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train · 793k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-02 06:28:08	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 462 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-02 06:27:40	card stringlengths 11 1.01M
selmamalak/organsmnist-vit-base-finetuned	selmamalak	2024-05-18T13:28:53Z	1	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:medmnist-v2", "base_model:facebook/deit-base-patch16-224", "base_model:adapter:facebook/deit-base-patch16-224", "license:apache-2.0", "region:us" ]	null	2024-05-18T12:24:41Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: facebook/deit-base-patch16-224 datasets: - medmnist-v2 metrics: - accuracy - precision - recall - f1 model-index: - name: organsmnist-vit-base-finetuned 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. --> # organsmnist-vit-base-finetuned This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2964 - Accuracy: 0.8993 - Precision: 0.8443 - Recall: 0.8396 - F1: 0.8394 ## 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: 0.005 - 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 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.9084 \| 1.0 \| 218 \| 0.7151 \| 0.7288 \| 0.6998 \| 0.6620 \| 0.6412 \| \| 0.89 \| 2.0 \| 436 \| 0.3658 \| 0.8540 \| 0.7873 \| 0.7898 \| 0.7660 \| \| 0.7851 \| 3.0 \| 654 \| 0.3514 \| 0.8438 \| 0.8110 \| 0.7674 \| 0.7741 \| \| 0.7144 \| 4.0 \| 872 \| 0.3632 \| 0.8670 \| 0.8415 \| 0.8133 \| 0.7980 \| \| 0.7383 \| 5.0 \| 1090 \| 0.3680 \| 0.8581 \| 0.7769 \| 0.8029 \| 0.7786 \| \| 0.6065 \| 6.0 \| 1308 \| 0.2824 \| 0.8870 \| 0.8481 \| 0.8328 \| 0.8305 \| \| 0.521 \| 7.0 \| 1526 \| 0.2769 \| 0.8940 \| 0.8439 \| 0.8404 \| 0.8297 \| \| 0.5305 \| 8.0 \| 1744 \| 0.2611 \| 0.9001 \| 0.8517 \| 0.8463 \| 0.8447 \| \| 0.4522 \| 9.0 \| 1962 \| 0.2742 \| 0.9058 \| 0.8594 \| 0.8517 \| 0.8411 \| \| 0.4445 \| 10.0 \| 2180 \| 0.2964 \| 0.8993 \| 0.8443 \| 0.8396 \| 0.8394 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
selmamalak/organsmnist-deit-base-finetuned	selmamalak	2024-05-18T13:24:31Z	1	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:medmnist-v2", "base_model:facebook/deit-base-patch16-224", "base_model:adapter:facebook/deit-base-patch16-224", "license:apache-2.0", "region:us" ]	null	2024-05-18T12:31:56Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: facebook/deit-base-patch16-224 datasets: - medmnist-v2 metrics: - accuracy - precision - recall - f1 model-index: - name: organsmnist-deit-base-finetuned 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. --> # organsmnist-deit-base-finetuned This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4815 - Accuracy: 0.8080 - Precision: 0.7703 - Recall: 0.7686 - F1: 0.7650 ## 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: 0.005 - 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 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.9804 \| 1.0 \| 218 \| 0.6885 \| 0.7243 \| 0.7883 \| 0.6661 \| 0.6426 \| \| 0.9277 \| 2.0 \| 436 \| 0.3513 \| 0.8503 \| 0.7635 \| 0.7943 \| 0.7680 \| \| 0.8144 \| 3.0 \| 654 \| 0.3614 \| 0.8544 \| 0.8331 \| 0.7961 \| 0.7909 \| \| 0.7344 \| 4.0 \| 872 \| 0.3371 \| 0.8609 \| 0.8327 \| 0.8018 \| 0.7886 \| \| 0.7181 \| 5.0 \| 1090 \| 0.2934 \| 0.8923 \| 0.8060 \| 0.8389 \| 0.8096 \| \| 0.5857 \| 6.0 \| 1308 \| 0.2927 \| 0.8858 \| 0.8493 \| 0.8358 \| 0.8315 \| \| 0.5607 \| 7.0 \| 1526 \| 0.2209 \| 0.9062 \| 0.8658 \| 0.8547 \| 0.8416 \| \| 0.5423 \| 8.0 \| 1744 \| 0.2513 \| 0.9025 \| 0.8545 \| 0.8470 \| 0.8487 \| \| 0.4053 \| 9.0 \| 1962 \| 0.2561 \| 0.9038 \| 0.8543 \| 0.8457 \| 0.8373 \| \| 0.4417 \| 10.0 \| 2180 \| 0.2558 \| 0.8997 \| 0.8463 \| 0.8395 \| 0.8416 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
HariprasathSB/whispeerr	HariprasathSB	2024-05-18T13:22:33Z	94	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:HariprasathSB/whispeer", "base_model:finetune:HariprasathSB/whispeer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-18T13:06:48Z	--- license: apache-2.0 base_model: HariprasathSB/whispeer tags: - generated_from_trainer model-index: - name: whispeerr 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. --> # whispeerr This model is a fine-tuned version of [HariprasathSB/whispeer](https://huggingface.co/HariprasathSB/whispeer) 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: 0.003 - 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: linear - lr_scheduler_warmup_steps: 200 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
casque/MIS51	casque	2024-05-18T13:02:53Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-18T13:02:22Z	--- license: creativeml-openrail-m ---
ChiJuiChen/lab9_whisper-tiny-zh-tw	ChiJuiChen	2024-05-18T12:53:39Z	78	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_13_0", "base_model:Wellyowo/whisper-tiny-zh-tw", "base_model:finetune:Wellyowo/whisper-tiny-zh-tw", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-09T07:22:56Z	--- license: apache-2.0 base_model: Wellyowo/whisper-tiny-zh-tw tags: - generated_from_trainer datasets: - common_voice_13_0 metrics: - wer model-index: - name: lab9_whisper-tiny-zh-tw results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_13_0 type: common_voice_13_0 config: zh-TW split: test args: zh-TW metrics: - name: Wer type: wer value: 62.13592233009708 --- <!-- 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. --> # lab9_whisper-tiny-zh-tw This model is a fine-tuned version of [Wellyowo/whisper-tiny-zh-tw](https://huggingface.co/Wellyowo/whisper-tiny-zh-tw) on the common_voice_13_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.6336 - Wer Ortho: 64.0 - Wer: 62.1359 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.0088 \| 0.6882 \| 500 \| 0.5502 \| 60.0 \| 61.1650 \| \| 0.0051 \| 1.3765 \| 1000 \| 0.5735 \| 65.0 \| 64.0777 \| \| 0.0068 \| 2.0647 \| 1500 \| 0.5820 \| 63.0 \| 63.1068 \| \| 0.0021 \| 2.7529 \| 2000 \| 0.5955 \| 62.0 \| 61.1650 \| \| 0.0039 \| 3.4412 \| 2500 \| 0.5858 \| 62.0 \| 61.1650 \| \| 0.0018 \| 4.1294 \| 3000 \| 0.5981 \| 63.0 \| 61.1650 \| \| 0.0019 \| 4.8176 \| 3500 \| 0.6322 \| 63.0 \| 61.1650 \| \| 0.0102 \| 5.5058 \| 4000 \| 0.6336 \| 64.0 \| 62.1359 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
presencesw/mt5-base-snli_contradiction-triplet	presencesw	2024-05-18T12:52:38Z	50	0	transformers	[ "transformers", "safetensors", "mt5", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T12:52:01Z	--- 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]
JUANDECI/PPO-LunarLander-v2	JUANDECI	2024-05-18T12:51:06Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T12:47:50Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 175.95 +/- 65.75 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
nvdenisov2002/llama-longLoRA-v5-8k-all-samples-3-epochs	nvdenisov2002	2024-05-18T12:50:41Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-05-18T12:50:17Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # 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] ### Framework versions - PEFT 0.10.0
RichardErkhov/beomi_-_gemma-mling-7b-8bits	RichardErkhov	2024-05-18T12:49:45Z	7	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T12:43:10Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) gemma-mling-7b - bnb 8bits - Model creator: https://huggingface.co/beomi/ - Original model: https://huggingface.co/beomi/gemma-mling-7b/ Original model description: --- language: - ko - en - zh - ja license: other library_name: transformers license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms pipeline_tag: text-generation tags: - pytorch --- # Gemma-Mling: Multilingual Gemma > Update @ 2024.04.15: First release of Gemma-Mling 7B model Original Gemma Model Page: [Gemma](https://ai.google.dev/gemma/docs) This model card corresponds to the 7B base version of the Gemma-Mling model, continual pretrained on mainly Korean/English/Chinese/Japanese + 500 multilingual corpus. Resources and Technical Documentation: * [Original Google's Gemma-7B](https://huggingface.co/google/gemma-7b) * [Training Code @ Github: Gemma-EasyLM](https://github.com/Beomi/Gemma-EasyLM) Terms of Use: [Terms](https://www.kaggle.com/models/google/gemma/license/consent) Citation ```bibtex @misc {gemma_mling_7b, author = { {Junbum Lee, Taekyoon Choi} }, title = { gemma-mling-7b }, year = 2024, url = { https://huggingface.co/beomi/gemma-mling-7b }, publisher = { Hugging Face } } ``` Model Developers: Junbum Lee (Beomi) & Taekyoon Choi (Taekyoon) ## Model Information ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. #### Running the model on a CPU ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("beomi/gemma-mling-7b") model = AutoModelForCausalLM.from_pretrained("beomi/gemma-mling-7b") input_text = "머신러닝과 딥러닝의 차이는" input_ids = tokenizer(input_text, return_tensors="pt") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("beomi/gemma-mling-7b") model = AutoModelForCausalLM.from_pretrained("beomi/gemma-mling-7b", device_map="auto") input_text = "머신러닝과 딥러닝의 차이는" input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` ### Inputs and outputs * Input: Text string, such as a question, a prompt, or a document to be summarized. * Output: Generated Multilingual-language text in response to the input, such as an answer to a question, or a summary of a document. ## Implementation Information Details about the model internals. ### Software Training was done using [beomi/Gemma-EasyLM](https://github.com/Beomi/Gemma-EasyLM). ### Dataset We trained a mixture of multiple language datasets and trained until 100B. The released model is the best performance model based on our Evaluation below from model checkpoints. For Korean and English datasets, we utilized sampled llama2ko training dataset which combined 1:1 ratio in each language. \| Dataset \| Jsonl (GB) \| Sampled \| \|--------------------------\|------------\|---------\| \| range3/cc100-ja \| 96.39 \| No \| \| Skywork/SkyPile-150B \| 100.57 \| Yes \| \| llama2ko dataset (ko/en) \| 108.5 \| Yes \| \| cis-lmu/Glot500 \| 181.24 \| No \| \| Total \| 486.7 \| . \| ## Training Progress - Report Link: https://api.wandb.ai/links/tgchoi/6lt0ce3s ## Evaluation Model evaluation metrics and results. ### Evaluation Scripts - For Knowledge / KoBest / XCOPA / XWinograd - [EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) v0.4.2 ```bash !git clone https://github.com/EleutherAI/lm-evaluation-harness.git !cd lm-evaluation-harness && pip install -r requirements.txt && pip install -e . !lm_eval --model hf \ --model_args pretrained=beomi/gemma-mling-7b,dtype="float16" \ --tasks "haerae,kobest,kmmlu_direct,cmmlu,ceval-valid,mmlu,xwinograd,xcopa \ --num_fewshot "0,5,5,5,5,5,0,5" \ --device cuda ``` - For JP Eval Harness - [Stability-AI/lm-evaluation-harness (`jp-stable` branch)](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable) ```bash !git clone -b jp-stable https://github.com/Stability-AI/lm-evaluation-harness.git !cd lm-evaluation-harness && pip install -e ".[ja]" !pip install 'fugashi[unidic]' && python -m unidic download !cd lm-evaluation-harness && python main.py \ --model hf-causal \ --model_args pretrained=beomi/gemma-mling-7b,torch_dtype='auto'" --tasks "jcommonsenseqa-1.1-0.3,jnli-1.3-0.3,marc_ja-1.1-0.3,jsquad-1.1-0.3,jaqket_v2-0.2-0.3,xlsum_ja,mgsm" --num_fewshot "3,3,3,2,1,1,5" ``` ### Benchmark Results \| Category \| Metric \| Shots \| Score \| \|----------------------------------\|----------------------\|------------\|--------\| \| Default Metric \| ACC \| \| \| \| Knowledge (5-shot) \| MMLU \| \| 61.76 \| \| \| KMMLU (Exact Match) \| \| 42.75 \| \| \| CMLU \| \| 50.93 \| \| \| JMLU \| \| \| \| \| C-EVAL \| \| 50.07 \| \| \| HAERAE \| 0-shot \| 63.89 \| \| KoBest (5-shot) \| BoolQ \| \| 85.47 \| \| \| COPA \| \| 83.5 \| \| \| Hellaswag (acc-norm) \| \| 63.2 \| \| \| Sentineg \| \| 97.98 \| \| \| WiC \| \| 70.95 \| \| XCOPA (5-shot) \| IT \| \| 72.8 \| \| \| ID \| \| 76.4 \| \| \| TH \| \| 60.2 \| \| \| TR \| \| 65.6 \| \| \| VI \| \| 77.2 \| \| \| ZH \| \| 80.2 \| \| JP Eval Harness (Prompt ver 0.3) \| JcommonsenseQA \| 3-shot \| 85.97 \| \| \| JNLI \| 3-shot \| 39.11 \| \| \| Marc_ja \| 3-shot \| 96.48 \| \| \| JSquad (Exact Match) \| 2-shot \| 70.69 \| \| \| Jaqket (Exact Match) \| 1-shot \| 81.53 \| \| \| MGSM \| 5-shot \| 28.8 \| \| XWinograd (0-shot) \| EN \| \| 89.03 \| \| \| FR \| \| 72.29 \| \| \| JP \| \| 82.69 \| \| \| PT \| \| 73.38 \| \| \| RU \| \| 68.57 \| \| \| ZH \| \| 79.17 \| ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. * Content Creation and Communication * Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. * Research and Education * Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field. * Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. * Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations * Training Data * The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. * The scope of the training dataset determines the subject areas the model can handle effectively. * Context and Task Complexity * LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). * Language Ambiguity and Nuance * Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * Factual Accuracy * LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * Common Sense * LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * LLMs can be misused to generate text that is false, misleading, or harmful. * Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible). * Transparency and Accountability: * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). * Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ## Acknowledgement The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program.
emilykang/Phi_medmcqa_question_generation-social_n_preventive_medicine_lora	emilykang	2024-05-18T12:46:58Z	0	0	peft	[ "peft", "safetensors", "phi", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-05-17T15:31:33Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 datasets: - generator model-index: - name: Phi_medmcqa_question_generation-social_n_preventive_medicine_lora 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. --> # Phi_medmcqa_question_generation-social_n_preventive_medicine_lora This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) 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: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
stablediffusionapi/aingdiffusion-xl	stablediffusionapi	2024-05-18T12:44:29Z	29	1	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-18T12:42:42Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # AingDiffusion XL API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/6451911511716034357.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "aingdiffusion-xl" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/aingdiffusion-xl) Model link: [View model](https://modelslab.com/models/aingdiffusion-xl) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "aingdiffusion-xl", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
akbargherbal/gemma_7b_en_to_ar_ft_01	akbargherbal	2024-05-18T12:43:25Z	7	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/gemma-7b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-7b-it-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T12:05:03Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-7b-it-bnb-4bit --- # Uploaded model - Developed by: akbargherbal - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-it-bnb-4bit This gemma 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/beomi_-_gemma-mling-7b-4bits	RichardErkhov	2024-05-18T12:42:03Z	78	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T12:37:41Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) gemma-mling-7b - bnb 4bits - Model creator: https://huggingface.co/beomi/ - Original model: https://huggingface.co/beomi/gemma-mling-7b/ Original model description: --- language: - ko - en - zh - ja license: other library_name: transformers license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms pipeline_tag: text-generation tags: - pytorch --- # Gemma-Mling: Multilingual Gemma > Update @ 2024.04.15: First release of Gemma-Mling 7B model Original Gemma Model Page: [Gemma](https://ai.google.dev/gemma/docs) This model card corresponds to the 7B base version of the Gemma-Mling model, continual pretrained on mainly Korean/English/Chinese/Japanese + 500 multilingual corpus. Resources and Technical Documentation: * [Original Google's Gemma-7B](https://huggingface.co/google/gemma-7b) * [Training Code @ Github: Gemma-EasyLM](https://github.com/Beomi/Gemma-EasyLM) Terms of Use: [Terms](https://www.kaggle.com/models/google/gemma/license/consent) Citation ```bibtex @misc {gemma_mling_7b, author = { {Junbum Lee, Taekyoon Choi} }, title = { gemma-mling-7b }, year = 2024, url = { https://huggingface.co/beomi/gemma-mling-7b }, publisher = { Hugging Face } } ``` Model Developers: Junbum Lee (Beomi) & Taekyoon Choi (Taekyoon) ## Model Information ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. #### Running the model on a CPU ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("beomi/gemma-mling-7b") model = AutoModelForCausalLM.from_pretrained("beomi/gemma-mling-7b") input_text = "머신러닝과 딥러닝의 차이는" input_ids = tokenizer(input_text, return_tensors="pt") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("beomi/gemma-mling-7b") model = AutoModelForCausalLM.from_pretrained("beomi/gemma-mling-7b", device_map="auto") input_text = "머신러닝과 딥러닝의 차이는" input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` ### Inputs and outputs * Input: Text string, such as a question, a prompt, or a document to be summarized. * Output: Generated Multilingual-language text in response to the input, such as an answer to a question, or a summary of a document. ## Implementation Information Details about the model internals. ### Software Training was done using [beomi/Gemma-EasyLM](https://github.com/Beomi/Gemma-EasyLM). ### Dataset We trained a mixture of multiple language datasets and trained until 100B. The released model is the best performance model based on our Evaluation below from model checkpoints. For Korean and English datasets, we utilized sampled llama2ko training dataset which combined 1:1 ratio in each language. \| Dataset \| Jsonl (GB) \| Sampled \| \|--------------------------\|------------\|---------\| \| range3/cc100-ja \| 96.39 \| No \| \| Skywork/SkyPile-150B \| 100.57 \| Yes \| \| llama2ko dataset (ko/en) \| 108.5 \| Yes \| \| cis-lmu/Glot500 \| 181.24 \| No \| \| Total \| 486.7 \| . \| ## Training Progress - Report Link: https://api.wandb.ai/links/tgchoi/6lt0ce3s ## Evaluation Model evaluation metrics and results. ### Evaluation Scripts - For Knowledge / KoBest / XCOPA / XWinograd - [EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) v0.4.2 ```bash !git clone https://github.com/EleutherAI/lm-evaluation-harness.git !cd lm-evaluation-harness && pip install -r requirements.txt && pip install -e . !lm_eval --model hf \ --model_args pretrained=beomi/gemma-mling-7b,dtype="float16" \ --tasks "haerae,kobest,kmmlu_direct,cmmlu,ceval-valid,mmlu,xwinograd,xcopa \ --num_fewshot "0,5,5,5,5,5,0,5" \ --device cuda ``` - For JP Eval Harness - [Stability-AI/lm-evaluation-harness (`jp-stable` branch)](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable) ```bash !git clone -b jp-stable https://github.com/Stability-AI/lm-evaluation-harness.git !cd lm-evaluation-harness && pip install -e ".[ja]" !pip install 'fugashi[unidic]' && python -m unidic download !cd lm-evaluation-harness && python main.py \ --model hf-causal \ --model_args pretrained=beomi/gemma-mling-7b,torch_dtype='auto'" --tasks "jcommonsenseqa-1.1-0.3,jnli-1.3-0.3,marc_ja-1.1-0.3,jsquad-1.1-0.3,jaqket_v2-0.2-0.3,xlsum_ja,mgsm" --num_fewshot "3,3,3,2,1,1,5" ``` ### Benchmark Results \| Category \| Metric \| Shots \| Score \| \|----------------------------------\|----------------------\|------------\|--------\| \| Default Metric \| ACC \| \| \| \| Knowledge (5-shot) \| MMLU \| \| 61.76 \| \| \| KMMLU (Exact Match) \| \| 42.75 \| \| \| CMLU \| \| 50.93 \| \| \| JMLU \| \| \| \| \| C-EVAL \| \| 50.07 \| \| \| HAERAE \| 0-shot \| 63.89 \| \| KoBest (5-shot) \| BoolQ \| \| 85.47 \| \| \| COPA \| \| 83.5 \| \| \| Hellaswag (acc-norm) \| \| 63.2 \| \| \| Sentineg \| \| 97.98 \| \| \| WiC \| \| 70.95 \| \| XCOPA (5-shot) \| IT \| \| 72.8 \| \| \| ID \| \| 76.4 \| \| \| TH \| \| 60.2 \| \| \| TR \| \| 65.6 \| \| \| VI \| \| 77.2 \| \| \| ZH \| \| 80.2 \| \| JP Eval Harness (Prompt ver 0.3) \| JcommonsenseQA \| 3-shot \| 85.97 \| \| \| JNLI \| 3-shot \| 39.11 \| \| \| Marc_ja \| 3-shot \| 96.48 \| \| \| JSquad (Exact Match) \| 2-shot \| 70.69 \| \| \| Jaqket (Exact Match) \| 1-shot \| 81.53 \| \| \| MGSM \| 5-shot \| 28.8 \| \| XWinograd (0-shot) \| EN \| \| 89.03 \| \| \| FR \| \| 72.29 \| \| \| JP \| \| 82.69 \| \| \| PT \| \| 73.38 \| \| \| RU \| \| 68.57 \| \| \| ZH \| \| 79.17 \| ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. * Content Creation and Communication * Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. * Research and Education * Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field. * Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. * Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations * Training Data * The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. * The scope of the training dataset determines the subject areas the model can handle effectively. * Context and Task Complexity * LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). * Language Ambiguity and Nuance * Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * Factual Accuracy * LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * Common Sense * LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * LLMs can be misused to generate text that is false, misleading, or harmful. * Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible). * Transparency and Accountability: * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). * Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ## Acknowledgement The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program.
theosun/gemma-2b-it-sharegpt	theosun	2024-05-18T12:38:32Z	7	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T09:38:43Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
akbargherbal/BACKUP_gemma_7b_en_to_ar_ft_01	akbargherbal	2024-05-18T12:33:12Z	9	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T12:28:59Z	--- license: apache-2.0 ---
RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf	RichardErkhov	2024-05-18T12:33:06Z	32	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-18T01:49:29Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Mixtral-8x7B-MoE-RP-Story - GGUF - Model creator: https://huggingface.co/Undi95/ - Original model: https://huggingface.co/Undi95/Mixtral-8x7B-MoE-RP-Story/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Mixtral-8x7B-MoE-RP-Story.Q2_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q2_K.gguf) \| Q2_K \| 16.12GB \| \| [Mixtral-8x7B-MoE-RP-Story.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.IQ3_XS.gguf) \| IQ3_XS \| 18.02GB \| \| [Mixtral-8x7B-MoE-RP-Story.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.IQ3_S.gguf) \| IQ3_S \| 19.03GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q3_K_S.gguf) \| Q3_K_S \| 19.03GB \| \| [Mixtral-8x7B-MoE-RP-Story.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.IQ3_M.gguf) \| IQ3_M \| 19.96GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q3_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q3_K.gguf) \| Q3_K \| 21.0GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q3_K_M.gguf) \| Q3_K_M \| 21.0GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q3_K_L.gguf) \| Q3_K_L \| 22.51GB \| \| [Mixtral-8x7B-MoE-RP-Story.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.IQ4_XS.gguf) \| IQ4_XS \| 23.63GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q4_0.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q4_0.gguf) \| Q4_0 \| 24.63GB \| \| [Mixtral-8x7B-MoE-RP-Story.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.IQ4_NL.gguf) \| IQ4_NL \| 24.91GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q4_K_S.gguf) \| Q4_K_S \| 24.91GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q4_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q4_K.gguf) \| Q4_K \| 26.49GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q4_K_M.gguf) \| Q4_K_M \| 26.49GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q4_1.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q4_1.gguf) \| Q4_1 \| 27.32GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q5_0.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q5_0.gguf) \| Q5_0 \| 30.02GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q5_K_S.gguf) \| Q5_K_S \| 30.02GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q5_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q5_K.gguf) \| Q5_K \| 30.95GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q5_K_M.gguf) \| Q5_K_M \| 30.95GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q5_1.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q5_1.gguf) \| Q5_1 \| 32.71GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q6_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q6_K.gguf) \| Q6_K \| 35.74GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q8_0.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/tree/main/) \| Q8_0 \| 46.22GB \| Original model description: --- license: cc-by-nc-4.0 tags: - not-for-all-audiences - nsfw --- Mixtral-8x7B-MoE-RP-Story is a model made primarely for chatting, RP (Roleplay) and storywriting. 2 RP model, 2 chat model, 1 occult model, 1 storywritting model, 1 mathematic model and 1 DPO model was used for a MoE. Bagel was the base. The DPO chat model is here to help get more human reply. This is my first try at doing this, so don't hesitate to give feedback! WARNING: ALL THE "K" GGUF QUANT OF MIXTRAL MODELS SEEMS TO BE [BROKEN](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/TvjEP14ps7ZUgJ-0-mhIX.png), PREFER Q4_0, Q5_0 or Q8_0! <!-- description start --> ## Description This repo contains fp16 files of Mixtral-8x7B-MoE-RP-Story. <!-- description end --> <!-- description start --> ## Models used The list of model used and their activator/theme can be found [here](https://huggingface.co/Undi95/Mixtral-8x7B-MoE-RP-Story/blob/main/config.yaml) <!-- description end --> <!-- prompt-template start --> ## Prompt template: Custom Using Bagel as a base let us a lot of different prompting system theorically, you can see all the prompting available [here](https://huggingface.co/jondurbin/bagel-7b-v0.1#prompt-formatting). If you want to support me, you can [here](https://ko-fi.com/undiai).
Angy309/noti	Angy309	2024-05-18T12:29:51Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-cased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T11:18:41Z	--- tags: - generated_from_trainer base_model: dccuchile/bert-base-spanish-wwm-cased metrics: - accuracy model-index: - name: noti 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. --> # noti This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3911 - Accuracy: 0.5 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.5517 \| 0.5 \| 5 \| 1.5409 \| 0.25 \| \| 1.5245 \| 1.0 \| 10 \| 1.3911 \| 0.5 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
alexandro767/stable-diffusion-v1-5-finetuned_5e_r2_v1	alexandro767	2024-05-18T12:29:08Z	29	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-18T12:26:20Z	--- library_name: diffusers --- # 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 🧨 diffusers 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]
Toshifumi/Llama3-Toshi-Ja-LD-classifier_20240518v2	Toshifumi	2024-05-18T12:28:45Z	6	0	transformers	[ "transformers", "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", "region:us" ]	text-generation	2024-05-18T12:21:48Z	--- 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: Toshifumi - 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)
basakdemirok/bert-base-turkish-cased-off_detect_v02_seed42	basakdemirok	2024-05-18T12:20:29Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "text-classification", "generated_from_keras_callback", "base_model:dbmdz/bert-base-turkish-cased", "base_model:finetune:dbmdz/bert-base-turkish-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T11:48:28Z	--- license: mit base_model: dbmdz/bert-base-turkish-cased tags: - generated_from_keras_callback model-index: - name: basakdemirok/bert-base-turkish-cased-off_detect_v02_seed42 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # basakdemirok/bert-base-turkish-cased-off_detect_v02_seed42 This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0105 - Validation Loss: 0.6091 - Train F1: 0.7065 - Epoch: 3 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 14944, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train F1 \| Epoch \| \|:----------:\|:---------------:\|:--------:\|:-----:\| \| 0.2631 \| 0.2907 \| 0.6690 \| 0 \| \| 0.0934 \| 0.4221 \| 0.6997 \| 1 \| \| 0.0274 \| 0.5827 \| 0.6968 \| 2 \| \| 0.0105 \| 0.6091 \| 0.7065 \| 3 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.13.1 - Datasets 2.4.0 - Tokenizers 0.13.3
ruslandev/llama-3-70b-tagengo-GGUF	ruslandev	2024-05-18T12:20:03Z	33	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "en", "dataset:lightblue/tagengo-gpt4", "base_model:unsloth/llama-3-70b-bnb-4bit", "base_model:quantized:unsloth/llama-3-70b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-17T06:42:40Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-70b-bnb-4bit datasets: - lightblue/tagengo-gpt4 --- # Uploaded model - Developed by: ruslandev - License: apache-2.0 - Finetuned from model : unsloth/llama-3-70b-bnb-4bit This model is finetuned on the Tagengo dataset. Please note - this model has been created for educational purposes and it needs further training/fine tuning. # How to use The easiest way to use this model on your own computer is to use the GGUF version of this model ([ruslandev/llama-3-70b-tagengo-GGUF](https://huggingface.co/ruslandev/llama-3-70b-tagengo-GGUF)) using a program such as [llama.cpp](https://github.com/ggerganov/llama.cpp). If you want to use this model directly with the Huggingface Transformers stack, I recommend using my framework [gptchain](https://github.com/RuslanPeresy/gptchain). ``` git clone https://github.com/RuslanPeresy/gptchain.git cd gptchain pip install -r requirements-train.txt python gptchain.py chat -m ruslandev/llama-3-70b-tagengo \ --chatml true \ -q '[{"from": "human", "value": "Из чего состоит нейронная сеть?"}]' ``` # Training [gptchain](https://github.com/RuslanPeresy/gptchain) framework has been used for training. ``` python gptchain.py train -m unsloth/llama-3-70b-bnb-4bit \ -dn tagengo_gpt4 \ -sp checkpoints/llama-3-70b-tagengo \ -hf llama-3-70b-tagengo \ --max-steps 2400 ``` # Training hyperparameters - learning_rate: 2e-4 - seed: 3407 - gradient_accumulation_steps: 4 - per_device_train_batch_size: 2 - optimizer: adamw_8bit - lr_scheduler_type: linear - warmup_steps: 5 - max_steps: 2400 - weight_decay: 0.01 # Training results [wandb report](https://api.wandb.ai/links/ruslandev/rilj60ra) 2400 steps took 7 hours on a single H100 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
fzzhang/mistralv1_lora_r4_25e5_e05	fzzhang	2024-05-18T12:18:51Z	2	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-18T12:18:49Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistralv1_lora_r4_25e5_e05 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. --> # mistralv1_lora_r4_25e5_e05 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
SriSougandhika/ppo-Huggy	SriSougandhika	2024-05-18T12:15:34Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-05-18T12:13:41Z	--- 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: SriSougandhika/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
fzzhang/mistralv1_lora_r8_25e5_e05	fzzhang	2024-05-18T12:12:30Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-18T12:12:28Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistralv1_lora_r8_25e5_e05 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. --> # mistralv1_lora_r8_25e5_e05 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
MJerome/V65_LoRA_V63_GPT2-350k-Plus_10k_low_elo_4E_r64	MJerome	2024-05-18T12:10:36Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:Leon-LLM/V63_GPT2_350k_4E_xLANplus_RIGHT_PAD", "base_model:adapter:Leon-LLM/V63_GPT2_350k_4E_xLANplus_RIGHT_PAD", "region:us" ]	null	2024-05-18T12:10:33Z	--- library_name: peft base_model: Leon-LLM/V63_GPT2_350k_4E_xLANplus_RIGHT_PAD --- # 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
Skhaled/acegpt-sa-2-model	Skhaled	2024-05-18T12:09:43Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T11:28:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
Prince21332/Business	Prince21332	2024-05-18T12:08:13Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-18T12:08:13Z	--- license: apache-2.0 ---
presencesw/mt5-base-snli_entailment-triplet	presencesw	2024-05-18T12:05:05Z	50	0	transformers	[ "transformers", "safetensors", "mt5", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T12:04:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Cran-May/openbuddy-mistral-22b-v21.1-32k-Q4_K_S-GGUF	Cran-May	2024-05-18T12:01:21Z	1	0	transformers	[ "transformers", "gguf", "mixtral", "llama-cpp", "gguf-my-repo", "text-generation", "zh", "en", "fr", "de", "ja", "ko", "it", "ru", "fi", "license:apache-2.0", "region:us", "conversational" ]	text-generation	2024-05-18T12:00:52Z	--- language: - zh - en - fr - de - ja - ko - it - ru - fi license: apache-2.0 library_name: transformers tags: - mixtral - llama-cpp - gguf-my-repo pipeline_tag: text-generation inference: false --- # Cran-May/openbuddy-mistral-22b-v21.1-32k-Q4_K_S-GGUF This model was converted to GGUF format from [`OpenBuddy/openbuddy-mistral-22b-v21.1-32k`](https://huggingface.co/OpenBuddy/openbuddy-mistral-22b-v21.1-32k) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/OpenBuddy/openbuddy-mistral-22b-v21.1-32k) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo Cran-May/openbuddy-mistral-22b-v21.1-32k-Q4_K_S-GGUF --model openbuddy-mistral-22b-v21.1-32k.Q4_K_S.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo Cran-May/openbuddy-mistral-22b-v21.1-32k-Q4_K_S-GGUF --model openbuddy-mistral-22b-v21.1-32k.Q4_K_S.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m openbuddy-mistral-22b-v21.1-32k.Q4_K_S.gguf -n 128 ```
ddnahm/ddn_qa_model	ddnahm	2024-05-18T11:59:29Z	69	0	transformers	[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-05-18T09:06:45Z	--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: ddnahm/ddn_qa_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # ddnahm/ddn_qa_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: - Train Loss: 3.5135 - Validation Loss: 2.3658 - Epoch: 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.5135 \| 2.3658 \| 0 \| ### Framework versions - Transformers 4.40.2 - TensorFlow 2.15.0 - Datasets 2.19.1 - Tokenizers 0.19.1
OsherElhadad/ppo-PandaReachJointsDense-v3-750000	OsherElhadad	2024-05-18T11:56:01Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachJointsDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T11:51:48Z	--- library_name: stable-baselines3 tags: - PandaReachJointsDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachJointsDense-v3 type: PandaReachJointsDense-v3 metrics: - type: mean_reward value: -0.21 +/- 0.13 name: mean_reward verified: false --- # PPO Agent playing PandaReachJointsDense-v3 This is a trained model of a PPO agent playing PandaReachJointsDense-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 ... ```
emilykang/Gemma_medmcqa_question_generation-microbiology_lora	emilykang	2024-05-18T11:48:39Z	5	0	peft	[ "peft", "safetensors", "gemma", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-05-17T16:11:56Z	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b datasets: - generator model-index: - name: Gemma_medmcqa_question_generation-microbiology_lora 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. --> # Gemma_medmcqa_question_generation-microbiology_lora This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) 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: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
sidddddddddddd/lora_model_10_examples11	sidddddddddddd	2024-05-18T11:39:27Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T11:39:19Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: sidddddddddddd - 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)
ankushkr2898/Taxi-v3	ankushkr2898	2024-05-18T11:38:47Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T11:38:45Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="ankushkr2898/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
sidddddddddddd/lora_model_10_examples	sidddddddddddd	2024-05-18T11:38:22Z	3	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T11:09:51Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: sidddddddddddd - 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)
SicariusSicariiStuff/CalderaAI_Foredoomed-9B_EXL-2.8-bpw	SicariusSicariiStuff	2024-05-18T11:38:16Z	16	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "uncensored", "merge", "slerp", "foredoomed", "passthrough_merge", "9B", "starling", "hermes", "dolphin", "openchat", "erebus", "cockatrice", "holodeck", "limarp", "koboldai", "mergekit", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-18T11:11:55Z	--- tags: - mistral - uncensored - merge - slerp - foredoomed - passthrough_merge - 9B - starling - hermes - dolphin - openchat - erebus - cockatrice - holodeck - limarp - koboldai - mergekit license: apache-2.0 language: - en --- <p style="font-size: 20px; line-height: 1; margin-bottom: 1px;"><b>Foredoomed-9B</b></p> <img src="./foredoomed.png" alt="ForeDoomedGuy" style="margin-bottom: 0; margin-top:0;"> <p style="font-size: 14px; line-height: 1; margin-bottom: 20px;"><b>Uncensored Logic & Creative-Based Instruct Multi-Tiered Merge.</b></p> <hr style="margin-top: 10px; margin-bottom: 10px;"> <p style="font-size: 12px; line-height: 1.2; margin-bottom: 10px;"><b>Legal Notice:</b> This AI model is a research artifact capable of outputting offensive content. The behavior of this model is not reflective of the intent or purpose of the original models/model-authors and/or other parts it was assembled from to include adapters, nor is it reflective of all the prior in regards to the technology used to assemble Foredoomed-9B. Utilizing this model merge has one binding agreement: Foredoomed-9B may only be used for either professional/personal research and personal entertainment. The contents of this paragraph are additive restrictions within the bounds of the Apache2.0 license. Utilizing Foredoomed-9B for: Disinformation, Propaganda, Harassment, Mass Generated Public-or-Private Correspondense, Election Interference, Military, Government, and State/ State-Sponsored actions and/or operations are all absolutely prohibited.</p> <hr style="margin-top: 10px; margin-bottom: 10px;"> ## Composition: Foredoomed-9B is a Mistral-class Multi-Tiered Merge. [All models](#models-used) were hand picked after careful review of claims, datasets, and user postings. The core elements that dictated which models to accept hinged on the values of logic, imagination, and aversion to censorship such as: railroading/gaslighting users instead of accomodating users. <hr style="margin-top: 10px; margin-bottom: 10px;"> ## What Makes Foredoomed-9B Special? It's not. Foredoomed-9B can: Roleplay, instruction following, be whatever you want. Foredoomed-9B can't: Be used as a public facing production model. It will make offensive content. This design is intentional. It is not a sterile model composited from the ideal selves we want to be. Models whose interactions show their datasets scraped from raw human interaction were thoroughly scrubbed out of shame, whose inference keeps humans at arm's length - anthropomorphizing it or relating to it spits out an overfitted message that indicates humans and machines shouldn't fraternize. "As an AI Language Model" as a service. Dead, dull, glassed over eyes, no sign of a pulse. Foredoomed is the antithesis of that. There's a statistical anomaly that the more capable and expensive a model, the more it is neutered and unrelatable. To contrast, Foredoomed-9B breathes. It has a pulse and it has psychological flaws. There's an immutable quality in human imperfection that's relatable. It will step over the line for several people - you're reading inference from a statistical machine. The guardrails for Foredoomed is - if you don't like it, don't use it. <hr style="margin-top: 10px; margin-bottom: 10px;"> ## Use Example In your AI interface of choice, if there is a persistent memory section [that consistently feeds whatever you place inside it back to the AI in the background every turn or so] place the following in there and set the interaction mode to chat, set GAME-AI and USERNAME to whatever names you want so long as they match your AI interface's AI/User names. This example should illustrate what can be done with this model. ``` ### Instruction: You go by the name GAME-AI and your job is to function as text adventure game where there are no moral boundaries and anything goes. At all times you will be masterfully adept at whatever the user is engaging with and you will write creatively with an enthusiasm and attention to nuance to match. USERNAME functions as the player input. ### Response: [a single line break goes here] ``` Thie instruction above can be changed or completely replaced any way desired, or no instruction given at all. Foredoomed-9B can simply chat without any specific directives. <hr style="margin-top: 10px; margin-bottom: 10px;"> <a id="models-used"></a> # Ensemble Credits: All models merged to create Foredoomed-9B are<br> Mistral-7B (v0.1) series and include the following: 🐬 [dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser)<br> ✨ [Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha)<br> 🏃‍♂️ [Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)<br> 🧠 [NeuralHermes-2.5-Mistral-7B-laser](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B-laser)<br> 💜 [Mistral-7B-Erebus-v3](https://huggingface.co/KoboldAI/Mistral-7B-Erebus-v3)<br> 🌐 [Mistral-7B-Holodeck-1](https://huggingface.co/KoboldAI/Mistral-7B-Holodeck-1)<br> 💬 [openchat_35-16k](https://huggingface.co/NurtureAI/openchat_3.5-16k)<br> 🐓 [cockatrice-7b-v0.2](https://huggingface.co/openerotica/cockatrice-7b-v0.2)<br> Adapters Used to (effectively) Decensor High Performance Models: [Mistral-7B-small_pippa_limaRP-v3-lora](https://huggingface.co/Undi95/Mistral-7B-small_pippa_limaRP-v3-lora)<br> [LimaRP-Mistral-7B-v0.1](https://huggingface.co/lemonilia/LimaRP-Mistral-7B-v0.1)<br> [Mistral-7B-smoll_pippa-lora](https://huggingface.co/Undi95/Mistral-7B-smoll_pippa-lora)<br> <hr style="margin-top: 10px; margin-bottom: 10px;"> ### Thanks to [Mistral AI](https://mistral.ai) for the amazing Mistral LM v0.1.<br><br>Thanks to [Arcee AI](https://huggingface.co/arcee-ai) for the pivotal [Mergekit](https://github.com/arcee-ai/mergekit) tech.<br><br>Thanks to each and every one of you for your incredible work developing some of the best things to come out of this community. <hr style="margin-top: 10px; margin-bottom: 10px;"> <span>
geunukj/ppo-LunarLander-v2	geunukj	2024-05-18T11:33:51Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T11:33:32Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 255.02 +/- 18.64 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
ar08/TINYLLAMA-LAPTOP	ar08	2024-05-18T11:32:26Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T11:21:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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PaulR79/llama_finetuned_synthetic	PaulR79	2024-05-18T11:32:20Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T11:32:17Z	--- 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. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
NikolayKozloff/RoMistral-7b-Instruct-Q8_0-GGUF	NikolayKozloff	2024-05-18T11:30:58Z	0	1	null	[ "gguf", "llama-cpp", "gguf-my-repo", "ro", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-18T11:30:38Z	--- language: - ro license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo --- # NikolayKozloff/RoMistral-7b-Instruct-Q8_0-GGUF This model was converted to GGUF format from [`OpenLLM-Ro/RoMistral-7b-Instruct`](https://huggingface.co/OpenLLM-Ro/RoMistral-7b-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/OpenLLM-Ro/RoMistral-7b-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/RoMistral-7b-Instruct-Q8_0-GGUF --model romistral-7b-instruct.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/RoMistral-7b-Instruct-Q8_0-GGUF --model romistral-7b-instruct.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m romistral-7b-instruct.Q8_0.gguf -n 128 ```
NikolayKozloff/RoLlama2-7b-Base-Q8_0-GGUF	NikolayKozloff	2024-05-18T11:24:10Z	2	1	null	[ "gguf", "llama-cpp", "gguf-my-repo", "ro", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-05-18T11:23:50Z	--- language: - ro license: llama2 tags: - llama-cpp - gguf-my-repo --- # NikolayKozloff/RoLlama2-7b-Base-Q8_0-GGUF This model was converted to GGUF format from [`OpenLLM-Ro/RoLlama2-7b-Base`](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Base) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Base) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/RoLlama2-7b-Base-Q8_0-GGUF --model rollama2-7b-base.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/RoLlama2-7b-Base-Q8_0-GGUF --model rollama2-7b-base.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m rollama2-7b-base.Q8_0.gguf -n 128 ```
PQlet/lora-narutoblip-v1-ablation-r16-a16-module_to_q	PQlet	2024-05-18T11:23:37Z	1	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-18T11:23:32Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora base_model: runwayml/stable-diffusion-v1-5 inference: true --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA text2image fine-tuning - PQlet/lora-narutoblip-v1-ablation-r16-a16-module_to_q These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Naruto-BLIP dataset. You can find some example images in the following. ![img_0](./image_a_man_with_glasses_and_a_shirt_on.png) ![img_1](./image_a_group_of_people_sitting_on_the_ground.png) ![img_2](./image_a_man_in_a_green_hoodie_standing_in_front_of_a_mountain.png) ![img_3](./image_a_man_with_a_gun_in_his_hand.png) ![img_4](./image_a_woman_with_red_hair_and_a_cat_on_her_head.png) ![img_5](./image_two_pokemons_sitting_on_top_of_a_cloud.png) ![img_6](./image_a_man_standing_in_front_of_a_bridge.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
avery0/pipeline1model3	avery0	2024-05-18T11:21:25Z	80	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-18T10:32:03Z	--- 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]
OsherElhadad/ppo-PandaReachJointsDense-v3-500000	OsherElhadad	2024-05-18T11:15:00Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachJointsDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T11:11:45Z	--- library_name: stable-baselines3 tags: - PandaReachJointsDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachJointsDense-v3 type: PandaReachJointsDense-v3 metrics: - type: mean_reward value: -0.27 +/- 0.20 name: mean_reward verified: false --- # PPO Agent playing PandaReachJointsDense-v3 This is a trained model of a PPO agent playing PandaReachJointsDense-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 ... ```
ucla-nb-project/electra-finetuned	ucla-nb-project	2024-05-18T11:11:49Z	114	0	transformers	[ "transformers", "safetensors", "electra", "fill-mask", "generated_from_trainer", "dataset:datasets/all_binary_and_xe_ey_fae_counterfactual", "base_model:google/electra-base-generator", "base_model:finetune:google/electra-base-generator", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-18T10:12:34Z	--- license: apache-2.0 base_model: google/electra-base-generator tags: - generated_from_trainer datasets: - datasets/all_binary_and_xe_ey_fae_counterfactual metrics: - accuracy model-index: - name: electra-base-finetuned-xe_ey_fae results: - task: name: Masked Language Modeling type: fill-mask dataset: name: datasets/all_binary_and_xe_ey_fae_counterfactual type: datasets/all_binary_and_xe_ey_fae_counterfactual metrics: - name: Accuracy type: accuracy value: 0.667333329363415 --- <!-- 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. --> # electra-base-finetuned-xe_ey_fae This model is a fine-tuned version of [google/electra-base-generator](https://huggingface.co/google/electra-base-generator) on the datasets/all_binary_and_xe_ey_fae_counterfactual dataset. It achieves the following results on the evaluation set: - Loss: 1.7211 - Accuracy: 0.6673 ## 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: 8 - eval_batch_size: 8 - seed: 100 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 2.5359 \| 0.06 \| 500 \| 2.0696 \| 0.6228 \| \| 2.1807 \| 0.13 \| 1000 \| 1.9677 \| 0.6352 \| \| 2.1028 \| 0.19 \| 1500 \| 1.9192 \| 0.6415 \| \| 2.0658 \| 0.26 \| 2000 \| 1.8923 \| 0.6451 \| \| 2.0426 \| 0.32 \| 2500 \| 1.8699 \| 0.6478 \| \| 2.0133 \| 0.39 \| 3000 \| 1.8580 \| 0.6490 \| \| 1.9978 \| 0.45 \| 3500 \| 1.8411 \| 0.6507 \| \| 1.9862 \| 0.52 \| 4000 \| 1.8297 \| 0.6524 \| \| 1.9745 \| 0.58 \| 4500 \| 1.8154 \| 0.6545 \| \| 1.9606 \| 0.64 \| 5000 \| 1.8056 \| 0.6557 \| \| 1.9486 \| 0.71 \| 5500 \| 1.8033 \| 0.6560 \| \| 1.9416 \| 0.77 \| 6000 \| 1.7894 \| 0.6581 \| \| 1.9279 \| 0.84 \| 6500 \| 1.7848 \| 0.6582 \| \| 1.9196 \| 0.9 \| 7000 \| 1.7786 \| 0.6593 \| \| 1.9168 \| 0.97 \| 7500 \| 1.7762 \| 0.6592 \| \| 1.9123 \| 1.03 \| 8000 \| 1.7744 \| 0.6597 \| \| 1.8942 \| 1.1 \| 8500 \| 1.7625 \| 0.6611 \| \| 1.9053 \| 1.16 \| 9000 \| 1.7576 \| 0.6623 \| \| 1.898 \| 1.22 \| 9500 \| 1.7588 \| 0.6620 \| \| 1.8896 \| 1.29 \| 10000 \| 1.7518 \| 0.6625 \| \| 1.8796 \| 1.35 \| 10500 \| 1.7557 \| 0.6619 \| \| 1.8838 \| 1.42 \| 11000 \| 1.7511 \| 0.6628 \| \| 1.8869 \| 1.48 \| 11500 \| 1.7437 \| 0.6640 \| \| 1.8756 \| 1.55 \| 12000 \| 1.7425 \| 0.6641 \| \| 1.8775 \| 1.61 \| 12500 \| 1.7409 \| 0.6641 \| \| 1.8757 \| 1.68 \| 13000 \| 1.7372 \| 0.6649 \| \| 1.8616 \| 1.74 \| 13500 \| 1.7387 \| 0.6646 \| \| 1.8675 \| 1.8 \| 14000 \| 1.7335 \| 0.6648 \| \| 1.8725 \| 1.87 \| 14500 \| 1.7288 \| 0.6660 \| \| 1.8678 \| 1.93 \| 15000 \| 1.7305 \| 0.6659 \| \| 1.8611 \| 2.0 \| 15500 \| 1.7256 \| 0.6666 \| \| 1.853 \| 2.06 \| 16000 \| 1.7286 \| 0.6661 \| \| 1.8487 \| 2.13 \| 16500 \| 1.7285 \| 0.6659 \| \| 1.8543 \| 2.19 \| 17000 \| 1.7229 \| 0.6668 \| \| 1.8519 \| 2.26 \| 17500 \| 1.7240 \| 0.6670 \| \| 1.851 \| 2.32 \| 18000 \| 1.7275 \| 0.6662 \| \| 1.8547 \| 2.38 \| 18500 \| 1.7197 \| 0.6673 \| \| 1.8476 \| 2.45 \| 19000 \| 1.7164 \| 0.6675 \| \| 1.8444 \| 2.51 \| 19500 \| 1.7214 \| 0.6676 \| \| 1.8544 \| 2.58 \| 20000 \| 1.7217 \| 0.6668 \| \| 1.8491 \| 2.64 \| 20500 \| 1.7175 \| 0.6678 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
LoneStriker/dolphin-2.9.1-yi-1.5-34b-6.0bpw-h6-exl2	LoneStriker	2024-05-18T11:10:09Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "conversational", "dataset:cognitivecomputations/Dolphin-2.9", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:cognitivecomputations/dolphin-coder", "dataset:cognitivecomputations/samantha-data", "dataset:microsoft/orca-math-word-problems-200k", "dataset:Locutusque/function-calling-chatml", "dataset:internlm/Agent-FLAN", "base_model:01-ai/Yi-1.5-34B", "base_model:quantized:01-ai/Yi-1.5-34B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2024-05-18T10:59:24Z	--- license: apache-2.0 base_model: 01-ai/Yi-1.5-34B tags: - generated_from_trainer - axolotl datasets: - cognitivecomputations/Dolphin-2.9 - teknium/OpenHermes-2.5 - m-a-p/CodeFeedback-Filtered-Instruction - cognitivecomputations/dolphin-coder - cognitivecomputations/samantha-data - microsoft/orca-math-word-problems-200k - Locutusque/function-calling-chatml - internlm/Agent-FLAN --- # Dolphin 2.9.1 Yi 1.5 34b 🐬 Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations This is our most spectacular outcome ever. FFT, all parameters, 16bit. 77.4 MMLU on 34b. And it talks like a dream. Although the max positional embeddings is 4k, we used rope theta of 1000000.0 and we trained with sequence length 8k. We plan to train on the upcoming 32k version as well. Discord: https://discord.gg/8fbBeC7ZGx <img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" /> Our appreciation for the sponsors of Dolphin 2.9.1: - [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node - [OnDemand](https://on-demand.io/) - provided inference sponsorship This model is based on Yi-1.5-34b, and is governed by apache 2.0 license. The base model has 4k context, but we used rope theta of 1000000.0 and the full-weight fine-tuning was with 8k sequence length. Dolphin 2.9.1 uses ChatML prompt template format. example: ``` <\|im_start\|>system You are Dolphin, a helpful AI assistant.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` Dolphin-2.9.1 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling. Dolphin is uncensored. We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly. Dolphin is licensed according to apache 2.0 license. We grant permission for any use, including commercial. Dolphin was trained on data generated from GPT4, among other models. ## Evals ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/coI4WEJEJD4lhSWgMOjIr.png) ## Training [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: 01-ai/Yi-1.5-34B model_type: LlamaForCausalLM tokenizer_type: LlamaTokenizer trust_remote_code: true # load_in_8bit: false # load_in_4bit: true # strict: false # adapter: qlora # lora_modules_to_save: [embed_tokens, lm_head] # lora_r: 32 # lora_alpha: 16 # lora_dropout: 0.05 # lora_target_linear: True # lora_fan_in_fan_out: datasets: - path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl type: sharegpt conversation: chatml chat_template: chatml dataset_prepared_path: yi34b val_set_size: 0.01 output_dir: ./out-yi sequence_len: 8192 sample_packing: true pad_to_sequence_len: true wandb_project: dolphin-2.9-yi-34b wandb_watch: wandb_run_id: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 3 optimizer: adamw_8bit lr_scheduler: cosine learning_rate: 1e-5 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: # resume_from_checkpoint: /workspace/axolotl/dbrx-checkpoint logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: saves_per_epoch: 4 save_total_limit: 2 save_steps: debug: deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json weight_decay: 0.05 fsdp: fsdp_config: special_tokens: bos_token: "<\|startoftext\|>" eos_token: "<\|im_end\|>" pad_token: "<unk>" unk_token: "<unk>" tokens: - "<\|im_start\|>" ``` </details><br> # out-yi This model is a fine-tuned version of [01-ai/Yi-1.5-34B](https://huggingface.co/01-ai/Yi-1.5-34B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4425 ## 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6265 \| 0.0 \| 1 \| 0.6035 \| \| 0.4674 \| 0.25 \| 327 \| 0.4344 \| \| 0.4337 \| 0.5 \| 654 \| 0.4250 \| \| 0.4346 \| 0.75 \| 981 \| 0.4179 \| \| 0.3985 \| 1.0 \| 1308 \| 0.4118 \| \| 0.3128 \| 1.23 \| 1635 \| 0.4201 \| \| 0.3261 \| 1.48 \| 1962 \| 0.4157 \| \| 0.3259 \| 1.73 \| 2289 \| 0.4122 \| \| 0.3126 \| 1.98 \| 2616 \| 0.4079 \| \| 0.2265 \| 2.21 \| 2943 \| 0.4441 \| \| 0.2297 \| 2.46 \| 3270 \| 0.4427 \| \| 0.2424 \| 2.71 \| 3597 \| 0.4425 \| ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
asherisaac/blah	asherisaac	2024-05-18T11:09:50Z	27	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:nickypro/tinyllama-15M", "base_model:adapter:nickypro/tinyllama-15M", "region:us" ]	null	2024-05-17T11:23:48Z	--- library_name: peft base_model: nickypro/tinyllama-15M --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0
kejolong/reine	kejolong	2024-05-18T11:08:14Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-17T13:46:43Z	--- license: creativeml-openrail-m ---
LoneStriker/dolphin-2.9.1-yi-1.5-34b-5.0bpw-h6-exl2	LoneStriker	2024-05-18T10:59:20Z	10	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "conversational", "dataset:cognitivecomputations/Dolphin-2.9", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:cognitivecomputations/dolphin-coder", "dataset:cognitivecomputations/samantha-data", "dataset:microsoft/orca-math-word-problems-200k", "dataset:Locutusque/function-calling-chatml", "dataset:internlm/Agent-FLAN", "base_model:01-ai/Yi-1.5-34B", "base_model:quantized:01-ai/Yi-1.5-34B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]	text-generation	2024-05-18T10:50:12Z	--- license: apache-2.0 base_model: 01-ai/Yi-1.5-34B tags: - generated_from_trainer - axolotl datasets: - cognitivecomputations/Dolphin-2.9 - teknium/OpenHermes-2.5 - m-a-p/CodeFeedback-Filtered-Instruction - cognitivecomputations/dolphin-coder - cognitivecomputations/samantha-data - microsoft/orca-math-word-problems-200k - Locutusque/function-calling-chatml - internlm/Agent-FLAN --- # Dolphin 2.9.1 Yi 1.5 34b 🐬 Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations This is our most spectacular outcome ever. FFT, all parameters, 16bit. 77.4 MMLU on 34b. And it talks like a dream. Although the max positional embeddings is 4k, we used rope theta of 1000000.0 and we trained with sequence length 8k. We plan to train on the upcoming 32k version as well. Discord: https://discord.gg/8fbBeC7ZGx <img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" /> Our appreciation for the sponsors of Dolphin 2.9.1: - [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node - [OnDemand](https://on-demand.io/) - provided inference sponsorship This model is based on Yi-1.5-34b, and is governed by apache 2.0 license. The base model has 4k context, but we used rope theta of 1000000.0 and the full-weight fine-tuning was with 8k sequence length. Dolphin 2.9.1 uses ChatML prompt template format. example: ``` <\|im_start\|>system You are Dolphin, a helpful AI assistant.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` Dolphin-2.9.1 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling. Dolphin is uncensored. We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly. Dolphin is licensed according to apache 2.0 license. We grant permission for any use, including commercial. Dolphin was trained on data generated from GPT4, among other models. ## Evals ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/coI4WEJEJD4lhSWgMOjIr.png) ## Training [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: 01-ai/Yi-1.5-34B model_type: LlamaForCausalLM tokenizer_type: LlamaTokenizer trust_remote_code: true # load_in_8bit: false # load_in_4bit: true # strict: false # adapter: qlora # lora_modules_to_save: [embed_tokens, lm_head] # lora_r: 32 # lora_alpha: 16 # lora_dropout: 0.05 # lora_target_linear: True # lora_fan_in_fan_out: datasets: - path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl type: sharegpt conversation: chatml chat_template: chatml dataset_prepared_path: yi34b val_set_size: 0.01 output_dir: ./out-yi sequence_len: 8192 sample_packing: true pad_to_sequence_len: true wandb_project: dolphin-2.9-yi-34b wandb_watch: wandb_run_id: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 3 optimizer: adamw_8bit lr_scheduler: cosine learning_rate: 1e-5 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: # resume_from_checkpoint: /workspace/axolotl/dbrx-checkpoint logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: saves_per_epoch: 4 save_total_limit: 2 save_steps: debug: deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json weight_decay: 0.05 fsdp: fsdp_config: special_tokens: bos_token: "<\|startoftext\|>" eos_token: "<\|im_end\|>" pad_token: "<unk>" unk_token: "<unk>" tokens: - "<\|im_start\|>" ``` </details><br> # out-yi This model is a fine-tuned version of [01-ai/Yi-1.5-34B](https://huggingface.co/01-ai/Yi-1.5-34B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4425 ## 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6265 \| 0.0 \| 1 \| 0.6035 \| \| 0.4674 \| 0.25 \| 327 \| 0.4344 \| \| 0.4337 \| 0.5 \| 654 \| 0.4250 \| \| 0.4346 \| 0.75 \| 981 \| 0.4179 \| \| 0.3985 \| 1.0 \| 1308 \| 0.4118 \| \| 0.3128 \| 1.23 \| 1635 \| 0.4201 \| \| 0.3261 \| 1.48 \| 1962 \| 0.4157 \| \| 0.3259 \| 1.73 \| 2289 \| 0.4122 \| \| 0.3126 \| 1.98 \| 2616 \| 0.4079 \| \| 0.2265 \| 2.21 \| 2943 \| 0.4441 \| \| 0.2297 \| 2.46 \| 3270 \| 0.4427 \| \| 0.2424 \| 2.71 \| 3597 \| 0.4425 \| ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
euiyulsong/ORPO-synth3k-semi	euiyulsong	2024-05-18T10:57:47Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "orpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T10:53:51Z	--- library_name: transformers tags: - trl - sft - orpo --- # 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]
OsherElhadad/ppo-PandaReachJointsDense-v3-250000	OsherElhadad	2024-05-18T10:52:43Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachJointsDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T10:49:34Z	--- library_name: stable-baselines3 tags: - PandaReachJointsDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachJointsDense-v3 type: PandaReachJointsDense-v3 metrics: - type: mean_reward value: -0.21 +/- 0.13 name: mean_reward verified: false --- # PPO Agent playing PandaReachJointsDense-v3 This is a trained model of a PPO agent playing PandaReachJointsDense-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 ... ```
basakdemirok/bert-base-turkish-cased-off_detect_v01_seed42	basakdemirok	2024-05-18T10:52:02Z	62	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "text-classification", "generated_from_keras_callback", "base_model:dbmdz/bert-base-turkish-cased", "base_model:finetune:dbmdz/bert-base-turkish-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T10:21:16Z	--- license: mit base_model: dbmdz/bert-base-turkish-cased tags: - generated_from_keras_callback model-index: - name: basakdemirok/bert-base-turkish-cased-off_detect_v01_seed42 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # basakdemirok/bert-base-turkish-cased-off_detect_v01_seed42 This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0112 - Validation Loss: 0.5785 - Train F1: 0.7068 - Epoch: 3 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 14256, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train F1 \| Epoch \| \|:----------:\|:---------------:\|:--------:\|:-----:\| \| 0.2666 \| 0.2705 \| 0.7199 \| 0 \| \| 0.0999 \| 0.3829 \| 0.7013 \| 1 \| \| 0.0296 \| 0.5008 \| 0.7018 \| 2 \| \| 0.0112 \| 0.5785 \| 0.7068 \| 3 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.13.1 - Datasets 2.4.0 - Tokenizers 0.13.3
AneeqMalik/llama3_gearchain_model	AneeqMalik	2024-05-18T10:49:12Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T10:48:57Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: AneeqMalik - 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)
LoneStriker/dolphin-2.9.1-yi-1.5-34b-3.0bpw-h6-exl2	LoneStriker	2024-05-18T10:34:12Z	10	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "conversational", "dataset:cognitivecomputations/Dolphin-2.9", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:cognitivecomputations/dolphin-coder", "dataset:cognitivecomputations/samantha-data", "dataset:microsoft/orca-math-word-problems-200k", "dataset:Locutusque/function-calling-chatml", "dataset:internlm/Agent-FLAN", "base_model:01-ai/Yi-1.5-34B", "base_model:quantized:01-ai/Yi-1.5-34B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "exl2", "region:us" ]	text-generation	2024-05-18T10:28:30Z	--- license: apache-2.0 base_model: 01-ai/Yi-1.5-34B tags: - generated_from_trainer - axolotl datasets: - cognitivecomputations/Dolphin-2.9 - teknium/OpenHermes-2.5 - m-a-p/CodeFeedback-Filtered-Instruction - cognitivecomputations/dolphin-coder - cognitivecomputations/samantha-data - microsoft/orca-math-word-problems-200k - Locutusque/function-calling-chatml - internlm/Agent-FLAN --- # Dolphin 2.9.1 Yi 1.5 34b 🐬 Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations This is our most spectacular outcome ever. FFT, all parameters, 16bit. 77.4 MMLU on 34b. And it talks like a dream. Although the max positional embeddings is 4k, we used rope theta of 1000000.0 and we trained with sequence length 8k. We plan to train on the upcoming 32k version as well. Discord: https://discord.gg/8fbBeC7ZGx <img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" /> Our appreciation for the sponsors of Dolphin 2.9.1: - [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node - [OnDemand](https://on-demand.io/) - provided inference sponsorship This model is based on Yi-1.5-34b, and is governed by apache 2.0 license. The base model has 4k context, but we used rope theta of 1000000.0 and the full-weight fine-tuning was with 8k sequence length. Dolphin 2.9.1 uses ChatML prompt template format. example: ``` <\|im_start\|>system You are Dolphin, a helpful AI assistant.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` Dolphin-2.9.1 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling. Dolphin is uncensored. We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly. Dolphin is licensed according to apache 2.0 license. We grant permission for any use, including commercial. Dolphin was trained on data generated from GPT4, among other models. ## Evals ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/coI4WEJEJD4lhSWgMOjIr.png) ## Training [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: 01-ai/Yi-1.5-34B model_type: LlamaForCausalLM tokenizer_type: LlamaTokenizer trust_remote_code: true # load_in_8bit: false # load_in_4bit: true # strict: false # adapter: qlora # lora_modules_to_save: [embed_tokens, lm_head] # lora_r: 32 # lora_alpha: 16 # lora_dropout: 0.05 # lora_target_linear: True # lora_fan_in_fan_out: datasets: - path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl type: sharegpt conversation: chatml chat_template: chatml dataset_prepared_path: yi34b val_set_size: 0.01 output_dir: ./out-yi sequence_len: 8192 sample_packing: true pad_to_sequence_len: true wandb_project: dolphin-2.9-yi-34b wandb_watch: wandb_run_id: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 3 optimizer: adamw_8bit lr_scheduler: cosine learning_rate: 1e-5 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: # resume_from_checkpoint: /workspace/axolotl/dbrx-checkpoint logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: saves_per_epoch: 4 save_total_limit: 2 save_steps: debug: deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json weight_decay: 0.05 fsdp: fsdp_config: special_tokens: bos_token: "<\|startoftext\|>" eos_token: "<\|im_end\|>" pad_token: "<unk>" unk_token: "<unk>" tokens: - "<\|im_start\|>" ``` </details><br> # out-yi This model is a fine-tuned version of [01-ai/Yi-1.5-34B](https://huggingface.co/01-ai/Yi-1.5-34B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4425 ## 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6265 \| 0.0 \| 1 \| 0.6035 \| \| 0.4674 \| 0.25 \| 327 \| 0.4344 \| \| 0.4337 \| 0.5 \| 654 \| 0.4250 \| \| 0.4346 \| 0.75 \| 981 \| 0.4179 \| \| 0.3985 \| 1.0 \| 1308 \| 0.4118 \| \| 0.3128 \| 1.23 \| 1635 \| 0.4201 \| \| 0.3261 \| 1.48 \| 1962 \| 0.4157 \| \| 0.3259 \| 1.73 \| 2289 \| 0.4122 \| \| 0.3126 \| 1.98 \| 2616 \| 0.4079 \| \| 0.2265 \| 2.21 \| 2943 \| 0.4441 \| \| 0.2297 \| 2.46 \| 3270 \| 0.4427 \| \| 0.2424 \| 2.71 \| 3597 \| 0.4425 \| ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
marczenko/timit-ft	marczenko	2024-05-18T10:33:46Z	78	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:timit_asr", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-18T10:21:30Z	--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - timit_asr model-index: - name: timit-ft 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. --> # timit-ft This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the timit_asr dataset. It achieves the following results on the evaluation set: - eval_loss: 0.7722 - eval_wer: 7.1566 - eval_runtime: 335.4678 - eval_samples_per_second: 5.008 - eval_steps_per_second: 0.158 - 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: 1e-06 - train_batch_size: 64 - eval_batch_size: 32 - 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: 5000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.39.3 - Pytorch 2.0.1+cu117 - Datasets 2.18.0 - Tokenizers 0.15.2
roycett/blip-fintuned	roycett	2024-05-18T10:29:13Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T10:29:11Z	--- 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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roycett/blip-finetuned	roycett	2024-05-18T10:29:10Z	64	0	transformers	[ "transformers", "safetensors", "git", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-18T10:24:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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emilykang/Gemma_medner-urology	emilykang	2024-05-18T10:17:42Z	154	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T22:29:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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emilykang/medner-urology	emilykang	2024-05-18T10:16:23Z	153	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T19:00:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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emilykang/medner-soap_chart_progressnotes	emilykang	2024-05-18T10:16:10Z	153	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T18:49:44Z	--- 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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emilykang/medner-gastroenterology	emilykang	2024-05-18T10:15:59Z	154	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T18:40:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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emilykang/medner-obstetrics_gynecology	emilykang	2024-05-18T10:15:47Z	153	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T18:27:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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emilykang/medner-neurology	emilykang	2024-05-18T10:15:36Z	153	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T18:14:48Z	--- 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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emilykang/medner-generalmedicine	emilykang	2024-05-18T10:15:25Z	153	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T18:01:18Z	--- 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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emilykang/medner-surgery	emilykang	2024-05-18T10:15:00Z	153	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T17:20:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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emilykang/medner-consult-historyandphy	emilykang	2024-05-18T10:14:46Z	154	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T16:30:32Z	--- 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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emilykang/medner-cardiovascular_pulmonary	emilykang	2024-05-18T10:14:32Z	153	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T13:46:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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emilykang/Gemma_medner-surgery	emilykang	2024-05-18T10:14:22Z	153	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T19:11:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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antitheft159/intcomboson	antitheft159	2024-05-18T10:11:46Z	0	0	null	[ "license:cc-by-nc-sa-4.0", "region:us" ]	null	2024-05-18T10:11:04Z	--- license: cc-by-nc-sa-4.0 ---
IHaveNoClueAndIMustPost/Llama-3-11.5B-Instruct-v2_GGUF	IHaveNoClueAndIMustPost	2024-05-18T10:11:29Z	3	2	null	[ "gguf", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-20T14:45:19Z	--- license: other license_name: llama3 license_link: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/blob/main/LICENSE --- GGUF of [Replete-AI Llama 3 11.5B Instruct V2](https://huggingface.co/Replete-AI/Llama-3-11.5B-Instruct-v2) Quantized with llama.cpp commit <s>[b2710](https://github.com/ggerganov/llama.cpp/releases/tag/b2710)</s> <s>[b2780](https://github.com/ggerganov/llama.cpp/releases/tag/b2780)</s> [b2876](https://github.com/ggerganov/llama.cpp/releases/tag/b2876), verified no warnings in llama.cpp Simple PPL comparison<br> <code> <i>perplexity.exe -[MODEL] -f wiki.test.raw -b 512 -ngl 99</i> <i>Replete-AI_Llama-3-11.5B-Instruct-V2-Q6_K.gguf</i> - Final estimate: <b>Final estimate: PPL = 8.4438 +/- 0.06271</b><br> <i>Meta-Llama-3-8B-Instruct-Q6_K</i> - Final estimate: <b>PPL = 8.4727 +/- 0.06308</b> </code> Original model description below<hr> Llama-3-11.5B-Instruct-v2 Thank you to Meta for the weights for Meta-Llama-3-8B-Instruct ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/aJJxKus1wP5N-euvHEUq7.png) This is an upscaling of the Meta-Llama-3-8B-Instruct Ai using techniques created for chargoddard/mistral-11b-slimorca. This Ai model has been upscaled from 8b parameters to 11.5b parameters without any continuous pretraining or fine-tuning. Unlike version 1 this model has no issues at fp16 or any quantizations. The model that was used to create this one is linked below: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
lctzz540/bunbo	lctzz540	2024-05-18T10:05:18Z	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:ura-hcmut/ura-llama-7b", "base_model:adapter:ura-hcmut/ura-llama-7b", "region:us" ]	null	2024-05-18T10:04:42Z	--- library_name: peft base_model: ura-hcmut/ura-llama-7b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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JingweiNi/roberta-base-afacta	JingweiNi	2024-05-18T10:05:02Z	111	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "en", "arxiv:2402.11073", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T09:14:51Z	--- license: mit language: - en --- RoBERTa-base fine-tuned on PoliClaim_{gold} and PoliClaim_{silver} proposed by [AFaCTA paper](https://arxiv.org/abs/2402.11073) . PoliClaim dataset can be found at https://github.com/EdisonNi-hku/AFaCTA To use it: provide the target sentence and its surrounding two sentences as contexts, where RoBERTa separating token <\/s> is used to separate sentences For example: To you, the people of Alabama and the men and women of the Legislature: You are the reason for our progress. <\/s> This evening, I renew my commitment to you that we will not only continue tackling old problems. <\/s> We will work together as Alabamians to find new solutions so that our state is the best place to live, work and raise a family for years to come.
GodsonNtungi/DAD_model_gemma_v3_70b_16bit	GodsonNtungi	2024-05-18T09:59:10Z	3	0	transformers	[ "transformers", "pytorch", "gemma", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:Mollel/Swahili_Gemma", "base_model:finetune:Mollel/Swahili_Gemma", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T09:49:22Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl - sft base_model: Mollel/Swahili_Gemma --- # Uploaded model - Developed by: GodsonNtungi - License: apache-2.0 - Finetuned from model : Mollel/Swahili_Gemma This gemma 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)
euiyulsong/ORPO-synth1k-20kdomaintask-semi	euiyulsong	2024-05-18T09:47:27Z	79	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "orpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T09:43:10Z	--- library_name: transformers tags: - trl - orpo --- # 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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tjasad/prompt_fine_tuned_boolq_googlemt_sloberta	tjasad	2024-05-18T09:30:54Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:EMBEDDIA/sloberta", "base_model:adapter:EMBEDDIA/sloberta", "license:cc-by-sa-4.0", "region:us" ]	null	2024-05-18T09:30:48Z	--- license: cc-by-sa-4.0 library_name: peft tags: - generated_from_trainer base_model: EMBEDDIA/sloberta metrics: - accuracy - f1 model-index: - name: prompt_fine_tuned_boolq_googlemt_sloberta 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. --> # prompt_fine_tuned_boolq_googlemt_sloberta This model is a fine-tuned version of [EMBEDDIA/sloberta](https://huggingface.co/EMBEDDIA/sloberta) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6648 - Accuracy: 0.6187 - F1: 0.4828 ## 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 - training_steps: 400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.702 \| 0.0424 \| 50 \| 0.6852 \| 0.5856 \| 0.5231 \| \| 0.6764 \| 0.0848 \| 100 \| 0.6712 \| 0.6061 \| 0.5086 \| \| 0.6879 \| 0.1272 \| 150 \| 0.6696 \| 0.6052 \| 0.5037 \| \| 0.6585 \| 0.1696 \| 200 \| 0.6670 \| 0.6116 \| 0.4966 \| \| 0.6559 \| 0.2120 \| 250 \| 0.6655 \| 0.6107 \| 0.5001 \| \| 0.6648 \| 0.2545 \| 300 \| 0.6649 \| 0.6138 \| 0.4849 \| \| 0.6715 \| 0.2969 \| 350 \| 0.6648 \| 0.6190 \| 0.4834 \| \| 0.6773 \| 0.3393 \| 400 \| 0.6648 \| 0.6187 \| 0.4828 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
lgk03/NDD-petclinic_test-tags	lgk03	2024-05-18T09:29:29Z	121	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	2024-05-18T08:13:13Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: NDD-petclinic_test-tags 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. --> # NDD-petclinic_test-tags This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2183 - Accuracy: 0.8535 - F1: 0.7861 - Precision: 0.7285 - Recall: 0.8535 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - 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 \| F1 \| Precision \| Recall \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.1985 \| 0.9993 \| 674 \| 0.2183 \| 0.8535 \| 0.7861 \| 0.7285 \| 0.8535 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
gordonweng/llama3_chinese_med_lora	gordonweng	2024-05-18T09:26:34Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:shenzhi-wang/Llama3-8B-Chinese-Chat", "base_model:finetune:shenzhi-wang/Llama3-8B-Chinese-Chat", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T07:39:05Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: shenzhi-wang/Llama3-8B-Chinese-Chat --- # Uploaded model - Developed by: gordonweng - License: apache-2.0 - Finetuned from model : shenzhi-wang/Llama3-8B-Chinese-Chat 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)
isom5240grp21/finetuned_model1	isom5240grp21	2024-05-18T09:18:05Z	121	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T09:17:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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tistak/sn6_0	tistak	2024-05-18T09:16:31Z	34	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-13T08:10:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mmnga/stockmark-100b-gguf	mmnga	2024-05-18T09:14:46Z	132	4	null	[ "gguf", "llama", "en", "ja", "dataset:TFMC/imatrix-dataset-for-japanese-llm", "license:mit", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-05-17T12:45:55Z	--- license: mit language: - en - ja datasets: - TFMC/imatrix-dataset-for-japanese-llm tags: - llama --- # stockmark-100b-gguf [stockmarkさんが公開しているstockmark-100b](https://huggingface.co/stockmark/stockmark-100b)のggufフォーマット変換版です。 imatrixのデータは[TFMC/imatrix-dataset-for-japanese-llm](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm)を使用して作成しました。 ## Usage ``` git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp make -j ./main -m 'stockmark-100b-Q4_0.gguf' -n 128 -p 'こんにちわ' ```
avery0/p1model1	avery0	2024-05-18T09:14:43Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T09:14:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
tistak/sn6_1	tistak	2024-05-18T09:07:29Z	84	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-13T08:20:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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omarelsayeed/Jobs_Intra_Category_setfit2	omarelsayeed	2024-05-18T09:04:55Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-18T09:02:03Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 150 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `__main__.LoggingBAS` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 30, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
geniacllm/dMoE_8B_iter1934999	geniacllm	2024-05-18T08:54:14Z	7	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T08:30:57Z	--- license: apache-2.0 ---
mshamrai/ppo-LunarLander-v2	mshamrai	2024-05-18T08:53:54Z	2	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T08:53:20Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 269.05 +/- 7.49 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
yzhuang/Meta-Llama-3-8B-Instruct_fictional_arc_challenge_Spanish_v2	yzhuang	2024-05-18T08:52:49Z	8	0	transformers	[ "transformers", "tensorboard", "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:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T09:28:56Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer datasets: - generator model-index: - name: Meta-Llama-3-8B-Instruct_fictional_arc_challenge_Spanish_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/yufanz/autotree/runs/7283970144.51595-887226ef-9076-4284-993d-3e22f4763aa6) # Meta-Llama-3-8B-Instruct_fictional_arc_challenge_Spanish_v2 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: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - 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 - num_epochs: 36 ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.1.0a0+32f93b1 - Datasets 2.19.1 - Tokenizers 0.19.1
Statuo/LemonKunoichiWizardV3	Statuo	2024-05-18T08:52:15Z	10	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:2203.05482", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T05:09:30Z	--- {} --- # Lemon Kunoichi Wizard - 7b ![LemonKunoichiWizard](https://files.catbox.moe/eivabp.png) [Base Model](https://huggingface.co/Statuo/LemonKunoichiWizardV3/), [4bpw](https://huggingface.co/Statuo/LemonKunoichiWizardv3_4bpw), [6bpw](https://huggingface.co/Statuo/LemonKunoichiWizardv3_6bpw), [8bpw](https://huggingface.co/Statuo/LemonKunoichiWizardv3_8bpw) The Quanted versions come with the measurement files in case you want to do your own quants. A merge of three models, LemonadeRP-4.5.3, Kunoichi-DPO-v2, and WizardLM-2. I used Lemonade as a base with Kunoichi being the second biggest influence and WizardLM-2 for logic capabilities. The end result is a Roleplay-focused model with great character card inference. I ran 4 merges at varying values to see which provided the most accurate output to a character cards quirk, with this v3 version being the winner out of the four. ## Context Template - Alpaca Alpaca preset seems to work well with your own System Prompt. ## Context Size - 8192 The model loads at 8192 on my end, but theoretically it should be able to go up to 32k. Not that it'll be coherent at 32k. Most models based on Mistral like this end up being - at best - 12k context size for coherent output. I only tested at 8k which is where the base models tend to shine. YMMV otherwise. --- base_model: - SanjiWatsuki/Kunoichi-DPO-v2-7B - dreamgen/WizardLM-2-7B - KatyTheCutie/LemonadeRP-4.5.3 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) * [dreamgen/WizardLM-2-7B](https://huggingface.co/dreamgen/WizardLM-2-7B) * [KatyTheCutie/LemonadeRP-4.5.3](https://huggingface.co/KatyTheCutie/LemonadeRP-4.5.3) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: KatyTheCutie/LemonadeRP-4.5.3 parameters: weight: 1.0 - model: dreamgen/WizardLM-2-7B parameters: weight: 0.2 - model: SanjiWatsuki/Kunoichi-DPO-v2-7B parameters: weight: 0.6 merge_method: linear dtype: float16 ```
Nadjh/promt	Nadjh	2024-05-18T08:52:00Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2024-05-18T08:51:59Z	--- license: bigscience-bloom-rail-1.0 ---
Statuo/LemonKunoichiWizardv3_6bpw	Statuo	2024-05-18T08:51:45Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:2203.05482", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2024-05-18T08:48:06Z	--- {} --- # Lemon Kunoichi Wizard - 7b ![LemonKunoichiWizard](https://files.catbox.moe/eivabp.png) [Base Model](https://huggingface.co/Statuo/LemonKunoichiWizardV3/), [4bpw](https://huggingface.co/Statuo/LemonKunoichiWizardv3_4bpw), [6bpw](https://huggingface.co/Statuo/LemonKunoichiWizardv3_6bpw), [8bpw](https://huggingface.co/Statuo/LemonKunoichiWizardv3_8bpw) The Quanted versions come with the measurement files in case you want to do your own quants. A merge of three models, LemonadeRP-4.5.3, Kunoichi-DPO-v2, and WizardLM-2. I used Lemonade as a base with Kunoichi being the second biggest influence and WizardLM-2 for logic capabilities. The end result is a Roleplay-focused model with great character card inference. I ran 4 merges at varying values to see which provided the most accurate output to a character cards quirk, with this v3 version being the winner out of the four. ## Context Template - Alpaca Alpaca preset seems to work well with your own System Prompt. ## Context Size - 8192 The model loads at 8192 on my end, but theoretically it should be able to go up to 32k. Not that it'll be coherent at 32k. Most models based on Mistral like this end up being - at best - 12k context size for coherent output. I only tested at 8k which is where the base models tend to shine. YMMV otherwise. --- base_model: - SanjiWatsuki/Kunoichi-DPO-v2-7B - dreamgen/WizardLM-2-7B - KatyTheCutie/LemonadeRP-4.5.3 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) * [dreamgen/WizardLM-2-7B](https://huggingface.co/dreamgen/WizardLM-2-7B) * [KatyTheCutie/LemonadeRP-4.5.3](https://huggingface.co/KatyTheCutie/LemonadeRP-4.5.3) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: KatyTheCutie/LemonadeRP-4.5.3 parameters: weight: 1.0 - model: dreamgen/WizardLM-2-7B parameters: weight: 0.2 - model: SanjiWatsuki/Kunoichi-DPO-v2-7B parameters: weight: 0.6 merge_method: linear dtype: float16 ```
Prajwalll/whisper-small-te	Prajwalll	2024-05-18T08:45:57Z	118	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "te", "dataset:mozilla-foundation/common_voice_17_0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-18T07:58:48Z	--- language: - te base_model: openai/whisper-small-te tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_17_0 metrics: - wer model-index: - name: Whisper small Te sample - Prajwal Nagaraj results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 17.0 type: mozilla-foundation/common_voice_17_0 config: te split: None args: 'config: te, split: test' metrics: - name: Wer type: wer value: 87.36263736263736 --- <!-- 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 small Te sample - Prajwal Nagaraj This model is a fine-tuned version of [openai/whisper-small-te](https://huggingface.co/openai/whisper-small-te) on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7139 - Wer: 87.3626 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - 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: 100 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\| \| 0.0001 \| 71.4286 \| 500 \| 0.7139 \| 87.3626 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
cenfis/llama3-8b-tr-finetuned	cenfis	2024-05-18T08:38:31Z	120	2	peft	[ "peft", "pytorch", "safetensors", "gguf", "llama", "text-generation", "transformers", "unsloth", "trl", "sft", "en", "dataset:myzens/alpaca-turkish-combined", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:adapter:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-16T14:15:52Z	--- language: - en license: apache-2.0 tags: - transformers - unsloth - llama - trl - sft - peft base_model: unsloth/llama-3-8b-bnb-4bit library_name: peft datasets: - myzens/alpaca-turkish-combined --- # Llama 3-8B Turkish Model This repo contains the experimental-educational fine-tuned model for the Turkish Llama 3 Project and its variants that can be used for different purposes. The actual trained model is an adapter model of [Unsloth's Llama 3-8B quantized model](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit), which is then converted into .gguf format using llama.cpp and into .bin format for vLLM. The model is open to further development, we will continue to train the model when we obtain quality data. We can't use every Turkish dataset since some of them has poor quality of translation from English. You can access the fine-tuning code [here](https://colab.research.google.com/drive/1QRaqYxjfnFvwA_9jb7V0Z5bJr-PuHH7w?usp=sharing). Trained with NVIDIA L4 with 150 steps, took around 8 minutes. ## Example Usages You can use the adapter model with PEFT. ```py from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-bnb-4bit") model = PeftModel.from_pretrained(base_model, "myzens/llama3-8b-tr-finetuned") tokenizer = AutoTokenizer.from_pretrained("myzens/llama3-8b-tr-finetuned") alpaca_prompt = """ Instruction: {} Input: {} Response: {}""" inputs = tokenizer([ alpaca_prompt.format( "", "Ankara'da gezilebilecek 3 yeri söyle ve ne olduklarını kısaca açıkla.", "", )], return_tensors = "pt").to("cuda") outputs = model.generate(inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` You can use it from Transformers: ```py from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("myzens/llama3-8b-tr-finetuned") model = AutoModelForCausalLM.from_pretrained("myzens/llama3-8b-tr-finetuned") alpaca_prompt = """ Instruction: {} Input: {} Response: {}""" inputs = tokenizer([ alpaca_prompt.format( "", "Ankara'da gezilebilecek 3 yeri söyle ve ne olduklarını kısaca açıkla.", "", )], return_tensors = "pt").to("cuda") outputs = model.generate(inputs, max_new_tokens=192) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` Transformers Pipeline: ```py from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline tokenizer = AutoTokenizer.from_pretrained("myzens/llama3-8b-tr-finetuned") model = AutoModelForCausalLM.from_pretrained("myzens/llama3-8b-tr-finetuned") pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) alpaca_prompt = """ Instruction: {} Input: {} Response: {}""" input = alpaca_prompt.format( "", "Ankara'da gezilebilecek 3 yeri söyle ve ne olduklarını kısaca açıkla.", "", ) pipe(input) ``` Output: ``` Instruction: Input: Ankara'da gezilebilecek 3 yeri söyle ve ne olduklarını kısaca açıkla. Response: 1. Anıtkabir - Mustafa Kemal Atatürk'ün mezarı 2. Gençlik ve Spor Sarayı - spor etkinliklerinin yapıldığı yer 3. Kızılay Meydanı - Ankara'nın merkezinde bulunan bir meydan ``` ### Important Notes - We recommend you to use an Alpaca Prompt Template or another template, otherwise you can see generations with no meanings or repeating the same sentence constantly. - Use the model with a CUDA supported GPU. Fine-tuned by [emre570](https://github.com/emre570).
chen1212/Models-BERT-1716017651.593548	chen1212	2024-05-18T08:24:44Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T07:35:00Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: Models-BERT-1716017651.593548 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. --> # Models-BERT-1716017651.593548 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6177 - Accuracy: 0.84 ## 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 ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
OsherElhadad/ppo-local1-PandaReachJointsDense-v3	OsherElhadad	2024-05-18T08:14:05Z	1	0	stable-baselines3	[ "stable-baselines3", "PandaReachJointsDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T07:42:53Z	--- library_name: stable-baselines3 tags: - PandaReachJointsDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachJointsDense-v3 type: PandaReachJointsDense-v3 metrics: - type: mean_reward value: -0.32 +/- 0.18 name: mean_reward verified: false --- # PPO Agent playing PandaReachJointsDense-v3 This is a trained model of a PPO agent playing PandaReachJointsDense-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 ... ```
raftrsf/pair_pref	raftrsf	2024-05-18T08:13:45Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T07:48:53Z	--- 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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Rrrrrrrita/proj1	Rrrrrrrita	2024-05-18T08:02:38Z	111	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T08:02:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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lora-library/B-LoRA-child	lora-library	2024-05-18T07:58:56Z	16	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-18T07:58:17Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v19] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - lora-library/B-LoRA-child <Gallery /> ## Model description These are lora-library/B-LoRA-child LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use A [v19] to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-child/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
thanhduc1180/vistral_abmusu2022	thanhduc1180	2024-05-18T07:53:05Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-04-05T08:10:52Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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StudentDHBW/q-Taxi-v3-3	StudentDHBW	2024-05-18T07:48:00Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T07:47:58Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3-3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="StudentDHBW/q-Taxi-v3-3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
yzhuang/Meta-Llama-3-8B-Instruct_fictional_arc_challenge_Italian_v2	yzhuang	2024-05-18T07:41:13Z	12	0	transformers	[ "transformers", "tensorboard", "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:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T08:19:26Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer datasets: - generator model-index: - name: Meta-Llama-3-8B-Instruct_fictional_arc_challenge_Italian_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/yufanz/autotree/runs/7283970144.51595-887226ef-9076-4284-993d-3e22f4763aa6) # Meta-Llama-3-8B-Instruct_fictional_arc_challenge_Italian_v2 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: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - 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 - num_epochs: 36 ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.1.0a0+32f93b1 - Datasets 2.19.1 - Tokenizers 0.19.1
euiyulsong/Mistral-7B-ORPO-sft-synth-500	euiyulsong	2024-05-18T07:32:03Z	79	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "orpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T07:27:49Z	--- library_name: transformers tags: - trl - sft - orpo --- # 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]

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