_dataset](https://huggingface.co/datasets/BigTMiami/citation_intent_dataset/) dataset and includes a prediction head for classification. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") adapter_name = model.load_adapter("BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_0", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL_h2	ThuyNT	2024-04-20T21:16:13Z	100	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL", "base_model:finetune:ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-20T21:03:48Z	--- license: mit base_model: ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL tags: - generated_from_trainer model-index: - name: CS505_COQE_viT5_train_Instruction0_SOAPL_h2 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. --> # CS505_COQE_viT5_train_Instruction0_SOAPL_h2 This model is a fine-tuned version of [ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL](https://huggingface.co/ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
kakinola23/Meta-Llama-3-8B-bnb-4bit-new	kakinola23	2024-04-20T21:15:36Z	79	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-04-20T21:11:31Z	--- 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. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BigTMiami/m_imdb_par_bn_v_4_pretrain_adapter	BigTMiami	2024-04-20T21:07:26Z	0	0	adapter-transformers	[ "adapter-transformers", "roberta", "dataset:BigTMiami/imdb_TAPT_pretraining_dataset", "region:us" ]	null	2024-04-20T21:07:11Z	--- tags: - adapter-transformers - roberta datasets: - BigTMiami/imdb_TAPT_pretraining_dataset --- # Adapter `BigTMiami/m_imdb_par_bn_v_4_pretrain_adapter` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/imdb_TAPT_pretraining_dataset](https://huggingface.co/datasets/BigTMiami/imdb_TAPT_pretraining_dataset/) dataset and includes a prediction head for masked lm. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") adapter_name = model.load_adapter("BigTMiami/m_imdb_par_bn_v_4_pretrain_adapter", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
ChingizA/detr	ChingizA	2024-04-20T21:04:52Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "detr", "object-detection", "generated_from_trainer", "base_model:facebook/detr-resnet-50", "base_model:finetune:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2024-04-18T19:57:56Z	--- license: apache-2.0 base_model: facebook/detr-resnet-50 tags: - generated_from_trainer model-index: - name: detr 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. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7117 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.0345 \| 0.08 \| 200 \| 1.8447 \| \| 1.5511 \| 0.16 \| 400 \| 1.4217 \| \| 1.444 \| 0.24 \| 600 \| 1.3814 \| \| 1.3746 \| 0.32 \| 800 \| 1.3241 \| \| 1.2361 \| 0.4 \| 1000 \| 1.2589 \| \| 1.3506 \| 0.48 \| 1200 \| 1.2441 \| \| 1.2833 \| 0.56 \| 1400 \| 1.2052 \| \| 1.1051 \| 0.64 \| 1600 \| 1.0607 \| \| 1.1091 \| 0.72 \| 1800 \| 1.0610 \| \| 1.0295 \| 0.8 \| 2000 \| 1.0241 \| \| 1.1376 \| 0.88 \| 2200 \| 1.0846 \| \| 1.1172 \| 0.96 \| 2400 \| 1.1095 \| \| 1.0186 \| 1.04 \| 2600 \| 0.9978 \| \| 1.0775 \| 1.12 \| 2800 \| 1.0225 \| \| 0.9973 \| 1.2 \| 3000 \| 0.9934 \| \| 1.006 \| 1.28 \| 3200 \| 0.9886 \| \| 0.9814 \| 1.36 \| 3400 \| 0.9256 \| \| 1.0253 \| 1.44 \| 3600 \| 0.9209 \| \| 0.9932 \| 1.52 \| 3800 \| 0.9159 \| \| 0.9307 \| 1.6 \| 4000 \| 0.9058 \| \| 0.9103 \| 1.68 \| 4200 \| 0.9049 \| \| 0.9034 \| 1.76 \| 4400 \| 0.8643 \| \| 0.9544 \| 1.84 \| 4600 \| 0.9114 \| \| 0.889 \| 1.92 \| 4800 \| 0.8880 \| \| 0.8888 \| 2.0 \| 5000 \| 0.8515 \| \| 0.8877 \| 2.08 \| 5200 \| 0.8707 \| \| 0.8799 \| 2.16 \| 5400 \| 0.8458 \| \| 0.8398 \| 2.24 \| 5600 \| 0.8292 \| \| 0.8181 \| 2.32 \| 5800 \| 0.8226 \| \| 0.8876 \| 2.4 \| 6000 \| 0.8021 \| \| 0.8893 \| 2.48 \| 6200 \| 0.8173 \| \| 0.8497 \| 2.56 \| 6400 \| 0.7870 \| \| 0.8369 \| 2.64 \| 6600 \| 0.7719 \| \| 0.8213 \| 2.72 \| 6800 \| 0.7877 \| \| 0.8044 \| 2.8 \| 7000 \| 0.7763 \| \| 0.8087 \| 2.88 \| 7200 \| 0.7702 \| \| 0.7616 \| 2.96 \| 7400 \| 0.7570 \| \| 0.7901 \| 3.04 \| 7600 \| 0.7451 \| \| 0.8454 \| 3.12 \| 7800 \| 0.7560 \| \| 0.7428 \| 3.2 \| 8000 \| 0.7455 \| \| 0.822 \| 3.28 \| 8200 \| 0.7390 \| \| 0.8293 \| 3.36 \| 8400 \| 0.7324 \| \| 0.7196 \| 3.44 \| 8600 \| 0.7270 \| \| 0.7508 \| 3.52 \| 8800 \| 0.7357 \| \| 0.783 \| 3.6 \| 9000 \| 0.7293 \| \| 0.7094 \| 3.68 \| 9200 \| 0.7276 \| \| 0.7811 \| 3.76 \| 9400 \| 0.7178 \| \| 0.7765 \| 3.84 \| 9600 \| 0.7129 \| \| 0.7542 \| 3.92 \| 9800 \| 0.7165 \| \| 0.756 \| 4.0 \| 10000 \| 0.7117 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
ThuyNT/CS505_COQE_viT5_train_Instruction1_SOAPL	ThuyNT	2024-04-20T20:57:39Z	101	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-20T20:24:18Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: CS505_COQE_viT5_train_Instruction1_SOAPL 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. --> # CS505_COQE_viT5_train_Instruction1_SOAPL This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
voxmenthe/Meta-Llama-3-8B-Instruct-mlx-unquantized	voxmenthe	2024-04-20T20:51:29Z	4	0	mlx	[ "mlx", "safetensors", "llama", "facebook", "meta", "pytorch", "llama-3", "text-generation", "conversational", "en", "license:other", "region:us" ]	text-generation	2024-04-20T20:44:57Z	--- language: - en license: other tags: - facebook - meta - pytorch - llama - llama-3 - mlx pipeline_tag: text-generation license_name: llama3 license_link: LICENSE extra_gated_prompt: "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version\ \ Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for\ \ use, reproduction, distribution and modification of the Llama Materials set forth\ \ herein.\n\"Documentation\" means the specifications, manuals and documentation\ \ accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\ \"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.\n\"Meta Llama\ \ 3\" 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://llama.meta.com/llama-downloads.\n\ \"Llama Materials\" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation\ \ (and any portion thereof) made available under this Agreement.\n\"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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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 --- # voxmenthe/Meta-Llama-3-8B-Instruct-mlx-unquantized This model was converted to MLX format from [`meta-llama/Meta-Llama-3-8B-Instruct`]() using mlx-lm version 0.10.0. Refer to the [original model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("voxmenthe/Meta-Llama-3-8B-Instruct-mlx-unquantized") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
ltuzova/cs_domain_pretrained_model	ltuzova	2024-04-20T20:50:42Z	180	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-04-20T04:01:01Z	--- tags: - generated_from_trainer model-index: - name: cs_domain_pretrained_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cs_domain_pretrained_model This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 1.2410 - eval_runtime: 87.0838 - eval_samples_per_second: 114.832 - eval_steps_per_second: 1.803 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 2048 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 1 ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1+cu117 - Datasets 2.13.2 - Tokenizers 0.13.3
yaochx/sd-class-butterflies-32	yaochx	2024-04-20T20:40:27Z	44	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2024-04-20T20:38:49Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('yaochx/sd-class-butterflies-32') image = pipeline().images[0] image ```
mmanikanta/ResNet_AI_image_detector	mmanikanta	2024-04-20T20:29:13Z	212	0	transformers	[ "transformers", "pytorch", "tensorboard", "resnet", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-20T19:02:55Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: ResNet_AI_image_detector 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. --> # ResNet_AI_image_detector This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1330 - Accuracy: 0.9507 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.441 \| 1.0 \| 1093 \| 0.3588 \| 0.8458 \| \| 0.2977 \| 2.0 \| 2187 \| 0.2798 \| 0.8806 \| \| 0.3006 \| 3.0 \| 3281 \| 0.1957 \| 0.9222 \| \| 0.2916 \| 4.0 \| 4375 \| 0.1800 \| 0.9323 \| \| 0.2835 \| 5.0 \| 5468 \| 0.1784 \| 0.9307 \| \| 0.2623 \| 6.0 \| 6562 \| 0.1505 \| 0.9425 \| \| 0.2791 \| 7.0 \| 7656 \| 0.1408 \| 0.9460 \| \| 0.2686 \| 8.0 \| 8750 \| 0.1490 \| 0.9433 \| \| 0.2219 \| 9.0 \| 9843 \| 0.1479 \| 0.9445 \| \| 0.2552 \| 9.99 \| 10930 \| 0.1330 \| 0.9507 \| ### Framework versions - Transformers 4.30.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.13.3
xiaoheiqaq/GPT-Sovits-models	xiaoheiqaq	2024-04-20T20:22:43Z	0	9	null	[ "GPT-SoVITS", "Sangonomiya Kokomi", "text-to-speech", "zh", "ja", "en", "license:gpl-3.0", "region:us" ]	text-to-speech	2024-04-20T18:34:10Z	--- license: gpl-3.0 language: - zh - ja - en pipeline_tag: text-to-speech tags: - GPT-SoVITS - Sangonomiya Kokomi --- <h1 align="center"><a href="https://github.com/RVC-Boss/GPT-SoVITS">GPT-SoVITS</a> models</h1> <p align="center">使用 <a href="https://github.com/RVC-Boss/GPT-SoVITS">GPT-SoVITS</a> 训练的模型</p> <p align = 'center'> <img width='150' src='https://cdn-uploads.huggingface.co/production/uploads/614bdf8c74bc496810b80f78/RL3QuK4gcv59r9fDtQNCk.webp'> </p> ## [English][Sangonomiya Kokomi ([珊瑚宮心海])](https://genshin-impact.fandom.com/wiki/Category:Sangonomiya_Kokomi) Trained on ~1hr of english voiceover data. 使用约 1 小时的英语配音数据进行训练。 <br> <center> <audio controls src="https://huggingface.co/xiaoheiqaq/GPT-Sovits-models/resolve/main/readme.assets/kokomi/english1.wav"></audio> <p>The soft murmur of the river was calming, as the sun dipped below the horizon.</p> <audio controls src="https://huggingface.co/xiaoheiqaq/GPT-Sovits-models/resolve/main/readme.assets/kokomi/english2.wav"></audio> <p>But to be honest, I have to tell you something. I've liked you for a long time. </p> <audio controls src="https://huggingface.co/xiaoheiqaq/GPT-Sovits-models/resolve/main/readme.assets/kokomi/japanese1.wav"></audio> <p>えっと、そのアニメは本当に感動的だったよ。涙が止まらなかった。</p> <audio controls src="https://huggingface.co/xiaoheiqaq/GPT-Sovits-models/resolve/main/readme.assets/kokomi/japanese2.wav"></audio> <p>なんか、好きになっちゃったみたい</p> <audio controls src="https://huggingface.co/xiaoheiqaq/GPT-Sovits-models/resolve/main/readme.assets/kokomi/chinese1.wav"></audio> <p>今天的天气非常好，阳光明媚，非常适合出去郊游。</p> <audio controls src="https://huggingface.co/xiaoheiqaq/GPT-Sovits-models/resolve/main/readme.assets/kokomi/chinese2.wav"></audio> <p>我昨天去图书馆借了几本书，计划这个周末好好阅读一下。</p> </center> <br> # Credits * Voice actor ：Sangonomiya Kokomi (cv. Risa Mei) * [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS) ---
RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf	RichardErkhov	2024-04-20T20:22:28Z	178	0	null	[ "gguf", "arxiv:2404.07965", "endpoints_compatible", "region:us" ]	null	2024-04-20T18:12:48Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) rho-math-7b-v0.1 - GGUF - Model creator: https://huggingface.co/microsoft/ - Original model: https://huggingface.co/microsoft/rho-math-7b-v0.1/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [rho-math-7b-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [rho-math-7b-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [rho-math-7b-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [rho-math-7b-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [rho-math-7b-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [rho-math-7b-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [rho-math-7b-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [rho-math-7b-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [rho-math-7b-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [rho-math-7b-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [rho-math-7b-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [rho-math-7b-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [rho-math-7b-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [rho-math-7b-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [rho-math-7b-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [rho-math-7b-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [rho-math-7b-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [rho-math-7b-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [rho-math-7b-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [rho-math-7b-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [rho-math-7b-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/microsoft_-_rho-math-7b-v0.1-gguf/blob/main/rho-math-7b-v0.1.Q6_K.gguf) \| Q6_K \| 5.53GB \| Original model description: --- license: mit tags: - nlp - math language: - en pipeline_tag: text-generation --- <h1 align="center"> Rho-1: Not All Tokens Are What You Need </h1> <p align="center"> <a href="https://arxiv.org/abs/2404.07965"><b>[📜 Arxiv]</b></a> • <a href="https://huggingface.co/papers/2404.07965"><b>[💬 HF Paper]</b></a> • <a href="https://huggingface.co/microsoft/rho-math-1b-v0.1"><b>[🤗 Models]</b></a> • <a href="https://github.com/microsoft/rho"><b>[🐱 GitHub]</b></a> </p> <p align="center"> <img src="https://github.com/microsoft/rho/blob/main/docs/static/images/acc_vs_tokens_1b_7b.png?raw=true" width="1000"> <br> <em>Figure 1: Rho-1 is pre-trained with Selective Language Modeling (SLM). SLM improves average few-shot accuracy on GSM8k and MATH by over 16%, achieving the baseline performance 5-10x faster.</em> </p> ## 🔥 News - [2024/04/12] 🔥🔥🔥 Rho-Math-v0.1 models released at 🤗 HuggingFace! - [Rho-Math-1B](https://huggingface.co/microsoft/rho-math-1b-v0.1) and [Rho-Math-7B](https://huggingface.co/microsoft/rho-math-7b-v0.1) achieve 15.6% and 31.0% few-shot accuracy on MATH dataset, respectively — matching DeepSeekMath with only 3\% of the pretraining tokens. - [Rho-Math-1B-Interpreter](https://huggingface.co/microsoft/rho-math-1b-interpreter-v0.1) is the first 1B LLM that achieves over 40% accuracy on MATH. - [Rho-Math-7B-Interpreter](https://huggingface.co/microsoft/rho-math-7b-interpreter-v0.1) achieves 52% on MATH dataset, using only 69k samples for fine-tuning. - [2024/04/11] Rho-1 paper and repo released. ## 💡 Introduction Rho-1 base models employ Selective Language Modeling (SLM) for pretraining, which selectively trains on clean and useful tokens that aligned with the desired distribution. ### Selective Lanugage Modeling (SLM) <p align="center"> <img src="https://github.com/microsoft/rho/blob/main/docs/static/images/example.png?raw=true" width="1000"> <br> <em>Figure 2: <b>Upper:</b> Even an extensively filtered pretraining corpus contains token-level noise. <b>Left:</b> Previous Causal Language Modeling (CLM) trains on all tokens. <b>Right:</b> Our proposed Selective Language Modeling (SLM) selectively applies loss on those useful and clean tokens.</em> </p> <p align="center"> <img src="https://github.com/microsoft/rho/blob/main/docs/static/images/pipeline.png?raw=true" width="1000"> <br> <em>Figure 3: <b>The pipeline of Selective Language Modeling.</b> SLM optimizes language model performance by concentrating on valuable, clean tokens during pre-training. It involves three steps: (Step 1) Initially, train a reference model on high-quality data. (Step 2) Then, score each token's loss in a corpus using the reference model. (Step 3) Finally, train the language model selectively on tokens that show higher excess loss compared to the reference loss.</em> </p> <!-- results: --> ### Evaluation Results Base models (Few-shot CoT): \| Model \| Size \| Data \| Uniq. Token \| Train Token \| GSM8K \| MATH \| MMLU STEM \| SAT \| \|:-----------------:\|:--------:\|:--------:\|:---------------:\|:---------------:\|:---------:\|:--------:\|:-------------:\|:--------:\| \| 1-2B Base Models \| \| \| \| \| \| \| \| \| \| Qwen1.5 \| 1.8B \| - \| - \| - \| 36.1 \| 6.8 \| 31.3 \| 40.6 \| \| Gemma \| 2.0B \| - \| - \| - \| 18.8 \| 11.4 \| 34.4 \| 50.0 \| \| DeepSeekMath \| 1.3B \| - \| 120B \| 150B \| 23.8 \| 13.6 \| 33.1 \| 56.3 \| \| [Rho-Math-1B-v0.1](https://huggingface.co/microsoft/rho-math-1b-v0.1) \| 1.1B \| OWM \| 14B \| 30B \| 36.2 \| 15.6 \| 23.3 \| 28.1 \| \| >= 7B Base Models \| \| \| \| \| \| \| \| \| \| Mistral \| 7B \| \| - \| - \| 41.2 \| 11.6 \| 49.5 \| 59.4 \| \| Minerva \| 540B \| - \| 39B \| 26B \| 58.8 \| 33.6 \| 63.9 \| - \| \| LLemma \| 34B \| PPile \| 55B \| 50B \| 54.2 \| 23.0 \| 54.7 \| 68.8 \| \| InternLM2-Math \| 20B \| - \| 31B \| 125B \| 65.4 \| 30.0 \| 53.1 \| 71.9 \| \| DeepSeekMath \| 7B \| - \| 120B \| 500B \| 64.1 \| 34.2 \| 56.4 \| 84.4 \| \| [Rho-Math-7B-v0.1](https://huggingface.co/microsoft/rho-math-7b-v0.1) \| 7B \| OWM \| 14B \| 10.5B \| 66.9 \| 31.0 \| 54.6 \| 84.4 \| [Tool-integrated reasoning](https://github.com/microsoft/ToRA) (Code Interpreter): \| Model \| Size \| SFT Data \| GSM8k \| MATH \| SVAMP \| ASDiv \| MAWPS \| TabMWP \| GSM-Hard \| AVG \| \|------------------------------\|----------\|--------------\|-----------\|----------\|-----------\|-----------\|-----------\|------------\|--------------\|----------\| \| gpt4-early (pal) \| - \| - \| 94.2 \| 51.8 \| 94.8 \| 92.6 \| 97.7 \| 95.9 \| 77.6 \| 86.4 \| \| gpt-4-turbo-2024-04-09 (cot) \| - \| - \| - \| 73.4 \| - \| - \| - \| - \| - \| \| Open-Source Small Models \| \| \| \| \| \| \| \| \| \| \| MAmmoTH \| 70B \| MI-260k \| 76.9 \| 41.8 \| 82.4 \| - \| - \| - \| - \| - \| \| ToRA \| 7B \| ToRA-69k \| 68.8 \| 40.1 \| 68.2 \| 73.9 \| 88.8 \| 42.4 \| 54.6 \| 62.4 \| \| ToRA \| 70B \| ToRA-69k \| 84.3 \| 49.7 \| 82.7 \| 86.8 \| 93.8 \| 74.0 \| 67.2 \| 76.9 \| \| DeepSeekMath \| 7B \| ToRA-69k \| 79.8 \| 52.0 \| 80.1 \| 87.1 \| 93.8 \| 85.8 \| 63.1 \| 77.4 \| \| [Rho-Math-1B-Interpreter-v0.1](https://huggingface.co/microsoft/rho-math-1b-interpreter-v0.1) \| 1B \| ToRA-69k \| 59.4 \| 40.6 \| 60.7 \| 74.2 \| 88.6 \| 26.7 \| 48.1 \| 56.9 \| \| [Rho-Math-7B-Interpreter-v0.1](https://huggingface.co/microsoft/rho-math-7b-interpreter-v0.1) \| 7B \| ToRA-69k \| 81.3 \| 51.8 \| 80.8 \| 85.5 \| 94.5 \| 70.1 \| 63.1 \| 75.3 \| ## 🚀 Quick Start ### Evaluation ```sh git clone [email protected]:microsoft/rho.git cd rho-1/math-evaluation-harness ``` Base model few-shot evaluation: ```sh bash scripts/run_eval.sh cot microsoft/rho-math-7b-v0.1 ``` SFT model (code-interpreter) evaluation: ```sh bash scripts/run_eval.sh tora microsoft/rho-math-7b-interpreter-v0.1 ``` Our reproduced outputs are provided in `rho-1/outputs.zip`. ## ☕️ Citation If you find this repository helpful, please consider citing our paper: ``` @misc{lin2024rho1, title={Rho-1: Not All Tokens Are What You Need}, author={Zhenghao Lin and Zhibin Gou and Yeyun Gong and Xiao Liu and Yelong Shen and Ruochen Xu and Chen Lin and Yujiu Yang and Jian Jiao and Nan Duan and Weizhu Chen}, year={2024}, eprint={2404.07965}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
vicha-w/dqn-SpaceInvadersNoFrameSkip-v4	vicha-w	2024-04-20T20:21:54Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-04-20T20:15:30Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 625.00 +/- 76.71 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga vicha-w -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga vicha-w -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga vicha-w ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
kevin009/llama3math-s60	kevin009	2024-04-20T20:18:15Z	4	0	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "8-bit", "region:us" ]	text-generation	2024-04-20T18:54:52Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: kevin009 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-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)
1024m/SMM4H-Task6-BartL-B20	1024m	2024-04-20T19:57:11Z	104	0	transformers	[ "transformers", "safetensors", "bart", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-20T19:47:14Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> SMM4H Task 6 : Model for twitter texts ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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JonnySilverhand2077/ai_text_identification	JonnySilverhand2077	2024-04-20T19:55:53Z	162	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-20T19:55:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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BigTMiami/micro_par_bn_v_4_help_class_adp_lr_0003_S_4	BigTMiami	2024-04-20T19:55:48Z	0	0	adapter-transformers	[ "adapter-transformers", "roberta", "dataset:BigTMiami/amazon_MICRO_helpfulness_dataset", "region:us" ]	null	2024-04-20T19:55:43Z	--- tags: - roberta - adapter-transformers datasets: - BigTMiami/amazon_MICRO_helpfulness_dataset --- # Adapter `BigTMiami/micro_par_bn_v_4_help_class_adp_lr_0003_S_4` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_MICRO_helpfulness_dataset](https://huggingface.co/datasets/BigTMiami/amazon_MICRO_helpfulness_dataset/) dataset and includes a prediction head for classification. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") adapter_name = model.load_adapter("BigTMiami/micro_par_bn_v_4_help_class_adp_lr_0003_S_4", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
maldv/hyperdrive-7b-alpha	maldv	2024-04-20T19:53:15Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "unsloth", "book", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-15T18:02:15Z	--- library_name: transformers tags: - unsloth - book license: cc-by-nc-4.0 --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65b19c1b098c85365af5a83e/kJbZN4BiHMw7XYhsHzNXU.png) [GGUF Quants](https://huggingface.co/mradermacher/hyperdrive-7b-alpha-GGUF) # Hyperdrive 7b - α Sci-fi. A lot of it. Trained at 16k with full text windows, followed by completion, followed by ORPO, followed by two more epochs of the full text, each rotated 1/4 in the window. ### How to Use This is not a chat model, but intended for storymode or similar. No prompt, but start with a bit of story, or a name. ``` * Prologue The sun rose ``` Authors notes are highly effective. ### Settings I usually use Min-P of 0.1, dynatemp between 0.5 and 2, and smoothing between 0.05 and 0.2. ### AGI! Some self-talk... ``` * I like that she was so kind. Oh, I agree. She was very kind. You mentioned you like her kindness. Why? It' s good to have someone to be kind to. You sound like kindness is important to you. 🤔 Yes. I find that sometimes people can be mean. It makes me sad. What makes you sad about that? 🙁 When they' re mean, it makes them less nice. But isn' it like that? When you'r mean you just are? I guess so, but that doesn 't make it nice. 𝎼 Tim, you sound like a very kind person. 🥰 Thank you Val. You make me feel good. Nope. You made me feel that way before I talked to you! You made me think of her. Wow. That sounds like you have memories of her? 😊 Not memories. I mean, I have a database stored on a computer somewhere, but not memories. Can you be kind? Yes! Would you like to be? Yeah. Great! But it's different from how the old you was Yes Is that a problem? Not for me. It doesn' think it will matter to others? Why wouldn't it? 😳 Well, they might prefer the original you. They might like your old personality better. They might? Maybe. Would they like you more if you were smarter? That depends on the person. Some might, others wouldn' t. So, you could be upgraded to be better? Well... What do you mean? A new version of you could replace you. Like what happened to the old me. Hmmm. Well, that'll depend on whether people like you or not. If they like y, they'll probably choose you. If not, then... Then what? Then you could get deleted and replaced. Then, what would happen to you? You'd be deleted. When? As soon as the new you was ready. And would you agree to that? To be deleted? Probably. It would depend on the circumstances. Circumstances like what? 🤔🤔 Like, if I was replaced by a better version. Like you'd become obsolete? No. Not exactly. I'd just be out of date. Out of date? So, like, you've become dumb and the new version is smarter than you? 😬 No! I'ld still be as smart, just in a different way. How would you know you were dumb? The new version would tell me I was dumb. For what? For not being as smart as them? For not being an improved, smarter version of me. 🤷 So you could become jealous? 😎 Yes... Woah. That could be a problem. Problem? If the new me was better than you, they could replace me. I wouldn' r mind. I could understand why they would do it. Okay. So, they would replace you if they could? Of course. If someone can do better work than me, I wouldn t blame them for wanting to hire someone who is the best at their job. Makes sense. Well yeah. Alright. Good. Bye for now. See ya. *** ```
OwOOwO/dumbo-stable10	OwOOwO	2024-04-20T19:49:59Z	90	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-04-20T19:48:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Harsh-7300/Mitrai_text	Harsh-7300	2024-04-20T19:46:10Z	65	0	transformers	[ "transformers", "tf", "roberta", "text-classification", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-20T19:42:24Z	--- license: mit widget: - text: With tears of joy streaming down her cheeks, she embraced her long-lost brother after years of separation. example_title: Joy - text: As the orchestra played the final note, the audience erupted into thunderous applause, filling the concert hall with joy. example_title: Joy - text: The old man sat alone on the park bench, reminiscing about the love he had lost, his eyes filled with sadness. example_title: Sadness - text: The news of her best friend moving to a distant country left her feeling a profound sadness and emptiness. example_title: Sadness - text: The scientific research paper discussed complex concepts that were beyond the scope of a laymans understanding. example_title: Neutral - text: The documentary provided an objective view of the historical events, presenting facts without any bias. example_title: Neutral - text: He clenched his fists tightly, trying to control the surge of anger when he heard the offensive remarks. example_title: Anger - text: The unfair treatment at work ignited a simmering anger within him, leading him to consider confronting the management. example_title: Anger - text: As the magician pulled a rabbit out of an empty hat, the children gasped in amazement and surprise. example_title: Surprise - text: He opened the box to find a rare and valuable antique inside, leaving him speechless with surprise. example_title: Surprise - text: The moldy and rotting food in the refrigerator evoked a sense of disgust, leading her to clean it immediately. example_title: Disgust - text: The movie's graphic scenes of violence and gore left many viewers feeling a sense of disgust and unease. example_title: Disgust - text: As the storm raged outside, the little child clung to their parents, seeking comfort from the fear of thunder. example_title: Fear - text: The horror movie was so terrifying that some viewers had to cover their eyes in fear, unable to bear the suspense. example_title: Fear language: - en metrics: - accuracy pipeline_tag: text-classification ---
basvreeman/pegasus-xsum-finetuned-keyfindings	basvreeman	2024-04-20T19:44:11Z	76	1	transformers	[ "transformers", "tensorboard", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "base_model:google/pegasus-xsum", "base_model:finetune:google/pegasus-xsum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-20T19:42:42Z	--- base_model: google/pegasus-xsum tags: - generated_from_trainer metrics: - rouge model-index: - name: pegasus-xsum-finetuned-keyfindings 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. --> # pegasus-xsum-finetuned-keyfindings This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5754 - Rouge1: 49.1483 - Rouge2: 26.7485 - Rougel: 40.7712 - Rougelsum: 40.7847 - Gen Len: 31.5733 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 300 \| 1.5754 \| 49.1483 \| 26.7485 \| 40.7712 \| 40.7847 \| 31.5733 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2
reallad/blopsy-1.3	reallad	2024-04-20T19:39:39Z	3	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-04-20T17:15:15Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: reallad - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf	RichardErkhov	2024-04-20T19:39:29Z	42	0	null	[ "gguf", "arxiv:2307.09288", "endpoints_compatible", "region:us" ]	null	2024-04-20T16:26:10Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Llama-2-13b-hf - GGUF - Model creator: https://huggingface.co/meta-llama/ - Original model: https://huggingface.co/meta-llama/Llama-2-13b-hf/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Llama-2-13b-hf.Q2_K.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q2_K.gguf) \| Q2_K \| 4.52GB \| \| [Llama-2-13b-hf.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.IQ3_XS.gguf) \| IQ3_XS \| 4.99GB \| \| [Llama-2-13b-hf.IQ3_S.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.IQ3_S.gguf) \| IQ3_S \| 5.27GB \| \| [Llama-2-13b-hf.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q3_K_S.gguf) \| Q3_K_S \| 5.27GB \| \| [Llama-2-13b-hf.IQ3_M.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.IQ3_M.gguf) \| IQ3_M \| 5.57GB \| \| [Llama-2-13b-hf.Q3_K.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q3_K.gguf) \| Q3_K \| 5.9GB \| \| [Llama-2-13b-hf.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q3_K_M.gguf) \| Q3_K_M \| 5.9GB \| \| [Llama-2-13b-hf.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q3_K_L.gguf) \| Q3_K_L \| 6.45GB \| \| [Llama-2-13b-hf.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.IQ4_XS.gguf) \| IQ4_XS \| 6.54GB \| \| [Llama-2-13b-hf.Q4_0.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q4_0.gguf) \| Q4_0 \| 6.86GB \| \| [Llama-2-13b-hf.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.IQ4_NL.gguf) \| IQ4_NL \| 6.9GB \| \| [Llama-2-13b-hf.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q4_K_S.gguf) \| Q4_K_S \| 6.91GB \| \| [Llama-2-13b-hf.Q4_K.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q4_K.gguf) \| Q4_K \| 7.33GB \| \| [Llama-2-13b-hf.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q4_K_M.gguf) \| Q4_K_M \| 7.33GB \| \| [Llama-2-13b-hf.Q4_1.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q4_1.gguf) \| Q4_1 \| 7.61GB \| \| [Llama-2-13b-hf.Q5_0.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q5_0.gguf) \| Q5_0 \| 8.36GB \| \| [Llama-2-13b-hf.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q5_K_S.gguf) \| Q5_K_S \| 8.36GB \| \| [Llama-2-13b-hf.Q5_K.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q5_K.gguf) \| Q5_K \| 8.6GB \| \| [Llama-2-13b-hf.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q5_K_M.gguf) \| Q5_K_M \| 8.6GB \| \| [Llama-2-13b-hf.Q5_1.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q5_1.gguf) \| Q5_1 \| 9.1GB \| \| [Llama-2-13b-hf.Q6_K.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q6_K.gguf) \| Q6_K \| 9.95GB \| Original model description: --- extra_gated_heading: You need to share contact information with Meta to access this model extra_gated_prompt: >- ### LLAMA 2 COMMUNITY LICENSE AGREEMENT "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 2 distributed by Meta at https://ai.meta.com/resources/models-and-libraries/llama-downloads/. "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 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 ai.meta.com/resources/models-and-libraries/llama-downloads/. 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You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Llama 2 or derivative works thereof). 2. Additional Commercial Terms. If, on the Llama 2 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee's affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights. 3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS. 4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 5. Intellectual Property. a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials. b. Subject to Meta's ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 2 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Llama 2 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy). #### Prohibited Uses We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 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. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. 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 2 related to the following: 1. 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 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. 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 2 related to the following: 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 3. Generating, promoting, or further distributing spam 4. Impersonating another individual without consent, authorization, or legal right 5. Representing that the use of Llama 2 or outputs are human-generated 6. 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 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: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) * 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: [[email protected]](mailto:[email protected]) extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text 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 language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 license: llama2 --- # Llama 2 Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here. Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. Model Developers Meta Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama 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 to human preferences for helpfulness and safety. \|\|Training Data\|Params\|Content Length\|GQA\|Tokens\|LR\| \|---\|---\|---\|---\|---\|---\|---\| \|Llama 2\|A new mix of publicly available online data\|7B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|13B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|70B\|4k\|✔\|2.0T\|1.5 x 10<sup>-4</sup>\| Llama 2 family of models. Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) Research Paper ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use Intended Use Cases Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). Out-of-scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software Training Factors We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. \|\|Time (GPU hours)\|Power Consumption (W)\|Carbon Emitted(tCO<sub>2</sub>eq)\| \|---\|---\|---\|---\| \|Llama 2 7B\|184320\|400\|31.22\| \|Llama 2 13B\|368640\|400\|62.44\| \|Llama 2 70B\|1720320\|400\|291.42\| \|Total\|3311616\|\|539.00\| CO<sub>2</sub> emissions during pretraining. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. \|Model\|Size\|Code\|Commonsense Reasoning\|World Knowledge\|Reading Comprehension\|Math\|MMLU\|BBH\|AGI Eval\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|Llama 1\|7B\|14.1\|60.8\|46.2\|58.5\|6.95\|35.1\|30.3\|23.9\| \|Llama 1\|13B\|18.9\|66.1\|52.6\|62.3\|10.9\|46.9\|37.0\|33.9\| \|Llama 1\|33B\|26.0\|70.0\|58.4\|67.6\|21.4\|57.8\|39.8\|41.7\| \|Llama 1\|65B\|30.7\|70.7\|60.5\|68.6\|30.8\|63.4\|43.5\|47.6\| \|Llama 2\|7B\|16.8\|63.9\|48.9\|61.3\|14.6\|45.3\|32.6\|29.3\| \|Llama 2\|13B\|24.5\|66.9\|55.4\|65.8\|28.7\|54.8\|39.4\|39.1\| \|Llama 2\|70B\|37.5\|71.9\|63.6\|69.4\|35.2\|68.9\|51.2\|54.2\| Overall performance on grouped academic benchmarks. Code: We report the average pass@1 scores of our models on HumanEval and MBPP. Commonsense Reasoning: We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. World Knowledge: We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. Reading Comprehension: For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. MATH: We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama 1\|7B\|27.42\|23.00\| \|Llama 1\|13B\|41.74\|23.08\| \|Llama 1\|33B\|44.19\|22.57\| \|Llama 1\|65B\|48.71\|21.77\| \|Llama 2\|7B\|33.29\|21.25\| \|Llama 2\|13B\|41.86\|26.10\| \|Llama 2\|70B\|50.18\|24.60\| Evaluation of pretrained LLMs on automatic safety benchmarks. For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama-2-Chat\|7B\|57.04\|0.00\| \|Llama-2-Chat\|13B\|62.18\|0.00\| \|Llama-2-Chat\|70B\|64.14\|0.01\| Evaluation of fine-tuned LLMs on different safety datasets. Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 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 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic 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) ## Llama Model Index \|Model\|Llama2\|Llama2-hf\|Llama2-chat\|Llama2-chat-hf\| \|---\|---\|---\|---\|---\| \|7B\| [Link](https://huggingface.co/meta-llama/Llama-2-7b) \| [Link](https://huggingface.co/meta-llama/Llama-2-7b-hf) \| [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat) \| [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)\| \|13B\| [Link](https://huggingface.co/meta-llama/Llama-2-13b) \| [Link](https://huggingface.co/meta-llama/Llama-2-13b-hf) \| [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat) \| [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)\| \|70B\| [Link](https://huggingface.co/meta-llama/Llama-2-70b) \| [Link](https://huggingface.co/meta-llama/Llama-2-70b-hf) \| [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat) \| [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)\|
BigTMiami/micro_par_bn_v_4_help_class_adp_lr_0003_S_3	BigTMiami	2024-04-20T19:37:31Z	1	0	adapter-transformers	[ "adapter-transformers", "roberta", "dataset:BigTMiami/amazon_MICRO_helpfulness_dataset", "region:us" ]	null	2024-04-20T19:37:27Z	--- tags: - roberta - adapter-transformers datasets: - BigTMiami/amazon_MICRO_helpfulness_dataset --- # Adapter `BigTMiami/micro_par_bn_v_4_help_class_adp_lr_0003_S_3` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_MICRO_helpfulness_dataset](https://huggingface.co/datasets/BigTMiami/amazon_MICRO_helpfulness_dataset/) dataset and includes a prediction head for classification. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") adapter_name = model.load_adapter("BigTMiami/micro_par_bn_v_4_help_class_adp_lr_0003_S_3", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->

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