ftopal/huggingface-models-raw · Datasets at Hugging Face

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text-generation	transformers	# cognitivecomputations/dolphin-2.9-llama3-8b-1m AWQ - Model creator: [cognitivecomputations](https://huggingface.co/cognitivecomputations) - Original model: [dolphin-2.9-llama3-8b-1m](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b-1m) ## How to use ### Install the necessary packages ```bash pip install --upgrade autoawq autoawq-kernels ``` ### Example Python code ```python from awq import AutoAWQForCausalLM from transformers import AutoTokenizer, TextStreamer model_path = "solidrust/dolphin-2.9-llama3-8b-1m-AWQ" system_message = "You are dolphin-2.9-llama3-8b-1m, incarnated as a powerful AI. You were created by cognitivecomputations." # Load model model = AutoAWQForCausalLM.from_quantized(model_path, fuse_layers=True) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) # Convert prompt to tokens prompt_template = """\ <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant""" prompt = "You're standing on the surface of the Earth. "\ "You walk one mile south, one mile west and one mile north. "\ "You end up exactly where you started. Where are you?" tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt), return_tensors='pt').input_ids.cuda() # Generate output generation_output = model.generate(tokens, streamer=streamer, max_new_tokens=512) ``` ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code	{"library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"}	solidrust/dolphin-2.9-llama3-8b-1m-AWQ	null	[ "transformers", "safetensors", "llama", "text-generation", "4-bit", "AWQ", "autotrain_compatible", "endpoints_compatible", "conversational", "text-generation-inference", "region:us" ]	null	2024-05-03T16:01:32+00:00
null	null		{}	optimum-internal-testing/optimum-neuron-cache-for-testing-arwim	null	[ "region:us" ]	null	2024-05-03T16:02:09+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama-2-ko-7b - bnb 8bits - Model creator: https://huggingface.co/beomi/ - Original model: https://huggingface.co/beomi/llama-2-ko-7b/ Original model description: --- language: - en - ko pipeline_tag: text-generation inference: false tags: - facebook - meta - pytorch - llama - llama-2 - kollama - llama-2-ko --- Update Log - 2023.12.27 - New Model is here! Trained with only open-accessible Korean text corpus: https://huggingface.co/beomi/open-llama-2-ko-7b - 2023.10.19 - Fix Tokenizer bug(space not applied when decoding) after `transforemrs>=4.34.0` # Llama-2-Ko 🦙🇰🇷 Llama-2-Ko serves as an advanced iteration of Llama 2, benefiting from an expanded vocabulary and the inclusion of a Korean corpus in its further pretraining. Just like its predecessor, Llama-2-Ko operates within the broad range of generative text models that stretch from 7 billion to 70 billion parameters. This repository focuses on the 7B pretrained version, which is tailored to fit the Hugging Face Transformers format. For access to the other models, feel free to consult the index provided below. ## Model Details Model Developers Junbum Lee (Beomi) Variations Llama-2-Ko will come in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama-2-Ko is an auto-regressive language model that uses an optimized transformer architecture based on Llama-2. \|\|Training Data\|Params\|Content Length\|GQA\|Tokens\|LR\| \|---\|---\|---\|---\|---\|---\|---\| \|Llama 2\|A new mix of Korean online data\|7B\|4k\|✗\|>40B\|1e<sup>-5</sup>\| Plan to train upto 200B tokens Vocab Expansion \| Model Name \| Vocabulary Size \| Description \| \| --- \| --- \| --- \| \| Original Llama-2 \| 32000 \| Sentencepiece BPE \| \| Expanded Llama-2-Ko \| 46336 \| Sentencepiece BPE. Added Korean vocab and merges \| Tokenizing "안녕하세요, 오늘은 날씨가 좋네요." \| Model \| Tokens \| \| --- \| --- \| \| Llama-2 \| `['▁', '안', '<0xEB>', '<0x85>', '<0x95>', '하', '세', '요', ',', '▁', '오', '<0xEB>', '<0x8A>', '<0x98>', '은', '▁', '<0xEB>', '<0x82>', '<0xA0>', '씨', '가', '▁', '<0xEC>', '<0xA2>', '<0x8B>', '<0xEB>', '<0x84>', '<0xA4>', '요']` \| \| Llama-2-Ko \| `['▁안녕', '하세요', ',', '▁오늘은', '▁날', '씨가', '▁좋네요']` \| Tokenizing "Llama 2: Open Foundation and Fine-Tuned Chat Models" \| Model \| Tokens \| \| --- \| --- \| \| Llama-2 \| `['▁L', 'l', 'ama', '▁', '2', ':', '▁Open', '▁Foundation', '▁and', '▁Fine', '-', 'T', 'un', 'ed', '▁Ch', 'at', '▁Mod', 'els']` \| \| Llama-2-Ko \| `['▁L', 'l', 'ama', '▁', '2', ':', '▁Open', '▁Foundation', '▁and', '▁Fine', '-', 'T', 'un', 'ed', '▁Ch', 'at', '▁Mod', 'els']` \| # Model Benchmark ## LM Eval Harness - Korean (polyglot branch) - Used EleutherAI's lm-evaluation-harness https://github.com/EleutherAI/lm-evaluation-harness/tree/polyglot ### NSMC (Acc) - 50000 full test TBD ### COPA (F1) <img src=https://user-images.githubusercontent.com/11323660/255575809-c037bc6e-0566-436a-a6c1-2329ac92187a.png style="max-width: 700px; width: 100%" /> \| Model \| 0-shot \| 5-shot \| 10-shot \| 50-shot \| \| --- \| --- \| --- \| --- \| --- \| \| https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 \| 0.6696 \| 0.6477 \| 0.6419 \| 0.6514 \| \| https://huggingface.co/kakaobrain/kogpt \| 0.7345 \| 0.7287 \| 0.7277 \| 0.7479 \| \| https://huggingface.co/facebook/xglm-7.5B \| 0.6723 \| 0.6731 \| 0.6769 \| 0.7119 \| \| https://huggingface.co/EleutherAI/polyglot-ko-1.3b \| 0.7196 \| 0.7193 \| 0.7204 \| 0.7206 \| \| https://huggingface.co/EleutherAI/polyglot-ko-3.8b \| 0.7595 \| 0.7608 \| 0.7638 \| 0.7788 \| \| https://huggingface.co/EleutherAI/polyglot-ko-5.8b \| 0.7745 \| 0.7676 \| 0.7775 \| 0.7887 \| \| https://huggingface.co/EleutherAI/polyglot-ko-12.8b \| 0.7937 \| 0.8108 \| 0.8037 \| 0.8369 \| \| Llama-2 Original 7B* \| 0.562033 \| 0.575982 \| 0.576216 \| 0.595532 \| \| Llama-2-Ko-7b 20B (10k) \| 0.738780 \| 0.762639 \| 0.780761 \| 0.797863 \| \| Llama-2-Ko-7b 40B (20k) \| 0.743630 \| 0.792716 \| 0.803746 \| 0.825944 \| Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ### HellaSwag (F1) <img src=https://user-images.githubusercontent.com/11323660/255576090-a2bfc1ae-d117-44b7-9f7b-262e41179ec1.png style="max-width: 700px; width: 100%" /> \| Model \| 0-shot \| 5-shot \| 10-shot \| 50-shot \| \| --- \| --- \| --- \| --- \| --- \| \| https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 \| 0.5243 \| 0.5272 \| 0.5166 \| 0.5352 \| \| https://huggingface.co/kakaobrain/kogpt \| 0.5590 \| 0.5833 \| 0.5828 \| 0.5907 \| \| https://huggingface.co/facebook/xglm-7.5B \| 0.5665 \| 0.5689 \| 0.5565 \| 0.5622 \| \| https://huggingface.co/EleutherAI/polyglot-ko-1.3b \| 0.5247 \| 0.5260 \| 0.5278 \| 0.5427 \| \| https://huggingface.co/EleutherAI/polyglot-ko-3.8b \| 0.5707 \| 0.5830 \| 0.5670 \| 0.5787 \| \| https://huggingface.co/EleutherAI/polyglot-ko-5.8b \| 0.5976 \| 0.5998 \| 0.5979 \| 0.6208 \| \| https://huggingface.co/EleutherAI/polyglot-ko-12.8b \| 0.5954 \| 0.6306 \| 0.6098 \| 0.6118 \| \| Llama-2 Original 7B \| 0.415390 \| 0.431382 \| 0.421342 \| 0.442003 \| \| Llama-2-Ko-7b 20B (10k) \| 0.451757 \| 0.466751 \| 0.472607 \| 0.482776 \| \| Llama-2-Ko-7b 40B (20k) \| 0.456246 \| 0.465665 \| 0.469810 \| 0.477374 \| Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ### BoolQ (F1) <img src=https://user-images.githubusercontent.com/11323660/255576343-5d847a6f-3b6a-41a7-af37-0f11940a5ea4.png style="max-width: 700px; width: 100%" /> \| Model \| 0-shot \| 5-shot \| 10-shot \| 50-shot \| \| --- \| --- \| --- \| --- \| --- \| \| https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 \| 0.3356 \| 0.4014 \| 0.3640 \| 0.3560 \| \| https://huggingface.co/kakaobrain/kogpt \| 0.4514 \| 0.5981 \| 0.5499 \| 0.5202 \| \| https://huggingface.co/facebook/xglm-7.5B \| 0.4464 \| 0.3324 \| 0.3324 \| 0.3324 \| \| https://huggingface.co/EleutherAI/polyglot-ko-1.3b \| 0.3552 \| 0.4751 \| 0.4109 \| 0.4038 \| \| https://huggingface.co/EleutherAI/polyglot-ko-3.8b \| 0.4320 \| 0.5263 \| 0.4930 \| 0.4038 \| \| https://huggingface.co/EleutherAI/polyglot-ko-5.8b \| 0.4356 \| 0.5698 \| 0.5187 \| 0.5236 \| \| https://huggingface.co/EleutherAI/polyglot-ko-12.8b \| 0.4818 \| 0.6041 \| 0.6289 \| 0.6448 \| \| Llama-2 Original 7B \| 0.352050 \| 0.563238 \| 0.474788 \| 0.419222 \| \| Llama-2-Ko-7b 20B (10k) \| 0.360656 \| 0.679743 \| 0.680109 \| 0.662152 \| \| Llama-2-Ko-7b 40B (20k) \| 0.578640 \| 0.697747 \| 0.708358 \| 0.714423 \| Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ### SentiNeg (F1) <img src=https://user-images.githubusercontent.com/11323660/255576572-b005a81d-fa4d-4709-b48a-f0fe4eed17a3.png style="max-width: 700px; width: 100%" /> \| Model \| 0-shot \| 5-shot \| 10-shot \| 50-shot \| \| --- \| --- \| --- \| --- \| --- \| \| https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 \| 0.6065 \| 0.6878 \| 0.7280 \| 0.8413 \| \| https://huggingface.co/kakaobrain/kogpt \| 0.3747 \| 0.8942 \| 0.9294 \| 0.9698 \| \| https://huggingface.co/facebook/xglm-7.5B \| 0.3578 \| 0.4471 \| 0.3964 \| 0.5271 \| \| https://huggingface.co/EleutherAI/polyglot-ko-1.3b \| 0.6790 \| 0.6257 \| 0.5514 \| 0.7851 \| \| https://huggingface.co/EleutherAI/polyglot-ko-3.8b \| 0.4858 \| 0.7950 \| 0.7320 \| 0.7851 \| \| https://huggingface.co/EleutherAI/polyglot-ko-5.8b \| 0.3394 \| 0.8841 \| 0.8808 \| 0.9521 \| \| https://huggingface.co/EleutherAI/polyglot-ko-12.8b \| 0.9117 \| 0.9015 \| 0.9345 \| 0.9723 \| \| Llama-2 Original 7B \| 0.347502 \| 0.529124 \| 0.480641 \| 0.788457 \| \| Llama-2-Ko-7b 20B (10k) \| 0.485546 \| 0.829503 \| 0.871141 \| 0.851253 \| \| Llama-2-Ko-7b 40B (20k) \| 0.459447 \| 0.761079 \| 0.727611 \| 0.936988 \| *Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ## Note for oobabooga/text-generation-webui Remove `ValueError` at `load_tokenizer` function(line 109 or near), in `modules/models.py`. ```python diff --git a/modules/models.py b/modules/models.py index 232d5fa..de5b7a0 100644 --- a/modules/models.py +++ b/modules/models.py @@ -106,7 +106,7 @@ def load_tokenizer(model_name, model): trust_remote_code=shared.args.trust_remote_code, use_fast=False ) - except ValueError: + except: tokenizer = AutoTokenizer.from_pretrained( path_to_model, trust_remote_code=shared.args.trust_remote_code, ``` Since Llama-2-Ko uses FastTokenizer provided by HF tokenizers NOT sentencepiece package, it is required to use `use_fast=True` option when initialize tokenizer. Apple Sillicon does not support BF16 computing, use CPU instead. (BF16 is supported when using NVIDIA GPU) ## Citation ``` @misc {l._junbum_2023, author = { {L. Junbum} }, title = { llama-2-ko-7b (Revision 4a9993e) }, year = 2023, url = { https://huggingface.co/beomi/llama-2-ko-7b }, doi = { 10.57967/hf/1098 }, publisher = { Hugging Face } } ``` ## Acknowledgement The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_beomi__llama-2-ko-7b) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 39.43 \| \| ARC (25-shot) \| 48.46 \| \| HellaSwag (10-shot) \| 75.28 \| \| MMLU (5-shot) \| 39.56 \| \| TruthfulQA (0-shot) \| 34.49 \| \| Winogrande (5-shot) \| 72.14 \| \| GSM8K (5-shot) \| 1.97 \| \| DROP (3-shot) \| 4.1 \|	{}	RichardErkhov/beomi_-_llama-2-ko-7b-8bits	null	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-05-03T16:02:42+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	golf2248/uj5jfvc	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:03:11+00:00
text-generation	transformers		{}	sanchit-gandhi/Mistral-1.5B-Instruct-v0.2-first-6	null	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:03:11+00:00
null	transformers	# 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]	{"library_name": "transformers", "tags": []}	mehdisebai/CodeQwen-text-to-rule	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:03:14+00:00
fill-mask	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	CLASS-MATE/BERT-MLM-multilingual-cased	null	[ "transformers", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:03:20+00:00
null	peft	<!-- 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. --> # GUE_EMP_H4-seqsight_65536_512_47M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2880 - F1 Score: 0.8934 - Accuracy: 0.8932 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.3756 \| 2.17 \| 200 \| 0.2960 \| 0.8851 \| 0.8850 \| \| 0.2905 \| 4.35 \| 400 \| 0.3002 \| 0.8839 \| 0.8836 \| \| 0.2784 \| 6.52 \| 600 \| 0.2925 \| 0.8883 \| 0.8884 \| \| 0.2756 \| 8.7 \| 800 \| 0.3025 \| 0.8826 \| 0.8823 \| \| 0.2605 \| 10.87 \| 1000 \| 0.2880 \| 0.8905 \| 0.8905 \| \| 0.2556 \| 13.04 \| 1200 \| 0.2872 \| 0.8961 \| 0.8960 \| \| 0.2488 \| 15.22 \| 1400 \| 0.2880 \| 0.8949 \| 0.8946 \| \| 0.2435 \| 17.39 \| 1600 \| 0.2970 \| 0.8908 \| 0.8905 \| \| 0.2394 \| 19.57 \| 1800 \| 0.2875 \| 0.8992 \| 0.8994 \| \| 0.2346 \| 21.74 \| 2000 \| 0.2896 \| 0.8962 \| 0.8960 \| \| 0.2279 \| 23.91 \| 2200 \| 0.3006 \| 0.8921 \| 0.8919 \| \| 0.2257 \| 26.09 \| 2400 \| 0.2989 \| 0.8874 \| 0.8871 \| \| 0.2218 \| 28.26 \| 2600 \| 0.2959 \| 0.8921 \| 0.8919 \| \| 0.2164 \| 30.43 \| 2800 \| 0.2968 \| 0.8907 \| 0.8905 \| \| 0.215 \| 32.61 \| 3000 \| 0.2993 \| 0.9015 \| 0.9014 \| \| 0.2115 \| 34.78 \| 3200 \| 0.3025 \| 0.8913 \| 0.8912 \| \| 0.2074 \| 36.96 \| 3400 \| 0.3011 \| 0.8981 \| 0.8980 \| \| 0.2052 \| 39.13 \| 3600 \| 0.3075 \| 0.8901 \| 0.8898 \| \| 0.2008 \| 41.3 \| 3800 \| 0.3079 \| 0.8934 \| 0.8932 \| \| 0.1992 \| 43.48 \| 4000 \| 0.3171 \| 0.8913 \| 0.8912 \| \| 0.1985 \| 45.65 \| 4200 \| 0.3199 \| 0.8858 \| 0.8857 \| \| 0.1912 \| 47.83 \| 4400 \| 0.3155 \| 0.8891 \| 0.8891 \| \| 0.1905 \| 50.0 \| 4600 \| 0.3100 \| 0.8940 \| 0.8939 \| \| 0.19 \| 52.17 \| 4800 \| 0.3164 \| 0.8884 \| 0.8884 \| \| 0.1915 \| 54.35 \| 5000 \| 0.3157 \| 0.8935 \| 0.8932 \| \| 0.185 \| 56.52 \| 5200 \| 0.3235 \| 0.8887 \| 0.8884 \| \| 0.1806 \| 58.7 \| 5400 \| 0.3242 \| 0.8900 \| 0.8898 \| \| 0.1809 \| 60.87 \| 5600 \| 0.3224 \| 0.8880 \| 0.8877 \| \| 0.1787 \| 63.04 \| 5800 \| 0.3286 \| 0.8866 \| 0.8864 \| \| 0.1788 \| 65.22 \| 6000 \| 0.3372 \| 0.8859 \| 0.8857 \| \| 0.1762 \| 67.39 \| 6200 \| 0.3454 \| 0.8779 \| 0.8775 \| \| 0.1732 \| 69.57 \| 6400 \| 0.3405 \| 0.8826 \| 0.8823 \| \| 0.171 \| 71.74 \| 6600 \| 0.3395 \| 0.8914 \| 0.8912 \| \| 0.1726 \| 73.91 \| 6800 \| 0.3427 \| 0.8873 \| 0.8871 \| \| 0.169 \| 76.09 \| 7000 \| 0.3593 \| 0.8820 \| 0.8816 \| \| 0.1688 \| 78.26 \| 7200 \| 0.3436 \| 0.8846 \| 0.8843 \| \| 0.1674 \| 80.43 \| 7400 \| 0.3411 \| 0.8900 \| 0.8898 \| \| 0.1641 \| 82.61 \| 7600 \| 0.3489 \| 0.8892 \| 0.8891 \| \| 0.1646 \| 84.78 \| 7800 \| 0.3523 \| 0.8887 \| 0.8884 \| \| 0.1646 \| 86.96 \| 8000 \| 0.3448 \| 0.8899 \| 0.8898 \| \| 0.1629 \| 89.13 \| 8200 \| 0.3592 \| 0.8846 \| 0.8843 \| \| 0.1632 \| 91.3 \| 8400 \| 0.3495 \| 0.8865 \| 0.8864 \| \| 0.1578 \| 93.48 \| 8600 \| 0.3543 \| 0.8893 \| 0.8891 \| \| 0.1598 \| 95.65 \| 8800 \| 0.3551 \| 0.8878 \| 0.8877 \| \| 0.1612 \| 97.83 \| 9000 \| 0.3517 \| 0.8886 \| 0.8884 \| \| 0.163 \| 100.0 \| 9200 \| 0.3541 \| 0.8846 \| 0.8843 \| \| 0.163 \| 102.17 \| 9400 \| 0.3523 \| 0.8886 \| 0.8884 \| \| 0.1584 \| 104.35 \| 9600 \| 0.3553 \| 0.8880 \| 0.8877 \| \| 0.1561 \| 106.52 \| 9800 \| 0.3557 \| 0.8865 \| 0.8864 \| \| 0.1564 \| 108.7 \| 10000 \| 0.3554 \| 0.8886 \| 0.8884 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H4-seqsight_65536_512_47M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H4-seqsight_65536_512_47M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]	null	2024-05-03T16:03:23+00:00
text-generation	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 0.0001_withdpo_4iters_bs256_5102lr_misit_iter_2 This model is a fine-tuned version of [ShenaoZ/0.0001_withdpo_4iters_bs256_5102lr_misit_iter_1](https://huggingface.co/ShenaoZ/0.0001_withdpo_4iters_bs256_5102lr_misit_iter_1) on the updated and the original datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2	{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZ/0.0001_withdpo_4iters_bs256_5102lr_misit_iter_1", "model-index": [{"name": "0.0001_withdpo_4iters_bs256_5102lr_misit_iter_2", "results": []}]}	ShenaoZ/0.0001_withdpo_4iters_bs256_5102lr_misit_iter_2	null	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZ/0.0001_withdpo_4iters_bs256_5102lr_misit_iter_1", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:03:29+00:00
null	null		{}	optimum-internal-testing/optimum-neuron-cache-for-testing-ummro	null	[ "region:us" ]	null	2024-05-03T16:03:38+00:00
null	transformers	# Uploaded model - Developed by: animaRegem - License: apache-2.0 - Finetuned from model : unsloth/gemma-2b-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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl"], "base_model": "unsloth/gemma-2b-it-bnb-4bit"}	animaRegem/gemma-2b-it-lora-0_1-malayalam	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-2b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:04:03+00:00
null	peft	<!-- 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. --> # GUE_EMP_H4-seqsight_65536_512_47M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2723 - F1 Score: 0.8953 - Accuracy: 0.8953 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.3593 \| 2.17 \| 200 \| 0.2935 \| 0.8873 \| 0.8871 \| \| 0.2808 \| 4.35 \| 400 \| 0.2879 \| 0.8960 \| 0.8960 \| \| 0.2632 \| 6.52 \| 600 \| 0.2894 \| 0.8888 \| 0.8891 \| \| 0.252 \| 8.7 \| 800 \| 0.2906 \| 0.8888 \| 0.8884 \| \| 0.2352 \| 10.87 \| 1000 \| 0.2793 \| 0.9110 \| 0.9110 \| \| 0.2293 \| 13.04 \| 1200 \| 0.2952 \| 0.8901 \| 0.8898 \| \| 0.2172 \| 15.22 \| 1400 \| 0.2890 \| 0.8948 \| 0.8946 \| \| 0.2113 \| 17.39 \| 1600 \| 0.3144 \| 0.8909 \| 0.8905 \| \| 0.2004 \| 19.57 \| 1800 \| 0.3055 \| 0.8945 \| 0.8946 \| \| 0.1942 \| 21.74 \| 2000 \| 0.3162 \| 0.8907 \| 0.8905 \| \| 0.1835 \| 23.91 \| 2200 \| 0.3497 \| 0.8696 \| 0.8693 \| \| 0.1786 \| 26.09 \| 2400 \| 0.3230 \| 0.8819 \| 0.8816 \| \| 0.1698 \| 28.26 \| 2600 \| 0.3381 \| 0.8858 \| 0.8857 \| \| 0.1611 \| 30.43 \| 2800 \| 0.3506 \| 0.8852 \| 0.8850 \| \| 0.1532 \| 32.61 \| 3000 \| 0.3809 \| 0.8799 \| 0.8802 \| \| 0.1489 \| 34.78 \| 3200 \| 0.3671 \| 0.8791 \| 0.8789 \| \| 0.1385 \| 36.96 \| 3400 \| 0.3798 \| 0.8786 \| 0.8782 \| \| 0.1347 \| 39.13 \| 3600 \| 0.3871 \| 0.8758 \| 0.8754 \| \| 0.1278 \| 41.3 \| 3800 \| 0.4102 \| 0.8761 \| 0.8761 \| \| 0.1241 \| 43.48 \| 4000 \| 0.4262 \| 0.8790 \| 0.8789 \| \| 0.1173 \| 45.65 \| 4200 \| 0.4611 \| 0.8715 \| 0.8720 \| \| 0.1122 \| 47.83 \| 4400 \| 0.4375 \| 0.8797 \| 0.8795 \| \| 0.11 \| 50.0 \| 4600 \| 0.4266 \| 0.8786 \| 0.8789 \| \| 0.1039 \| 52.17 \| 4800 \| 0.4801 \| 0.8736 \| 0.8734 \| \| 0.1057 \| 54.35 \| 5000 \| 0.4509 \| 0.8775 \| 0.8775 \| \| 0.0953 \| 56.52 \| 5200 \| 0.4760 \| 0.8717 \| 0.8713 \| \| 0.0926 \| 58.7 \| 5400 \| 0.5029 \| 0.8683 \| 0.8679 \| \| 0.0903 \| 60.87 \| 5600 \| 0.4814 \| 0.8722 \| 0.8720 \| \| 0.0863 \| 63.04 \| 5800 \| 0.5023 \| 0.8729 \| 0.8727 \| \| 0.0856 \| 65.22 \| 6000 \| 0.5227 \| 0.8670 \| 0.8665 \| \| 0.0833 \| 67.39 \| 6200 \| 0.5262 \| 0.8677 \| 0.8672 \| \| 0.0783 \| 69.57 \| 6400 \| 0.5150 \| 0.8695 \| 0.8693 \| \| 0.0761 \| 71.74 \| 6600 \| 0.5296 \| 0.8734 \| 0.8734 \| \| 0.0727 \| 73.91 \| 6800 \| 0.5547 \| 0.8704 \| 0.8700 \| \| 0.0705 \| 76.09 \| 7000 \| 0.5961 \| 0.8663 \| 0.8658 \| \| 0.0718 \| 78.26 \| 7200 \| 0.5728 \| 0.8608 \| 0.8604 \| \| 0.0666 \| 80.43 \| 7400 \| 0.5711 \| 0.8695 \| 0.8693 \| \| 0.0657 \| 82.61 \| 7600 \| 0.5681 \| 0.8652 \| 0.8652 \| \| 0.0638 \| 84.78 \| 7800 \| 0.5880 \| 0.8697 \| 0.8693 \| \| 0.0616 \| 86.96 \| 8000 \| 0.5926 \| 0.8695 \| 0.8693 \| \| 0.0638 \| 89.13 \| 8200 \| 0.5964 \| 0.8641 \| 0.8638 \| \| 0.0638 \| 91.3 \| 8400 \| 0.5819 \| 0.8708 \| 0.8706 \| \| 0.0594 \| 93.48 \| 8600 \| 0.5993 \| 0.8680 \| 0.8679 \| \| 0.0574 \| 95.65 \| 8800 \| 0.5968 \| 0.8675 \| 0.8672 \| \| 0.0586 \| 97.83 \| 9000 \| 0.5952 \| 0.8640 \| 0.8638 \| \| 0.0584 \| 100.0 \| 9200 \| 0.6028 \| 0.8614 \| 0.8611 \| \| 0.0583 \| 102.17 \| 9400 \| 0.6088 \| 0.8640 \| 0.8638 \| \| 0.0575 \| 104.35 \| 9600 \| 0.6062 \| 0.8682 \| 0.8679 \| \| 0.0576 \| 106.52 \| 9800 \| 0.6077 \| 0.8668 \| 0.8665 \| \| 0.0553 \| 108.7 \| 10000 \| 0.6073 \| 0.8667 \| 0.8665 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H4-seqsight_65536_512_47M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H4-seqsight_65536_512_47M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]	null	2024-05-03T16:04:04+00:00
text-generation	transformers	# Llama-3-8B-Irene-v0.1 <img src="https://huggingface.co/Virt-io/Llama-3-8B-Irene-v0.1/resolve/main/Irene.png"> [SillyTavern Presest](https://huggingface.co/Virt-io/SillyTavern-Presets) 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 [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [NeverSleep/Llama-3-Lumimaid-8B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) as a base. ### Models Merged The following models were included in the merge: * [ResplendentAI/SOVL_Llama3_8B](https://huggingface.co/ResplendentAI/SOVL_Llama3_8B) * [ChaoticNeutrals/Poppy_Porpoise-0.72-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-0.72-L3-8B) * [cgato/L3-TheSpice-8b-v0.8.3](https://huggingface.co/cgato/L3-TheSpice-8b-v0.8.3) * [Endevor/InfinityRP-v2-8B](https://huggingface.co/Endevor/InfinityRP-v2-8B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: NeverSleep/Llama-3-Lumimaid-8B-v0.1 - model: ResplendentAI/SOVL_Llama3_8B parameters: density: 0.58 weight: [0.15, 0.1, 0.1, 0.33] - model: Endevor/InfinityRP-v2-8B parameters: density: 0.58 weight: [0.15, 0.1, 0.1, 0.25] - model: ChaoticNeutrals/Poppy_Porpoise-0.72-L3-8B parameters: density: 0.66 weight: [0.20, 0.35, 0.25, 0.25] - model: cgato/L3-TheSpice-8b-v0.8.3 parameters: density: 0.66 weight: [0.20, 0.15, 0.15, 0.25] merge_method: dare_ties base_model: NeverSleep/Llama-3-Lumimaid-8B-v0.1 parameters: normalize: true int8_mask: true dtype: bfloat16 ```	{"license": "other", "library_name": "transformers", "tags": ["mergekit", "merge", "facebook", "meta", "pytorch", "llama", "llama-3"], "base_model": ["ResplendentAI/SOVL_Llama3_8B", "ChaoticNeutrals/Poppy_Porpoise-0.72-L3-8B", "NeverSleep/Llama-3-Lumimaid-8B-v0.1", "cgato/L3-TheSpice-8b-v0.8.3", "Endevor/InfinityRP-v2-8B"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE"}	Virt-io/Llama-3-8B-Irene-v0.1	null	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "facebook", "meta", "pytorch", "llama-3", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:ResplendentAI/SOVL_Llama3_8B", "base_model:ChaoticNeutrals/Poppy_Porpoise-0.72-L3-8B", "base_model:NeverSleep/Llama-3-Lumimaid-8B-v0.1", "base_model:cgato/L3-TheSpice-8b-v0.8.3", "base_model:Endevor/InfinityRP-v2-8B", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:04:05+00:00
null	transformers	# 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]	{"library_name": "transformers", "tags": ["unsloth"]}	animaRegem/gemma-2b-it-lora-0_1-malayalam-tokenizer	null	[ "transformers", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:04:11+00:00
text-generation	transformers		{}	sajaw/Verifiedllama-2-7b-Generated10K_3epochs	null	[ "transformers", "pytorch", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:04:41+00:00
null	diffusers		{}	xkronosx/train_prior_ddpm-celeb-128	null	[ "diffusers", "safetensors", "diffusers:DDPMPipeline", "region:us" ]	null	2024-05-03T16:05:08+00:00
text-generation	transformers	# Gille/StrangeMerges_16-7B-slerp AWQ - Model creator: [Gille](https://huggingface.co/Gille) - Original model: [StrangeMerges_16-7B-slerp](https://huggingface.co/Gille/StrangeMerges_16-7B-slerp) ## How to use ### Install the necessary packages ```bash pip install --upgrade autoawq autoawq-kernels ``` ### Example Python code ```python from awq import AutoAWQForCausalLM from transformers import AutoTokenizer, TextStreamer model_path = "solidrust/StrangeMerges_16-7B-slerp-AWQ" system_message = "You are StrangeMerges_16-7B-slerp, incarnated as a powerful AI. You were created by Gille." # Load model model = AutoAWQForCausalLM.from_quantized(model_path, fuse_layers=True) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) # Convert prompt to tokens prompt_template = """\ <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant""" prompt = "You're standing on the surface of the Earth. "\ "You walk one mile south, one mile west and one mile north. "\ "You end up exactly where you started. Where are you?" tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt), return_tensors='pt').input_ids.cuda() # Generate output generation_output = model.generate(tokens, streamer=streamer, max_new_tokens=512) ``` ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code	{"library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"}	solidrust/StrangeMerges_16-7B-slerp-AWQ	null	[ "transformers", "safetensors", "mistral", "text-generation", "4-bit", "AWQ", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:05:18+00:00
null	null		{}	largenumber/Test3	null	[ "region:us" ]	null	2024-05-03T16:05:58+00:00
null	peft	<!-- 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. --> # lora_fine_tuned_copa This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6918 - Accuracy: 0.46 - F1: 0.4570 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.7088 \| 1.0 \| 50 \| 0.6921 \| 0.48 \| 0.48 \| \| 0.7024 \| 2.0 \| 100 \| 0.6922 \| 0.49 \| 0.4894 \| \| 0.6993 \| 3.0 \| 150 \| 0.6921 \| 0.46 \| 0.4587 \| \| 0.7005 \| 4.0 \| 200 \| 0.6920 \| 0.48 \| 0.4788 \| \| 0.6989 \| 5.0 \| 250 \| 0.6919 \| 0.47 \| 0.4679 \| \| 0.7018 \| 6.0 \| 300 \| 0.6919 \| 0.46 \| 0.4570 \| \| 0.6943 \| 7.0 \| 350 \| 0.6919 \| 0.46 \| 0.4570 \| \| 0.6943 \| 8.0 \| 400 \| 0.6918 \| 0.46 \| 0.4570 \| ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "google-bert/bert-base-uncased", "model-index": [{"name": "lora_fine_tuned_copa", "results": []}]}	lenatr99/lora_fine_tuned_copa	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "license:apache-2.0", "region:us" ]	null	2024-05-03T16:08:08+00:00
text2text-generation	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3247 - Rouge1: 0.1978 - Rouge2: 0.099 - Rougel: 0.1684 - Rougelsum: 0.1682 - Gen Len: 19.0 ## 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: 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: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 62 \| 2.3922 \| 0.1587 \| 0.0648 \| 0.1323 \| 0.1323 \| 19.0 \| \| No log \| 2.0 \| 124 \| 2.3515 \| 0.1894 \| 0.0903 \| 0.1611 \| 0.1607 \| 19.0 \| \| No log \| 3.0 \| 186 \| 2.3310 \| 0.1968 \| 0.0983 \| 0.1679 \| 0.1675 \| 19.0 \| \| No log \| 4.0 \| 248 \| 2.3247 \| 0.1978 \| 0.099 \| 0.1684 \| 0.1682 \| 19.0 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "t5-small", "model-index": [{"name": "my_awesome_billsum_model", "results": []}]}	ngthanhlong089/my_awesome_billsum_model	null	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:08:12+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	golf2248/art9e97	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:08:35+00:00
null	null		{}	ntmerl/jojoDiffusion	null	[ "region:us" ]	null	2024-05-03T16:09:42+00:00
null	null		{}	descansodj/Phi-3-mini-128k-instruct-dpo	null	[ "region:us" ]	null	2024-05-03T16:10:14+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	cilantro9246/mkon0fy	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:11:04+00:00
question-answering	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta_mrqa_v2 This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3580 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.1229 \| 1.0 \| 967 \| 1.3499 \| \| 1.1979 \| 2.0 \| 1934 \| 1.3192 \| \| 0.9852 \| 3.0 \| 2901 \| 1.3580 \| ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "FacebookAI/roberta-base", "model-index": [{"name": "roberta_mrqa_v2", "results": []}]}	enriquesaou/roberta_mrqa_v2	null	[ "transformers", "tensorboard", "safetensors", "roberta", "question-answering", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:11:49+00:00
null	transformers	# 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]	{"library_name": "transformers", "tags": []}	jeongmi/solar_text	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:12:37+00:00
null	null		{}	vup2p/model_sn25_45	null	[ "region:us" ]	null	2024-05-03T16:12:48+00:00
text2text-generation	transformers		{}	mika5883/pretrain_rugec	null	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:12:51+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3-seqsight_65536_512_47M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset. It achieves the following results on the evaluation set: - Loss: 0.3391 - F1 Score: 0.8676 - Accuracy: 0.8677 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.4898 \| 2.13 \| 200 \| 0.4183 \| 0.8196 \| 0.8196 \| \| 0.3827 \| 4.26 \| 400 \| 0.4199 \| 0.8255 \| 0.8263 \| \| 0.3609 \| 6.38 \| 600 \| 0.4122 \| 0.8241 \| 0.8250 \| \| 0.3487 \| 8.51 \| 800 \| 0.3864 \| 0.8301 \| 0.8303 \| \| 0.3376 \| 10.64 \| 1000 \| 0.3927 \| 0.8282 \| 0.8290 \| \| 0.3246 \| 12.77 \| 1200 \| 0.3862 \| 0.8331 \| 0.8337 \| \| 0.3197 \| 14.89 \| 1400 \| 0.3668 \| 0.8402 \| 0.8403 \| \| 0.3126 \| 17.02 \| 1600 \| 0.3676 \| 0.8407 \| 0.8410 \| \| 0.3023 \| 19.15 \| 1800 \| 0.4137 \| 0.8265 \| 0.8277 \| \| 0.299 \| 21.28 \| 2000 \| 0.3796 \| 0.8378 \| 0.8383 \| \| 0.2968 \| 23.4 \| 2200 \| 0.3519 \| 0.8523 \| 0.8524 \| \| 0.2882 \| 25.53 \| 2400 \| 0.3784 \| 0.8386 \| 0.8390 \| \| 0.2879 \| 27.66 \| 2600 \| 0.3634 \| 0.8487 \| 0.8490 \| \| 0.2888 \| 29.79 \| 2800 \| 0.3759 \| 0.8458 \| 0.8464 \| \| 0.2845 \| 31.91 \| 3000 \| 0.3722 \| 0.8452 \| 0.8457 \| \| 0.2801 \| 34.04 \| 3200 \| 0.3733 \| 0.8446 \| 0.8450 \| \| 0.2778 \| 36.17 \| 3400 \| 0.3864 \| 0.8438 \| 0.8444 \| \| 0.2752 \| 38.3 \| 3600 \| 0.3656 \| 0.8568 \| 0.8570 \| \| 0.2773 \| 40.43 \| 3800 \| 0.3683 \| 0.8527 \| 0.8530 \| \| 0.2727 \| 42.55 \| 4000 \| 0.3568 \| 0.8576 \| 0.8577 \| \| 0.2711 \| 44.68 \| 4200 \| 0.3499 \| 0.8596 \| 0.8597 \| \| 0.2708 \| 46.81 \| 4400 \| 0.3751 \| 0.8479 \| 0.8484 \| \| 0.2718 \| 48.94 \| 4600 \| 0.3834 \| 0.8463 \| 0.8470 \| \| 0.2679 \| 51.06 \| 4800 \| 0.3602 \| 0.8568 \| 0.8570 \| \| 0.2659 \| 53.19 \| 5000 \| 0.3933 \| 0.8455 \| 0.8464 \| \| 0.2692 \| 55.32 \| 5200 \| 0.3556 \| 0.8555 \| 0.8557 \| \| 0.2665 \| 57.45 \| 5400 \| 0.3706 \| 0.8508 \| 0.8510 \| \| 0.2626 \| 59.57 \| 5600 \| 0.3638 \| 0.8568 \| 0.8570 \| \| 0.2672 \| 61.7 \| 5800 \| 0.3498 \| 0.8589 \| 0.8591 \| \| 0.2595 \| 63.83 \| 6000 \| 0.3725 \| 0.8546 \| 0.8550 \| \| 0.2637 \| 65.96 \| 6200 \| 0.3743 \| 0.8580 \| 0.8584 \| \| 0.2606 \| 68.09 \| 6400 \| 0.3801 \| 0.8559 \| 0.8564 \| \| 0.2586 \| 70.21 \| 6600 \| 0.3770 \| 0.8546 \| 0.8550 \| \| 0.2613 \| 72.34 \| 6800 \| 0.3648 \| 0.8547 \| 0.8550 \| \| 0.2608 \| 74.47 \| 7000 \| 0.3994 \| 0.8434 \| 0.8444 \| \| 0.2586 \| 76.6 \| 7200 \| 0.3739 \| 0.8547 \| 0.8550 \| \| 0.2612 \| 78.72 \| 7400 \| 0.3657 \| 0.8567 \| 0.8570 \| \| 0.2575 \| 80.85 \| 7600 \| 0.3624 \| 0.8554 \| 0.8557 \| \| 0.2573 \| 82.98 \| 7800 \| 0.3695 \| 0.8573 \| 0.8577 \| \| 0.2579 \| 85.11 \| 8000 \| 0.3598 \| 0.8574 \| 0.8577 \| \| 0.2579 \| 87.23 \| 8200 \| 0.3569 \| 0.8595 \| 0.8597 \| \| 0.253 \| 89.36 \| 8400 \| 0.3685 \| 0.8534 \| 0.8537 \| \| 0.2559 \| 91.49 \| 8600 \| 0.3750 \| 0.8547 \| 0.8550 \| \| 0.2566 \| 93.62 \| 8800 \| 0.3699 \| 0.8553 \| 0.8557 \| \| 0.2546 \| 95.74 \| 9000 \| 0.3729 \| 0.8560 \| 0.8564 \| \| 0.2524 \| 97.87 \| 9200 \| 0.3649 \| 0.8567 \| 0.8570 \| \| 0.2533 \| 100.0 \| 9400 \| 0.3632 \| 0.8568 \| 0.8570 \| \| 0.2557 \| 102.13 \| 9600 \| 0.3663 \| 0.8573 \| 0.8577 \| \| 0.2527 \| 104.26 \| 9800 \| 0.3654 \| 0.8567 \| 0.8570 \| \| 0.2531 \| 106.38 \| 10000 \| 0.3667 \| 0.8573 \| 0.8577 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3-seqsight_65536_512_47M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3-seqsight_65536_512_47M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]	null	2024-05-03T16:12:53+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3-seqsight_65536_512_47M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset. It achieves the following results on the evaluation set: - Loss: 0.3253 - F1 Score: 0.8696 - Accuracy: 0.8697 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.455 \| 2.13 \| 200 \| 0.3926 \| 0.8357 \| 0.8357 \| \| 0.3495 \| 4.26 \| 400 \| 0.4119 \| 0.8128 \| 0.8143 \| \| 0.3143 \| 6.38 \| 600 \| 0.4219 \| 0.8202 \| 0.8216 \| \| 0.2963 \| 8.51 \| 800 \| 0.3801 \| 0.8343 \| 0.8350 \| \| 0.2842 \| 10.64 \| 1000 \| 0.3831 \| 0.8389 \| 0.8397 \| \| 0.2751 \| 12.77 \| 1200 \| 0.3779 \| 0.8493 \| 0.8497 \| \| 0.2711 \| 14.89 \| 1400 \| 0.3792 \| 0.8452 \| 0.8457 \| \| 0.2664 \| 17.02 \| 1600 \| 0.3620 \| 0.8549 \| 0.8550 \| \| 0.2571 \| 19.15 \| 1800 \| 0.4008 \| 0.8409 \| 0.8417 \| \| 0.2527 \| 21.28 \| 2000 \| 0.3837 \| 0.8473 \| 0.8477 \| \| 0.2544 \| 23.4 \| 2200 \| 0.3505 \| 0.8595 \| 0.8597 \| \| 0.2405 \| 25.53 \| 2400 \| 0.4114 \| 0.8446 \| 0.8450 \| \| 0.247 \| 27.66 \| 2600 \| 0.3592 \| 0.8628 \| 0.8631 \| \| 0.2449 \| 29.79 \| 2800 \| 0.3576 \| 0.8554 \| 0.8557 \| \| 0.2392 \| 31.91 \| 3000 \| 0.3568 \| 0.8624 \| 0.8624 \| \| 0.2344 \| 34.04 \| 3200 \| 0.3470 \| 0.8670 \| 0.8671 \| \| 0.2327 \| 36.17 \| 3400 \| 0.3992 \| 0.8511 \| 0.8517 \| \| 0.2284 \| 38.3 \| 3600 \| 0.3705 \| 0.8622 \| 0.8624 \| \| 0.2305 \| 40.43 \| 3800 \| 0.3835 \| 0.8572 \| 0.8577 \| \| 0.2239 \| 42.55 \| 4000 \| 0.3739 \| 0.8556 \| 0.8557 \| \| 0.2211 \| 44.68 \| 4200 \| 0.3594 \| 0.8651 \| 0.8651 \| \| 0.2194 \| 46.81 \| 4400 \| 0.3890 \| 0.8541 \| 0.8544 \| \| 0.2202 \| 48.94 \| 4600 \| 0.3940 \| 0.8511 \| 0.8517 \| \| 0.215 \| 51.06 \| 4800 \| 0.3617 \| 0.8610 \| 0.8611 \| \| 0.2124 \| 53.19 \| 5000 \| 0.4273 \| 0.8477 \| 0.8484 \| \| 0.2171 \| 55.32 \| 5200 \| 0.3822 \| 0.8595 \| 0.8597 \| \| 0.2099 \| 57.45 \| 5400 \| 0.3963 \| 0.8508 \| 0.8510 \| \| 0.2073 \| 59.57 \| 5600 \| 0.3901 \| 0.8549 \| 0.8550 \| \| 0.2075 \| 61.7 \| 5800 \| 0.3635 \| 0.8651 \| 0.8651 \| \| 0.2038 \| 63.83 \| 6000 \| 0.4093 \| 0.8520 \| 0.8524 \| \| 0.2061 \| 65.96 \| 6200 \| 0.4333 \| 0.8450 \| 0.8457 \| \| 0.2036 \| 68.09 \| 6400 \| 0.4143 \| 0.8540 \| 0.8544 \| \| 0.2006 \| 70.21 \| 6600 \| 0.4012 \| 0.8589 \| 0.8591 \| \| 0.2009 \| 72.34 \| 6800 \| 0.3996 \| 0.8588 \| 0.8591 \| \| 0.1995 \| 74.47 \| 7000 \| 0.4453 \| 0.8435 \| 0.8444 \| \| 0.1969 \| 76.6 \| 7200 \| 0.3989 \| 0.8541 \| 0.8544 \| \| 0.2014 \| 78.72 \| 7400 \| 0.3923 \| 0.8543 \| 0.8544 \| \| 0.199 \| 80.85 \| 7600 \| 0.4155 \| 0.8495 \| 0.8497 \| \| 0.1962 \| 82.98 \| 7800 \| 0.4144 \| 0.8549 \| 0.8550 \| \| 0.1938 \| 85.11 \| 8000 \| 0.3919 \| 0.8589 \| 0.8591 \| \| 0.1919 \| 87.23 \| 8200 \| 0.4019 \| 0.8523 \| 0.8524 \| \| 0.1905 \| 89.36 \| 8400 \| 0.4164 \| 0.8530 \| 0.8530 \| \| 0.1898 \| 91.49 \| 8600 \| 0.4227 \| 0.8541 \| 0.8544 \| \| 0.1908 \| 93.62 \| 8800 \| 0.4182 \| 0.8548 \| 0.8550 \| \| 0.1906 \| 95.74 \| 9000 \| 0.4258 \| 0.8514 \| 0.8517 \| \| 0.188 \| 97.87 \| 9200 \| 0.4151 \| 0.8555 \| 0.8557 \| \| 0.1886 \| 100.0 \| 9400 \| 0.4124 \| 0.8543 \| 0.8544 \| \| 0.1891 \| 102.13 \| 9600 \| 0.4163 \| 0.8522 \| 0.8524 \| \| 0.1867 \| 104.26 \| 9800 \| 0.4136 \| 0.8536 \| 0.8537 \| \| 0.1866 \| 106.38 \| 10000 \| 0.4139 \| 0.8542 \| 0.8544 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3-seqsight_65536_512_47M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3-seqsight_65536_512_47M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]	null	2024-05-03T16:12:54+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3-seqsight_65536_512_47M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset. It achieves the following results on the evaluation set: - Loss: 0.3472 - F1 Score: 0.8683 - Accuracy: 0.8684 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.4364 \| 2.13 \| 200 \| 0.3952 \| 0.8232 \| 0.8236 \| \| 0.3132 \| 4.26 \| 400 \| 0.3627 \| 0.8461 \| 0.8464 \| \| 0.2868 \| 6.38 \| 600 \| 0.4096 \| 0.8341 \| 0.8350 \| \| 0.275 \| 8.51 \| 800 \| 0.3467 \| 0.8561 \| 0.8564 \| \| 0.2648 \| 10.64 \| 1000 \| 0.3499 \| 0.8561 \| 0.8564 \| \| 0.2538 \| 12.77 \| 1200 \| 0.3487 \| 0.8589 \| 0.8591 \| \| 0.2482 \| 14.89 \| 1400 \| 0.4163 \| 0.8368 \| 0.8377 \| \| 0.2415 \| 17.02 \| 1600 \| 0.3507 \| 0.8671 \| 0.8671 \| \| 0.2295 \| 19.15 \| 1800 \| 0.3938 \| 0.8472 \| 0.8477 \| \| 0.2222 \| 21.28 \| 2000 \| 0.3751 \| 0.8637 \| 0.8637 \| \| 0.2213 \| 23.4 \| 2200 \| 0.3761 \| 0.8635 \| 0.8637 \| \| 0.2024 \| 25.53 \| 2400 \| 0.4104 \| 0.8596 \| 0.8597 \| \| 0.2089 \| 27.66 \| 2600 \| 0.4076 \| 0.8607 \| 0.8611 \| \| 0.198 \| 29.79 \| 2800 \| 0.4025 \| 0.8620 \| 0.8624 \| \| 0.1919 \| 31.91 \| 3000 \| 0.4010 \| 0.8570 \| 0.8570 \| \| 0.1828 \| 34.04 \| 3200 \| 0.3662 \| 0.8677 \| 0.8677 \| \| 0.1758 \| 36.17 \| 3400 \| 0.4293 \| 0.8629 \| 0.8631 \| \| 0.1685 \| 38.3 \| 3600 \| 0.4412 \| 0.8574 \| 0.8577 \| \| 0.1667 \| 40.43 \| 3800 \| 0.4562 \| 0.8585 \| 0.8591 \| \| 0.1578 \| 42.55 \| 4000 \| 0.4707 \| 0.8549 \| 0.8550 \| \| 0.1513 \| 44.68 \| 4200 \| 0.4599 \| 0.8575 \| 0.8577 \| \| 0.1463 \| 46.81 \| 4400 \| 0.4876 \| 0.8582 \| 0.8584 \| \| 0.1431 \| 48.94 \| 4600 \| 0.5163 \| 0.8484 \| 0.8490 \| \| 0.1349 \| 51.06 \| 4800 \| 0.4653 \| 0.8635 \| 0.8637 \| \| 0.1296 \| 53.19 \| 5000 \| 0.5254 \| 0.8593 \| 0.8597 \| \| 0.1278 \| 55.32 \| 5200 \| 0.5322 \| 0.8526 \| 0.8530 \| \| 0.1199 \| 57.45 \| 5400 \| 0.5515 \| 0.8533 \| 0.8537 \| \| 0.117 \| 59.57 \| 5600 \| 0.5362 \| 0.8581 \| 0.8584 \| \| 0.1133 \| 61.7 \| 5800 \| 0.4982 \| 0.8569 \| 0.8570 \| \| 0.1118 \| 63.83 \| 6000 \| 0.5740 \| 0.8453 \| 0.8457 \| \| 0.11 \| 65.96 \| 6200 \| 0.6111 \| 0.8408 \| 0.8417 \| \| 0.1016 \| 68.09 \| 6400 \| 0.6034 \| 0.8464 \| 0.8470 \| \| 0.1013 \| 70.21 \| 6600 \| 0.5935 \| 0.8506 \| 0.8510 \| \| 0.0944 \| 72.34 \| 6800 \| 0.5933 \| 0.8560 \| 0.8564 \| \| 0.0944 \| 74.47 \| 7000 \| 0.6320 \| 0.8443 \| 0.8450 \| \| 0.09 \| 76.6 \| 7200 \| 0.6099 \| 0.8539 \| 0.8544 \| \| 0.0905 \| 78.72 \| 7400 \| 0.6381 \| 0.8525 \| 0.8530 \| \| 0.093 \| 80.85 \| 7600 \| 0.6642 \| 0.8466 \| 0.8470 \| \| 0.0888 \| 82.98 \| 7800 \| 0.6228 \| 0.8553 \| 0.8557 \| \| 0.0854 \| 85.11 \| 8000 \| 0.6298 \| 0.8526 \| 0.8530 \| \| 0.0839 \| 87.23 \| 8200 \| 0.6514 \| 0.8498 \| 0.8504 \| \| 0.0789 \| 89.36 \| 8400 \| 0.6437 \| 0.8515 \| 0.8517 \| \| 0.0796 \| 91.49 \| 8600 \| 0.6850 \| 0.8465 \| 0.8470 \| \| 0.0812 \| 93.62 \| 8800 \| 0.6603 \| 0.8466 \| 0.8470 \| \| 0.0816 \| 95.74 \| 9000 \| 0.6931 \| 0.8450 \| 0.8457 \| \| 0.0772 \| 97.87 \| 9200 \| 0.6764 \| 0.8478 \| 0.8484 \| \| 0.075 \| 100.0 \| 9400 \| 0.6582 \| 0.8500 \| 0.8504 \| \| 0.0772 \| 102.13 \| 9600 \| 0.6674 \| 0.8493 \| 0.8497 \| \| 0.0715 \| 104.26 \| 9800 \| 0.6791 \| 0.8452 \| 0.8457 \| \| 0.0758 \| 106.38 \| 10000 \| 0.6793 \| 0.8465 \| 0.8470 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3-seqsight_65536_512_47M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3-seqsight_65536_512_47M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]	null	2024-05-03T16:12:56+00:00
null	peft	<!-- 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. --> # GUE_EMP_H4ac-seqsight_65536_512_47M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H4ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.5702 - F1 Score: 0.7087 - Accuracy: 0.7085 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6417 \| 0.93 \| 200 \| 0.5973 \| 0.6815 \| 0.6812 \| \| 0.6039 \| 1.87 \| 400 \| 0.6004 \| 0.6859 \| 0.6871 \| \| 0.5916 \| 2.8 \| 600 \| 0.5816 \| 0.7040 \| 0.7038 \| \| 0.5863 \| 3.74 \| 800 \| 0.5817 \| 0.7044 \| 0.7041 \| \| 0.5795 \| 4.67 \| 1000 \| 0.5882 \| 0.7058 \| 0.7062 \| \| 0.5751 \| 5.61 \| 1200 \| 0.5956 \| 0.7051 \| 0.7067 \| \| 0.5732 \| 6.54 \| 1400 \| 0.5767 \| 0.7128 \| 0.7126 \| \| 0.5653 \| 7.48 \| 1600 \| 0.5786 \| 0.7123 \| 0.7120 \| \| 0.5723 \| 8.41 \| 1800 \| 0.5774 \| 0.7119 \| 0.7117 \| \| 0.5682 \| 9.35 \| 2000 \| 0.5854 \| 0.7109 \| 0.7117 \| \| 0.5614 \| 10.28 \| 2200 \| 0.5768 \| 0.7124 \| 0.7123 \| \| 0.5653 \| 11.21 \| 2400 \| 0.5738 \| 0.7158 \| 0.7158 \| \| 0.5605 \| 12.15 \| 2600 \| 0.5763 \| 0.7136 \| 0.7138 \| \| 0.559 \| 13.08 \| 2800 \| 0.5887 \| 0.7114 \| 0.7126 \| \| 0.5598 \| 14.02 \| 3000 \| 0.5760 \| 0.7146 \| 0.7150 \| \| 0.5565 \| 14.95 \| 3200 \| 0.5703 \| 0.7176 \| 0.7176 \| \| 0.5541 \| 15.89 \| 3400 \| 0.5891 \| 0.7101 \| 0.7120 \| \| 0.552 \| 16.82 \| 3600 \| 0.5692 \| 0.7192 \| 0.7191 \| \| 0.5579 \| 17.76 \| 3800 \| 0.5672 \| 0.7212 \| 0.7211 \| \| 0.5528 \| 18.69 \| 4000 \| 0.5698 \| 0.7187 \| 0.7188 \| \| 0.5492 \| 19.63 \| 4200 \| 0.5783 \| 0.7161 \| 0.7170 \| \| 0.5525 \| 20.56 \| 4400 \| 0.5653 \| 0.7226 \| 0.7226 \| \| 0.5496 \| 21.5 \| 4600 \| 0.5951 \| 0.7070 \| 0.7103 \| \| 0.5495 \| 22.43 \| 4800 \| 0.5678 \| 0.7221 \| 0.7223 \| \| 0.5521 \| 23.36 \| 5000 \| 0.5792 \| 0.7182 \| 0.7196 \| \| 0.5458 \| 24.3 \| 5200 \| 0.5668 \| 0.7237 \| 0.7238 \| \| 0.5497 \| 25.23 \| 5400 \| 0.5603 \| 0.7257 \| 0.7255 \| \| 0.5482 \| 26.17 \| 5600 \| 0.5680 \| 0.7232 \| 0.7235 \| \| 0.5479 \| 27.1 \| 5800 \| 0.5718 \| 0.7214 \| 0.7223 \| \| 0.5439 \| 28.04 \| 6000 \| 0.5623 \| 0.7295 \| 0.7293 \| \| 0.5477 \| 28.97 \| 6200 \| 0.5758 \| 0.7186 \| 0.7196 \| \| 0.5463 \| 29.91 \| 6400 \| 0.5683 \| 0.7237 \| 0.7240 \| \| 0.5461 \| 30.84 \| 6600 \| 0.5867 \| 0.7164 \| 0.7185 \| \| 0.5448 \| 31.78 \| 6800 \| 0.5662 \| 0.7250 \| 0.7252 \| \| 0.5426 \| 32.71 \| 7000 \| 0.5676 \| 0.7240 \| 0.7243 \| \| 0.5419 \| 33.64 \| 7200 \| 0.5682 \| 0.7239 \| 0.7246 \| \| 0.5439 \| 34.58 \| 7400 \| 0.5696 \| 0.7216 \| 0.7223 \| \| 0.5425 \| 35.51 \| 7600 \| 0.5626 \| 0.7284 \| 0.7284 \| \| 0.5385 \| 36.45 \| 7800 \| 0.5638 \| 0.7287 \| 0.7287 \| \| 0.5443 \| 37.38 \| 8000 \| 0.5762 \| 0.7198 \| 0.7211 \| \| 0.5399 \| 38.32 \| 8200 \| 0.5670 \| 0.7270 \| 0.7276 \| \| 0.5409 \| 39.25 \| 8400 \| 0.5653 \| 0.7284 \| 0.7287 \| \| 0.5439 \| 40.19 \| 8600 \| 0.5633 \| 0.7277 \| 0.7279 \| \| 0.5406 \| 41.12 \| 8800 \| 0.5669 \| 0.7262 \| 0.7267 \| \| 0.5393 \| 42.06 \| 9000 \| 0.5684 \| 0.7268 \| 0.7273 \| \| 0.543 \| 42.99 \| 9200 \| 0.5738 \| 0.7209 \| 0.7220 \| \| 0.5384 \| 43.93 \| 9400 \| 0.5725 \| 0.7238 \| 0.7246 \| \| 0.5406 \| 44.86 \| 9600 \| 0.5664 \| 0.7266 \| 0.7270 \| \| 0.542 \| 45.79 \| 9800 \| 0.5679 \| 0.7259 \| 0.7264 \| \| 0.5386 \| 46.73 \| 10000 \| 0.5694 \| 0.7248 \| 0.7255 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H4ac-seqsight_65536_512_47M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H4ac-seqsight_65536_512_47M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]	null	2024-05-03T16:13:19+00:00
automatic-speech-recognition	transformers	<!-- 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 Base Noise Ko - Dearlie This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Noise Data dataset. It achieves the following results on the evaluation set: - Loss: 1.0157 - Cer: 41.4126 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 1.3649 \| 0.8780 \| 1000 \| 1.3839 \| 54.5000 \| \| 1.0173 \| 1.7559 \| 2000 \| 1.1473 \| 52.0300 \| \| 0.7373 \| 2.6339 \| 3000 \| 1.0454 \| 43.7234 \| \| 0.5197 \| 3.5119 \| 4000 \| 1.0157 \| 41.4126 \| ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"language": ["ko"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["AIHub/noise"], "base_model": "openai/whisper-base", "model-index": [{"name": "Whisper Base Noise Ko - Dearlie", "results": []}]}	Dearlie/whisper-noise3	null	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ko", "dataset:AIHub/noise", "base_model:openai/whisper-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:13:31+00:00
text-generation	transformers	<!-- 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. --> # BioGPT_DocBot_SonatafyAI_V1 This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8762 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.1291 \| 1.0 \| 1109 \| 2.9973 \| \| 2.8675 \| 2.0 \| 2218 \| 2.9057 \| \| 2.7264 \| 3.0 \| 3327 \| 2.8822 \| \| 2.6095 \| 4.0 \| 4436 \| 2.8706 \| \| 2.548 \| 5.0 \| 5545 \| 2.8762 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/biogpt", "model-index": [{"name": "BioGPT_DocBot_SonatafyAI_V1", "results": []}]}	Sonatafyai/BioGPT_DocBot_SonatafyAI_V1	null	[ "transformers", "tensorboard", "safetensors", "biogpt", "text-generation", "generated_from_trainer", "base_model:microsoft/biogpt", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:13:46+00:00
null	peft	<!-- 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. --> # GUE_EMP_H4ac-seqsight_65536_512_47M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H4ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.5638 - F1 Score: 0.7186 - Accuracy: 0.7185 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6275 \| 0.93 \| 200 \| 0.5856 \| 0.6927 \| 0.6924 \| \| 0.589 \| 1.87 \| 400 \| 0.5897 \| 0.7023 \| 0.7032 \| \| 0.5744 \| 2.8 \| 600 \| 0.5709 \| 0.7072 \| 0.7070 \| \| 0.5693 \| 3.74 \| 800 \| 0.5721 \| 0.7129 \| 0.7126 \| \| 0.5626 \| 4.67 \| 1000 \| 0.5702 \| 0.7176 \| 0.7173 \| \| 0.5563 \| 5.61 \| 1200 \| 0.5767 \| 0.7101 \| 0.7109 \| \| 0.5549 \| 6.54 \| 1400 \| 0.5737 \| 0.7128 \| 0.7135 \| \| 0.5438 \| 7.48 \| 1600 \| 0.5655 \| 0.7219 \| 0.7217 \| \| 0.5507 \| 8.41 \| 1800 \| 0.5624 \| 0.7231 \| 0.7229 \| \| 0.5442 \| 9.35 \| 2000 \| 0.5599 \| 0.7237 \| 0.7235 \| \| 0.5376 \| 10.28 \| 2200 \| 0.5564 \| 0.7277 \| 0.7276 \| \| 0.5401 \| 11.21 \| 2400 \| 0.5580 \| 0.7302 \| 0.7299 \| \| 0.534 \| 12.15 \| 2600 \| 0.5662 \| 0.7264 \| 0.7267 \| \| 0.5312 \| 13.08 \| 2800 \| 0.5639 \| 0.7289 \| 0.7287 \| \| 0.5321 \| 14.02 \| 3000 \| 0.5519 \| 0.7270 \| 0.7267 \| \| 0.5281 \| 14.95 \| 3200 \| 0.5523 \| 0.7317 \| 0.7314 \| \| 0.5244 \| 15.89 \| 3400 \| 0.5527 \| 0.7343 \| 0.7340 \| \| 0.5211 \| 16.82 \| 3600 \| 0.5606 \| 0.7300 \| 0.7299 \| \| 0.5255 \| 17.76 \| 3800 \| 0.5725 \| 0.7209 \| 0.7220 \| \| 0.522 \| 18.69 \| 4000 \| 0.5527 \| 0.7327 \| 0.7326 \| \| 0.5155 \| 19.63 \| 4200 \| 0.5498 \| 0.7376 \| 0.7372 \| \| 0.5197 \| 20.56 \| 4400 \| 0.5515 \| 0.7346 \| 0.7343 \| \| 0.5178 \| 21.5 \| 4600 \| 0.5649 \| 0.7226 \| 0.7235 \| \| 0.5147 \| 22.43 \| 4800 \| 0.5514 \| 0.7370 \| 0.7367 \| \| 0.5162 \| 23.36 \| 5000 \| 0.5602 \| 0.7330 \| 0.7331 \| \| 0.5122 \| 24.3 \| 5200 \| 0.5509 \| 0.7375 \| 0.7372 \| \| 0.5132 \| 25.23 \| 5400 \| 0.5460 \| 0.7381 \| 0.7378 \| \| 0.5129 \| 26.17 \| 5600 \| 0.5504 \| 0.7370 \| 0.7367 \| \| 0.5098 \| 27.1 \| 5800 \| 0.5520 \| 0.7352 \| 0.7349 \| \| 0.5072 \| 28.04 \| 6000 \| 0.5511 \| 0.7390 \| 0.7387 \| \| 0.5102 \| 28.97 \| 6200 \| 0.5572 \| 0.7351 \| 0.7349 \| \| 0.5079 \| 29.91 \| 6400 \| 0.5584 \| 0.7315 \| 0.7314 \| \| 0.5078 \| 30.84 \| 6600 \| 0.5681 \| 0.7282 \| 0.7287 \| \| 0.5028 \| 31.78 \| 6800 \| 0.5553 \| 0.7366 \| 0.7364 \| \| 0.5059 \| 32.71 \| 7000 \| 0.5563 \| 0.7352 \| 0.7349 \| \| 0.5042 \| 33.64 \| 7200 \| 0.5569 \| 0.7308 \| 0.7311 \| \| 0.5027 \| 34.58 \| 7400 \| 0.5531 \| 0.7366 \| 0.7364 \| \| 0.5031 \| 35.51 \| 7600 \| 0.5531 \| 0.7364 \| 0.7361 \| \| 0.499 \| 36.45 \| 7800 \| 0.5564 \| 0.7363 \| 0.7361 \| \| 0.5026 \| 37.38 \| 8000 \| 0.5586 \| 0.7339 \| 0.7340 \| \| 0.5005 \| 38.32 \| 8200 \| 0.5524 \| 0.7357 \| 0.7355 \| \| 0.4999 \| 39.25 \| 8400 \| 0.5557 \| 0.7345 \| 0.7343 \| \| 0.5014 \| 40.19 \| 8600 \| 0.5566 \| 0.7355 \| 0.7352 \| \| 0.4998 \| 41.12 \| 8800 \| 0.5579 \| 0.7347 \| 0.7346 \| \| 0.4986 \| 42.06 \| 9000 \| 0.5580 \| 0.7363 \| 0.7361 \| \| 0.5021 \| 42.99 \| 9200 \| 0.5613 \| 0.7319 \| 0.7320 \| \| 0.4965 \| 43.93 \| 9400 \| 0.5597 \| 0.7347 \| 0.7346 \| \| 0.4979 \| 44.86 \| 9600 \| 0.5571 \| 0.7357 \| 0.7355 \| \| 0.5001 \| 45.79 \| 9800 \| 0.5572 \| 0.7336 \| 0.7334 \| \| 0.4954 \| 46.73 \| 10000 \| 0.5587 \| 0.7353 \| 0.7352 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H4ac-seqsight_65536_512_47M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H4ac-seqsight_65536_512_47M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]	null	2024-05-03T16:14:32+00:00
null	peft	<!-- 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. --> # GUE_EMP_H4ac-seqsight_65536_512_47M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H4ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.5976 - F1 Score: 0.7230 - Accuracy: 0.7229 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6163 \| 0.93 \| 200 \| 0.5941 \| 0.6959 \| 0.6965 \| \| 0.5793 \| 1.87 \| 400 \| 0.5846 \| 0.7054 \| 0.7062 \| \| 0.5645 \| 2.8 \| 600 \| 0.5619 \| 0.7224 \| 0.7223 \| \| 0.5566 \| 3.74 \| 800 \| 0.5643 \| 0.7247 \| 0.7243 \| \| 0.5498 \| 4.67 \| 1000 \| 0.5582 \| 0.7258 \| 0.7255 \| \| 0.5412 \| 5.61 \| 1200 \| 0.5765 \| 0.7119 \| 0.7132 \| \| 0.5392 \| 6.54 \| 1400 \| 0.5703 \| 0.7175 \| 0.7182 \| \| 0.5263 \| 7.48 \| 1600 \| 0.5556 \| 0.7307 \| 0.7305 \| \| 0.5297 \| 8.41 \| 1800 \| 0.5542 \| 0.7281 \| 0.7279 \| \| 0.522 \| 9.35 \| 2000 \| 0.5545 \| 0.7337 \| 0.7334 \| \| 0.5143 \| 10.28 \| 2200 \| 0.5502 \| 0.7321 \| 0.7320 \| \| 0.5141 \| 11.21 \| 2400 \| 0.5602 \| 0.7309 \| 0.7308 \| \| 0.5068 \| 12.15 \| 2600 \| 0.5658 \| 0.7300 \| 0.7302 \| \| 0.5029 \| 13.08 \| 2800 \| 0.5543 \| 0.7323 \| 0.7320 \| \| 0.5013 \| 14.02 \| 3000 \| 0.5646 \| 0.7318 \| 0.7317 \| \| 0.4941 \| 14.95 \| 3200 \| 0.5613 \| 0.7361 \| 0.7358 \| \| 0.4883 \| 15.89 \| 3400 \| 0.5586 \| 0.7370 \| 0.7367 \| \| 0.4829 \| 16.82 \| 3600 \| 0.5657 \| 0.7337 \| 0.7337 \| \| 0.4853 \| 17.76 \| 3800 \| 0.5990 \| 0.7200 \| 0.7220 \| \| 0.479 \| 18.69 \| 4000 \| 0.5616 \| 0.7352 \| 0.7349 \| \| 0.4725 \| 19.63 \| 4200 \| 0.5568 \| 0.7369 \| 0.7367 \| \| 0.475 \| 20.56 \| 4400 \| 0.5594 \| 0.7378 \| 0.7375 \| \| 0.4682 \| 21.5 \| 4600 \| 0.5767 \| 0.7313 \| 0.7317 \| \| 0.4652 \| 22.43 \| 4800 \| 0.5581 \| 0.7384 \| 0.7381 \| \| 0.4614 \| 23.36 \| 5000 \| 0.5728 \| 0.7331 \| 0.7331 \| \| 0.4579 \| 24.3 \| 5200 \| 0.5709 \| 0.7407 \| 0.7405 \| \| 0.4564 \| 25.23 \| 5400 \| 0.5619 \| 0.7370 \| 0.7367 \| \| 0.4548 \| 26.17 \| 5600 \| 0.5749 \| 0.7372 \| 0.7370 \| \| 0.4519 \| 27.1 \| 5800 \| 0.5706 \| 0.7326 \| 0.7323 \| \| 0.4479 \| 28.04 \| 6000 \| 0.5742 \| 0.7396 \| 0.7393 \| \| 0.446 \| 28.97 \| 6200 \| 0.5767 \| 0.7378 \| 0.7375 \| \| 0.4445 \| 29.91 \| 6400 \| 0.5753 \| 0.7379 \| 0.7378 \| \| 0.4399 \| 30.84 \| 6600 \| 0.5980 \| 0.7372 \| 0.7372 \| \| 0.4351 \| 31.78 \| 6800 \| 0.5851 \| 0.7386 \| 0.7384 \| \| 0.4336 \| 32.71 \| 7000 \| 0.5883 \| 0.7352 \| 0.7349 \| \| 0.4319 \| 33.64 \| 7200 \| 0.5929 \| 0.7337 \| 0.7340 \| \| 0.4338 \| 34.58 \| 7400 \| 0.5883 \| 0.7369 \| 0.7370 \| \| 0.4277 \| 35.51 \| 7600 \| 0.5873 \| 0.7371 \| 0.7370 \| \| 0.424 \| 36.45 \| 7800 \| 0.5890 \| 0.7401 \| 0.7399 \| \| 0.4254 \| 37.38 \| 8000 \| 0.5903 \| 0.7411 \| 0.7411 \| \| 0.4238 \| 38.32 \| 8200 \| 0.5865 \| 0.7362 \| 0.7361 \| \| 0.4205 \| 39.25 \| 8400 \| 0.5941 \| 0.7387 \| 0.7384 \| \| 0.4205 \| 40.19 \| 8600 \| 0.5969 \| 0.7378 \| 0.7375 \| \| 0.4193 \| 41.12 \| 8800 \| 0.5965 \| 0.7401 \| 0.7399 \| \| 0.4182 \| 42.06 \| 9000 \| 0.5959 \| 0.7398 \| 0.7396 \| \| 0.4175 \| 42.99 \| 9200 \| 0.6042 \| 0.7358 \| 0.7358 \| \| 0.4139 \| 43.93 \| 9400 \| 0.6035 \| 0.7378 \| 0.7378 \| \| 0.4173 \| 44.86 \| 9600 \| 0.5966 \| 0.7403 \| 0.7402 \| \| 0.4187 \| 45.79 \| 9800 \| 0.5986 \| 0.7397 \| 0.7396 \| \| 0.4146 \| 46.73 \| 10000 \| 0.5997 \| 0.7376 \| 0.7375 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H4ac-seqsight_65536_512_47M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H4ac-seqsight_65536_512_47M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]	null	2024-05-03T16:14:32+00:00
null	peft	<!-- 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. --> # lora_fine_tuned_boolq This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5547 - Accuracy: 0.7778 - F1: 0.6806 ## 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 \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.6762 \| 4.1667 \| 50 \| 0.5947 \| 0.7778 \| 0.6806 \| \| 0.6639 \| 8.3333 \| 100 \| 0.5719 \| 0.7778 \| 0.6806 \| \| 0.6555 \| 12.5 \| 150 \| 0.5648 \| 0.7778 \| 0.6806 \| \| 0.6605 \| 16.6667 \| 200 \| 0.5615 \| 0.7778 \| 0.6806 \| \| 0.6612 \| 20.8333 \| 250 \| 0.5568 \| 0.7778 \| 0.6806 \| \| 0.6508 \| 25.0 \| 300 \| 0.5567 \| 0.7778 \| 0.6806 \| \| 0.6491 \| 29.1667 \| 350 \| 0.5550 \| 0.7778 \| 0.6806 \| \| 0.663 \| 33.3333 \| 400 \| 0.5547 \| 0.7778 \| 0.6806 \| ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "google-bert/bert-base-uncased", "model-index": [{"name": "lora_fine_tuned_boolq", "results": []}]}	lenatr99/lora_fine_tuned_boolq	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "license:apache-2.0", "region:us" ]	null	2024-05-03T16:14:40+00:00
text-to-image	diffusers	# Juggernaut X Hyper + RunDiffusion Official (Community Version) ![juggernaut X Hyper previews](https://storage.googleapis.com/run-diffusion-public-assets/juggernaut-x/juggernaut-x-hyper-0-832.webp) ![RunDiffusion Logo](https://imagedelivery.net/siANnpeNAc_S2q1M3-eDrA/ca2b388d-a835-490c-dec0-e764bee8d000/micro) This model is not permitted to be used behind API services. Please contact [[email protected]](mailto:[email protected]) for business inquires, commercial licensing, custom models, and consultation. Juggernaut X (SAFE) is available exclusivly on [RunDiffusion.com](http://rundiffusion.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=juggernautv10hyper) 🎉 Juggernaut X Hyper World Wide Release! 🌍 After almost two months, we are thrilled to announce the next version of Juggernaut is ready to launch! Introducing Juggernaut X Hyper. 🚀 If you would have been following us on Twitter (X) you would have been seeing the test images. If you aren't following us, do it now! https://x.com/RunDiffusion & Kandoo's new account needs some new followers. Help him out. https://x.com/Juggernaut_AI - TWO Versions of Juggernaut X Hyper! SFW 🌟 and NSFW 🔥 - Fully trained from the ground up using the GPT4 Vision Captioning tool by LEOSAM 🛠️ - Much improved prompt adherence ✅ - Expanded and cleaner dataset with higher quality images 🖼️ - Improved classifications of shots (Full Body, Midshots, Portraits, etc) 📸 - Enhanced text generation capability 📝 - Two different prompting techniques, Natural and Tagging style 🏷️ - Enhanced by RunDiffusion Photo for refinement of details 🧐 Read more about this version here https://rundiffusion.com/juggernaut-xl Dual Version Release 🔄 A Safe for Work (SFW) and a Not Safe for Work (NSFW) version of Juggernaut X Hyper will be available. This dual release strategy is designed to cater to diverse preferences and ensure inclusivity, offering the perfect solution for every user. Our newest Safe for Work edition is available right now exclusively through Fooocus on RunDiffusion.com. Launch Fooocus on RunDiffusion Find Juggernaut X_RunDiffusion_Hyper.safetensors and start generating! It allows users to generate high-quality, suitable images while adhering to safe content guidelines. This version is particularly user-friendly, requiring only simple, straightforward prompts. It's ideal for the workplace, students, educators, and families. SAFE stands for Suitable Ai For Everyone. 🌈 Conversely, the Not Safe for Work version offers unrestricted creative freedom across all categories and spectrums. This model is perfect for those seeking less constrained artistic expression and is available for free on Civitai.com, though a license is required for commercial use. 🎨 Both models of Juggernaut X Hyper (v10) represent our commitment to fostering a creative community that respects diverse needs and preferences. 🤝 Prompting Guide 📘 Because everything has been trained from the ground up, prompting is a bit different. (Simpler, don't worry) @Kandoo has created a guide to help you seamlessly integrate this powerful model into your workflow, enabling you to leverage its advanced capabilities without feeling overwhelmed. Download it here: https://rundiffusion.com/juggernaut-xl#nav As always, we love our community and feel so lucky to be in this position to bring these awesome tools and models to you amazing diffusers. Thanks for supporting us since our first day back in 2022. Going on TWO YEARS since we first started using generative Ai. Time flies when you're having fun. wow! Don't forget to follow us on Twitter where we have way more updates on big things we're working on. The future is bright https://x.com/RunDiffusion -RunDiffusion Team ![https://rundiffusion.com?utm_source=huggingface&utm_medium=referral&utm_campaign=juggernautv10hyper](https://i.imgur.com/fKPEqSu.jpg)	{"language": ["en"], "license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["art", "people", "diffusion", "Cinematic", "Photography", "Landscape", "Interior", "Food", "Car", "Wildlife", "Architecture"], "thumbnail": "https://storage.googleapis.com/run-diffusion-public-assets/juggernaut-x/juggernaut-x-hyper-0-256.webp", "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "pipeline_tag": "text-to-image"}	RunDiffusion/Juggernaut-X-Hyper	null	[ "diffusers", "art", "people", "diffusion", "Cinematic", "Photography", "Landscape", "Interior", "Food", "Car", "Wildlife", "Architecture", "text-to-image", "en", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	null	2024-05-03T16:14:58+00:00
automatic-speech-recognition	transformers	<!-- 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 Tiny chinese - VingeNie This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 16.1 dataset. It achieves the following results on the evaluation set: - Loss: 0.8984 - Cer Ortho: 82.5463 - Cer: 30.3139 ## 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-08 - train_batch_size: 32 - 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: 25 - training_steps: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer Ortho \| Cer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.2618 \| 0.0400 \| 50 \| 0.8984 \| 82.5463 \| 30.3139 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.0.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1	{"language": ["zh"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_16_1"], "base_model": "openai/whisper-tiny", "model-index": [{"name": "Whisper Tiny chinese - VingeNie", "results": []}]}	VingeNie/whisper-tiny-zh_CN_lr4_3600	null	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "zh", "dataset:mozilla-foundation/common_voice_16_1", "base_model:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:15:03+00:00
null	null	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama-2-ko-7b - GGUF - Model creator: https://huggingface.co/beomi/ - Original model: https://huggingface.co/beomi/llama-2-ko-7b/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [llama-2-ko-7b.Q2_K.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q2_K.gguf) \| Q2_K \| 2.42GB \| \| [llama-2-ko-7b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.IQ3_XS.gguf) \| IQ3_XS \| 2.67GB \| \| [llama-2-ko-7b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.IQ3_S.gguf) \| IQ3_S \| 2.81GB \| \| [llama-2-ko-7b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q3_K_S.gguf) \| Q3_K_S \| 2.81GB \| \| [llama-2-ko-7b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.IQ3_M.gguf) \| IQ3_M \| 2.97GB \| \| [llama-2-ko-7b.Q3_K.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q3_K.gguf) \| Q3_K \| 3.14GB \| \| [llama-2-ko-7b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.14GB \| \| [llama-2-ko-7b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q3_K_L.gguf) \| Q3_K_L \| 3.42GB \| \| [llama-2-ko-7b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.IQ4_XS.gguf) \| IQ4_XS \| 3.47GB \| \| [llama-2-ko-7b.Q4_0.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q4_0.gguf) \| Q4_0 \| 3.64GB \| \| [llama-2-ko-7b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.IQ4_NL.gguf) \| IQ4_NL \| 3.66GB \| \| [llama-2-ko-7b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q4_K_S.gguf) \| Q4_K_S \| 3.67GB \| \| [llama-2-ko-7b.Q4_K.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q4_K.gguf) \| Q4_K \| 3.88GB \| \| [llama-2-ko-7b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q4_K_M.gguf) \| Q4_K_M \| 3.88GB \| \| [llama-2-ko-7b.Q4_1.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q4_1.gguf) \| Q4_1 \| 4.03GB \| \| [llama-2-ko-7b.Q5_0.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q5_0.gguf) \| Q5_0 \| 4.42GB \| \| [llama-2-ko-7b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q5_K_S.gguf) \| Q5_K_S \| 4.42GB \| \| [llama-2-ko-7b.Q5_K.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q5_K.gguf) \| Q5_K \| 4.54GB \| \| [llama-2-ko-7b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q5_K_M.gguf) \| Q5_K_M \| 4.54GB \| \| [llama-2-ko-7b.Q5_1.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q5_1.gguf) \| Q5_1 \| 4.8GB \| \| [llama-2-ko-7b.Q6_K.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q6_K.gguf) \| Q6_K \| 5.24GB \| Original model description: --- language: - en - ko pipeline_tag: text-generation inference: false tags: - facebook - meta - pytorch - llama - llama-2 - kollama - llama-2-ko --- Update Log - 2023.12.27 - New Model is here! Trained with only open-accessible Korean text corpus: https://huggingface.co/beomi/open-llama-2-ko-7b - 2023.10.19 - Fix Tokenizer bug(space not applied when decoding) after `transforemrs>=4.34.0` # Llama-2-Ko 🦙🇰🇷 Llama-2-Ko serves as an advanced iteration of Llama 2, benefiting from an expanded vocabulary and the inclusion of a Korean corpus in its further pretraining. Just like its predecessor, Llama-2-Ko operates within the broad range of generative text models that stretch from 7 billion to 70 billion parameters. This repository focuses on the 7B pretrained version, which is tailored to fit the Hugging Face Transformers format. For access to the other models, feel free to consult the index provided below. ## Model Details Model Developers Junbum Lee (Beomi) Variations Llama-2-Ko will come in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama-2-Ko is an auto-regressive language model that uses an optimized transformer architecture based on Llama-2. \|\|Training Data\|Params\|Content Length\|GQA\|Tokens\|LR\| \|---\|---\|---\|---\|---\|---\|---\| \|Llama 2\|A new mix of Korean online data\|7B\|4k\|✗\|>40B\|1e<sup>-5</sup>\| Plan to train upto 200B tokens Vocab Expansion \| Model Name \| Vocabulary Size \| Description \| \| --- \| --- \| --- \| \| Original Llama-2 \| 32000 \| Sentencepiece BPE \| \| Expanded Llama-2-Ko \| 46336 \| Sentencepiece BPE. Added Korean vocab and merges \| Tokenizing "안녕하세요, 오늘은 날씨가 좋네요." \| Model \| Tokens \| \| --- \| --- \| \| Llama-2 \| `['▁', '안', '<0xEB>', '<0x85>', '<0x95>', '하', '세', '요', ',', '▁', '오', '<0xEB>', '<0x8A>', '<0x98>', '은', '▁', '<0xEB>', '<0x82>', '<0xA0>', '씨', '가', '▁', '<0xEC>', '<0xA2>', '<0x8B>', '<0xEB>', '<0x84>', '<0xA4>', '요']` \| \| Llama-2-Ko \| `['▁안녕', '하세요', ',', '▁오늘은', '▁날', '씨가', '▁좋네요']` \| Tokenizing "Llama 2: Open Foundation and Fine-Tuned Chat Models" \| Model \| Tokens \| \| --- \| --- \| \| Llama-2 \| `['▁L', 'l', 'ama', '▁', '2', ':', '▁Open', '▁Foundation', '▁and', '▁Fine', '-', 'T', 'un', 'ed', '▁Ch', 'at', '▁Mod', 'els']` \| \| Llama-2-Ko \| `['▁L', 'l', 'ama', '▁', '2', ':', '▁Open', '▁Foundation', '▁and', '▁Fine', '-', 'T', 'un', 'ed', '▁Ch', 'at', '▁Mod', 'els']` \| # Model Benchmark ## LM Eval Harness - Korean (polyglot branch) - Used EleutherAI's lm-evaluation-harness https://github.com/EleutherAI/lm-evaluation-harness/tree/polyglot ### NSMC (Acc) - 50000 full test TBD ### COPA (F1) <img src=https://user-images.githubusercontent.com/11323660/255575809-c037bc6e-0566-436a-a6c1-2329ac92187a.png style="max-width: 700px; width: 100%" /> \| Model \| 0-shot \| 5-shot \| 10-shot \| 50-shot \| \| --- \| --- \| --- \| --- \| --- \| \| https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 \| 0.6696 \| 0.6477 \| 0.6419 \| 0.6514 \| \| https://huggingface.co/kakaobrain/kogpt \| 0.7345 \| 0.7287 \| 0.7277 \| 0.7479 \| \| https://huggingface.co/facebook/xglm-7.5B \| 0.6723 \| 0.6731 \| 0.6769 \| 0.7119 \| \| https://huggingface.co/EleutherAI/polyglot-ko-1.3b \| 0.7196 \| 0.7193 \| 0.7204 \| 0.7206 \| \| https://huggingface.co/EleutherAI/polyglot-ko-3.8b \| 0.7595 \| 0.7608 \| 0.7638 \| 0.7788 \| \| https://huggingface.co/EleutherAI/polyglot-ko-5.8b \| 0.7745 \| 0.7676 \| 0.7775 \| 0.7887 \| \| https://huggingface.co/EleutherAI/polyglot-ko-12.8b \| 0.7937 \| 0.8108 \| 0.8037 \| 0.8369 \| \| Llama-2 Original 7B* \| 0.562033 \| 0.575982 \| 0.576216 \| 0.595532 \| \| Llama-2-Ko-7b 20B (10k) \| 0.738780 \| 0.762639 \| 0.780761 \| 0.797863 \| \| Llama-2-Ko-7b 40B (20k) \| 0.743630 \| 0.792716 \| 0.803746 \| 0.825944 \| Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ### HellaSwag (F1) <img src=https://user-images.githubusercontent.com/11323660/255576090-a2bfc1ae-d117-44b7-9f7b-262e41179ec1.png style="max-width: 700px; width: 100%" /> \| Model \| 0-shot \| 5-shot \| 10-shot \| 50-shot \| \| --- \| --- \| --- \| --- \| --- \| \| https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 \| 0.5243 \| 0.5272 \| 0.5166 \| 0.5352 \| \| https://huggingface.co/kakaobrain/kogpt \| 0.5590 \| 0.5833 \| 0.5828 \| 0.5907 \| \| https://huggingface.co/facebook/xglm-7.5B \| 0.5665 \| 0.5689 \| 0.5565 \| 0.5622 \| \| https://huggingface.co/EleutherAI/polyglot-ko-1.3b \| 0.5247 \| 0.5260 \| 0.5278 \| 0.5427 \| \| https://huggingface.co/EleutherAI/polyglot-ko-3.8b \| 0.5707 \| 0.5830 \| 0.5670 \| 0.5787 \| \| https://huggingface.co/EleutherAI/polyglot-ko-5.8b \| 0.5976 \| 0.5998 \| 0.5979 \| 0.6208 \| \| https://huggingface.co/EleutherAI/polyglot-ko-12.8b \| 0.5954 \| 0.6306 \| 0.6098 \| 0.6118 \| \| Llama-2 Original 7B \| 0.415390 \| 0.431382 \| 0.421342 \| 0.442003 \| \| Llama-2-Ko-7b 20B (10k) \| 0.451757 \| 0.466751 \| 0.472607 \| 0.482776 \| \| Llama-2-Ko-7b 40B (20k) \| 0.456246 \| 0.465665 \| 0.469810 \| 0.477374 \| Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ### BoolQ (F1) <img src=https://user-images.githubusercontent.com/11323660/255576343-5d847a6f-3b6a-41a7-af37-0f11940a5ea4.png style="max-width: 700px; width: 100%" /> \| Model \| 0-shot \| 5-shot \| 10-shot \| 50-shot \| \| --- \| --- \| --- \| --- \| --- \| \| https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 \| 0.3356 \| 0.4014 \| 0.3640 \| 0.3560 \| \| https://huggingface.co/kakaobrain/kogpt \| 0.4514 \| 0.5981 \| 0.5499 \| 0.5202 \| \| https://huggingface.co/facebook/xglm-7.5B \| 0.4464 \| 0.3324 \| 0.3324 \| 0.3324 \| \| https://huggingface.co/EleutherAI/polyglot-ko-1.3b \| 0.3552 \| 0.4751 \| 0.4109 \| 0.4038 \| \| https://huggingface.co/EleutherAI/polyglot-ko-3.8b \| 0.4320 \| 0.5263 \| 0.4930 \| 0.4038 \| \| https://huggingface.co/EleutherAI/polyglot-ko-5.8b \| 0.4356 \| 0.5698 \| 0.5187 \| 0.5236 \| \| https://huggingface.co/EleutherAI/polyglot-ko-12.8b \| 0.4818 \| 0.6041 \| 0.6289 \| 0.6448 \| \| Llama-2 Original 7B \| 0.352050 \| 0.563238 \| 0.474788 \| 0.419222 \| \| Llama-2-Ko-7b 20B (10k) \| 0.360656 \| 0.679743 \| 0.680109 \| 0.662152 \| \| Llama-2-Ko-7b 40B (20k) \| 0.578640 \| 0.697747 \| 0.708358 \| 0.714423 \| Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ### SentiNeg (F1) <img src=https://user-images.githubusercontent.com/11323660/255576572-b005a81d-fa4d-4709-b48a-f0fe4eed17a3.png style="max-width: 700px; width: 100%" /> \| Model \| 0-shot \| 5-shot \| 10-shot \| 50-shot \| \| --- \| --- \| --- \| --- \| --- \| \| https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 \| 0.6065 \| 0.6878 \| 0.7280 \| 0.8413 \| \| https://huggingface.co/kakaobrain/kogpt \| 0.3747 \| 0.8942 \| 0.9294 \| 0.9698 \| \| https://huggingface.co/facebook/xglm-7.5B \| 0.3578 \| 0.4471 \| 0.3964 \| 0.5271 \| \| https://huggingface.co/EleutherAI/polyglot-ko-1.3b \| 0.6790 \| 0.6257 \| 0.5514 \| 0.7851 \| \| https://huggingface.co/EleutherAI/polyglot-ko-3.8b \| 0.4858 \| 0.7950 \| 0.7320 \| 0.7851 \| \| https://huggingface.co/EleutherAI/polyglot-ko-5.8b \| 0.3394 \| 0.8841 \| 0.8808 \| 0.9521 \| \| https://huggingface.co/EleutherAI/polyglot-ko-12.8b \| 0.9117 \| 0.9015 \| 0.9345 \| 0.9723 \| \| Llama-2 Original 7B \| 0.347502 \| 0.529124 \| 0.480641 \| 0.788457 \| \| Llama-2-Ko-7b 20B (10k) \| 0.485546 \| 0.829503 \| 0.871141 \| 0.851253 \| \| Llama-2-Ko-7b 40B (20k) \| 0.459447 \| 0.761079 \| 0.727611 \| 0.936988 \| *Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ## Note for oobabooga/text-generation-webui Remove `ValueError` at `load_tokenizer` function(line 109 or near), in `modules/models.py`. ```python diff --git a/modules/models.py b/modules/models.py index 232d5fa..de5b7a0 100644 --- a/modules/models.py +++ b/modules/models.py @@ -106,7 +106,7 @@ def load_tokenizer(model_name, model): trust_remote_code=shared.args.trust_remote_code, use_fast=False ) - except ValueError: + except: tokenizer = AutoTokenizer.from_pretrained( path_to_model, trust_remote_code=shared.args.trust_remote_code, ``` Since Llama-2-Ko uses FastTokenizer provided by HF tokenizers NOT sentencepiece package, it is required to use `use_fast=True` option when initialize tokenizer. Apple Sillicon does not support BF16 computing, use CPU instead. (BF16 is supported when using NVIDIA GPU) ## Citation ``` @misc {l._junbum_2023, author = { {L. Junbum} }, title = { llama-2-ko-7b (Revision 4a9993e) }, year = 2023, url = { https://huggingface.co/beomi/llama-2-ko-7b }, doi = { 10.57967/hf/1098 }, publisher = { Hugging Face } } ``` ## Acknowledgement The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_beomi__llama-2-ko-7b) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 39.43 \| \| ARC (25-shot) \| 48.46 \| \| HellaSwag (10-shot) \| 75.28 \| \| MMLU (5-shot) \| 39.56 \| \| TruthfulQA (0-shot) \| 34.49 \| \| Winogrande (5-shot) \| 72.14 \| \| GSM8K (5-shot) \| 1.97 \| \| DROP (3-shot) \| 4.1 \|	{}	RichardErkhov/beomi_-_llama-2-ko-7b-gguf	null	[ "gguf", "region:us" ]	null	2024-05-03T16:15:23+00:00
null	null		{}	vup2p/model_sn25_44	null	[ "region:us" ]	null	2024-05-03T16:15:33+00:00
null	null		{}	Sebasvargas/CursoAI	null	[ "region:us" ]	null	2024-05-03T16:15:38+00:00
text-classification	transformers		{}	Paral1ax/vk_model_clf_distilbert	null	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:15:48+00:00
audio-to-audio	null	Jack from Genndy Tartakovsky's "Samurai Jack" Cartoon Network/Adult Swim show! For RVC/RVCv2 AI Covers Made with Weights.gg Feel free to use! Don't forget to credit if used Note: Jack's voice is naturally deep so don't forget to adjust your song's pitch so he can fully sound like himself Model by Radaverse \| @samurairad	{"language": ["en"], "license": "openrail", "tags": ["RVC", "RVCv2", "AI", "Cover", "Voice", "Cartoon", "Samurai Jack"], "pipeline_tag": "audio-to-audio"}	Radaverse/SamuraiJack-RVCv2	null	[ "RVC", "RVCv2", "AI", "Cover", "Voice", "Cartoon", "Samurai Jack", "audio-to-audio", "en", "license:openrail", "region:us" ]	null	2024-05-03T16:15:49+00:00
null	null		{}	FanFierik/NoShaveNoHaircut	null	[ "region:us" ]	null	2024-05-03T16:15:57+00:00
null	null		{}	GeorgeImmanuel/autonomous_taxi_ride	null	[ "region:us" ]	null	2024-05-03T16:16:19+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K79me3-seqsight_65536_512_47M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3K79me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K79me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.4367 - F1 Score: 0.8170 - Accuracy: 0.8173 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5342 \| 1.1 \| 200 \| 0.4669 \| 0.8027 \| 0.8027 \| \| 0.4727 \| 2.21 \| 400 \| 0.4585 \| 0.7966 \| 0.7982 \| \| 0.4678 \| 3.31 \| 600 \| 0.4500 \| 0.8050 \| 0.8058 \| \| 0.4585 \| 4.42 \| 800 \| 0.4482 \| 0.8051 \| 0.8062 \| \| 0.4586 \| 5.52 \| 1000 \| 0.4469 \| 0.8050 \| 0.8062 \| \| 0.4519 \| 6.63 \| 1200 \| 0.4499 \| 0.8032 \| 0.8048 \| \| 0.4567 \| 7.73 \| 1400 \| 0.4412 \| 0.8097 \| 0.8103 \| \| 0.4482 \| 8.84 \| 1600 \| 0.4460 \| 0.8039 \| 0.8051 \| \| 0.4492 \| 9.94 \| 1800 \| 0.4426 \| 0.8105 \| 0.8103 \| \| 0.4476 \| 11.05 \| 2000 \| 0.4397 \| 0.8074 \| 0.8083 \| \| 0.4472 \| 12.15 \| 2200 \| 0.4359 \| 0.8109 \| 0.8114 \| \| 0.4424 \| 13.26 \| 2400 \| 0.4347 \| 0.8093 \| 0.8100 \| \| 0.4412 \| 14.36 \| 2600 \| 0.4350 \| 0.8097 \| 0.8100 \| \| 0.4441 \| 15.47 \| 2800 \| 0.4438 \| 0.8012 \| 0.8031 \| \| 0.4389 \| 16.57 \| 3000 \| 0.4347 \| 0.8085 \| 0.8089 \| \| 0.4408 \| 17.68 \| 3200 \| 0.4338 \| 0.8093 \| 0.8100 \| \| 0.4352 \| 18.78 \| 3400 \| 0.4318 \| 0.8126 \| 0.8128 \| \| 0.4363 \| 19.89 \| 3600 \| 0.4363 \| 0.8085 \| 0.8096 \| \| 0.4377 \| 20.99 \| 3800 \| 0.4340 \| 0.8094 \| 0.8100 \| \| 0.4367 \| 22.1 \| 4000 \| 0.4326 \| 0.8103 \| 0.8110 \| \| 0.4356 \| 23.2 \| 4200 \| 0.4325 \| 0.8113 \| 0.8121 \| \| 0.436 \| 24.31 \| 4400 \| 0.4342 \| 0.8125 \| 0.8131 \| \| 0.4275 \| 25.41 \| 4600 \| 0.4359 \| 0.8140 \| 0.8148 \| \| 0.4331 \| 26.52 \| 4800 \| 0.4318 \| 0.8132 \| 0.8135 \| \| 0.4341 \| 27.62 \| 5000 \| 0.4310 \| 0.8130 \| 0.8135 \| \| 0.4297 \| 28.73 \| 5200 \| 0.4298 \| 0.8112 \| 0.8117 \| \| 0.428 \| 29.83 \| 5400 \| 0.4309 \| 0.8138 \| 0.8141 \| \| 0.4299 \| 30.94 \| 5600 \| 0.4318 \| 0.8105 \| 0.8107 \| \| 0.4299 \| 32.04 \| 5800 \| 0.4303 \| 0.8141 \| 0.8141 \| \| 0.4309 \| 33.15 \| 6000 \| 0.4284 \| 0.8149 \| 0.8152 \| \| 0.4284 \| 34.25 \| 6200 \| 0.4307 \| 0.8125 \| 0.8128 \| \| 0.4275 \| 35.36 \| 6400 \| 0.4322 \| 0.8123 \| 0.8131 \| \| 0.4272 \| 36.46 \| 6600 \| 0.4292 \| 0.8162 \| 0.8162 \| \| 0.4286 \| 37.57 \| 6800 \| 0.4303 \| 0.8141 \| 0.8145 \| \| 0.4263 \| 38.67 \| 7000 \| 0.4320 \| 0.8136 \| 0.8141 \| \| 0.4246 \| 39.78 \| 7200 \| 0.4304 \| 0.8165 \| 0.8166 \| \| 0.4268 \| 40.88 \| 7400 \| 0.4290 \| 0.8150 \| 0.8152 \| \| 0.4263 \| 41.99 \| 7600 \| 0.4290 \| 0.8153 \| 0.8155 \| \| 0.4243 \| 43.09 \| 7800 \| 0.4303 \| 0.8161 \| 0.8166 \| \| 0.4262 \| 44.2 \| 8000 \| 0.4295 \| 0.8141 \| 0.8145 \| \| 0.4233 \| 45.3 \| 8200 \| 0.4301 \| 0.8152 \| 0.8155 \| \| 0.4256 \| 46.41 \| 8400 \| 0.4286 \| 0.8148 \| 0.8152 \| \| 0.4238 \| 47.51 \| 8600 \| 0.4293 \| 0.8156 \| 0.8159 \| \| 0.4236 \| 48.62 \| 8800 \| 0.4312 \| 0.8136 \| 0.8141 \| \| 0.4221 \| 49.72 \| 9000 \| 0.4301 \| 0.8142 \| 0.8145 \| \| 0.4283 \| 50.83 \| 9200 \| 0.4296 \| 0.8131 \| 0.8135 \| \| 0.4232 \| 51.93 \| 9400 \| 0.4299 \| 0.8142 \| 0.8145 \| \| 0.4238 \| 53.04 \| 9600 \| 0.4297 \| 0.8142 \| 0.8145 \| \| 0.4218 \| 54.14 \| 9800 \| 0.4295 \| 0.8149 \| 0.8152 \| \| 0.424 \| 55.25 \| 10000 \| 0.4300 \| 0.8145 \| 0.8148 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3K79me3-seqsight_65536_512_47M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K79me3-seqsight_65536_512_47M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]	null	2024-05-03T16:16:34+00:00
text-generation	transformers	<img src="./llama-3-merges.webp" alt="Llama-3 DPO Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4 This model is a fine-tune (DPO) of `meta-llama/Meta-Llama-3-70B-Instruct` model. # Quantized GGUF All GGUF models are available here: [MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4-GGUF) # Prompt Template This model uses `ChatML` prompt template: ``` <\|im_start\|>system {System} <\|im_end\|> <\|im_start\|>user {User} <\|im_end\|> <\|im_start\|>assistant {Assistant} ```` # How to use You can use this model by using `MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4` as the model name in Hugging Face's transformers library. ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer from transformers import pipeline import torch model_id = "MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4" model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True, # attn_implementation="flash_attention_2" ) tokenizer = AutoTokenizer.from_pretrained( model_id, trust_remote_code=True ) streamer = TextStreamer(tokenizer) pipeline = pipeline( "text-generation", model=model, tokenizer=tokenizer, model_kwargs={"torch_dtype": torch.bfloat16}, streamer=streamer ) # Then you can use the pipeline to generate text. messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<\|im_end\|>"), tokenizer.convert_tokens_to_ids("<\|eot_id\|>") # safer to have this too ] outputs = pipeline( prompt, max_new_tokens=2048, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.95, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## Sample Response ``` <\|im_start\|>system You are a helpful assistant.<\|im_end\|> <\|im_start\|>user describe about pros and cons of docker system.<\|im_end\|> <\|im_start\|>assistant ``` ``` Here are the pros and cons of the Docker system: Pros: 1. Lightweight: Docker containers are lightweight and use fewer resources compared to traditional virtual machines. 2. Isolation: Docker provides a high level of isolation between containers, ensuring that each application runs independently without interfering with others. 3. Portability: Docker containers are highly portable and can run on any system that supports Docker, without requiring specific dependencies or configurations. 4. Efficient Use of Resources: Docker containers can share the same kernel and use fewer resources, making them more efficient than traditional virtual machines. 5. Fast Deployment: Docker containers can be spun up quickly, making it ideal for rapid deployment and scaling of applications. 6. Easy Maintenance: Docker provides a simple way to manage and update applications, with features like rolling updates and rollbacks. 7. Security: Docker provides a secure environment for applications, with features like network isolation and encryption. 8. Large Ecosystem: Docker has a large and active ecosystem, with a wide range of tools and plugins available for various tasks. 9. Version Control: Docker provides an easy way to manage different versions of an application, with features like version tagging and rollbacks. 10. Cost-Effective: Docker can help reduce costs by minimizing the need for virtual machines and reducing the overhead of resource allocation. Cons: 1. Steep Learning Curve: Docker has a complex architecture and requires a good understanding of containerization, networking, and storage. 2. Overhead: While Docker containers are lightweight, they still require some overhead in terms of resources and management. 3. Security Risks: If not properly configured, Docker containers can introduce security risks, such as unauthorized access to the host system. 4. Limited Support for GUI Applications: Docker is primarily designed for running command-line applications, and running GUI applications can be challenging. 5. Networking Complexity: Docker's networking model can be complex, especially in large-scale deployments. 6. Storage Complexity: Docker's storage model can be complex, especially when dealing with persistent data. 7. Limited Support for Legacy Applications: Docker is designed for modern, cloud-native applications, and may not be suitable for legacy applications that rely on older technologies. 8. Dependence on Docker Hub: Docker relies heavily on Docker Hub, which can be a single point of failure if the hub is down or experiencing issues. 9. Orchestration Complexity: Docker provides a built-in orchestration tool, Docker Swarm, but it can be complex to set up and manage. 10. Vendor Lock-in: Docker is a proprietary technology, and while it has a large ecosystem, it can be difficult to switch to alternative containerization platforms. Overall, Docker provides a powerful and flexible way to deploy and manage applications, but it requires careful planning, configuration, and management to ensure optimal performance and security. ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__Llama-3-70B-Instruct-DPO-v0.4) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|78.89\| \|AI2 Reasoning Challenge (25-Shot)\|72.61\| \|HellaSwag (10-Shot) \|86.03\| \|MMLU (5-Shot) \|80.50\| \|TruthfulQA (0-shot) \|63.26\| \|Winogrande (5-shot) \|83.58\| \|GSM8k (5-shot) \|87.34\|	{"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["axolotl", "finetune", "dpo", "facebook", "meta", "pytorch", "llama", "llama-3", "chatml"], "datasets": ["argilla/ultrafeedback-binarized-preferences"], "base_model": "meta-llama/Meta-Llama-3-70B-Instruct", "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "inference": false, "model_creator": "MaziyarPanahi", "quantized_by": "MaziyarPanahi", "model-index": [{"name": "Llama-3-70B-Instruct-DPO-v0.4", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 72.61, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 86.03, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 80.5, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 63.26}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 83.58, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 87.34, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4", "name": "Open LLM Leaderboard"}}]}]}	blockblockblock/Llama-3-70B-Instruct-DPO-v0.4-bpw2.5-exl2	null	[ "transformers", "safetensors", "llama", "text-generation", "axolotl", "finetune", "dpo", "facebook", "meta", "pytorch", "llama-3", "chatml", "conversational", "en", "dataset:argilla/ultrafeedback-binarized-preferences", "base_model:meta-llama/Meta-Llama-3-70B-Instruct", "license:llama3", "model-index", "autotrain_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:17:38+00:00
null	null		{}	nzsadvdfgzv/test	null	[ "region:us" ]	null	2024-05-03T16:18:13+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	ar08/ar08	null	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:19:14+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K79me3-seqsight_65536_512_47M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3K79me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K79me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.4309 - F1 Score: 0.8185 - Accuracy: 0.8187 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5148 \| 1.1 \| 200 \| 0.4601 \| 0.8090 \| 0.8089 \| \| 0.4624 \| 2.21 \| 400 \| 0.4479 \| 0.8057 \| 0.8065 \| \| 0.4592 \| 3.31 \| 600 \| 0.4440 \| 0.8066 \| 0.8076 \| \| 0.4474 \| 4.42 \| 800 \| 0.4400 \| 0.8033 \| 0.8044 \| \| 0.4463 \| 5.52 \| 1000 \| 0.4429 \| 0.8030 \| 0.8048 \| \| 0.4386 \| 6.63 \| 1200 \| 0.4471 \| 0.8024 \| 0.8048 \| \| 0.4403 \| 7.73 \| 1400 \| 0.4353 \| 0.8077 \| 0.8089 \| \| 0.4328 \| 8.84 \| 1600 \| 0.4478 \| 0.8019 \| 0.8041 \| \| 0.4305 \| 9.94 \| 1800 \| 0.4266 \| 0.8190 \| 0.8190 \| \| 0.4265 \| 11.05 \| 2000 \| 0.4371 \| 0.8041 \| 0.8051 \| \| 0.4265 \| 12.15 \| 2200 \| 0.4269 \| 0.8185 \| 0.8183 \| \| 0.4207 \| 13.26 \| 2400 \| 0.4243 \| 0.8151 \| 0.8155 \| \| 0.4176 \| 14.36 \| 2600 \| 0.4245 \| 0.8184 \| 0.8183 \| \| 0.4192 \| 15.47 \| 2800 \| 0.4285 \| 0.8111 \| 0.8117 \| \| 0.414 \| 16.57 \| 3000 \| 0.4283 \| 0.8175 \| 0.8173 \| \| 0.4149 \| 17.68 \| 3200 \| 0.4244 \| 0.8161 \| 0.8162 \| \| 0.4094 \| 18.78 \| 3400 \| 0.4262 \| 0.8172 \| 0.8176 \| \| 0.4091 \| 19.89 \| 3600 \| 0.4239 \| 0.8140 \| 0.8141 \| \| 0.4087 \| 20.99 \| 3800 \| 0.4302 \| 0.8091 \| 0.8100 \| \| 0.4076 \| 22.1 \| 4000 \| 0.4246 \| 0.8108 \| 0.8114 \| \| 0.4059 \| 23.2 \| 4200 \| 0.4253 \| 0.8144 \| 0.8148 \| \| 0.4057 \| 24.31 \| 4400 \| 0.4300 \| 0.8124 \| 0.8131 \| \| 0.3982 \| 25.41 \| 4600 \| 0.4299 \| 0.8159 \| 0.8162 \| \| 0.4019 \| 26.52 \| 4800 \| 0.4289 \| 0.8184 \| 0.8187 \| \| 0.4036 \| 27.62 \| 5000 \| 0.4294 \| 0.8112 \| 0.8121 \| \| 0.3975 \| 28.73 \| 5200 \| 0.4243 \| 0.8114 \| 0.8121 \| \| 0.3938 \| 29.83 \| 5400 \| 0.4255 \| 0.8134 \| 0.8138 \| \| 0.3966 \| 30.94 \| 5600 \| 0.4280 \| 0.8160 \| 0.8162 \| \| 0.3953 \| 32.04 \| 5800 \| 0.4275 \| 0.8214 \| 0.8214 \| \| 0.3972 \| 33.15 \| 6000 \| 0.4261 \| 0.8150 \| 0.8155 \| \| 0.3931 \| 34.25 \| 6200 \| 0.4297 \| 0.8170 \| 0.8173 \| \| 0.3914 \| 35.36 \| 6400 \| 0.4287 \| 0.8140 \| 0.8145 \| \| 0.393 \| 36.46 \| 6600 \| 0.4275 \| 0.8181 \| 0.8183 \| \| 0.3901 \| 37.57 \| 6800 \| 0.4299 \| 0.8136 \| 0.8141 \| \| 0.3893 \| 38.67 \| 7000 \| 0.4314 \| 0.8153 \| 0.8159 \| \| 0.3881 \| 39.78 \| 7200 \| 0.4304 \| 0.8184 \| 0.8187 \| \| 0.3886 \| 40.88 \| 7400 \| 0.4277 \| 0.8189 \| 0.8190 \| \| 0.3859 \| 41.99 \| 7600 \| 0.4314 \| 0.8162 \| 0.8166 \| \| 0.3869 \| 43.09 \| 7800 \| 0.4308 \| 0.8169 \| 0.8173 \| \| 0.3859 \| 44.2 \| 8000 \| 0.4329 \| 0.8149 \| 0.8155 \| \| 0.3839 \| 45.3 \| 8200 \| 0.4341 \| 0.8159 \| 0.8162 \| \| 0.3871 \| 46.41 \| 8400 \| 0.4291 \| 0.8184 \| 0.8187 \| \| 0.3848 \| 47.51 \| 8600 \| 0.4327 \| 0.8172 \| 0.8176 \| \| 0.3837 \| 48.62 \| 8800 \| 0.4334 \| 0.8164 \| 0.8169 \| \| 0.383 \| 49.72 \| 9000 \| 0.4334 \| 0.8158 \| 0.8162 \| \| 0.388 \| 50.83 \| 9200 \| 0.4328 \| 0.8160 \| 0.8166 \| \| 0.3826 \| 51.93 \| 9400 \| 0.4316 \| 0.8169 \| 0.8173 \| \| 0.3819 \| 53.04 \| 9600 \| 0.4315 \| 0.8166 \| 0.8169 \| \| 0.3815 \| 54.14 \| 9800 \| 0.4318 \| 0.8170 \| 0.8173 \| \| 0.3831 \| 55.25 \| 10000 \| 0.4325 \| 0.8166 \| 0.8169 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3K79me3-seqsight_65536_512_47M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K79me3-seqsight_65536_512_47M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]	null	2024-05-03T16:19:22+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K79me3-seqsight_65536_512_47M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3K79me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K79me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.4374 - F1 Score: 0.8193 - Accuracy: 0.8193 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5042 \| 1.1 \| 200 \| 0.4523 \| 0.8134 \| 0.8135 \| \| 0.4575 \| 2.21 \| 400 \| 0.4432 \| 0.8135 \| 0.8135 \| \| 0.453 \| 3.31 \| 600 \| 0.4381 \| 0.8068 \| 0.8079 \| \| 0.4367 \| 4.42 \| 800 \| 0.4333 \| 0.8096 \| 0.8107 \| \| 0.4327 \| 5.52 \| 1000 \| 0.4302 \| 0.8134 \| 0.8145 \| \| 0.423 \| 6.63 \| 1200 \| 0.4528 \| 0.8043 \| 0.8065 \| \| 0.4233 \| 7.73 \| 1400 \| 0.4418 \| 0.8010 \| 0.8031 \| \| 0.4154 \| 8.84 \| 1600 \| 0.4534 \| 0.7936 \| 0.7961 \| \| 0.4116 \| 9.94 \| 1800 \| 0.4231 \| 0.8144 \| 0.8145 \| \| 0.4052 \| 11.05 \| 2000 \| 0.4394 \| 0.8028 \| 0.8037 \| \| 0.4028 \| 12.15 \| 2200 \| 0.4245 \| 0.8196 \| 0.8197 \| \| 0.397 \| 13.26 \| 2400 \| 0.4251 \| 0.8144 \| 0.8148 \| \| 0.3917 \| 14.36 \| 2600 \| 0.4285 \| 0.8201 \| 0.8200 \| \| 0.3907 \| 15.47 \| 2800 \| 0.4296 \| 0.8129 \| 0.8131 \| \| 0.3827 \| 16.57 \| 3000 \| 0.4302 \| 0.8171 \| 0.8169 \| \| 0.3821 \| 17.68 \| 3200 \| 0.4380 \| 0.8186 \| 0.8187 \| \| 0.3754 \| 18.78 \| 3400 \| 0.4418 \| 0.8105 \| 0.8110 \| \| 0.371 \| 19.89 \| 3600 \| 0.4367 \| 0.8177 \| 0.8176 \| \| 0.3684 \| 20.99 \| 3800 \| 0.4477 \| 0.8107 \| 0.8110 \| \| 0.3639 \| 22.1 \| 4000 \| 0.4422 \| 0.8158 \| 0.8159 \| \| 0.3605 \| 23.2 \| 4200 \| 0.4480 \| 0.8144 \| 0.8145 \| \| 0.3561 \| 24.31 \| 4400 \| 0.4502 \| 0.8163 \| 0.8166 \| \| 0.3478 \| 25.41 \| 4600 \| 0.4584 \| 0.8175 \| 0.8173 \| \| 0.3503 \| 26.52 \| 4800 \| 0.4596 \| 0.8121 \| 0.8121 \| \| 0.3491 \| 27.62 \| 5000 \| 0.4524 \| 0.8113 \| 0.8117 \| \| 0.3407 \| 28.73 \| 5200 \| 0.4644 \| 0.8110 \| 0.8117 \| \| 0.3349 \| 29.83 \| 5400 \| 0.4509 \| 0.8151 \| 0.8152 \| \| 0.3364 \| 30.94 \| 5600 \| 0.4585 \| 0.8171 \| 0.8169 \| \| 0.3328 \| 32.04 \| 5800 \| 0.4492 \| 0.8199 \| 0.8197 \| \| 0.3307 \| 33.15 \| 6000 \| 0.4530 \| 0.8164 \| 0.8166 \| \| 0.3277 \| 34.25 \| 6200 \| 0.4746 \| 0.8175 \| 0.8173 \| \| 0.3223 \| 35.36 \| 6400 \| 0.4711 \| 0.8181 \| 0.8183 \| \| 0.3192 \| 36.46 \| 6600 \| 0.4757 \| 0.8187 \| 0.8187 \| \| 0.3178 \| 37.57 \| 6800 \| 0.4753 \| 0.8139 \| 0.8141 \| \| 0.3153 \| 38.67 \| 7000 \| 0.4703 \| 0.8165 \| 0.8169 \| \| 0.3129 \| 39.78 \| 7200 \| 0.4812 \| 0.8196 \| 0.8197 \| \| 0.3105 \| 40.88 \| 7400 \| 0.4763 \| 0.8143 \| 0.8141 \| \| 0.3064 \| 41.99 \| 7600 \| 0.4652 \| 0.8180 \| 0.8180 \| \| 0.306 \| 43.09 \| 7800 \| 0.4787 \| 0.8145 \| 0.8145 \| \| 0.3041 \| 44.2 \| 8000 \| 0.4898 \| 0.8150 \| 0.8152 \| \| 0.3014 \| 45.3 \| 8200 \| 0.4882 \| 0.8173 \| 0.8173 \| \| 0.3005 \| 46.41 \| 8400 \| 0.4859 \| 0.8173 \| 0.8173 \| \| 0.3006 \| 47.51 \| 8600 \| 0.4895 \| 0.8143 \| 0.8145 \| \| 0.2973 \| 48.62 \| 8800 \| 0.4882 \| 0.8124 \| 0.8124 \| \| 0.2961 \| 49.72 \| 9000 \| 0.4937 \| 0.8140 \| 0.8141 \| \| 0.3008 \| 50.83 \| 9200 \| 0.4829 \| 0.8128 \| 0.8131 \| \| 0.2934 \| 51.93 \| 9400 \| 0.4918 \| 0.8133 \| 0.8135 \| \| 0.2928 \| 53.04 \| 9600 \| 0.4910 \| 0.8149 \| 0.8148 \| \| 0.2936 \| 54.14 \| 9800 \| 0.4936 \| 0.8156 \| 0.8155 \| \| 0.2934 \| 55.25 \| 10000 \| 0.4941 \| 0.8135 \| 0.8135 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3K79me3-seqsight_65536_512_47M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K79me3-seqsight_65536_512_47M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]	null	2024-05-03T16:19:50+00:00
null	null		{}	barrybadpak/agfdrvgb	null	[ "region:us" ]	null	2024-05-03T16:21:59+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	InayaKripa/gemma-2b-toxic-ConvoV1	null	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:23:48+00:00
text-classification	transformers	This model is a finetuned version of ```distilbert/distilbert-base-multilingual-cased``` model in the Bengali and Hindi languages. The dataset used is a Kaggle Dataset - [Modified-hate-speech-bengali-hindi](https://www.kaggle.com/datasets/abirmondal/modified-hate-speech-bengali-hindi) This model can classify Bengali and Hindi texts into the following 5 classes: - defamation - hate - non-hate - violence - vulgar	{"language": ["bn", "hi"], "license": "apache-2.0"}	kingshukroy/distilbert-base-multilingual-cased-hate-speech-ben-hin	null	[ "transformers", "safetensors", "distilbert", "text-classification", "bn", "hi", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:23:54+00:00
null	null		{}	vinven7/Llama2-ft-MatKG	null	[ "region:us" ]	null	2024-05-03T16:24:37+00:00
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral_finetued_on_scigen_server This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) 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: 1e-06 - train_batch_size: 256 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 256 - total_train_batch_size: 65536 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 30 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "mistral_finetued_on_scigen_server", "results": []}]}	moetezsa/mistral_finetued_on_scigen_server	null	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-05-03T16:24:44+00:00
feature-extraction	transformers		{}	MahmoudTaktak/LEGAL_E2	null	[ "transformers", "pytorch", "bert", "feature-extraction", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:25:14+00:00
text-classification	transformers	<!-- 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. --> # Prototipo_5_EMI This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4215 - Accuracy: 0.538 ## 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: 20 - eval_batch_size: 20 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 1.2459 \| 0.1481 \| 200 \| 1.2168 \| 0.4493 \| \| 1.1445 \| 0.2963 \| 400 \| 1.0823 \| 0.512 \| \| 1.1117 \| 0.4444 \| 600 \| 1.0979 \| 0.5053 \| \| 1.0618 \| 0.5926 \| 800 \| 1.0457 \| 0.5273 \| \| 1.0343 \| 0.7407 \| 1000 \| 1.0219 \| 0.537 \| \| 1.1239 \| 0.8889 \| 1200 \| 1.0353 \| 0.5257 \| \| 0.9012 \| 1.0370 \| 1400 \| 1.0637 \| 0.5383 \| \| 0.86 \| 1.1852 \| 1600 \| 1.0682 \| 0.5333 \| \| 0.898 \| 1.3333 \| 1800 \| 1.0341 \| 0.5483 \| \| 0.929 \| 1.4815 \| 2000 \| 1.0437 \| 0.5363 \| \| 0.9921 \| 1.6296 \| 2200 \| 0.9968 \| 0.5473 \| \| 0.9776 \| 1.7778 \| 2400 \| 1.0418 \| 0.5553 \| \| 0.9166 \| 1.9259 \| 2600 \| 0.9874 \| 0.5573 \| \| 0.703 \| 2.0741 \| 2800 \| 1.0564 \| 0.556 \| \| 0.8123 \| 2.2222 \| 3000 \| 1.0582 \| 0.561 \| \| 0.6727 \| 2.3704 \| 3200 \| 1.0942 \| 0.5483 \| \| 0.6843 \| 2.5185 \| 3400 \| 1.1128 \| 0.558 \| \| 0.7528 \| 2.6667 \| 3600 \| 1.0823 \| 0.5547 \| \| 0.7747 \| 2.8148 \| 3800 \| 1.0744 \| 0.5497 \| \| 0.7471 \| 2.9630 \| 4000 \| 1.0749 \| 0.5527 \| \| 0.5774 \| 3.1111 \| 4200 \| 1.1422 \| 0.552 \| \| 0.6105 \| 3.2593 \| 4400 \| 1.2226 \| 0.543 \| \| 0.573 \| 3.4074 \| 4600 \| 1.2427 \| 0.5417 \| \| 0.6047 \| 3.5556 \| 4800 \| 1.2403 \| 0.537 \| \| 0.5334 \| 3.7037 \| 5000 \| 1.2470 \| 0.5413 \| \| 0.5688 \| 3.8519 \| 5200 \| 1.2585 \| 0.5507 \| \| 0.4928 \| 4.0 \| 5400 \| 1.2653 \| 0.5437 \| \| 0.4314 \| 4.1481 \| 5600 \| 1.3419 \| 0.541 \| \| 0.4556 \| 4.2963 \| 5800 \| 1.3677 \| 0.5413 \| \| 0.4815 \| 4.4444 \| 6000 \| 1.3912 \| 0.5407 \| \| 0.4431 \| 4.5926 \| 6200 \| 1.4004 \| 0.5347 \| \| 0.4312 \| 4.7407 \| 6400 \| 1.4161 \| 0.5397 \| \| 0.459 \| 4.8889 \| 6600 \| 1.4215 \| 0.538 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "dccuchile/bert-base-spanish-wwm-uncased", "model-index": [{"name": "Prototipo_5_EMI", "results": []}]}	Armandodelca/Prototipo_5_EMI	null	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:25:41+00:00
text-generation	transformers	Barcenas 3.8b Based on the Phi-3-Mini-4K-Boost from DeepMount00 and trained with the pinzhenchen/alpaca-cleaned-es dataset, to improve Spanish conversations. The goal of this model is to have a small LLM that can express itself correctly and fluently in the Spanish language. Made with ❤️ in Guadalupe, Nuevo Leon, Mexico 🇲🇽	{"language": ["es", "en"], "license": "mit", "tags": ["phi"]}	Danielbrdz/Barcenas-3.8b	null	[ "transformers", "safetensors", "mistral", "text-generation", "phi", "conversational", "es", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:25:51+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	OwOpeepeepoopoo/herewegoagain15c	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:25:57+00:00
null	null		{"license": "unknown"}	charaatasnim/chekpointzip	null	[ "license:unknown", "region:us" ]	null	2024-05-03T16:27:42+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	golf2248/l9w0l2v	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:27:49+00:00
text-generation	transformers		{"license": "mit"}	sheikhfoysal/chatEDU-v2	null	[ "transformers", "safetensors", "llama", "text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:28:49+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me1-seqsight_65536_512_47M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me1) dataset. It achieves the following results on the evaluation set: - Loss: 0.5417 - F1 Score: 0.7453 - Accuracy: 0.7465 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6298 \| 1.01 \| 200 \| 0.6057 \| 0.6909 \| 0.6929 \| \| 0.6045 \| 2.02 \| 400 \| 0.6003 \| 0.6998 \| 0.7004 \| \| 0.5948 \| 3.03 \| 600 \| 0.5930 \| 0.7061 \| 0.7074 \| \| 0.5909 \| 4.04 \| 800 \| 0.5868 \| 0.7121 \| 0.7146 \| \| 0.5809 \| 5.05 \| 1000 \| 0.5821 \| 0.7155 \| 0.7172 \| \| 0.578 \| 6.06 \| 1200 \| 0.5811 \| 0.7148 \| 0.7191 \| \| 0.573 \| 7.07 \| 1400 \| 0.5783 \| 0.7180 \| 0.7213 \| \| 0.5737 \| 8.08 \| 1600 \| 0.5748 \| 0.7206 \| 0.7235 \| \| 0.5703 \| 9.09 \| 1800 \| 0.5703 \| 0.7262 \| 0.7279 \| \| 0.5664 \| 10.1 \| 2000 \| 0.5725 \| 0.7213 \| 0.7222 \| \| 0.5643 \| 11.11 \| 2200 \| 0.5712 \| 0.7248 \| 0.7270 \| \| 0.5647 \| 12.12 \| 2400 \| 0.5695 \| 0.7278 \| 0.7292 \| \| 0.563 \| 13.13 \| 2600 \| 0.5682 \| 0.7251 \| 0.7270 \| \| 0.5629 \| 14.14 \| 2800 \| 0.5641 \| 0.7292 \| 0.7314 \| \| 0.5582 \| 15.15 \| 3000 \| 0.5625 \| 0.7289 \| 0.7307 \| \| 0.5586 \| 16.16 \| 3200 \| 0.5639 \| 0.7267 \| 0.7295 \| \| 0.5564 \| 17.17 \| 3400 \| 0.5630 \| 0.7295 \| 0.7323 \| \| 0.5565 \| 18.18 \| 3600 \| 0.5582 \| 0.7325 \| 0.7336 \| \| 0.5531 \| 19.19 \| 3800 \| 0.5613 \| 0.7311 \| 0.7336 \| \| 0.5546 \| 20.2 \| 4000 \| 0.5590 \| 0.7314 \| 0.7330 \| \| 0.5507 \| 21.21 \| 4200 \| 0.5631 \| 0.7337 \| 0.7367 \| \| 0.552 \| 22.22 \| 4400 \| 0.5596 \| 0.7344 \| 0.7358 \| \| 0.5501 \| 23.23 \| 4600 \| 0.5615 \| 0.7342 \| 0.7367 \| \| 0.5545 \| 24.24 \| 4800 \| 0.5566 \| 0.7385 \| 0.7405 \| \| 0.5478 \| 25.25 \| 5000 \| 0.5563 \| 0.7372 \| 0.7386 \| \| 0.5501 \| 26.26 \| 5200 \| 0.5585 \| 0.7345 \| 0.7355 \| \| 0.5459 \| 27.27 \| 5400 \| 0.5563 \| 0.7350 \| 0.7367 \| \| 0.5483 \| 28.28 \| 5600 \| 0.5585 \| 0.7332 \| 0.7339 \| \| 0.5521 \| 29.29 \| 5800 \| 0.5566 \| 0.7362 \| 0.7386 \| \| 0.5451 \| 30.3 \| 6000 \| 0.5552 \| 0.7358 \| 0.7371 \| \| 0.5469 \| 31.31 \| 6200 \| 0.5547 \| 0.7378 \| 0.7396 \| \| 0.5478 \| 32.32 \| 6400 \| 0.5564 \| 0.7350 \| 0.7380 \| \| 0.5417 \| 33.33 \| 6600 \| 0.5552 \| 0.7365 \| 0.7390 \| \| 0.5433 \| 34.34 \| 6800 \| 0.5562 \| 0.7347 \| 0.7377 \| \| 0.5425 \| 35.35 \| 7000 \| 0.5530 \| 0.7407 \| 0.7421 \| \| 0.5473 \| 36.36 \| 7200 \| 0.5529 \| 0.7363 \| 0.7380 \| \| 0.5431 \| 37.37 \| 7400 \| 0.5531 \| 0.7360 \| 0.7374 \| \| 0.542 \| 38.38 \| 7600 \| 0.5538 \| 0.7373 \| 0.7383 \| \| 0.5421 \| 39.39 \| 7800 \| 0.5536 \| 0.7358 \| 0.7371 \| \| 0.544 \| 40.4 \| 8000 \| 0.5536 \| 0.7371 \| 0.7386 \| \| 0.5428 \| 41.41 \| 8200 \| 0.5535 \| 0.7369 \| 0.7393 \| \| 0.5474 \| 42.42 \| 8400 \| 0.5529 \| 0.7382 \| 0.7405 \| \| 0.5415 \| 43.43 \| 8600 \| 0.5530 \| 0.7345 \| 0.7364 \| \| 0.54 \| 44.44 \| 8800 \| 0.5529 \| 0.7367 \| 0.7383 \| \| 0.5416 \| 45.45 \| 9000 \| 0.5526 \| 0.7380 \| 0.7396 \| \| 0.5424 \| 46.46 \| 9200 \| 0.5527 \| 0.7364 \| 0.7383 \| \| 0.5451 \| 47.47 \| 9400 \| 0.5525 \| 0.7361 \| 0.7380 \| \| 0.5367 \| 48.48 \| 9600 \| 0.5528 \| 0.7359 \| 0.7377 \| \| 0.5461 \| 49.49 \| 9800 \| 0.5524 \| 0.7362 \| 0.7380 \| \| 0.5409 \| 50.51 \| 10000 \| 0.5525 \| 0.7369 \| 0.7386 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3K4me1-seqsight_65536_512_47M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me1-seqsight_65536_512_47M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]	null	2024-05-03T16:28:56+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me1-seqsight_65536_512_47M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me1) dataset. It achieves the following results on the evaluation set: - Loss: 0.5342 - F1 Score: 0.7479 - Accuracy: 0.7503 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6119 \| 1.01 \| 200 \| 0.5885 \| 0.7076 \| 0.7118 \| \| 0.5758 \| 2.02 \| 400 \| 0.5650 \| 0.7279 \| 0.7295 \| \| 0.5634 \| 3.03 \| 600 \| 0.5565 \| 0.7319 \| 0.7342 \| \| 0.5565 \| 4.04 \| 800 \| 0.5557 \| 0.7387 \| 0.7405 \| \| 0.5482 \| 5.05 \| 1000 \| 0.5473 \| 0.7424 \| 0.7434 \| \| 0.543 \| 6.06 \| 1200 \| 0.5546 \| 0.7293 \| 0.7345 \| \| 0.5323 \| 7.07 \| 1400 \| 0.5504 \| 0.7414 \| 0.7440 \| \| 0.531 \| 8.08 \| 1600 \| 0.5479 \| 0.7375 \| 0.7402 \| \| 0.5291 \| 9.09 \| 1800 \| 0.5429 \| 0.7434 \| 0.7459 \| \| 0.5196 \| 10.1 \| 2000 \| 0.5444 \| 0.7473 \| 0.7494 \| \| 0.5147 \| 11.11 \| 2200 \| 0.5500 \| 0.7482 \| 0.7513 \| \| 0.5142 \| 12.12 \| 2400 \| 0.5434 \| 0.7444 \| 0.7462 \| \| 0.5088 \| 13.13 \| 2600 \| 0.5517 \| 0.7404 \| 0.7431 \| \| 0.5083 \| 14.14 \| 2800 \| 0.5411 \| 0.7478 \| 0.7494 \| \| 0.4974 \| 15.15 \| 3000 \| 0.5450 \| 0.7427 \| 0.7449 \| \| 0.4994 \| 16.16 \| 3200 \| 0.5419 \| 0.7409 \| 0.7440 \| \| 0.4928 \| 17.17 \| 3400 \| 0.5453 \| 0.7485 \| 0.7503 \| \| 0.4909 \| 18.18 \| 3600 \| 0.5473 \| 0.7428 \| 0.7443 \| \| 0.4837 \| 19.19 \| 3800 \| 0.5471 \| 0.7458 \| 0.7478 \| \| 0.4813 \| 20.2 \| 4000 \| 0.5473 \| 0.7402 \| 0.7421 \| \| 0.4778 \| 21.21 \| 4200 \| 0.5463 \| 0.7410 \| 0.7440 \| \| 0.473 \| 22.22 \| 4400 \| 0.5585 \| 0.7418 \| 0.7434 \| \| 0.4706 \| 23.23 \| 4600 \| 0.5573 \| 0.7435 \| 0.7446 \| \| 0.4706 \| 24.24 \| 4800 \| 0.5544 \| 0.7398 \| 0.7424 \| \| 0.4626 \| 25.25 \| 5000 \| 0.5588 \| 0.7457 \| 0.7465 \| \| 0.463 \| 26.26 \| 5200 \| 0.5579 \| 0.7399 \| 0.7408 \| \| 0.4582 \| 27.27 \| 5400 \| 0.5557 \| 0.7385 \| 0.7415 \| \| 0.4599 \| 28.28 \| 5600 \| 0.5634 \| 0.7389 \| 0.7399 \| \| 0.4575 \| 29.29 \| 5800 \| 0.5552 \| 0.7401 \| 0.7431 \| \| 0.453 \| 30.3 \| 6000 \| 0.5668 \| 0.7389 \| 0.7405 \| \| 0.4525 \| 31.31 \| 6200 \| 0.5550 \| 0.7407 \| 0.7421 \| \| 0.4521 \| 32.32 \| 6400 \| 0.5617 \| 0.7406 \| 0.7434 \| \| 0.4442 \| 33.33 \| 6600 \| 0.5689 \| 0.7395 \| 0.7424 \| \| 0.442 \| 34.34 \| 6800 \| 0.5624 \| 0.7455 \| 0.7472 \| \| 0.4384 \| 35.35 \| 7000 \| 0.5687 \| 0.7431 \| 0.7449 \| \| 0.4474 \| 36.36 \| 7200 \| 0.5577 \| 0.7393 \| 0.7412 \| \| 0.4343 \| 37.37 \| 7400 \| 0.5663 \| 0.7425 \| 0.7443 \| \| 0.4362 \| 38.38 \| 7600 \| 0.5668 \| 0.7413 \| 0.7424 \| \| 0.4359 \| 39.39 \| 7800 \| 0.5707 \| 0.7376 \| 0.7386 \| \| 0.4341 \| 40.4 \| 8000 \| 0.5797 \| 0.7384 \| 0.7396 \| \| 0.4292 \| 41.41 \| 8200 \| 0.5780 \| 0.7390 \| 0.7421 \| \| 0.4375 \| 42.42 \| 8400 \| 0.5716 \| 0.7386 \| 0.7405 \| \| 0.4293 \| 43.43 \| 8600 \| 0.5735 \| 0.7406 \| 0.7424 \| \| 0.4273 \| 44.44 \| 8800 \| 0.5755 \| 0.7391 \| 0.7412 \| \| 0.4243 \| 45.45 \| 9000 \| 0.5755 \| 0.7413 \| 0.7427 \| \| 0.426 \| 46.46 \| 9200 \| 0.5778 \| 0.7400 \| 0.7418 \| \| 0.4296 \| 47.47 \| 9400 \| 0.5708 \| 0.7387 \| 0.7408 \| \| 0.4172 \| 48.48 \| 9600 \| 0.5781 \| 0.7385 \| 0.7408 \| \| 0.4309 \| 49.49 \| 9800 \| 0.5737 \| 0.7396 \| 0.7418 \| \| 0.4227 \| 50.51 \| 10000 \| 0.5749 \| 0.7402 \| 0.7421 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3K4me1-seqsight_65536_512_47M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me1-seqsight_65536_512_47M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]	null	2024-05-03T16:29:06+00:00
null	null		{}	lngalmsyr/indobert-hoaxClass	null	[ "region:us" ]	null	2024-05-03T16:29:10+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me1-seqsight_65536_512_47M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me1) dataset. It achieves the following results on the evaluation set: - Loss: 0.5308 - F1 Score: 0.7509 - Accuracy: 0.7532 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6204 \| 1.01 \| 200 \| 0.5983 \| 0.6993 \| 0.7027 \| \| 0.5905 \| 2.02 \| 400 \| 0.5808 \| 0.7134 \| 0.7165 \| \| 0.5754 \| 3.03 \| 600 \| 0.5707 \| 0.7255 \| 0.7279 \| \| 0.5712 \| 4.04 \| 800 \| 0.5692 \| 0.7207 \| 0.7235 \| \| 0.5627 \| 5.05 \| 1000 \| 0.5617 \| 0.7320 \| 0.7339 \| \| 0.5601 \| 6.06 \| 1200 \| 0.5642 \| 0.7224 \| 0.7266 \| \| 0.5519 \| 7.07 \| 1400 \| 0.5591 \| 0.7345 \| 0.7371 \| \| 0.5513 \| 8.08 \| 1600 \| 0.5567 \| 0.7322 \| 0.7352 \| \| 0.5475 \| 9.09 \| 1800 \| 0.5500 \| 0.7391 \| 0.7412 \| \| 0.5416 \| 10.1 \| 2000 \| 0.5502 \| 0.7406 \| 0.7424 \| \| 0.5385 \| 11.11 \| 2200 \| 0.5543 \| 0.7401 \| 0.7427 \| \| 0.5387 \| 12.12 \| 2400 \| 0.5486 \| 0.7430 \| 0.7443 \| \| 0.5355 \| 13.13 \| 2600 \| 0.5536 \| 0.7396 \| 0.7418 \| \| 0.5353 \| 14.14 \| 2800 \| 0.5485 \| 0.7441 \| 0.7462 \| \| 0.5297 \| 15.15 \| 3000 \| 0.5485 \| 0.7435 \| 0.7456 \| \| 0.5287 \| 16.16 \| 3200 \| 0.5456 \| 0.7401 \| 0.7431 \| \| 0.5269 \| 17.17 \| 3400 \| 0.5484 \| 0.7426 \| 0.7453 \| \| 0.5257 \| 18.18 \| 3600 \| 0.5428 \| 0.7473 \| 0.7487 \| \| 0.5213 \| 19.19 \| 3800 \| 0.5433 \| 0.7402 \| 0.7431 \| \| 0.5215 \| 20.2 \| 4000 \| 0.5433 \| 0.7452 \| 0.7472 \| \| 0.5196 \| 21.21 \| 4200 \| 0.5514 \| 0.7434 \| 0.7465 \| \| 0.5188 \| 22.22 \| 4400 \| 0.5465 \| 0.7448 \| 0.7472 \| \| 0.5182 \| 23.23 \| 4600 \| 0.5449 \| 0.7456 \| 0.7481 \| \| 0.5198 \| 24.24 \| 4800 \| 0.5446 \| 0.7427 \| 0.7456 \| \| 0.514 \| 25.25 \| 5000 \| 0.5429 \| 0.7467 \| 0.7481 \| \| 0.5147 \| 26.26 \| 5200 \| 0.5465 \| 0.7484 \| 0.7491 \| \| 0.5109 \| 27.27 \| 5400 \| 0.5419 \| 0.7468 \| 0.7487 \| \| 0.514 \| 28.28 \| 5600 \| 0.5464 \| 0.7447 \| 0.7453 \| \| 0.5148 \| 29.29 \| 5800 \| 0.5456 \| 0.7457 \| 0.7487 \| \| 0.5083 \| 30.3 \| 6000 \| 0.5455 \| 0.7457 \| 0.7472 \| \| 0.509 \| 31.31 \| 6200 \| 0.5444 \| 0.7466 \| 0.7481 \| \| 0.5105 \| 32.32 \| 6400 \| 0.5460 \| 0.7460 \| 0.7484 \| \| 0.5053 \| 33.33 \| 6600 \| 0.5483 \| 0.7447 \| 0.7475 \| \| 0.5054 \| 34.34 \| 6800 \| 0.5445 \| 0.7463 \| 0.7487 \| \| 0.5027 \| 35.35 \| 7000 \| 0.5424 \| 0.7487 \| 0.7503 \| \| 0.5086 \| 36.36 \| 7200 \| 0.5405 \| 0.7464 \| 0.7481 \| \| 0.5017 \| 37.37 \| 7400 \| 0.5435 \| 0.7440 \| 0.7456 \| \| 0.4999 \| 38.38 \| 7600 \| 0.5433 \| 0.7486 \| 0.7497 \| \| 0.5025 \| 39.39 \| 7800 \| 0.5442 \| 0.7475 \| 0.7484 \| \| 0.5038 \| 40.4 \| 8000 \| 0.5467 \| 0.7470 \| 0.7484 \| \| 0.501 \| 41.41 \| 8200 \| 0.5445 \| 0.7416 \| 0.7443 \| \| 0.5077 \| 42.42 \| 8400 \| 0.5422 \| 0.7472 \| 0.7494 \| \| 0.4974 \| 43.43 \| 8600 \| 0.5435 \| 0.7464 \| 0.7481 \| \| 0.4987 \| 44.44 \| 8800 \| 0.5445 \| 0.7446 \| 0.7462 \| \| 0.4971 \| 45.45 \| 9000 \| 0.5449 \| 0.7468 \| 0.7484 \| \| 0.499 \| 46.46 \| 9200 \| 0.5438 \| 0.7470 \| 0.7487 \| \| 0.503 \| 47.47 \| 9400 \| 0.5428 \| 0.7437 \| 0.7456 \| \| 0.4927 \| 48.48 \| 9600 \| 0.5450 \| 0.7463 \| 0.7481 \| \| 0.5038 \| 49.49 \| 9800 \| 0.5438 \| 0.7460 \| 0.7478 \| \| 0.4975 \| 50.51 \| 10000 \| 0.5441 \| 0.7454 \| 0.7472 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3K4me1-seqsight_65536_512_47M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me1-seqsight_65536_512_47M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]	null	2024-05-03T16:29:21+00:00
text-generation	transformers		{}	Zekunli/Llama-2-7b-mlp2x-mmncphysical-text-only-v2	null	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:29:25+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K36me3-seqsight_65536_512_47M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3K36me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K36me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5000 - F1 Score: 0.7751 - Accuracy: 0.7769 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5805 \| 0.92 \| 200 \| 0.5475 \| 0.7329 \| 0.7351 \| \| 0.533 \| 1.83 \| 400 \| 0.5360 \| 0.7384 \| 0.7411 \| \| 0.5243 \| 2.75 \| 600 \| 0.5261 \| 0.7465 \| 0.7483 \| \| 0.5216 \| 3.67 \| 800 \| 0.5199 \| 0.7555 \| 0.7563 \| \| 0.5103 \| 4.59 \| 1000 \| 0.5198 \| 0.7563 \| 0.7583 \| \| 0.5074 \| 5.5 \| 1200 \| 0.5137 \| 0.7593 \| 0.7615 \| \| 0.5047 \| 6.42 \| 1400 \| 0.5086 \| 0.7731 \| 0.7738 \| \| 0.5017 \| 7.34 \| 1600 \| 0.5109 \| 0.7695 \| 0.7712 \| \| 0.4951 \| 8.26 \| 1800 \| 0.5114 \| 0.7696 \| 0.7718 \| \| 0.499 \| 9.17 \| 2000 \| 0.5101 \| 0.7674 \| 0.7701 \| \| 0.4968 \| 10.09 \| 2200 \| 0.5107 \| 0.7670 \| 0.7704 \| \| 0.4928 \| 11.01 \| 2400 \| 0.5085 \| 0.7655 \| 0.7689 \| \| 0.4914 \| 11.93 \| 2600 \| 0.5024 \| 0.7741 \| 0.7764 \| \| 0.4898 \| 12.84 \| 2800 \| 0.5021 \| 0.7707 \| 0.7732 \| \| 0.4886 \| 13.76 \| 3000 \| 0.5087 \| 0.7676 \| 0.7709 \| \| 0.4853 \| 14.68 \| 3200 \| 0.4988 \| 0.7759 \| 0.7775 \| \| 0.489 \| 15.6 \| 3400 \| 0.5080 \| 0.7675 \| 0.7712 \| \| 0.4866 \| 16.51 \| 3600 \| 0.5003 \| 0.7750 \| 0.7769 \| \| 0.4851 \| 17.43 \| 3800 \| 0.4924 \| 0.7816 \| 0.7830 \| \| 0.4856 \| 18.35 \| 4000 \| 0.4995 \| 0.7763 \| 0.7787 \| \| 0.4816 \| 19.27 \| 4200 \| 0.4990 \| 0.7754 \| 0.7775 \| \| 0.4845 \| 20.18 \| 4400 \| 0.5034 \| 0.7717 \| 0.7749 \| \| 0.4832 \| 21.1 \| 4600 \| 0.4975 \| 0.7765 \| 0.7787 \| \| 0.4828 \| 22.02 \| 4800 \| 0.5014 \| 0.7756 \| 0.7778 \| \| 0.4829 \| 22.94 \| 5000 \| 0.4969 \| 0.7744 \| 0.7769 \| \| 0.4803 \| 23.85 \| 5200 \| 0.4996 \| 0.7732 \| 0.7761 \| \| 0.4788 \| 24.77 \| 5400 \| 0.5065 \| 0.7725 \| 0.7758 \| \| 0.4817 \| 25.69 \| 5600 \| 0.5004 \| 0.7760 \| 0.7784 \| \| 0.4796 \| 26.61 \| 5800 \| 0.4973 \| 0.7755 \| 0.7778 \| \| 0.4758 \| 27.52 \| 6000 \| 0.5100 \| 0.7729 \| 0.7764 \| \| 0.4787 \| 28.44 \| 6200 \| 0.5018 \| 0.7717 \| 0.7747 \| \| 0.4762 \| 29.36 \| 6400 \| 0.5042 \| 0.7713 \| 0.7747 \| \| 0.4794 \| 30.28 \| 6600 \| 0.5040 \| 0.7725 \| 0.7758 \| \| 0.4762 \| 31.19 \| 6800 \| 0.4930 \| 0.7812 \| 0.7827 \| \| 0.476 \| 32.11 \| 7000 \| 0.4992 \| 0.7733 \| 0.7764 \| \| 0.4767 \| 33.03 \| 7200 \| 0.5005 \| 0.7742 \| 0.7769 \| \| 0.4753 \| 33.94 \| 7400 \| 0.5002 \| 0.7756 \| 0.7781 \| \| 0.4756 \| 34.86 \| 7600 \| 0.4983 \| 0.7750 \| 0.7778 \| \| 0.4743 \| 35.78 \| 7800 \| 0.4978 \| 0.7738 \| 0.7767 \| \| 0.476 \| 36.7 \| 8000 \| 0.4983 \| 0.7744 \| 0.7772 \| \| 0.4736 \| 37.61 \| 8200 \| 0.5032 \| 0.7712 \| 0.7747 \| \| 0.4758 \| 38.53 \| 8400 \| 0.4928 \| 0.7799 \| 0.7818 \| \| 0.4734 \| 39.45 \| 8600 \| 0.4986 \| 0.7745 \| 0.7772 \| \| 0.4725 \| 40.37 \| 8800 \| 0.5023 \| 0.7729 \| 0.7761 \| \| 0.4773 \| 41.28 \| 9000 \| 0.4986 \| 0.7734 \| 0.7764 \| \| 0.4743 \| 42.2 \| 9200 \| 0.4955 \| 0.7774 \| 0.7798 \| \| 0.4721 \| 43.12 \| 9400 \| 0.4984 \| 0.7755 \| 0.7781 \| \| 0.4744 \| 44.04 \| 9600 \| 0.4979 \| 0.7750 \| 0.7778 \| \| 0.4732 \| 44.95 \| 9800 \| 0.5005 \| 0.7721 \| 0.7752 \| \| 0.4742 \| 45.87 \| 10000 \| 0.4987 \| 0.7755 \| 0.7784 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3K36me3-seqsight_65536_512_47M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K36me3-seqsight_65536_512_47M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]	null	2024-05-03T16:29:45+00:00
text-generation	transformers	# dareties 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 [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using ./models2/Meta-Llama-3-8B-instruct as a base. ### Models Merged The following models were included in the merge: * ./models2/Llama-3-Kafka-8B-v0.1 * [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B) * ./models2/Llama3_DiscoLM_German_8b_v0.1_experimental * ./models2/Llama-3-SauerkrautLM-8b-Instruct ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: ./models2/Meta-Llama-3-8B-instruct # No parameters necessary for base model - model: ./models2/Llama-3-SauerkrautLM-8b-Instruct parameters: density: 0.6 weight: 0.25 - model: ./models2/Llama3_DiscoLM_German_8b_v0.1_experimental parameters: density: 0.6 weight: 0.25 - model: ./models2/Llama-3-Kafka-8B-v0.1 parameters: density: 0.6 weight: 0.25 - model: NousResearch/Hermes-2-Pro-Llama-3-8B parameters: density: 0.6 weight: 0.25 merge_method: dare_ties base_model: ./models2/Meta-Llama-3-8B-instruct tokenizer_source: model:NousResearch/Hermes-2-Pro-Llama-3-8B parameters: int8_mask: true dtype: bfloat16 ```	{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["NousResearch/Hermes-2-Pro-Llama-3-8B"]}	johannhartmann/llama8_dt_b	null	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:NousResearch/Hermes-2-Pro-Llama-3-8B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:30:05+00:00
null	null		{}	Gokulapriyan9677/videomae-base-finetuned-ucf101-subset	null	[ "region:us" ]	null	2024-05-03T16:30:15+00:00
object-detection	transformers		{}	nsugianto/tabletransstructrecog_finetuned_pubt1m_lstabletransstrucrecogv1_session6	null	[ "transformers", "tensorboard", "safetensors", "table-transformer", "object-detection", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:30:25+00:00
null	transformers	# Uploaded model - Developed by: johannoriel - 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	johannoriel/medllama_model	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:30:46+00:00
null	null	This model is finetuned with an ML Q&A dataset - hanyueshf/ml-arxiv-papers-qa. It outperforms both its base Llama-2-7B-Chat and Llama-3-8B-Instruct, as shown in below figure. Note: improvement = (finetuned_llama2_scores - base_llama2_scores) / base_llama2_scores. The finetuning code is available on Github at https://github.com/hanyuesgithub/QA-ml-arxiv-papers. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65ab7449356bf23b4ac0f556/vx-VNfMq3q4iNRqmFz1iw.png)	{}	hanyueshf/llama-2-7b-chat-ml-qa	null	[ "region:us" ]	null	2024-05-03T16:31:05+00:00
text-classification	transformers	<!-- 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. --> # fine_tuned_boolq_bert This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5736 - Accuracy: 0.7222 - F1: 0.7325 ## 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 \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.6443 \| 4.1667 \| 50 \| 0.5606 \| 0.7778 \| 0.6806 \| \| 0.3932 \| 8.3333 \| 100 \| 0.6016 \| 0.6111 \| 0.6255 \| \| 0.126 \| 12.5 \| 150 \| 1.0887 \| 0.5 \| 0.5418 \| \| 0.0166 \| 16.6667 \| 200 \| 1.5543 \| 0.5556 \| 0.5829 \| \| 0.0041 \| 20.8333 \| 250 \| 1.5032 \| 0.7222 \| 0.7325 \| \| 0.0022 \| 25.0 \| 300 \| 1.7354 \| 0.6667 \| 0.6872 \| \| 0.0018 \| 29.1667 \| 350 \| 1.5756 \| 0.6667 \| 0.6667 \| \| 0.0016 \| 33.3333 \| 400 \| 1.5736 \| 0.7222 \| 0.7325 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "google-bert/bert-base-uncased", "model-index": [{"name": "fine_tuned_boolq_bert", "results": []}]}	lenatr99/fine_tuned_boolq_bert	null	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:31:44+00:00
reinforcement-learning	null	# Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="TeoGal/q-FrozenLake-v1-4x4-noSlippery", 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"]) ```	{"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}	TeoGal/q-FrozenLake-v1-4x4-noSlippery	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	null	2024-05-03T16:32:44+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	golf2248/mdxmtky	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:32:49+00:00
null	transformers	# LLOROv2 - GGUF VERSION Este repositório contém o modelo Llorov2 de 7B de parâmetros em formato GGUF, na versão 16 bits e também nas versão quantizada de 8 bits. Lloro, desenvolvido pelos Laboratórios de Pesquisa Semantix, é um Modelo de Linguagem que foi treinado para realizar efetivamente Análise de Dados em Português no Python. É uma versão aprimorada de codellama/CodeLlama-7b-Instruct-hf, que foi treinado em conjuntos de dados sintéticos. O processo de aprimoramento foi realizado usando a metodologia QLORA em uma GPU V100 com 16 GB de RAM. Acesse o [site](https://semantix.ai/conheca-o-lloro-o-primeiro-modelo-de-ia-expert-em-analise-de-dados-100-brasileiro/) para mais informações sobre o Lloro. # Sobre o formato GGUF O modelo no formato GGUF permite seu uso para inferência usando o llama.cpp, permitindo tanto o uso de CPU como de GPU, e outras bibliotecas e ferramentas compatíveis, como: * [text-generation-webui](https://github.com/oobabooga/text-generation-webui) * [KoboldCpp](https://github.com/LostRuins/koboldcpp) * [LM Studio](https://lmstudio.ai/) * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) * [ctransformers](https://github.com/marella/ctransformers) * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) ## Detalhes do Modelo - Modelo Base: CodeLlama-7b-Instruct-hf - Dataset de Treinamento: Synthetic - Idioma: Português ## Contribuições Contribuições para a melhoria deste modelo são bem-vindas. Sinta-se à vontade para abrir problemas e solicitações pull.	{"language": ["pt"], "license": "llama2", "library_name": "transformers", "tags": ["LLM", "Portuguese", "Lloro", "Llama 2", "Q&A"], "datasets": ["semantixai/Test-Dataset-Lloro"], "base_model": "codellama/CodeLlama-7b-Instruct-hf"}	anaxsouza/llorov2-gguf	null	[ "transformers", "gguf", "LLM", "Portuguese", "Lloro", "Llama 2", "Q&A", "pt", "dataset:semantixai/Test-Dataset-Lloro", "base_model:codellama/CodeLlama-7b-Instruct-hf", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:33:25+00:00
null	null		{"license": "cc-by-4.0"}	unileon-robotics/home-objects	null	[ "license:cc-by-4.0", "region:us" ]	null	2024-05-03T16:34:43+00:00
text-generation	transformers		{}	Yeobin/llama-3-8b-math	null	[ "transformers", "pytorch", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:34:49+00:00
text-generation	transformers	<!-- 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. --> # sft-fsi This model is a fine-tuned version of [dynamofl/dynamo-1.6B-v0.4-mosaic-dynamoDPO-iter0-2978](https://huggingface.co/dynamofl/dynamo-1.6B-v0.4-mosaic-dynamoDPO-iter0-2978) on the dynamofl/train-default-FSI-PersonalFinancialAdvice-input-formatted-chatml dataset. It achieves the following results on the evaluation set: - Loss: 0.7006 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 5.8867 \| 0.5333 \| 1 \| 5.7356 \| \| 5.8867 \| 1.6 \| 3 \| 2.7050 \| \| 3.7066 \| 2.6667 \| 5 \| 1.9496 \| \| 3.7066 \| 3.7333 \| 7 \| 1.5918 \| \| 3.7066 \| 4.8 \| 9 \| 1.3194 \| \| 1.6243 \| 5.8667 \| 11 \| 1.0055 \| \| 1.6243 \| 6.9333 \| 13 \| 0.8459 \| \| 0.9667 \| 8.0 \| 15 \| 0.7559 \| \| 0.9667 \| 8.5333 \| 16 \| 0.7331 \| \| 0.9667 \| 9.6 \| 18 \| 0.7034 \| \| 0.7508 \| 10.6667 \| 20 \| 0.7006 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer", "trl", "sft", "generated_from_trainer"], "datasets": ["dynamofl/train-default-FSI-PersonalFinancialAdvice-input-formatted-chatml"], "base_model": "dynamofl/dynamo-1.6B-v0.4-mosaic-dynamoDPO-iter0-2978", "model-index": [{"name": "sft-fsi", "results": []}]}	jamesoneill12/sft-fsi	null	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "dataset:dynamofl/train-default-FSI-PersonalFinancialAdvice-input-formatted-chatml", "base_model:dynamofl/dynamo-1.6B-v0.4-mosaic-dynamoDPO-iter0-2978", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:34:49+00:00
null	null		{}	vinven7/Llama2-ft-CrystalStructure	null	[ "region:us" ]	null	2024-05-03T16:35:07+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K36me3-seqsight_65536_512_47M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3K36me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K36me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.4908 - F1 Score: 0.7777 - Accuracy: 0.7790 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5667 \| 0.92 \| 200 \| 0.5382 \| 0.7367 \| 0.7397 \| \| 0.5207 \| 1.83 \| 400 \| 0.5229 \| 0.7512 \| 0.7537 \| \| 0.5071 \| 2.75 \| 600 \| 0.5121 \| 0.7597 \| 0.7618 \| \| 0.505 \| 3.67 \| 800 \| 0.5060 \| 0.7706 \| 0.7724 \| \| 0.4938 \| 4.59 \| 1000 \| 0.5047 \| 0.7715 \| 0.7735 \| \| 0.4908 \| 5.5 \| 1200 \| 0.5033 \| 0.7695 \| 0.7724 \| \| 0.4881 \| 6.42 \| 1400 \| 0.4938 \| 0.7790 \| 0.7801 \| \| 0.4832 \| 7.34 \| 1600 \| 0.5067 \| 0.7749 \| 0.7775 \| \| 0.4775 \| 8.26 \| 1800 \| 0.4963 \| 0.7822 \| 0.7838 \| \| 0.4815 \| 9.17 \| 2000 \| 0.4922 \| 0.7798 \| 0.7815 \| \| 0.478 \| 10.09 \| 2200 \| 0.5153 \| 0.7620 \| 0.7675 \| \| 0.4733 \| 11.01 \| 2400 \| 0.4963 \| 0.7765 \| 0.7801 \| \| 0.4707 \| 11.93 \| 2600 \| 0.4824 \| 0.7864 \| 0.7873 \| \| 0.4677 \| 12.84 \| 2800 \| 0.4894 \| 0.7764 \| 0.7792 \| \| 0.4668 \| 13.76 \| 3000 \| 0.5010 \| 0.7719 \| 0.7761 \| \| 0.4613 \| 14.68 \| 3200 \| 0.4897 \| 0.7811 \| 0.7833 \| \| 0.4644 \| 15.6 \| 3400 \| 0.4857 \| 0.7769 \| 0.7795 \| \| 0.463 \| 16.51 \| 3600 \| 0.4989 \| 0.7776 \| 0.7807 \| \| 0.4594 \| 17.43 \| 3800 \| 0.4825 \| 0.7863 \| 0.7878 \| \| 0.4594 \| 18.35 \| 4000 \| 0.4870 \| 0.7809 \| 0.7833 \| \| 0.4559 \| 19.27 \| 4200 \| 0.4896 \| 0.7829 \| 0.7850 \| \| 0.4579 \| 20.18 \| 4400 \| 0.4996 \| 0.7734 \| 0.7772 \| \| 0.4552 \| 21.1 \| 4600 \| 0.4861 \| 0.7824 \| 0.7847 \| \| 0.4564 \| 22.02 \| 4800 \| 0.4899 \| 0.7840 \| 0.7861 \| \| 0.4525 \| 22.94 \| 5000 \| 0.4892 \| 0.7759 \| 0.7792 \| \| 0.4504 \| 23.85 \| 5200 \| 0.4890 \| 0.7818 \| 0.7847 \| \| 0.4467 \| 24.77 \| 5400 \| 0.5002 \| 0.7733 \| 0.7775 \| \| 0.4512 \| 25.69 \| 5600 \| 0.4926 \| 0.7807 \| 0.7835 \| \| 0.4492 \| 26.61 \| 5800 \| 0.4851 \| 0.7833 \| 0.7856 \| \| 0.4436 \| 27.52 \| 6000 \| 0.5050 \| 0.7786 \| 0.7821 \| \| 0.4465 \| 28.44 \| 6200 \| 0.4897 \| 0.7824 \| 0.7853 \| \| 0.4451 \| 29.36 \| 6400 \| 0.4890 \| 0.7758 \| 0.7792 \| \| 0.4446 \| 30.28 \| 6600 \| 0.4969 \| 0.7771 \| 0.7810 \| \| 0.4429 \| 31.19 \| 6800 \| 0.4843 \| 0.7854 \| 0.7876 \| \| 0.441 \| 32.11 \| 7000 \| 0.4919 \| 0.7806 \| 0.7838 \| \| 0.4424 \| 33.03 \| 7200 \| 0.4934 \| 0.7819 \| 0.7850 \| \| 0.4413 \| 33.94 \| 7400 \| 0.4864 \| 0.7825 \| 0.7850 \| \| 0.4409 \| 34.86 \| 7600 \| 0.4901 \| 0.7825 \| 0.7853 \| \| 0.4398 \| 35.78 \| 7800 \| 0.4866 \| 0.7823 \| 0.7847 \| \| 0.4412 \| 36.7 \| 8000 \| 0.4897 \| 0.7805 \| 0.7835 \| \| 0.4369 \| 37.61 \| 8200 \| 0.4985 \| 0.7776 \| 0.7815 \| \| 0.4408 \| 38.53 \| 8400 \| 0.4874 \| 0.7825 \| 0.7853 \| \| 0.4359 \| 39.45 \| 8600 \| 0.4935 \| 0.7800 \| 0.7833 \| \| 0.4366 \| 40.37 \| 8800 \| 0.4989 \| 0.7804 \| 0.7838 \| \| 0.4396 \| 41.28 \| 9000 \| 0.4934 \| 0.7810 \| 0.7844 \| \| 0.4359 \| 42.2 \| 9200 \| 0.4899 \| 0.7829 \| 0.7858 \| \| 0.4332 \| 43.12 \| 9400 \| 0.4930 \| 0.7831 \| 0.7861 \| \| 0.4371 \| 44.04 \| 9600 \| 0.4909 \| 0.7831 \| 0.7861 \| \| 0.4348 \| 44.95 \| 9800 \| 0.4961 \| 0.7813 \| 0.7847 \| \| 0.4362 \| 45.87 \| 10000 \| 0.4933 \| 0.7830 \| 0.7861 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3K36me3-seqsight_65536_512_47M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K36me3-seqsight_65536_512_47M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]	null	2024-05-03T16:35:35+00:00
null	peft	<!-- 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. --> # GUE_mouse_0-seqsight_65536_512_47M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_mouse_0](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_0) dataset. It achieves the following results on the evaluation set: - Loss: 0.5918 - F1 Score: 0.6924 - Accuracy: 0.6926 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6506 \| 3.92 \| 200 \| 0.6065 \| 0.6652 \| 0.6691 \| \| 0.6137 \| 7.84 \| 400 \| 0.5919 \| 0.6697 \| 0.6716 \| \| 0.6024 \| 11.76 \| 600 \| 0.5871 \| 0.6856 \| 0.6864 \| \| 0.5927 \| 15.69 \| 800 \| 0.5826 \| 0.6963 \| 0.6963 \| \| 0.5819 \| 19.61 \| 1000 \| 0.5840 \| 0.6903 \| 0.6914 \| \| 0.5778 \| 23.53 \| 1200 \| 0.5791 \| 0.7016 \| 0.7025 \| \| 0.5674 \| 27.45 \| 1400 \| 0.5823 \| 0.6957 \| 0.6963 \| \| 0.5655 \| 31.37 \| 1600 \| 0.5750 \| 0.7000 \| 0.7 \| \| 0.5593 \| 35.29 \| 1800 \| 0.5737 \| 0.7037 \| 0.7037 \| \| 0.5532 \| 39.22 \| 2000 \| 0.5797 \| 0.6943 \| 0.6951 \| \| 0.5519 \| 43.14 \| 2200 \| 0.5756 \| 0.6985 \| 0.6988 \| \| 0.5508 \| 47.06 \| 2400 \| 0.5696 \| 0.7145 \| 0.7148 \| \| 0.5412 \| 50.98 \| 2600 \| 0.5818 \| 0.6816 \| 0.6827 \| \| 0.5392 \| 54.9 \| 2800 \| 0.5716 \| 0.7124 \| 0.7136 \| \| 0.5385 \| 58.82 \| 3000 \| 0.5700 \| 0.7047 \| 0.7049 \| \| 0.5367 \| 62.75 \| 3200 \| 0.5681 \| 0.7013 \| 0.7012 \| \| 0.5329 \| 66.67 \| 3400 \| 0.5713 \| 0.6976 \| 0.6975 \| \| 0.5304 \| 70.59 \| 3600 \| 0.5742 \| 0.7010 \| 0.7012 \| \| 0.5282 \| 74.51 \| 3800 \| 0.5724 \| 0.6975 \| 0.6975 \| \| 0.5279 \| 78.43 \| 4000 \| 0.5690 \| 0.6988 \| 0.6988 \| \| 0.5261 \| 82.35 \| 4200 \| 0.5696 \| 0.6998 \| 0.7 \| \| 0.5231 \| 86.27 \| 4400 \| 0.5752 \| 0.6985 \| 0.6988 \| \| 0.5214 \| 90.2 \| 4600 \| 0.5722 \| 0.7013 \| 0.7012 \| \| 0.5186 \| 94.12 \| 4800 \| 0.5787 \| 0.6983 \| 0.6988 \| \| 0.5183 \| 98.04 \| 5000 \| 0.5680 \| 0.7085 \| 0.7086 \| \| 0.5154 \| 101.96 \| 5200 \| 0.5731 \| 0.7036 \| 0.7037 \| \| 0.514 \| 105.88 \| 5400 \| 0.5663 \| 0.7121 \| 0.7123 \| \| 0.5163 \| 109.8 \| 5600 \| 0.5668 \| 0.7148 \| 0.7148 \| \| 0.5144 \| 113.73 \| 5800 \| 0.5673 \| 0.7184 \| 0.7185 \| \| 0.5153 \| 117.65 \| 6000 \| 0.5702 \| 0.7037 \| 0.7037 \| \| 0.5117 \| 121.57 \| 6200 \| 0.5666 \| 0.7161 \| 0.7160 \| \| 0.5099 \| 125.49 \| 6400 \| 0.5759 \| 0.7047 \| 0.7049 \| \| 0.5131 \| 129.41 \| 6600 \| 0.5691 \| 0.7112 \| 0.7111 \| \| 0.5109 \| 133.33 \| 6800 \| 0.5681 \| 0.7124 \| 0.7123 \| \| 0.5097 \| 137.25 \| 7000 \| 0.5711 \| 0.7086 \| 0.7086 \| \| 0.5056 \| 141.18 \| 7200 \| 0.5727 \| 0.7112 \| 0.7111 \| \| 0.5074 \| 145.1 \| 7400 \| 0.5751 \| 0.7060 \| 0.7062 \| \| 0.5065 \| 149.02 \| 7600 \| 0.5696 \| 0.7136 \| 0.7136 \| \| 0.5063 \| 152.94 \| 7800 \| 0.5720 \| 0.7099 \| 0.7099 \| \| 0.5041 \| 156.86 \| 8000 \| 0.5691 \| 0.7112 \| 0.7111 \| \| 0.5059 \| 160.78 \| 8200 \| 0.5727 \| 0.7099 \| 0.7099 \| \| 0.5064 \| 164.71 \| 8400 \| 0.5724 \| 0.7111 \| 0.7111 \| \| 0.5049 \| 168.63 \| 8600 \| 0.5717 \| 0.7099 \| 0.7099 \| \| 0.5042 \| 172.55 \| 8800 \| 0.5697 \| 0.7149 \| 0.7148 \| \| 0.5008 \| 176.47 \| 9000 \| 0.5716 \| 0.7149 \| 0.7148 \| \| 0.4982 \| 180.39 \| 9200 \| 0.5729 \| 0.7136 \| 0.7136 \| \| 0.4989 \| 184.31 \| 9400 \| 0.5742 \| 0.7099 \| 0.7099 \| \| 0.4989 \| 188.24 \| 9600 \| 0.5733 \| 0.7111 \| 0.7111 \| \| 0.504 \| 192.16 \| 9800 \| 0.5722 \| 0.7124 \| 0.7123 \| \| 0.4969 \| 196.08 \| 10000 \| 0.5727 \| 0.7111 \| 0.7111 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_mouse_0-seqsight_65536_512_47M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_mouse_0-seqsight_65536_512_47M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]	null	2024-05-03T16:35:35+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K36me3-seqsight_65536_512_47M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3K36me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K36me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5059 - F1 Score: 0.7826 - Accuracy: 0.7850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5563 \| 0.92 \| 200 \| 0.5396 \| 0.7412 \| 0.7451 \| \| 0.5106 \| 1.83 \| 400 \| 0.5126 \| 0.7627 \| 0.7655 \| \| 0.4971 \| 2.75 \| 600 \| 0.5045 \| 0.7687 \| 0.7709 \| \| 0.4962 \| 3.67 \| 800 \| 0.4940 \| 0.7739 \| 0.7752 \| \| 0.4826 \| 4.59 \| 1000 \| 0.4927 \| 0.7786 \| 0.7801 \| \| 0.4764 \| 5.5 \| 1200 \| 0.4988 \| 0.7700 \| 0.7738 \| \| 0.4737 \| 6.42 \| 1400 \| 0.4823 \| 0.7788 \| 0.7798 \| \| 0.4665 \| 7.34 \| 1600 \| 0.4999 \| 0.7720 \| 0.7752 \| \| 0.4582 \| 8.26 \| 1800 \| 0.4894 \| 0.7817 \| 0.7830 \| \| 0.4631 \| 9.17 \| 2000 \| 0.4824 \| 0.7819 \| 0.7833 \| \| 0.4574 \| 10.09 \| 2200 \| 0.5118 \| 0.7611 \| 0.7663 \| \| 0.4517 \| 11.01 \| 2400 \| 0.4879 \| 0.7811 \| 0.7841 \| \| 0.4455 \| 11.93 \| 2600 \| 0.4735 \| 0.7886 \| 0.7893 \| \| 0.4428 \| 12.84 \| 2800 \| 0.4840 \| 0.7766 \| 0.7798 \| \| 0.4382 \| 13.76 \| 3000 \| 0.5024 \| 0.7712 \| 0.7761 \| \| 0.4323 \| 14.68 \| 3200 \| 0.4920 \| 0.7870 \| 0.7893 \| \| 0.4336 \| 15.6 \| 3400 \| 0.4818 \| 0.7847 \| 0.7876 \| \| 0.4307 \| 16.51 \| 3600 \| 0.4985 \| 0.7775 \| 0.7807 \| \| 0.4247 \| 17.43 \| 3800 \| 0.4830 \| 0.7893 \| 0.7910 \| \| 0.4217 \| 18.35 \| 4000 \| 0.4860 \| 0.7895 \| 0.7913 \| \| 0.4195 \| 19.27 \| 4200 \| 0.5007 \| 0.7898 \| 0.7919 \| \| 0.4169 \| 20.18 \| 4400 \| 0.5000 \| 0.7856 \| 0.7884 \| \| 0.4126 \| 21.1 \| 4600 \| 0.4923 \| 0.7923 \| 0.7939 \| \| 0.4123 \| 22.02 \| 4800 \| 0.4984 \| 0.7824 \| 0.7850 \| \| 0.4059 \| 22.94 \| 5000 \| 0.4944 \| 0.7830 \| 0.7861 \| \| 0.4024 \| 23.85 \| 5200 \| 0.4952 \| 0.7799 \| 0.7830 \| \| 0.399 \| 24.77 \| 5400 \| 0.5144 \| 0.7755 \| 0.7798 \| \| 0.3994 \| 25.69 \| 5600 \| 0.5067 \| 0.7887 \| 0.7913 \| \| 0.3975 \| 26.61 \| 5800 \| 0.4957 \| 0.7832 \| 0.7858 \| \| 0.3908 \| 27.52 \| 6000 \| 0.5261 \| 0.7785 \| 0.7818 \| \| 0.392 \| 28.44 \| 6200 \| 0.4996 \| 0.7841 \| 0.7867 \| \| 0.3887 \| 29.36 \| 6400 \| 0.5025 \| 0.7812 \| 0.7844 \| \| 0.3849 \| 30.28 \| 6600 \| 0.5128 \| 0.7742 \| 0.7781 \| \| 0.3824 \| 31.19 \| 6800 \| 0.5134 \| 0.7831 \| 0.7858 \| \| 0.3811 \| 32.11 \| 7000 \| 0.5071 \| 0.7820 \| 0.7847 \| \| 0.3815 \| 33.03 \| 7200 \| 0.5100 \| 0.7833 \| 0.7858 \| \| 0.3787 \| 33.94 \| 7400 \| 0.5030 \| 0.7860 \| 0.7881 \| \| 0.3754 \| 34.86 \| 7600 \| 0.5084 \| 0.7831 \| 0.7858 \| \| 0.373 \| 35.78 \| 7800 \| 0.5119 \| 0.7851 \| 0.7876 \| \| 0.3724 \| 36.7 \| 8000 \| 0.5201 \| 0.7838 \| 0.7870 \| \| 0.3728 \| 37.61 \| 8200 \| 0.5250 \| 0.7716 \| 0.7758 \| \| 0.3755 \| 38.53 \| 8400 \| 0.5147 \| 0.7827 \| 0.7853 \| \| 0.3679 \| 39.45 \| 8600 \| 0.5214 \| 0.7767 \| 0.7804 \| \| 0.3668 \| 40.37 \| 8800 \| 0.5325 \| 0.7759 \| 0.7795 \| \| 0.3704 \| 41.28 \| 9000 \| 0.5190 \| 0.7773 \| 0.7807 \| \| 0.3628 \| 42.2 \| 9200 \| 0.5186 \| 0.7821 \| 0.7850 \| \| 0.3624 \| 43.12 \| 9400 \| 0.5233 \| 0.7824 \| 0.7853 \| \| 0.3623 \| 44.04 \| 9600 \| 0.5235 \| 0.7788 \| 0.7821 \| \| 0.3628 \| 44.95 \| 9800 \| 0.5291 \| 0.7771 \| 0.7807 \| \| 0.3665 \| 45.87 \| 10000 \| 0.5250 \| 0.7809 \| 0.7841 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3K36me3-seqsight_65536_512_47M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K36me3-seqsight_65536_512_47M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]	null	2024-05-03T16:35:35+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_300_notata-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.1181 - F1 Score: 0.9561 - Accuracy: 0.9561 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.2897 \| 0.6 \| 200 \| 0.1532 \| 0.9391 \| 0.9391 \| \| 0.1588 \| 1.2 \| 400 \| 0.1377 \| 0.9472 \| 0.9472 \| \| 0.1482 \| 1.81 \| 600 \| 0.1222 \| 0.9521 \| 0.9521 \| \| 0.1344 \| 2.41 \| 800 \| 0.1194 \| 0.9534 \| 0.9535 \| \| 0.1341 \| 3.01 \| 1000 \| 0.1171 \| 0.9550 \| 0.9550 \| \| 0.1298 \| 3.61 \| 1200 \| 0.1143 \| 0.9559 \| 0.9559 \| \| 0.1276 \| 4.22 \| 1400 \| 0.1130 \| 0.9550 \| 0.9550 \| \| 0.1226 \| 4.82 \| 1600 \| 0.1121 \| 0.9572 \| 0.9572 \| \| 0.1228 \| 5.42 \| 1800 \| 0.1124 \| 0.9565 \| 0.9565 \| \| 0.1177 \| 6.02 \| 2000 \| 0.1118 \| 0.9540 \| 0.9540 \| \| 0.1176 \| 6.63 \| 2200 \| 0.1085 \| 0.9582 \| 0.9582 \| \| 0.1165 \| 7.23 \| 2400 \| 0.1094 \| 0.9585 \| 0.9585 \| \| 0.1131 \| 7.83 \| 2600 \| 0.1084 \| 0.9582 \| 0.9582 \| \| 0.1148 \| 8.43 \| 2800 \| 0.1112 \| 0.9576 \| 0.9576 \| \| 0.1169 \| 9.04 \| 3000 \| 0.1116 \| 0.9580 \| 0.9580 \| \| 0.1117 \| 9.64 \| 3200 \| 0.1169 \| 0.9550 \| 0.9550 \| \| 0.1118 \| 10.24 \| 3400 \| 0.1079 \| 0.9593 \| 0.9593 \| \| 0.1096 \| 10.84 \| 3600 \| 0.1060 \| 0.9582 \| 0.9582 \| \| 0.1065 \| 11.45 \| 3800 \| 0.1081 \| 0.9597 \| 0.9597 \| \| 0.1082 \| 12.05 \| 4000 \| 0.1082 \| 0.9585 \| 0.9585 \| \| 0.1083 \| 12.65 \| 4200 \| 0.1047 \| 0.9606 \| 0.9606 \| \| 0.1077 \| 13.25 \| 4400 \| 0.1059 \| 0.9600 \| 0.9601 \| \| 0.1055 \| 13.86 \| 4600 \| 0.1057 \| 0.9597 \| 0.9597 \| \| 0.1076 \| 14.46 \| 4800 \| 0.1057 \| 0.9587 \| 0.9587 \| \| 0.1028 \| 15.06 \| 5000 \| 0.1041 \| 0.9585 \| 0.9585 \| \| 0.1048 \| 15.66 \| 5200 \| 0.1030 \| 0.9597 \| 0.9597 \| \| 0.1058 \| 16.27 \| 5400 \| 0.1035 \| 0.9612 \| 0.9612 \| \| 0.1016 \| 16.87 \| 5600 \| 0.1028 \| 0.9608 \| 0.9608 \| \| 0.1014 \| 17.47 \| 5800 \| 0.1027 \| 0.9604 \| 0.9604 \| \| 0.1062 \| 18.07 \| 6000 \| 0.1010 \| 0.9616 \| 0.9616 \| \| 0.1016 \| 18.67 \| 6200 \| 0.1018 \| 0.9595 \| 0.9595 \| \| 0.1031 \| 19.28 \| 6400 \| 0.1016 \| 0.9606 \| 0.9606 \| \| 0.097 \| 19.88 \| 6600 \| 0.1047 \| 0.9615 \| 0.9616 \| \| 0.1034 \| 20.48 \| 6800 \| 0.1034 \| 0.9608 \| 0.9608 \| \| 0.0985 \| 21.08 \| 7000 \| 0.1016 \| 0.9614 \| 0.9614 \| \| 0.096 \| 21.69 \| 7200 \| 0.1030 \| 0.9612 \| 0.9612 \| \| 0.098 \| 22.29 \| 7400 \| 0.1037 \| 0.9601 \| 0.9601 \| \| 0.0999 \| 22.89 \| 7600 \| 0.1004 \| 0.9616 \| 0.9616 \| \| 0.097 \| 23.49 \| 7800 \| 0.1019 \| 0.9616 \| 0.9616 \| \| 0.0988 \| 24.1 \| 8000 \| 0.1010 \| 0.9614 \| 0.9614 \| \| 0.0962 \| 24.7 \| 8200 \| 0.1023 \| 0.9608 \| 0.9608 \| \| 0.0973 \| 25.3 \| 8400 \| 0.1015 \| 0.9612 \| 0.9612 \| \| 0.0944 \| 25.9 \| 8600 \| 0.1014 \| 0.9614 \| 0.9614 \| \| 0.0974 \| 26.51 \| 8800 \| 0.1006 \| 0.9619 \| 0.9619 \| \| 0.0968 \| 27.11 \| 9000 \| 0.1001 \| 0.9616 \| 0.9616 \| \| 0.0971 \| 27.71 \| 9200 \| 0.1002 \| 0.9614 \| 0.9614 \| \| 0.0929 \| 28.31 \| 9400 \| 0.1005 \| 0.9612 \| 0.9612 \| \| 0.0997 \| 28.92 \| 9600 \| 0.1000 \| 0.9619 \| 0.9619 \| \| 0.0931 \| 29.52 \| 9800 \| 0.1003 \| 0.9617 \| 0.9617 \| \| 0.0973 \| 30.12 \| 10000 \| 0.1002 \| 0.9614 \| 0.9614 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_300_notata-seqsight_4096_512_15M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_300_notata-seqsight_4096_512_15M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T16:35:47+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_300_notata-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.1217 - F1 Score: 0.9582 - Accuracy: 0.9582 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.2508 \| 0.6 \| 200 \| 0.1359 \| 0.9487 \| 0.9487 \| \| 0.1422 \| 1.2 \| 400 \| 0.1312 \| 0.9479 \| 0.9480 \| \| 0.1383 \| 1.81 \| 600 \| 0.1152 \| 0.9548 \| 0.9548 \| \| 0.1266 \| 2.41 \| 800 \| 0.1144 \| 0.9572 \| 0.9572 \| \| 0.1273 \| 3.01 \| 1000 \| 0.1097 \| 0.9589 \| 0.9589 \| \| 0.1216 \| 3.61 \| 1200 \| 0.1115 \| 0.9591 \| 0.9591 \| \| 0.1195 \| 4.22 \| 1400 \| 0.1070 \| 0.9599 \| 0.9599 \| \| 0.1141 \| 4.82 \| 1600 \| 0.1052 \| 0.9597 \| 0.9597 \| \| 0.1124 \| 5.42 \| 1800 \| 0.1060 \| 0.9589 \| 0.9589 \| \| 0.1089 \| 6.02 \| 2000 \| 0.1047 \| 0.9585 \| 0.9585 \| \| 0.1082 \| 6.63 \| 2200 \| 0.1061 \| 0.9589 \| 0.9589 \| \| 0.1055 \| 7.23 \| 2400 \| 0.1037 \| 0.9591 \| 0.9591 \| \| 0.1022 \| 7.83 \| 2600 \| 0.1004 \| 0.9591 \| 0.9591 \| \| 0.1037 \| 8.43 \| 2800 \| 0.1056 \| 0.9617 \| 0.9617 \| \| 0.1061 \| 9.04 \| 3000 \| 0.1053 \| 0.9600 \| 0.9601 \| \| 0.0992 \| 9.64 \| 3200 \| 0.1105 \| 0.9584 \| 0.9584 \| \| 0.0982 \| 10.24 \| 3400 \| 0.1002 \| 0.9631 \| 0.9631 \| \| 0.0983 \| 10.84 \| 3600 \| 0.0987 \| 0.9608 \| 0.9608 \| \| 0.0929 \| 11.45 \| 3800 \| 0.1052 \| 0.9584 \| 0.9584 \| \| 0.0951 \| 12.05 \| 4000 \| 0.1039 \| 0.9608 \| 0.9608 \| \| 0.0946 \| 12.65 \| 4200 \| 0.0979 \| 0.9625 \| 0.9625 \| \| 0.0942 \| 13.25 \| 4400 \| 0.1007 \| 0.9625 \| 0.9625 \| \| 0.0916 \| 13.86 \| 4600 \| 0.1005 \| 0.9627 \| 0.9627 \| \| 0.0927 \| 14.46 \| 4800 \| 0.1026 \| 0.9612 \| 0.9612 \| \| 0.09 \| 15.06 \| 5000 \| 0.1008 \| 0.9623 \| 0.9623 \| \| 0.0889 \| 15.66 \| 5200 \| 0.1002 \| 0.9631 \| 0.9631 \| \| 0.0896 \| 16.27 \| 5400 \| 0.1003 \| 0.9631 \| 0.9631 \| \| 0.0856 \| 16.87 \| 5600 \| 0.0988 \| 0.9625 \| 0.9625 \| \| 0.0853 \| 17.47 \| 5800 \| 0.1013 \| 0.9606 \| 0.9606 \| \| 0.089 \| 18.07 \| 6000 \| 0.0973 \| 0.9631 \| 0.9631 \| \| 0.0842 \| 18.67 \| 6200 \| 0.0977 \| 0.9644 \| 0.9644 \| \| 0.0855 \| 19.28 \| 6400 \| 0.1026 \| 0.9627 \| 0.9627 \| \| 0.0804 \| 19.88 \| 6600 \| 0.1034 \| 0.9612 \| 0.9612 \| \| 0.0855 \| 20.48 \| 6800 \| 0.1006 \| 0.9640 \| 0.9640 \| \| 0.0813 \| 21.08 \| 7000 \| 0.1014 \| 0.9634 \| 0.9634 \| \| 0.0791 \| 21.69 \| 7200 \| 0.1038 \| 0.9606 \| 0.9606 \| \| 0.0798 \| 22.29 \| 7400 \| 0.1026 \| 0.9623 \| 0.9623 \| \| 0.0816 \| 22.89 \| 7600 \| 0.0986 \| 0.9629 \| 0.9629 \| \| 0.0788 \| 23.49 \| 7800 \| 0.1031 \| 0.9633 \| 0.9633 \| \| 0.0791 \| 24.1 \| 8000 \| 0.1003 \| 0.9636 \| 0.9636 \| \| 0.078 \| 24.7 \| 8200 \| 0.1035 \| 0.9632 \| 0.9633 \| \| 0.0779 \| 25.3 \| 8400 \| 0.1004 \| 0.9646 \| 0.9646 \| \| 0.0763 \| 25.9 \| 8600 \| 0.1020 \| 0.9631 \| 0.9631 \| \| 0.0785 \| 26.51 \| 8800 \| 0.1004 \| 0.9633 \| 0.9633 \| \| 0.0777 \| 27.11 \| 9000 \| 0.0999 \| 0.9631 \| 0.9631 \| \| 0.0782 \| 27.71 \| 9200 \| 0.0999 \| 0.9638 \| 0.9638 \| \| 0.0731 \| 28.31 \| 9400 \| 0.1000 \| 0.9634 \| 0.9634 \| \| 0.0791 \| 28.92 \| 9600 \| 0.1005 \| 0.9633 \| 0.9633 \| \| 0.0733 \| 29.52 \| 9800 \| 0.1013 \| 0.9631 \| 0.9631 \| \| 0.0759 \| 30.12 \| 10000 \| 0.1009 \| 0.9631 \| 0.9631 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_300_notata-seqsight_4096_512_15M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_300_notata-seqsight_4096_512_15M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T16:36:08+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	jeongmi/SOLAR_TG	null	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-03T16:36:28+00:00
text-classification	transformers	<!-- 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. --> # results This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3439 ## 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: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.3017 \| 1.0 \| 1563 \| 0.3571 \| \| 0.2894 \| 2.0 \| 3126 \| 0.3516 \| \| 0.2804 \| 3.0 \| 4689 \| 0.3439 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "prajjwal1/bert-tiny", "model-index": [{"name": "results", "results": []}]}	soheill/results	null	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:prajjwal1/bert-tiny", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:36:31+00:00
text-generation	transformers		{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "llama"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct"}	predibase/Meta-Llama-3-8B-Instruct-dequantized	null	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "conversational", "en", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:36:39+00:00
reinforcement-learning	null	# 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="TeoGal/q-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"]) ```	{"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.50 +/- 2.76", "name": "mean_reward", "verified": false}]}]}]}	TeoGal/q-Taxi-v3	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	null	2024-05-03T16:36:48+00:00
automatic-speech-recognition	transformers	# 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]	{"library_name": "transformers", "tags": []}	ezyyeah/murix-large-v3-1k-MERGED	null	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:37:32+00:00
text-classification	transformers	<!-- 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. --> # fine_tuned_boolq_bert_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3270 - Accuracy: 0.8333 - F1: 0.8243 ## 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 \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5567 \| 4.1667 \| 50 \| 0.5262 \| 0.7222 \| 0.6523 \| \| 0.1098 \| 8.3333 \| 100 \| 0.8949 \| 0.8333 \| 0.8243 \| \| 0.0031 \| 12.5 \| 150 \| 1.2237 \| 0.7778 \| 0.7778 \| \| 0.0011 \| 16.6667 \| 200 \| 1.2641 \| 0.7778 \| 0.7778 \| \| 0.0008 \| 20.8333 \| 250 \| 1.2343 \| 0.8333 \| 0.8243 \| \| 0.0007 \| 25.0 \| 300 \| 1.2852 \| 0.8333 \| 0.8243 \| \| 0.0005 \| 29.1667 \| 350 \| 1.3133 \| 0.8333 \| 0.8243 \| \| 0.0005 \| 33.3333 \| 400 \| 1.3270 \| 0.8333 \| 0.8243 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "fine_tuned_boolq_bert_croslo", "results": []}]}	lenatr99/fine_tuned_boolq_bert_croslo	null	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:EMBEDDIA/crosloengual-bert", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:40:39+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	cilantro9246/lji9v56	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T16:41:20+00:00
null	null		{}	ZurabDz/albert-geo-culturax-v2	null	[ "region:us" ]	null	2024-05-03T16:41:29+00:00
null	peft	<!-- 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. --> # GUE_tf_3-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_tf_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5549 - F1 Score: 0.7132 - Accuracy: 0.716 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.634 \| 0.93 \| 200 \| 0.5711 \| 0.7179 \| 0.718 \| \| 0.5965 \| 1.87 \| 400 \| 0.5680 \| 0.6977 \| 0.698 \| \| 0.5884 \| 2.8 \| 600 \| 0.5541 \| 0.7093 \| 0.712 \| \| 0.5816 \| 3.74 \| 800 \| 0.5569 \| 0.7101 \| 0.71 \| \| 0.5801 \| 4.67 \| 1000 \| 0.5477 \| 0.7180 \| 0.719 \| \| 0.572 \| 5.61 \| 1200 \| 0.5466 \| 0.7244 \| 0.725 \| \| 0.5667 \| 6.54 \| 1400 \| 0.5492 \| 0.7268 \| 0.727 \| \| 0.5625 \| 7.48 \| 1600 \| 0.5510 \| 0.7201 \| 0.721 \| \| 0.5596 \| 8.41 \| 1800 \| 0.5482 \| 0.7151 \| 0.715 \| \| 0.5585 \| 9.35 \| 2000 \| 0.5531 \| 0.7097 \| 0.712 \| \| 0.5525 \| 10.28 \| 2200 \| 0.5576 \| 0.7141 \| 0.714 \| \| 0.5513 \| 11.21 \| 2400 \| 0.5523 \| 0.7223 \| 0.723 \| \| 0.5457 \| 12.15 \| 2600 \| 0.5493 \| 0.7081 \| 0.711 \| \| 0.5403 \| 13.08 \| 2800 \| 0.5559 \| 0.7157 \| 0.72 \| \| 0.5398 \| 14.02 \| 3000 \| 0.5553 \| 0.7123 \| 0.713 \| \| 0.5364 \| 14.95 \| 3200 \| 0.5487 \| 0.7163 \| 0.718 \| \| 0.533 \| 15.89 \| 3400 \| 0.5521 \| 0.7217 \| 0.722 \| \| 0.5299 \| 16.82 \| 3600 \| 0.5576 \| 0.7101 \| 0.711 \| \| 0.5268 \| 17.76 \| 3800 \| 0.5548 \| 0.7180 \| 0.719 \| \| 0.5263 \| 18.69 \| 4000 \| 0.5564 \| 0.7150 \| 0.715 \| \| 0.525 \| 19.63 \| 4200 \| 0.5584 \| 0.7231 \| 0.725 \| \| 0.5235 \| 20.56 \| 4400 \| 0.5605 \| 0.7337 \| 0.734 \| \| 0.5209 \| 21.5 \| 4600 \| 0.5643 \| 0.7158 \| 0.716 \| \| 0.5176 \| 22.43 \| 4800 \| 0.5569 \| 0.7192 \| 0.721 \| \| 0.517 \| 23.36 \| 5000 \| 0.5774 \| 0.7150 \| 0.715 \| \| 0.5139 \| 24.3 \| 5200 \| 0.5743 \| 0.7049 \| 0.706 \| \| 0.5084 \| 25.23 \| 5400 \| 0.5705 \| 0.7313 \| 0.732 \| \| 0.5141 \| 26.17 \| 5600 \| 0.5550 \| 0.7319 \| 0.732 \| \| 0.5062 \| 27.1 \| 5800 \| 0.5617 \| 0.7247 \| 0.725 \| \| 0.5043 \| 28.04 \| 6000 \| 0.5665 \| 0.7436 \| 0.744 \| \| 0.5019 \| 28.97 \| 6200 \| 0.5656 \| 0.7231 \| 0.723 \| \| 0.5054 \| 29.91 \| 6400 \| 0.5654 \| 0.7313 \| 0.732 \| \| 0.5034 \| 30.84 \| 6600 \| 0.5657 \| 0.7329 \| 0.733 \| \| 0.5017 \| 31.78 \| 6800 \| 0.5651 \| 0.7250 \| 0.725 \| \| 0.4963 \| 32.71 \| 7000 \| 0.5708 \| 0.7240 \| 0.724 \| \| 0.502 \| 33.64 \| 7200 \| 0.5654 \| 0.7260 \| 0.726 \| \| 0.4912 \| 34.58 \| 7400 \| 0.5763 \| 0.7117 \| 0.712 \| \| 0.495 \| 35.51 \| 7600 \| 0.5726 \| 0.7140 \| 0.714 \| \| 0.495 \| 36.45 \| 7800 \| 0.5827 \| 0.7230 \| 0.723 \| \| 0.4953 \| 37.38 \| 8000 \| 0.5693 \| 0.7211 \| 0.721 \| \| 0.4878 \| 38.32 \| 8200 \| 0.5775 \| 0.7261 \| 0.726 \| \| 0.4885 \| 39.25 \| 8400 \| 0.5817 \| 0.7281 \| 0.728 \| \| 0.4866 \| 40.19 \| 8600 \| 0.5802 \| 0.7191 \| 0.719 \| \| 0.4858 \| 41.12 \| 8800 \| 0.5821 \| 0.7221 \| 0.722 \| \| 0.4915 \| 42.06 \| 9000 \| 0.5763 \| 0.7201 \| 0.72 \| \| 0.4871 \| 42.99 \| 9200 \| 0.5790 \| 0.7130 \| 0.713 \| \| 0.4845 \| 43.93 \| 9400 \| 0.5827 \| 0.7120 \| 0.712 \| \| 0.4834 \| 44.86 \| 9600 \| 0.5823 \| 0.7141 \| 0.714 \| \| 0.4872 \| 45.79 \| 9800 \| 0.5791 \| 0.7201 \| 0.72 \| \| 0.4824 \| 46.73 \| 10000 \| 0.5811 \| 0.7141 \| 0.714 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_tf_3-seqsight_4096_512_15M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_tf_3-seqsight_4096_512_15M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T16:41:31+00:00
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Mistral-7B-Instruct-v0.2-finetune-SWE_90_10_EN This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0346 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.0356 \| 0.9995 \| 1855 \| 1.4789 \| \| 0.9073 \| 1.9989 \| 3710 \| 1.4895 \| \| 0.349 \| 2.9984 \| 5565 \| 1.6255 \| \| 0.2672 \| 3.9978 \| 7420 \| 1.8033 \| \| 0.341 \| 4.9973 \| 9275 \| 2.0346 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "Mistral-7B-Instruct-v0.2-finetune-SWE_90_10_EN", "results": []}]}	JuanjoLopez19/Mistral-7B-Instruct-v0.2-finetune-SWE_90_10_EN	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-05-03T16:43:05+00:00
null	null		{}	mdiamore/mistral_instruct_coni_form_base_retrain	null	[ "region:us" ]	null	2024-05-03T16:43:34+00:00
text-classification	transformers	<!-- 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. --> # fine_tuned_cb_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3249 - Accuracy: 0.6818 - F1: 0.6390 ## 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 \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.7054 \| 3.5714 \| 50 \| 1.4024 \| 0.3182 \| 0.1536 \| \| 0.3117 \| 7.1429 \| 100 \| 1.0030 \| 0.6818 \| 0.6383 \| \| 0.0286 \| 10.7143 \| 150 \| 1.0108 \| 0.7273 \| 0.6791 \| \| 0.0038 \| 14.2857 \| 200 \| 1.1886 \| 0.6818 \| 0.6390 \| \| 0.0025 \| 17.8571 \| 250 \| 1.2342 \| 0.6818 \| 0.6390 \| \| 0.0019 \| 21.4286 \| 300 \| 1.2576 \| 0.7273 \| 0.6791 \| \| 0.0015 \| 25.0 \| 350 \| 1.2963 \| 0.6818 \| 0.6390 \| \| 0.0015 \| 28.5714 \| 400 \| 1.3249 \| 0.6818 \| 0.6390 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "fine_tuned_cb_croslo", "results": []}]}	lenatr99/fine_tuned_cb_croslo	null	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:EMBEDDIA/crosloengual-bert", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T16:44:34+00:00

text-generation

transformers

# cognitivecomputations/dolphin-2.9-llama3-8b-1m AWQ - Model creator: [cognitivecomputations](https://huggingface.co/cognitivecomputations) - Original model: [dolphin-2.9-llama3-8b-1m](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b-1m) ## How to use ### Install the necessary packages ```bash pip install --upgrade autoawq autoawq-kernels ``` ### Example Python code ```python from awq import AutoAWQForCausalLM from transformers import AutoTokenizer, TextStreamer model_path = "solidrust/dolphin-2.9-llama3-8b-1m-AWQ" system_message = "You are dolphin-2.9-llama3-8b-1m, incarnated as a powerful AI. You were created by cognitivecomputations." # Load model model = AutoAWQForCausalLM.from_quantized(model_path, fuse_layers=True) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) # Convert prompt to tokens prompt_template = """\ <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant""" prompt = "You're standing on the surface of the Earth. "\ "You walk one mile south, one mile west and one mile north. "\ "You end up exactly where you started. Where are you?" tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt), return_tensors='pt').input_ids.cuda() # Generate output generation_output = model.generate(tokens, streamer=streamer, max_new_tokens=512) ``` ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code

{"library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"}

solidrust/dolphin-2.9-llama3-8b-1m-AWQ

null

[ "transformers", "safetensors", "llama", "text-generation", "4-bit", "AWQ", "autotrain_compatible", "endpoints_compatible", "conversational", "text-generation-inference", "region:us" ]

null

2024-05-03T16:01:32+00:00

null

{}

optimum-internal-testing/optimum-neuron-cache-for-testing-arwim

null

[ "region:us" ]

null

2024-05-03T16:02:09+00:00

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama-2-ko-7b - bnb 8bits - Model creator: https://huggingface.co/beomi/ - Original model: https://huggingface.co/beomi/llama-2-ko-7b/ Original model description: --- language: - en - ko pipeline_tag: text-generation inference: false tags: - facebook - meta - pytorch - llama - llama-2 - kollama - llama-2-ko --- **Update Log** - 2023.12.27 - New Model is here! Trained with only open-accessible Korean text corpus: https://huggingface.co/beomi/open-llama-2-ko-7b - 2023.10.19 - Fix Tokenizer bug(space not applied when decoding) after `transforemrs>=4.34.0` # **Llama-2-Ko** 🦙🇰🇷 Llama-2-Ko serves as an advanced iteration of Llama 2, benefiting from an expanded vocabulary and the inclusion of a Korean corpus in its further pretraining. Just like its predecessor, Llama-2-Ko operates within the broad range of generative text models that stretch from 7 billion to 70 billion parameters. This repository focuses on the 7B pretrained version, which is tailored to fit the Hugging Face Transformers format. For access to the other models, feel free to consult the index provided below. ## Model Details **Model Developers** Junbum Lee (Beomi) **Variations** Llama-2-Ko will come in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama-2-Ko is an auto-regressive language model that uses an optimized transformer architecture based on Llama-2. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of Korean online data*|7B|4k|✗|>40B*|1e<sup>-5</sup>| *Plan to train upto 200B tokens **Vocab Expansion** | Model Name | Vocabulary Size | Description | | --- | --- | --- | | Original Llama-2 | 32000 | Sentencepiece BPE | | **Expanded Llama-2-Ko** | 46336 | Sentencepiece BPE. Added Korean vocab and merges | **Tokenizing "안녕하세요, 오늘은 날씨가 좋네요."** | Model | Tokens | | --- | --- | | Llama-2 | `['▁', '안', '<0xEB>', '<0x85>', '<0x95>', '하', '세', '요', ',', '▁', '오', '<0xEB>', '<0x8A>', '<0x98>', '은', '▁', '<0xEB>', '<0x82>', '<0xA0>', '씨', '가', '▁', '<0xEC>', '<0xA2>', '<0x8B>', '<0xEB>', '<0x84>', '<0xA4>', '요']` | | Llama-2-Ko | `['▁안녕', '하세요', ',', '▁오늘은', '▁날', '씨가', '▁좋네요']` | **Tokenizing "Llama 2: Open Foundation and Fine-Tuned Chat Models"** | Model | Tokens | | --- | --- | | Llama-2 | `['▁L', 'l', 'ama', '▁', '2', ':', '▁Open', '▁Foundation', '▁and', '▁Fine', '-', 'T', 'un', 'ed', '▁Ch', 'at', '▁Mod', 'els']` | | Llama-2-Ko | `['▁L', 'l', 'ama', '▁', '2', ':', '▁Open', '▁Foundation', '▁and', '▁Fine', '-', 'T', 'un', 'ed', '▁Ch', 'at', '▁Mod', 'els']` | # **Model Benchmark** ## LM Eval Harness - Korean (polyglot branch) - Used EleutherAI's lm-evaluation-harness https://github.com/EleutherAI/lm-evaluation-harness/tree/polyglot ### NSMC (Acc) - 50000 full test TBD ### COPA (F1) <img src=https://user-images.githubusercontent.com/11323660/255575809-c037bc6e-0566-436a-a6c1-2329ac92187a.png style="max-width: 700px; width: 100%" /> | Model | 0-shot | 5-shot | 10-shot | 50-shot | | --- | --- | --- | --- | --- | | https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 | 0.6696 | 0.6477 | 0.6419 | 0.6514 | | https://huggingface.co/kakaobrain/kogpt | 0.7345 | 0.7287 | 0.7277 | 0.7479 | | https://huggingface.co/facebook/xglm-7.5B | 0.6723 | 0.6731 | 0.6769 | 0.7119 | | https://huggingface.co/EleutherAI/polyglot-ko-1.3b | 0.7196 | 0.7193 | 0.7204 | 0.7206 | | https://huggingface.co/EleutherAI/polyglot-ko-3.8b | 0.7595 | 0.7608 | 0.7638 | 0.7788 | | https://huggingface.co/EleutherAI/polyglot-ko-5.8b | 0.7745 | 0.7676 | 0.7775 | 0.7887 | | https://huggingface.co/EleutherAI/polyglot-ko-12.8b | 0.7937 | 0.8108 | 0.8037 | 0.8369 | | Llama-2 Original 7B* | 0.562033 | 0.575982 | 0.576216 | 0.595532 | | Llama-2-Ko-7b 20B (10k) | 0.738780 | 0.762639 | 0.780761 | 0.797863 | | Llama-2-Ko-7b 40B (20k) | 0.743630 | 0.792716 | 0.803746 | 0.825944 | *Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ### HellaSwag (F1) <img src=https://user-images.githubusercontent.com/11323660/255576090-a2bfc1ae-d117-44b7-9f7b-262e41179ec1.png style="max-width: 700px; width: 100%" /> | Model | 0-shot | 5-shot | 10-shot | 50-shot | | --- | --- | --- | --- | --- | | https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 | 0.5243 | 0.5272 | 0.5166 | 0.5352 | | https://huggingface.co/kakaobrain/kogpt | 0.5590 | 0.5833 | 0.5828 | 0.5907 | | https://huggingface.co/facebook/xglm-7.5B | 0.5665 | 0.5689 | 0.5565 | 0.5622 | | https://huggingface.co/EleutherAI/polyglot-ko-1.3b | 0.5247 | 0.5260 | 0.5278 | 0.5427 | | https://huggingface.co/EleutherAI/polyglot-ko-3.8b | 0.5707 | 0.5830 | 0.5670 | 0.5787 | | https://huggingface.co/EleutherAI/polyglot-ko-5.8b | 0.5976 | 0.5998 | 0.5979 | 0.6208 | | https://huggingface.co/EleutherAI/polyglot-ko-12.8b | 0.5954 | 0.6306 | 0.6098 | 0.6118 | | Llama-2 Original 7B* | 0.415390 | 0.431382 | 0.421342 | 0.442003 | | Llama-2-Ko-7b 20B (10k) | 0.451757 | 0.466751 | 0.472607 | 0.482776 | | Llama-2-Ko-7b 40B (20k) | 0.456246 | 0.465665 | 0.469810 | 0.477374 | *Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ### BoolQ (F1) <img src=https://user-images.githubusercontent.com/11323660/255576343-5d847a6f-3b6a-41a7-af37-0f11940a5ea4.png style="max-width: 700px; width: 100%" /> | Model | 0-shot | 5-shot | 10-shot | 50-shot | | --- | --- | --- | --- | --- | | https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 | 0.3356 | 0.4014 | 0.3640 | 0.3560 | | https://huggingface.co/kakaobrain/kogpt | 0.4514 | 0.5981 | 0.5499 | 0.5202 | | https://huggingface.co/facebook/xglm-7.5B | 0.4464 | 0.3324 | 0.3324 | 0.3324 | | https://huggingface.co/EleutherAI/polyglot-ko-1.3b | 0.3552 | 0.4751 | 0.4109 | 0.4038 | | https://huggingface.co/EleutherAI/polyglot-ko-3.8b | 0.4320 | 0.5263 | 0.4930 | 0.4038 | | https://huggingface.co/EleutherAI/polyglot-ko-5.8b | 0.4356 | 0.5698 | 0.5187 | 0.5236 | | https://huggingface.co/EleutherAI/polyglot-ko-12.8b | 0.4818 | 0.6041 | 0.6289 | 0.6448 | | Llama-2 Original 7B* | 0.352050 | 0.563238 | 0.474788 | 0.419222 | | Llama-2-Ko-7b 20B (10k) | 0.360656 | 0.679743 | 0.680109 | 0.662152 | | Llama-2-Ko-7b 40B (20k) | 0.578640 | 0.697747 | 0.708358 | 0.714423 | *Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ### SentiNeg (F1) <img src=https://user-images.githubusercontent.com/11323660/255576572-b005a81d-fa4d-4709-b48a-f0fe4eed17a3.png style="max-width: 700px; width: 100%" /> | Model | 0-shot | 5-shot | 10-shot | 50-shot | | --- | --- | --- | --- | --- | | https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 | 0.6065 | 0.6878 | 0.7280 | 0.8413 | | https://huggingface.co/kakaobrain/kogpt | 0.3747 | 0.8942 | 0.9294 | 0.9698 | | https://huggingface.co/facebook/xglm-7.5B | 0.3578 | 0.4471 | 0.3964 | 0.5271 | | https://huggingface.co/EleutherAI/polyglot-ko-1.3b | 0.6790 | 0.6257 | 0.5514 | 0.7851 | | https://huggingface.co/EleutherAI/polyglot-ko-3.8b | 0.4858 | 0.7950 | 0.7320 | 0.7851 | | https://huggingface.co/EleutherAI/polyglot-ko-5.8b | 0.3394 | 0.8841 | 0.8808 | 0.9521 | | https://huggingface.co/EleutherAI/polyglot-ko-12.8b | 0.9117 | 0.9015 | 0.9345 | 0.9723 | | Llama-2 Original 7B* | 0.347502 | 0.529124 | 0.480641 | 0.788457 | | Llama-2-Ko-7b 20B (10k) | 0.485546 | 0.829503 | 0.871141 | 0.851253 | | Llama-2-Ko-7b 40B (20k) | 0.459447 | 0.761079 | 0.727611 | 0.936988 | *Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ## Note for oobabooga/text-generation-webui Remove `ValueError` at `load_tokenizer` function(line 109 or near), in `modules/models.py`. ```python diff --git a/modules/models.py b/modules/models.py index 232d5fa..de5b7a0 100644 --- a/modules/models.py +++ b/modules/models.py @@ -106,7 +106,7 @@ def load_tokenizer(model_name, model): trust_remote_code=shared.args.trust_remote_code, use_fast=False ) - except ValueError: + except: tokenizer = AutoTokenizer.from_pretrained( path_to_model, trust_remote_code=shared.args.trust_remote_code, ``` Since Llama-2-Ko uses FastTokenizer provided by HF tokenizers NOT sentencepiece package, it is required to use `use_fast=True` option when initialize tokenizer. Apple Sillicon does not support BF16 computing, use CPU instead. (BF16 is supported when using NVIDIA GPU) ## Citation ``` @misc {l._junbum_2023, author = { {L. Junbum} }, title = { llama-2-ko-7b (Revision 4a9993e) }, year = 2023, url = { https://huggingface.co/beomi/llama-2-ko-7b }, doi = { 10.57967/hf/1098 }, publisher = { Hugging Face } } ``` ## Acknowledgement The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_beomi__llama-2-ko-7b) | Metric | Value | |-----------------------|---------------------------| | Avg. | 39.43 | | ARC (25-shot) | 48.46 | | HellaSwag (10-shot) | 75.28 | | MMLU (5-shot) | 39.56 | | TruthfulQA (0-shot) | 34.49 | | Winogrande (5-shot) | 72.14 | | GSM8K (5-shot) | 1.97 | | DROP (3-shot) | 4.1 |

{}

RichardErkhov/beomi_-_llama-2-ko-7b-8bits

null

[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]

null

2024-05-03T16:02:42+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

golf2248/uj5jfvc

null

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:03:11+00:00

text-generation

transformers

{}

sanchit-gandhi/Mistral-1.5B-Instruct-v0.2-first-6

null

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:03:11+00:00

null

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

mehdisebai/CodeQwen-text-to-rule

null

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:03:14+00:00

fill-mask

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

CLASS-MATE/BERT-MLM-multilingual-cased

null

[ "transformers", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:03:20+00:00

null

peft

# GUE_EMP_H4-seqsight_65536_512_47M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2880 - F1 Score: 0.8934 - Accuracy: 0.8932 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.3756 | 2.17 | 200 | 0.2960 | 0.8851 | 0.8850 | | 0.2905 | 4.35 | 400 | 0.3002 | 0.8839 | 0.8836 | | 0.2784 | 6.52 | 600 | 0.2925 | 0.8883 | 0.8884 | | 0.2756 | 8.7 | 800 | 0.3025 | 0.8826 | 0.8823 | | 0.2605 | 10.87 | 1000 | 0.2880 | 0.8905 | 0.8905 | | 0.2556 | 13.04 | 1200 | 0.2872 | 0.8961 | 0.8960 | | 0.2488 | 15.22 | 1400 | 0.2880 | 0.8949 | 0.8946 | | 0.2435 | 17.39 | 1600 | 0.2970 | 0.8908 | 0.8905 | | 0.2394 | 19.57 | 1800 | 0.2875 | 0.8992 | 0.8994 | | 0.2346 | 21.74 | 2000 | 0.2896 | 0.8962 | 0.8960 | | 0.2279 | 23.91 | 2200 | 0.3006 | 0.8921 | 0.8919 | | 0.2257 | 26.09 | 2400 | 0.2989 | 0.8874 | 0.8871 | | 0.2218 | 28.26 | 2600 | 0.2959 | 0.8921 | 0.8919 | | 0.2164 | 30.43 | 2800 | 0.2968 | 0.8907 | 0.8905 | | 0.215 | 32.61 | 3000 | 0.2993 | 0.9015 | 0.9014 | | 0.2115 | 34.78 | 3200 | 0.3025 | 0.8913 | 0.8912 | | 0.2074 | 36.96 | 3400 | 0.3011 | 0.8981 | 0.8980 | | 0.2052 | 39.13 | 3600 | 0.3075 | 0.8901 | 0.8898 | | 0.2008 | 41.3 | 3800 | 0.3079 | 0.8934 | 0.8932 | | 0.1992 | 43.48 | 4000 | 0.3171 | 0.8913 | 0.8912 | | 0.1985 | 45.65 | 4200 | 0.3199 | 0.8858 | 0.8857 | | 0.1912 | 47.83 | 4400 | 0.3155 | 0.8891 | 0.8891 | | 0.1905 | 50.0 | 4600 | 0.3100 | 0.8940 | 0.8939 | | 0.19 | 52.17 | 4800 | 0.3164 | 0.8884 | 0.8884 | | 0.1915 | 54.35 | 5000 | 0.3157 | 0.8935 | 0.8932 | | 0.185 | 56.52 | 5200 | 0.3235 | 0.8887 | 0.8884 | | 0.1806 | 58.7 | 5400 | 0.3242 | 0.8900 | 0.8898 | | 0.1809 | 60.87 | 5600 | 0.3224 | 0.8880 | 0.8877 | | 0.1787 | 63.04 | 5800 | 0.3286 | 0.8866 | 0.8864 | | 0.1788 | 65.22 | 6000 | 0.3372 | 0.8859 | 0.8857 | | 0.1762 | 67.39 | 6200 | 0.3454 | 0.8779 | 0.8775 | | 0.1732 | 69.57 | 6400 | 0.3405 | 0.8826 | 0.8823 | | 0.171 | 71.74 | 6600 | 0.3395 | 0.8914 | 0.8912 | | 0.1726 | 73.91 | 6800 | 0.3427 | 0.8873 | 0.8871 | | 0.169 | 76.09 | 7000 | 0.3593 | 0.8820 | 0.8816 | | 0.1688 | 78.26 | 7200 | 0.3436 | 0.8846 | 0.8843 | | 0.1674 | 80.43 | 7400 | 0.3411 | 0.8900 | 0.8898 | | 0.1641 | 82.61 | 7600 | 0.3489 | 0.8892 | 0.8891 | | 0.1646 | 84.78 | 7800 | 0.3523 | 0.8887 | 0.8884 | | 0.1646 | 86.96 | 8000 | 0.3448 | 0.8899 | 0.8898 | | 0.1629 | 89.13 | 8200 | 0.3592 | 0.8846 | 0.8843 | | 0.1632 | 91.3 | 8400 | 0.3495 | 0.8865 | 0.8864 | | 0.1578 | 93.48 | 8600 | 0.3543 | 0.8893 | 0.8891 | | 0.1598 | 95.65 | 8800 | 0.3551 | 0.8878 | 0.8877 | | 0.1612 | 97.83 | 9000 | 0.3517 | 0.8886 | 0.8884 | | 0.163 | 100.0 | 9200 | 0.3541 | 0.8846 | 0.8843 | | 0.163 | 102.17 | 9400 | 0.3523 | 0.8886 | 0.8884 | | 0.1584 | 104.35 | 9600 | 0.3553 | 0.8880 | 0.8877 | | 0.1561 | 106.52 | 9800 | 0.3557 | 0.8865 | 0.8864 | | 0.1564 | 108.7 | 10000 | 0.3554 | 0.8886 | 0.8884 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H4-seqsight_65536_512_47M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H4-seqsight_65536_512_47M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]

null

2024-05-03T16:03:23+00:00

text-generation

transformers

# 0.0001_withdpo_4iters_bs256_5102lr_misit_iter_2 This model is a fine-tuned version of [ShenaoZ/0.0001_withdpo_4iters_bs256_5102lr_misit_iter_1](https://huggingface.co/ShenaoZ/0.0001_withdpo_4iters_bs256_5102lr_misit_iter_1) on the updated and the original datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2

{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZ/0.0001_withdpo_4iters_bs256_5102lr_misit_iter_1", "model-index": [{"name": "0.0001_withdpo_4iters_bs256_5102lr_misit_iter_2", "results": []}]}

ShenaoZ/0.0001_withdpo_4iters_bs256_5102lr_misit_iter_2

null

[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZ/0.0001_withdpo_4iters_bs256_5102lr_misit_iter_1", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:03:29+00:00

null

{}

optimum-internal-testing/optimum-neuron-cache-for-testing-ummro

null

[ "region:us" ]

null

2024-05-03T16:03:38+00:00

null

transformers

# Uploaded model - **Developed by:** animaRegem - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-2b-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)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl"], "base_model": "unsloth/gemma-2b-it-bnb-4bit"}

animaRegem/gemma-2b-it-lora-0_1-malayalam

null

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-2b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:04:03+00:00

null

peft

# GUE_EMP_H4-seqsight_65536_512_47M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2723 - F1 Score: 0.8953 - Accuracy: 0.8953 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.3593 | 2.17 | 200 | 0.2935 | 0.8873 | 0.8871 | | 0.2808 | 4.35 | 400 | 0.2879 | 0.8960 | 0.8960 | | 0.2632 | 6.52 | 600 | 0.2894 | 0.8888 | 0.8891 | | 0.252 | 8.7 | 800 | 0.2906 | 0.8888 | 0.8884 | | 0.2352 | 10.87 | 1000 | 0.2793 | 0.9110 | 0.9110 | | 0.2293 | 13.04 | 1200 | 0.2952 | 0.8901 | 0.8898 | | 0.2172 | 15.22 | 1400 | 0.2890 | 0.8948 | 0.8946 | | 0.2113 | 17.39 | 1600 | 0.3144 | 0.8909 | 0.8905 | | 0.2004 | 19.57 | 1800 | 0.3055 | 0.8945 | 0.8946 | | 0.1942 | 21.74 | 2000 | 0.3162 | 0.8907 | 0.8905 | | 0.1835 | 23.91 | 2200 | 0.3497 | 0.8696 | 0.8693 | | 0.1786 | 26.09 | 2400 | 0.3230 | 0.8819 | 0.8816 | | 0.1698 | 28.26 | 2600 | 0.3381 | 0.8858 | 0.8857 | | 0.1611 | 30.43 | 2800 | 0.3506 | 0.8852 | 0.8850 | | 0.1532 | 32.61 | 3000 | 0.3809 | 0.8799 | 0.8802 | | 0.1489 | 34.78 | 3200 | 0.3671 | 0.8791 | 0.8789 | | 0.1385 | 36.96 | 3400 | 0.3798 | 0.8786 | 0.8782 | | 0.1347 | 39.13 | 3600 | 0.3871 | 0.8758 | 0.8754 | | 0.1278 | 41.3 | 3800 | 0.4102 | 0.8761 | 0.8761 | | 0.1241 | 43.48 | 4000 | 0.4262 | 0.8790 | 0.8789 | | 0.1173 | 45.65 | 4200 | 0.4611 | 0.8715 | 0.8720 | | 0.1122 | 47.83 | 4400 | 0.4375 | 0.8797 | 0.8795 | | 0.11 | 50.0 | 4600 | 0.4266 | 0.8786 | 0.8789 | | 0.1039 | 52.17 | 4800 | 0.4801 | 0.8736 | 0.8734 | | 0.1057 | 54.35 | 5000 | 0.4509 | 0.8775 | 0.8775 | | 0.0953 | 56.52 | 5200 | 0.4760 | 0.8717 | 0.8713 | | 0.0926 | 58.7 | 5400 | 0.5029 | 0.8683 | 0.8679 | | 0.0903 | 60.87 | 5600 | 0.4814 | 0.8722 | 0.8720 | | 0.0863 | 63.04 | 5800 | 0.5023 | 0.8729 | 0.8727 | | 0.0856 | 65.22 | 6000 | 0.5227 | 0.8670 | 0.8665 | | 0.0833 | 67.39 | 6200 | 0.5262 | 0.8677 | 0.8672 | | 0.0783 | 69.57 | 6400 | 0.5150 | 0.8695 | 0.8693 | | 0.0761 | 71.74 | 6600 | 0.5296 | 0.8734 | 0.8734 | | 0.0727 | 73.91 | 6800 | 0.5547 | 0.8704 | 0.8700 | | 0.0705 | 76.09 | 7000 | 0.5961 | 0.8663 | 0.8658 | | 0.0718 | 78.26 | 7200 | 0.5728 | 0.8608 | 0.8604 | | 0.0666 | 80.43 | 7400 | 0.5711 | 0.8695 | 0.8693 | | 0.0657 | 82.61 | 7600 | 0.5681 | 0.8652 | 0.8652 | | 0.0638 | 84.78 | 7800 | 0.5880 | 0.8697 | 0.8693 | | 0.0616 | 86.96 | 8000 | 0.5926 | 0.8695 | 0.8693 | | 0.0638 | 89.13 | 8200 | 0.5964 | 0.8641 | 0.8638 | | 0.0638 | 91.3 | 8400 | 0.5819 | 0.8708 | 0.8706 | | 0.0594 | 93.48 | 8600 | 0.5993 | 0.8680 | 0.8679 | | 0.0574 | 95.65 | 8800 | 0.5968 | 0.8675 | 0.8672 | | 0.0586 | 97.83 | 9000 | 0.5952 | 0.8640 | 0.8638 | | 0.0584 | 100.0 | 9200 | 0.6028 | 0.8614 | 0.8611 | | 0.0583 | 102.17 | 9400 | 0.6088 | 0.8640 | 0.8638 | | 0.0575 | 104.35 | 9600 | 0.6062 | 0.8682 | 0.8679 | | 0.0576 | 106.52 | 9800 | 0.6077 | 0.8668 | 0.8665 | | 0.0553 | 108.7 | 10000 | 0.6073 | 0.8667 | 0.8665 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H4-seqsight_65536_512_47M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H4-seqsight_65536_512_47M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]

null

2024-05-03T16:04:04+00:00

text-generation

transformers

# Llama-3-8B-Irene-v0.1 <img src="https://huggingface.co/Virt-io/Llama-3-8B-Irene-v0.1/resolve/main/Irene.png"> [SillyTavern Presest](https://huggingface.co/Virt-io/SillyTavern-Presets) 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 [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [NeverSleep/Llama-3-Lumimaid-8B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) as a base. ### Models Merged The following models were included in the merge: * [ResplendentAI/SOVL_Llama3_8B](https://huggingface.co/ResplendentAI/SOVL_Llama3_8B) * [ChaoticNeutrals/Poppy_Porpoise-0.72-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-0.72-L3-8B) * [cgato/L3-TheSpice-8b-v0.8.3](https://huggingface.co/cgato/L3-TheSpice-8b-v0.8.3) * [Endevor/InfinityRP-v2-8B](https://huggingface.co/Endevor/InfinityRP-v2-8B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: NeverSleep/Llama-3-Lumimaid-8B-v0.1 - model: ResplendentAI/SOVL_Llama3_8B parameters: density: 0.58 weight: [0.15, 0.1, 0.1, 0.33] - model: Endevor/InfinityRP-v2-8B parameters: density: 0.58 weight: [0.15, 0.1, 0.1, 0.25] - model: ChaoticNeutrals/Poppy_Porpoise-0.72-L3-8B parameters: density: 0.66 weight: [0.20, 0.35, 0.25, 0.25] - model: cgato/L3-TheSpice-8b-v0.8.3 parameters: density: 0.66 weight: [0.20, 0.15, 0.15, 0.25] merge_method: dare_ties base_model: NeverSleep/Llama-3-Lumimaid-8B-v0.1 parameters: normalize: true int8_mask: true dtype: bfloat16 ```

{"license": "other", "library_name": "transformers", "tags": ["mergekit", "merge", "facebook", "meta", "pytorch", "llama", "llama-3"], "base_model": ["ResplendentAI/SOVL_Llama3_8B", "ChaoticNeutrals/Poppy_Porpoise-0.72-L3-8B", "NeverSleep/Llama-3-Lumimaid-8B-v0.1", "cgato/L3-TheSpice-8b-v0.8.3", "Endevor/InfinityRP-v2-8B"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE"}

Virt-io/Llama-3-8B-Irene-v0.1

null

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "facebook", "meta", "pytorch", "llama-3", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:ResplendentAI/SOVL_Llama3_8B", "base_model:ChaoticNeutrals/Poppy_Porpoise-0.72-L3-8B", "base_model:NeverSleep/Llama-3-Lumimaid-8B-v0.1", "base_model:cgato/L3-TheSpice-8b-v0.8.3", "base_model:Endevor/InfinityRP-v2-8B", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:04:05+00:00

null

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": ["unsloth"]}

animaRegem/gemma-2b-it-lora-0_1-malayalam-tokenizer

null

[ "transformers", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:04:11+00:00

text-generation

transformers

{}

sajaw/Verifiedllama-2-7b-Generated10K_3epochs

null

[ "transformers", "pytorch", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:04:41+00:00

null

diffusers

{}

xkronosx/train_prior_ddpm-celeb-128

null

[ "diffusers", "safetensors", "diffusers:DDPMPipeline", "region:us" ]

null

2024-05-03T16:05:08+00:00

text-generation

transformers

# Gille/StrangeMerges_16-7B-slerp AWQ - Model creator: [Gille](https://huggingface.co/Gille) - Original model: [StrangeMerges_16-7B-slerp](https://huggingface.co/Gille/StrangeMerges_16-7B-slerp) ## How to use ### Install the necessary packages ```bash pip install --upgrade autoawq autoawq-kernels ``` ### Example Python code ```python from awq import AutoAWQForCausalLM from transformers import AutoTokenizer, TextStreamer model_path = "solidrust/StrangeMerges_16-7B-slerp-AWQ" system_message = "You are StrangeMerges_16-7B-slerp, incarnated as a powerful AI. You were created by Gille." # Load model model = AutoAWQForCausalLM.from_quantized(model_path, fuse_layers=True) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) # Convert prompt to tokens prompt_template = """\ <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant""" prompt = "You're standing on the surface of the Earth. "\ "You walk one mile south, one mile west and one mile north. "\ "You end up exactly where you started. Where are you?" tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt), return_tensors='pt').input_ids.cuda() # Generate output generation_output = model.generate(tokens, streamer=streamer, max_new_tokens=512) ``` ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code

{"library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"}

solidrust/StrangeMerges_16-7B-slerp-AWQ

null

[ "transformers", "safetensors", "mistral", "text-generation", "4-bit", "AWQ", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:05:18+00:00

null

{}

largenumber/Test3

null

[ "region:us" ]

null

2024-05-03T16:05:58+00:00

null

peft

# lora_fine_tuned_copa This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6918 - Accuracy: 0.46 - F1: 0.4570 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7088 | 1.0 | 50 | 0.6921 | 0.48 | 0.48 | | 0.7024 | 2.0 | 100 | 0.6922 | 0.49 | 0.4894 | | 0.6993 | 3.0 | 150 | 0.6921 | 0.46 | 0.4587 | | 0.7005 | 4.0 | 200 | 0.6920 | 0.48 | 0.4788 | | 0.6989 | 5.0 | 250 | 0.6919 | 0.47 | 0.4679 | | 0.7018 | 6.0 | 300 | 0.6919 | 0.46 | 0.4570 | | 0.6943 | 7.0 | 350 | 0.6919 | 0.46 | 0.4570 | | 0.6943 | 8.0 | 400 | 0.6918 | 0.46 | 0.4570 | ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "google-bert/bert-base-uncased", "model-index": [{"name": "lora_fine_tuned_copa", "results": []}]}

lenatr99/lora_fine_tuned_copa

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "license:apache-2.0", "region:us" ]

null

2024-05-03T16:08:08+00:00

text2text-generation

transformers

# my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3247 - Rouge1: 0.1978 - Rouge2: 0.099 - Rougel: 0.1684 - Rougelsum: 0.1682 - Gen Len: 19.0 ## 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: 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: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 62 | 2.3922 | 0.1587 | 0.0648 | 0.1323 | 0.1323 | 19.0 | | No log | 2.0 | 124 | 2.3515 | 0.1894 | 0.0903 | 0.1611 | 0.1607 | 19.0 | | No log | 3.0 | 186 | 2.3310 | 0.1968 | 0.0983 | 0.1679 | 0.1675 | 19.0 | | No log | 4.0 | 248 | 2.3247 | 0.1978 | 0.099 | 0.1684 | 0.1682 | 19.0 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "t5-small", "model-index": [{"name": "my_awesome_billsum_model", "results": []}]}

ngthanhlong089/my_awesome_billsum_model

null

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:08:12+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

golf2248/art9e97

null

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:08:35+00:00

null

{}

ntmerl/jojoDiffusion

null

[ "region:us" ]

null

2024-05-03T16:09:42+00:00

null

{}

descansodj/Phi-3-mini-128k-instruct-dpo

null

[ "region:us" ]

null

2024-05-03T16:10:14+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

cilantro9246/mkon0fy

null

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:11:04+00:00

question-answering

transformers

# roberta_mrqa_v2 This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3580 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.1229 | 1.0 | 967 | 1.3499 | | 1.1979 | 2.0 | 1934 | 1.3192 | | 0.9852 | 3.0 | 2901 | 1.3580 | ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "FacebookAI/roberta-base", "model-index": [{"name": "roberta_mrqa_v2", "results": []}]}

enriquesaou/roberta_mrqa_v2

null

[ "transformers", "tensorboard", "safetensors", "roberta", "question-answering", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "license:mit", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:11:49+00:00

null

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

jeongmi/solar_text

null

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:12:37+00:00

null

{}

vup2p/model_sn25_45

null

[ "region:us" ]

null

2024-05-03T16:12:48+00:00

text2text-generation

transformers

{}

mika5883/pretrain_rugec

null

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:12:51+00:00

null

peft

# GUE_EMP_H3-seqsight_65536_512_47M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset. It achieves the following results on the evaluation set: - Loss: 0.3391 - F1 Score: 0.8676 - Accuracy: 0.8677 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.4898 | 2.13 | 200 | 0.4183 | 0.8196 | 0.8196 | | 0.3827 | 4.26 | 400 | 0.4199 | 0.8255 | 0.8263 | | 0.3609 | 6.38 | 600 | 0.4122 | 0.8241 | 0.8250 | | 0.3487 | 8.51 | 800 | 0.3864 | 0.8301 | 0.8303 | | 0.3376 | 10.64 | 1000 | 0.3927 | 0.8282 | 0.8290 | | 0.3246 | 12.77 | 1200 | 0.3862 | 0.8331 | 0.8337 | | 0.3197 | 14.89 | 1400 | 0.3668 | 0.8402 | 0.8403 | | 0.3126 | 17.02 | 1600 | 0.3676 | 0.8407 | 0.8410 | | 0.3023 | 19.15 | 1800 | 0.4137 | 0.8265 | 0.8277 | | 0.299 | 21.28 | 2000 | 0.3796 | 0.8378 | 0.8383 | | 0.2968 | 23.4 | 2200 | 0.3519 | 0.8523 | 0.8524 | | 0.2882 | 25.53 | 2400 | 0.3784 | 0.8386 | 0.8390 | | 0.2879 | 27.66 | 2600 | 0.3634 | 0.8487 | 0.8490 | | 0.2888 | 29.79 | 2800 | 0.3759 | 0.8458 | 0.8464 | | 0.2845 | 31.91 | 3000 | 0.3722 | 0.8452 | 0.8457 | | 0.2801 | 34.04 | 3200 | 0.3733 | 0.8446 | 0.8450 | | 0.2778 | 36.17 | 3400 | 0.3864 | 0.8438 | 0.8444 | | 0.2752 | 38.3 | 3600 | 0.3656 | 0.8568 | 0.8570 | | 0.2773 | 40.43 | 3800 | 0.3683 | 0.8527 | 0.8530 | | 0.2727 | 42.55 | 4000 | 0.3568 | 0.8576 | 0.8577 | | 0.2711 | 44.68 | 4200 | 0.3499 | 0.8596 | 0.8597 | | 0.2708 | 46.81 | 4400 | 0.3751 | 0.8479 | 0.8484 | | 0.2718 | 48.94 | 4600 | 0.3834 | 0.8463 | 0.8470 | | 0.2679 | 51.06 | 4800 | 0.3602 | 0.8568 | 0.8570 | | 0.2659 | 53.19 | 5000 | 0.3933 | 0.8455 | 0.8464 | | 0.2692 | 55.32 | 5200 | 0.3556 | 0.8555 | 0.8557 | | 0.2665 | 57.45 | 5400 | 0.3706 | 0.8508 | 0.8510 | | 0.2626 | 59.57 | 5600 | 0.3638 | 0.8568 | 0.8570 | | 0.2672 | 61.7 | 5800 | 0.3498 | 0.8589 | 0.8591 | | 0.2595 | 63.83 | 6000 | 0.3725 | 0.8546 | 0.8550 | | 0.2637 | 65.96 | 6200 | 0.3743 | 0.8580 | 0.8584 | | 0.2606 | 68.09 | 6400 | 0.3801 | 0.8559 | 0.8564 | | 0.2586 | 70.21 | 6600 | 0.3770 | 0.8546 | 0.8550 | | 0.2613 | 72.34 | 6800 | 0.3648 | 0.8547 | 0.8550 | | 0.2608 | 74.47 | 7000 | 0.3994 | 0.8434 | 0.8444 | | 0.2586 | 76.6 | 7200 | 0.3739 | 0.8547 | 0.8550 | | 0.2612 | 78.72 | 7400 | 0.3657 | 0.8567 | 0.8570 | | 0.2575 | 80.85 | 7600 | 0.3624 | 0.8554 | 0.8557 | | 0.2573 | 82.98 | 7800 | 0.3695 | 0.8573 | 0.8577 | | 0.2579 | 85.11 | 8000 | 0.3598 | 0.8574 | 0.8577 | | 0.2579 | 87.23 | 8200 | 0.3569 | 0.8595 | 0.8597 | | 0.253 | 89.36 | 8400 | 0.3685 | 0.8534 | 0.8537 | | 0.2559 | 91.49 | 8600 | 0.3750 | 0.8547 | 0.8550 | | 0.2566 | 93.62 | 8800 | 0.3699 | 0.8553 | 0.8557 | | 0.2546 | 95.74 | 9000 | 0.3729 | 0.8560 | 0.8564 | | 0.2524 | 97.87 | 9200 | 0.3649 | 0.8567 | 0.8570 | | 0.2533 | 100.0 | 9400 | 0.3632 | 0.8568 | 0.8570 | | 0.2557 | 102.13 | 9600 | 0.3663 | 0.8573 | 0.8577 | | 0.2527 | 104.26 | 9800 | 0.3654 | 0.8567 | 0.8570 | | 0.2531 | 106.38 | 10000 | 0.3667 | 0.8573 | 0.8577 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3-seqsight_65536_512_47M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3-seqsight_65536_512_47M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]

null

2024-05-03T16:12:53+00:00

null

peft

# GUE_EMP_H3-seqsight_65536_512_47M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset. It achieves the following results on the evaluation set: - Loss: 0.3253 - F1 Score: 0.8696 - Accuracy: 0.8697 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.455 | 2.13 | 200 | 0.3926 | 0.8357 | 0.8357 | | 0.3495 | 4.26 | 400 | 0.4119 | 0.8128 | 0.8143 | | 0.3143 | 6.38 | 600 | 0.4219 | 0.8202 | 0.8216 | | 0.2963 | 8.51 | 800 | 0.3801 | 0.8343 | 0.8350 | | 0.2842 | 10.64 | 1000 | 0.3831 | 0.8389 | 0.8397 | | 0.2751 | 12.77 | 1200 | 0.3779 | 0.8493 | 0.8497 | | 0.2711 | 14.89 | 1400 | 0.3792 | 0.8452 | 0.8457 | | 0.2664 | 17.02 | 1600 | 0.3620 | 0.8549 | 0.8550 | | 0.2571 | 19.15 | 1800 | 0.4008 | 0.8409 | 0.8417 | | 0.2527 | 21.28 | 2000 | 0.3837 | 0.8473 | 0.8477 | | 0.2544 | 23.4 | 2200 | 0.3505 | 0.8595 | 0.8597 | | 0.2405 | 25.53 | 2400 | 0.4114 | 0.8446 | 0.8450 | | 0.247 | 27.66 | 2600 | 0.3592 | 0.8628 | 0.8631 | | 0.2449 | 29.79 | 2800 | 0.3576 | 0.8554 | 0.8557 | | 0.2392 | 31.91 | 3000 | 0.3568 | 0.8624 | 0.8624 | | 0.2344 | 34.04 | 3200 | 0.3470 | 0.8670 | 0.8671 | | 0.2327 | 36.17 | 3400 | 0.3992 | 0.8511 | 0.8517 | | 0.2284 | 38.3 | 3600 | 0.3705 | 0.8622 | 0.8624 | | 0.2305 | 40.43 | 3800 | 0.3835 | 0.8572 | 0.8577 | | 0.2239 | 42.55 | 4000 | 0.3739 | 0.8556 | 0.8557 | | 0.2211 | 44.68 | 4200 | 0.3594 | 0.8651 | 0.8651 | | 0.2194 | 46.81 | 4400 | 0.3890 | 0.8541 | 0.8544 | | 0.2202 | 48.94 | 4600 | 0.3940 | 0.8511 | 0.8517 | | 0.215 | 51.06 | 4800 | 0.3617 | 0.8610 | 0.8611 | | 0.2124 | 53.19 | 5000 | 0.4273 | 0.8477 | 0.8484 | | 0.2171 | 55.32 | 5200 | 0.3822 | 0.8595 | 0.8597 | | 0.2099 | 57.45 | 5400 | 0.3963 | 0.8508 | 0.8510 | | 0.2073 | 59.57 | 5600 | 0.3901 | 0.8549 | 0.8550 | | 0.2075 | 61.7 | 5800 | 0.3635 | 0.8651 | 0.8651 | | 0.2038 | 63.83 | 6000 | 0.4093 | 0.8520 | 0.8524 | | 0.2061 | 65.96 | 6200 | 0.4333 | 0.8450 | 0.8457 | | 0.2036 | 68.09 | 6400 | 0.4143 | 0.8540 | 0.8544 | | 0.2006 | 70.21 | 6600 | 0.4012 | 0.8589 | 0.8591 | | 0.2009 | 72.34 | 6800 | 0.3996 | 0.8588 | 0.8591 | | 0.1995 | 74.47 | 7000 | 0.4453 | 0.8435 | 0.8444 | | 0.1969 | 76.6 | 7200 | 0.3989 | 0.8541 | 0.8544 | | 0.2014 | 78.72 | 7400 | 0.3923 | 0.8543 | 0.8544 | | 0.199 | 80.85 | 7600 | 0.4155 | 0.8495 | 0.8497 | | 0.1962 | 82.98 | 7800 | 0.4144 | 0.8549 | 0.8550 | | 0.1938 | 85.11 | 8000 | 0.3919 | 0.8589 | 0.8591 | | 0.1919 | 87.23 | 8200 | 0.4019 | 0.8523 | 0.8524 | | 0.1905 | 89.36 | 8400 | 0.4164 | 0.8530 | 0.8530 | | 0.1898 | 91.49 | 8600 | 0.4227 | 0.8541 | 0.8544 | | 0.1908 | 93.62 | 8800 | 0.4182 | 0.8548 | 0.8550 | | 0.1906 | 95.74 | 9000 | 0.4258 | 0.8514 | 0.8517 | | 0.188 | 97.87 | 9200 | 0.4151 | 0.8555 | 0.8557 | | 0.1886 | 100.0 | 9400 | 0.4124 | 0.8543 | 0.8544 | | 0.1891 | 102.13 | 9600 | 0.4163 | 0.8522 | 0.8524 | | 0.1867 | 104.26 | 9800 | 0.4136 | 0.8536 | 0.8537 | | 0.1866 | 106.38 | 10000 | 0.4139 | 0.8542 | 0.8544 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3-seqsight_65536_512_47M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3-seqsight_65536_512_47M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]

null

2024-05-03T16:12:54+00:00

null

peft

# GUE_EMP_H3-seqsight_65536_512_47M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset. It achieves the following results on the evaluation set: - Loss: 0.3472 - F1 Score: 0.8683 - Accuracy: 0.8684 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.4364 | 2.13 | 200 | 0.3952 | 0.8232 | 0.8236 | | 0.3132 | 4.26 | 400 | 0.3627 | 0.8461 | 0.8464 | | 0.2868 | 6.38 | 600 | 0.4096 | 0.8341 | 0.8350 | | 0.275 | 8.51 | 800 | 0.3467 | 0.8561 | 0.8564 | | 0.2648 | 10.64 | 1000 | 0.3499 | 0.8561 | 0.8564 | | 0.2538 | 12.77 | 1200 | 0.3487 | 0.8589 | 0.8591 | | 0.2482 | 14.89 | 1400 | 0.4163 | 0.8368 | 0.8377 | | 0.2415 | 17.02 | 1600 | 0.3507 | 0.8671 | 0.8671 | | 0.2295 | 19.15 | 1800 | 0.3938 | 0.8472 | 0.8477 | | 0.2222 | 21.28 | 2000 | 0.3751 | 0.8637 | 0.8637 | | 0.2213 | 23.4 | 2200 | 0.3761 | 0.8635 | 0.8637 | | 0.2024 | 25.53 | 2400 | 0.4104 | 0.8596 | 0.8597 | | 0.2089 | 27.66 | 2600 | 0.4076 | 0.8607 | 0.8611 | | 0.198 | 29.79 | 2800 | 0.4025 | 0.8620 | 0.8624 | | 0.1919 | 31.91 | 3000 | 0.4010 | 0.8570 | 0.8570 | | 0.1828 | 34.04 | 3200 | 0.3662 | 0.8677 | 0.8677 | | 0.1758 | 36.17 | 3400 | 0.4293 | 0.8629 | 0.8631 | | 0.1685 | 38.3 | 3600 | 0.4412 | 0.8574 | 0.8577 | | 0.1667 | 40.43 | 3800 | 0.4562 | 0.8585 | 0.8591 | | 0.1578 | 42.55 | 4000 | 0.4707 | 0.8549 | 0.8550 | | 0.1513 | 44.68 | 4200 | 0.4599 | 0.8575 | 0.8577 | | 0.1463 | 46.81 | 4400 | 0.4876 | 0.8582 | 0.8584 | | 0.1431 | 48.94 | 4600 | 0.5163 | 0.8484 | 0.8490 | | 0.1349 | 51.06 | 4800 | 0.4653 | 0.8635 | 0.8637 | | 0.1296 | 53.19 | 5000 | 0.5254 | 0.8593 | 0.8597 | | 0.1278 | 55.32 | 5200 | 0.5322 | 0.8526 | 0.8530 | | 0.1199 | 57.45 | 5400 | 0.5515 | 0.8533 | 0.8537 | | 0.117 | 59.57 | 5600 | 0.5362 | 0.8581 | 0.8584 | | 0.1133 | 61.7 | 5800 | 0.4982 | 0.8569 | 0.8570 | | 0.1118 | 63.83 | 6000 | 0.5740 | 0.8453 | 0.8457 | | 0.11 | 65.96 | 6200 | 0.6111 | 0.8408 | 0.8417 | | 0.1016 | 68.09 | 6400 | 0.6034 | 0.8464 | 0.8470 | | 0.1013 | 70.21 | 6600 | 0.5935 | 0.8506 | 0.8510 | | 0.0944 | 72.34 | 6800 | 0.5933 | 0.8560 | 0.8564 | | 0.0944 | 74.47 | 7000 | 0.6320 | 0.8443 | 0.8450 | | 0.09 | 76.6 | 7200 | 0.6099 | 0.8539 | 0.8544 | | 0.0905 | 78.72 | 7400 | 0.6381 | 0.8525 | 0.8530 | | 0.093 | 80.85 | 7600 | 0.6642 | 0.8466 | 0.8470 | | 0.0888 | 82.98 | 7800 | 0.6228 | 0.8553 | 0.8557 | | 0.0854 | 85.11 | 8000 | 0.6298 | 0.8526 | 0.8530 | | 0.0839 | 87.23 | 8200 | 0.6514 | 0.8498 | 0.8504 | | 0.0789 | 89.36 | 8400 | 0.6437 | 0.8515 | 0.8517 | | 0.0796 | 91.49 | 8600 | 0.6850 | 0.8465 | 0.8470 | | 0.0812 | 93.62 | 8800 | 0.6603 | 0.8466 | 0.8470 | | 0.0816 | 95.74 | 9000 | 0.6931 | 0.8450 | 0.8457 | | 0.0772 | 97.87 | 9200 | 0.6764 | 0.8478 | 0.8484 | | 0.075 | 100.0 | 9400 | 0.6582 | 0.8500 | 0.8504 | | 0.0772 | 102.13 | 9600 | 0.6674 | 0.8493 | 0.8497 | | 0.0715 | 104.26 | 9800 | 0.6791 | 0.8452 | 0.8457 | | 0.0758 | 106.38 | 10000 | 0.6793 | 0.8465 | 0.8470 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3-seqsight_65536_512_47M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3-seqsight_65536_512_47M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]

null

2024-05-03T16:12:56+00:00

null

peft

# GUE_EMP_H4ac-seqsight_65536_512_47M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H4ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.5702 - F1 Score: 0.7087 - Accuracy: 0.7085 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6417 | 0.93 | 200 | 0.5973 | 0.6815 | 0.6812 | | 0.6039 | 1.87 | 400 | 0.6004 | 0.6859 | 0.6871 | | 0.5916 | 2.8 | 600 | 0.5816 | 0.7040 | 0.7038 | | 0.5863 | 3.74 | 800 | 0.5817 | 0.7044 | 0.7041 | | 0.5795 | 4.67 | 1000 | 0.5882 | 0.7058 | 0.7062 | | 0.5751 | 5.61 | 1200 | 0.5956 | 0.7051 | 0.7067 | | 0.5732 | 6.54 | 1400 | 0.5767 | 0.7128 | 0.7126 | | 0.5653 | 7.48 | 1600 | 0.5786 | 0.7123 | 0.7120 | | 0.5723 | 8.41 | 1800 | 0.5774 | 0.7119 | 0.7117 | | 0.5682 | 9.35 | 2000 | 0.5854 | 0.7109 | 0.7117 | | 0.5614 | 10.28 | 2200 | 0.5768 | 0.7124 | 0.7123 | | 0.5653 | 11.21 | 2400 | 0.5738 | 0.7158 | 0.7158 | | 0.5605 | 12.15 | 2600 | 0.5763 | 0.7136 | 0.7138 | | 0.559 | 13.08 | 2800 | 0.5887 | 0.7114 | 0.7126 | | 0.5598 | 14.02 | 3000 | 0.5760 | 0.7146 | 0.7150 | | 0.5565 | 14.95 | 3200 | 0.5703 | 0.7176 | 0.7176 | | 0.5541 | 15.89 | 3400 | 0.5891 | 0.7101 | 0.7120 | | 0.552 | 16.82 | 3600 | 0.5692 | 0.7192 | 0.7191 | | 0.5579 | 17.76 | 3800 | 0.5672 | 0.7212 | 0.7211 | | 0.5528 | 18.69 | 4000 | 0.5698 | 0.7187 | 0.7188 | | 0.5492 | 19.63 | 4200 | 0.5783 | 0.7161 | 0.7170 | | 0.5525 | 20.56 | 4400 | 0.5653 | 0.7226 | 0.7226 | | 0.5496 | 21.5 | 4600 | 0.5951 | 0.7070 | 0.7103 | | 0.5495 | 22.43 | 4800 | 0.5678 | 0.7221 | 0.7223 | | 0.5521 | 23.36 | 5000 | 0.5792 | 0.7182 | 0.7196 | | 0.5458 | 24.3 | 5200 | 0.5668 | 0.7237 | 0.7238 | | 0.5497 | 25.23 | 5400 | 0.5603 | 0.7257 | 0.7255 | | 0.5482 | 26.17 | 5600 | 0.5680 | 0.7232 | 0.7235 | | 0.5479 | 27.1 | 5800 | 0.5718 | 0.7214 | 0.7223 | | 0.5439 | 28.04 | 6000 | 0.5623 | 0.7295 | 0.7293 | | 0.5477 | 28.97 | 6200 | 0.5758 | 0.7186 | 0.7196 | | 0.5463 | 29.91 | 6400 | 0.5683 | 0.7237 | 0.7240 | | 0.5461 | 30.84 | 6600 | 0.5867 | 0.7164 | 0.7185 | | 0.5448 | 31.78 | 6800 | 0.5662 | 0.7250 | 0.7252 | | 0.5426 | 32.71 | 7000 | 0.5676 | 0.7240 | 0.7243 | | 0.5419 | 33.64 | 7200 | 0.5682 | 0.7239 | 0.7246 | | 0.5439 | 34.58 | 7400 | 0.5696 | 0.7216 | 0.7223 | | 0.5425 | 35.51 | 7600 | 0.5626 | 0.7284 | 0.7284 | | 0.5385 | 36.45 | 7800 | 0.5638 | 0.7287 | 0.7287 | | 0.5443 | 37.38 | 8000 | 0.5762 | 0.7198 | 0.7211 | | 0.5399 | 38.32 | 8200 | 0.5670 | 0.7270 | 0.7276 | | 0.5409 | 39.25 | 8400 | 0.5653 | 0.7284 | 0.7287 | | 0.5439 | 40.19 | 8600 | 0.5633 | 0.7277 | 0.7279 | | 0.5406 | 41.12 | 8800 | 0.5669 | 0.7262 | 0.7267 | | 0.5393 | 42.06 | 9000 | 0.5684 | 0.7268 | 0.7273 | | 0.543 | 42.99 | 9200 | 0.5738 | 0.7209 | 0.7220 | | 0.5384 | 43.93 | 9400 | 0.5725 | 0.7238 | 0.7246 | | 0.5406 | 44.86 | 9600 | 0.5664 | 0.7266 | 0.7270 | | 0.542 | 45.79 | 9800 | 0.5679 | 0.7259 | 0.7264 | | 0.5386 | 46.73 | 10000 | 0.5694 | 0.7248 | 0.7255 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H4ac-seqsight_65536_512_47M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H4ac-seqsight_65536_512_47M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]

null

2024-05-03T16:13:19+00:00

automatic-speech-recognition

transformers

# Whisper Base Noise Ko - Dearlie This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Noise Data dataset. It achieves the following results on the evaluation set: - Loss: 1.0157 - Cer: 41.4126 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:------:|:----:|:---------------:|:-------:| | 1.3649 | 0.8780 | 1000 | 1.3839 | 54.5000 | | 1.0173 | 1.7559 | 2000 | 1.1473 | 52.0300 | | 0.7373 | 2.6339 | 3000 | 1.0454 | 43.7234 | | 0.5197 | 3.5119 | 4000 | 1.0157 | 41.4126 | ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"language": ["ko"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["AIHub/noise"], "base_model": "openai/whisper-base", "model-index": [{"name": "Whisper Base Noise Ko - Dearlie", "results": []}]}

Dearlie/whisper-noise3

null

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ko", "dataset:AIHub/noise", "base_model:openai/whisper-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:13:31+00:00

text-generation

transformers

# BioGPT_DocBot_SonatafyAI_V1 This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8762 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.1291 | 1.0 | 1109 | 2.9973 | | 2.8675 | 2.0 | 2218 | 2.9057 | | 2.7264 | 3.0 | 3327 | 2.8822 | | 2.6095 | 4.0 | 4436 | 2.8706 | | 2.548 | 5.0 | 5545 | 2.8762 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/biogpt", "model-index": [{"name": "BioGPT_DocBot_SonatafyAI_V1", "results": []}]}

Sonatafyai/BioGPT_DocBot_SonatafyAI_V1

null

[ "transformers", "tensorboard", "safetensors", "biogpt", "text-generation", "generated_from_trainer", "base_model:microsoft/biogpt", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:13:46+00:00

null

peft

# GUE_EMP_H4ac-seqsight_65536_512_47M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H4ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.5638 - F1 Score: 0.7186 - Accuracy: 0.7185 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6275 | 0.93 | 200 | 0.5856 | 0.6927 | 0.6924 | | 0.589 | 1.87 | 400 | 0.5897 | 0.7023 | 0.7032 | | 0.5744 | 2.8 | 600 | 0.5709 | 0.7072 | 0.7070 | | 0.5693 | 3.74 | 800 | 0.5721 | 0.7129 | 0.7126 | | 0.5626 | 4.67 | 1000 | 0.5702 | 0.7176 | 0.7173 | | 0.5563 | 5.61 | 1200 | 0.5767 | 0.7101 | 0.7109 | | 0.5549 | 6.54 | 1400 | 0.5737 | 0.7128 | 0.7135 | | 0.5438 | 7.48 | 1600 | 0.5655 | 0.7219 | 0.7217 | | 0.5507 | 8.41 | 1800 | 0.5624 | 0.7231 | 0.7229 | | 0.5442 | 9.35 | 2000 | 0.5599 | 0.7237 | 0.7235 | | 0.5376 | 10.28 | 2200 | 0.5564 | 0.7277 | 0.7276 | | 0.5401 | 11.21 | 2400 | 0.5580 | 0.7302 | 0.7299 | | 0.534 | 12.15 | 2600 | 0.5662 | 0.7264 | 0.7267 | | 0.5312 | 13.08 | 2800 | 0.5639 | 0.7289 | 0.7287 | | 0.5321 | 14.02 | 3000 | 0.5519 | 0.7270 | 0.7267 | | 0.5281 | 14.95 | 3200 | 0.5523 | 0.7317 | 0.7314 | | 0.5244 | 15.89 | 3400 | 0.5527 | 0.7343 | 0.7340 | | 0.5211 | 16.82 | 3600 | 0.5606 | 0.7300 | 0.7299 | | 0.5255 | 17.76 | 3800 | 0.5725 | 0.7209 | 0.7220 | | 0.522 | 18.69 | 4000 | 0.5527 | 0.7327 | 0.7326 | | 0.5155 | 19.63 | 4200 | 0.5498 | 0.7376 | 0.7372 | | 0.5197 | 20.56 | 4400 | 0.5515 | 0.7346 | 0.7343 | | 0.5178 | 21.5 | 4600 | 0.5649 | 0.7226 | 0.7235 | | 0.5147 | 22.43 | 4800 | 0.5514 | 0.7370 | 0.7367 | | 0.5162 | 23.36 | 5000 | 0.5602 | 0.7330 | 0.7331 | | 0.5122 | 24.3 | 5200 | 0.5509 | 0.7375 | 0.7372 | | 0.5132 | 25.23 | 5400 | 0.5460 | 0.7381 | 0.7378 | | 0.5129 | 26.17 | 5600 | 0.5504 | 0.7370 | 0.7367 | | 0.5098 | 27.1 | 5800 | 0.5520 | 0.7352 | 0.7349 | | 0.5072 | 28.04 | 6000 | 0.5511 | 0.7390 | 0.7387 | | 0.5102 | 28.97 | 6200 | 0.5572 | 0.7351 | 0.7349 | | 0.5079 | 29.91 | 6400 | 0.5584 | 0.7315 | 0.7314 | | 0.5078 | 30.84 | 6600 | 0.5681 | 0.7282 | 0.7287 | | 0.5028 | 31.78 | 6800 | 0.5553 | 0.7366 | 0.7364 | | 0.5059 | 32.71 | 7000 | 0.5563 | 0.7352 | 0.7349 | | 0.5042 | 33.64 | 7200 | 0.5569 | 0.7308 | 0.7311 | | 0.5027 | 34.58 | 7400 | 0.5531 | 0.7366 | 0.7364 | | 0.5031 | 35.51 | 7600 | 0.5531 | 0.7364 | 0.7361 | | 0.499 | 36.45 | 7800 | 0.5564 | 0.7363 | 0.7361 | | 0.5026 | 37.38 | 8000 | 0.5586 | 0.7339 | 0.7340 | | 0.5005 | 38.32 | 8200 | 0.5524 | 0.7357 | 0.7355 | | 0.4999 | 39.25 | 8400 | 0.5557 | 0.7345 | 0.7343 | | 0.5014 | 40.19 | 8600 | 0.5566 | 0.7355 | 0.7352 | | 0.4998 | 41.12 | 8800 | 0.5579 | 0.7347 | 0.7346 | | 0.4986 | 42.06 | 9000 | 0.5580 | 0.7363 | 0.7361 | | 0.5021 | 42.99 | 9200 | 0.5613 | 0.7319 | 0.7320 | | 0.4965 | 43.93 | 9400 | 0.5597 | 0.7347 | 0.7346 | | 0.4979 | 44.86 | 9600 | 0.5571 | 0.7357 | 0.7355 | | 0.5001 | 45.79 | 9800 | 0.5572 | 0.7336 | 0.7334 | | 0.4954 | 46.73 | 10000 | 0.5587 | 0.7353 | 0.7352 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H4ac-seqsight_65536_512_47M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H4ac-seqsight_65536_512_47M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]

null

2024-05-03T16:14:32+00:00

null

peft

# GUE_EMP_H4ac-seqsight_65536_512_47M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H4ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.5976 - F1 Score: 0.7230 - Accuracy: 0.7229 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6163 | 0.93 | 200 | 0.5941 | 0.6959 | 0.6965 | | 0.5793 | 1.87 | 400 | 0.5846 | 0.7054 | 0.7062 | | 0.5645 | 2.8 | 600 | 0.5619 | 0.7224 | 0.7223 | | 0.5566 | 3.74 | 800 | 0.5643 | 0.7247 | 0.7243 | | 0.5498 | 4.67 | 1000 | 0.5582 | 0.7258 | 0.7255 | | 0.5412 | 5.61 | 1200 | 0.5765 | 0.7119 | 0.7132 | | 0.5392 | 6.54 | 1400 | 0.5703 | 0.7175 | 0.7182 | | 0.5263 | 7.48 | 1600 | 0.5556 | 0.7307 | 0.7305 | | 0.5297 | 8.41 | 1800 | 0.5542 | 0.7281 | 0.7279 | | 0.522 | 9.35 | 2000 | 0.5545 | 0.7337 | 0.7334 | | 0.5143 | 10.28 | 2200 | 0.5502 | 0.7321 | 0.7320 | | 0.5141 | 11.21 | 2400 | 0.5602 | 0.7309 | 0.7308 | | 0.5068 | 12.15 | 2600 | 0.5658 | 0.7300 | 0.7302 | | 0.5029 | 13.08 | 2800 | 0.5543 | 0.7323 | 0.7320 | | 0.5013 | 14.02 | 3000 | 0.5646 | 0.7318 | 0.7317 | | 0.4941 | 14.95 | 3200 | 0.5613 | 0.7361 | 0.7358 | | 0.4883 | 15.89 | 3400 | 0.5586 | 0.7370 | 0.7367 | | 0.4829 | 16.82 | 3600 | 0.5657 | 0.7337 | 0.7337 | | 0.4853 | 17.76 | 3800 | 0.5990 | 0.7200 | 0.7220 | | 0.479 | 18.69 | 4000 | 0.5616 | 0.7352 | 0.7349 | | 0.4725 | 19.63 | 4200 | 0.5568 | 0.7369 | 0.7367 | | 0.475 | 20.56 | 4400 | 0.5594 | 0.7378 | 0.7375 | | 0.4682 | 21.5 | 4600 | 0.5767 | 0.7313 | 0.7317 | | 0.4652 | 22.43 | 4800 | 0.5581 | 0.7384 | 0.7381 | | 0.4614 | 23.36 | 5000 | 0.5728 | 0.7331 | 0.7331 | | 0.4579 | 24.3 | 5200 | 0.5709 | 0.7407 | 0.7405 | | 0.4564 | 25.23 | 5400 | 0.5619 | 0.7370 | 0.7367 | | 0.4548 | 26.17 | 5600 | 0.5749 | 0.7372 | 0.7370 | | 0.4519 | 27.1 | 5800 | 0.5706 | 0.7326 | 0.7323 | | 0.4479 | 28.04 | 6000 | 0.5742 | 0.7396 | 0.7393 | | 0.446 | 28.97 | 6200 | 0.5767 | 0.7378 | 0.7375 | | 0.4445 | 29.91 | 6400 | 0.5753 | 0.7379 | 0.7378 | | 0.4399 | 30.84 | 6600 | 0.5980 | 0.7372 | 0.7372 | | 0.4351 | 31.78 | 6800 | 0.5851 | 0.7386 | 0.7384 | | 0.4336 | 32.71 | 7000 | 0.5883 | 0.7352 | 0.7349 | | 0.4319 | 33.64 | 7200 | 0.5929 | 0.7337 | 0.7340 | | 0.4338 | 34.58 | 7400 | 0.5883 | 0.7369 | 0.7370 | | 0.4277 | 35.51 | 7600 | 0.5873 | 0.7371 | 0.7370 | | 0.424 | 36.45 | 7800 | 0.5890 | 0.7401 | 0.7399 | | 0.4254 | 37.38 | 8000 | 0.5903 | 0.7411 | 0.7411 | | 0.4238 | 38.32 | 8200 | 0.5865 | 0.7362 | 0.7361 | | 0.4205 | 39.25 | 8400 | 0.5941 | 0.7387 | 0.7384 | | 0.4205 | 40.19 | 8600 | 0.5969 | 0.7378 | 0.7375 | | 0.4193 | 41.12 | 8800 | 0.5965 | 0.7401 | 0.7399 | | 0.4182 | 42.06 | 9000 | 0.5959 | 0.7398 | 0.7396 | | 0.4175 | 42.99 | 9200 | 0.6042 | 0.7358 | 0.7358 | | 0.4139 | 43.93 | 9400 | 0.6035 | 0.7378 | 0.7378 | | 0.4173 | 44.86 | 9600 | 0.5966 | 0.7403 | 0.7402 | | 0.4187 | 45.79 | 9800 | 0.5986 | 0.7397 | 0.7396 | | 0.4146 | 46.73 | 10000 | 0.5997 | 0.7376 | 0.7375 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H4ac-seqsight_65536_512_47M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H4ac-seqsight_65536_512_47M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]

null

2024-05-03T16:14:32+00:00

null

peft

# lora_fine_tuned_boolq This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5547 - Accuracy: 0.7778 - F1: 0.6806 ## 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 | |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:| | 0.6762 | 4.1667 | 50 | 0.5947 | 0.7778 | 0.6806 | | 0.6639 | 8.3333 | 100 | 0.5719 | 0.7778 | 0.6806 | | 0.6555 | 12.5 | 150 | 0.5648 | 0.7778 | 0.6806 | | 0.6605 | 16.6667 | 200 | 0.5615 | 0.7778 | 0.6806 | | 0.6612 | 20.8333 | 250 | 0.5568 | 0.7778 | 0.6806 | | 0.6508 | 25.0 | 300 | 0.5567 | 0.7778 | 0.6806 | | 0.6491 | 29.1667 | 350 | 0.5550 | 0.7778 | 0.6806 | | 0.663 | 33.3333 | 400 | 0.5547 | 0.7778 | 0.6806 | ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "google-bert/bert-base-uncased", "model-index": [{"name": "lora_fine_tuned_boolq", "results": []}]}

lenatr99/lora_fine_tuned_boolq

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "license:apache-2.0", "region:us" ]

null

2024-05-03T16:14:40+00:00

text-to-image

diffusers

# Juggernaut X Hyper + RunDiffusion Official (Community Version) ![juggernaut X Hyper previews](https://storage.googleapis.com/run-diffusion-public-assets/juggernaut-x/juggernaut-x-hyper-0-832.webp) ![RunDiffusion Logo](https://imagedelivery.net/siANnpeNAc_S2q1M3-eDrA/ca2b388d-a835-490c-dec0-e764bee8d000/micro) This model is not permitted to be used behind API services. Please contact [[email protected]](mailto:[email protected]) for business inquires, commercial licensing, custom models, and consultation. Juggernaut X (SAFE) is available exclusivly on [RunDiffusion.com](http://rundiffusion.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=juggernautv10hyper) 🎉 Juggernaut X Hyper World Wide Release! 🌍 After almost two months, we are thrilled to announce the next version of Juggernaut is ready to launch! Introducing Juggernaut X Hyper. 🚀 If you would have been following us on Twitter (X) you would have been seeing the test images. If you aren't following us, do it now! https://x.com/RunDiffusion & Kandoo's new account needs some new followers. Help him out. https://x.com/Juggernaut_AI - TWO Versions of Juggernaut X Hyper! SFW 🌟 and NSFW 🔥 - Fully trained from the ground up using the GPT4 Vision Captioning tool by LEOSAM 🛠️ - Much improved prompt adherence ✅ - Expanded and cleaner dataset with higher quality images 🖼️ - Improved classifications of shots (Full Body, Midshots, Portraits, etc) 📸 - Enhanced text generation capability 📝 - Two different prompting techniques, Natural and Tagging style 🏷️ - Enhanced by RunDiffusion Photo for refinement of details 🧐 Read more about this version here https://rundiffusion.com/juggernaut-xl Dual Version Release 🔄 A Safe for Work (SFW) and a Not Safe for Work (NSFW) version of Juggernaut X Hyper will be available. This dual release strategy is designed to cater to diverse preferences and ensure inclusivity, offering the perfect solution for every user. Our newest Safe for Work edition is available right now exclusively through Fooocus on RunDiffusion.com. Launch Fooocus on RunDiffusion Find Juggernaut X_RunDiffusion_Hyper.safetensors and start generating! It allows users to generate high-quality, suitable images while adhering to safe content guidelines. This version is particularly user-friendly, requiring only simple, straightforward prompts. It's ideal for the workplace, students, educators, and families. SAFE stands for Suitable Ai For Everyone. 🌈 Conversely, the Not Safe for Work version offers unrestricted creative freedom across all categories and spectrums. This model is perfect for those seeking less constrained artistic expression and is available for free on Civitai.com, though a license is required for commercial use. 🎨 Both models of Juggernaut X Hyper (v10) represent our commitment to fostering a creative community that respects diverse needs and preferences. 🤝 Prompting Guide 📘 Because everything has been trained from the ground up, prompting is a bit different. (Simpler, don't worry) @Kandoo has created a guide to help you seamlessly integrate this powerful model into your workflow, enabling you to leverage its advanced capabilities without feeling overwhelmed. Download it here: https://rundiffusion.com/juggernaut-xl#nav As always, we love our community and feel so lucky to be in this position to bring these awesome tools and models to you amazing diffusers. Thanks for supporting us since our first day back in 2022. Going on TWO YEARS since we first started using generative Ai. Time flies when you're having fun. wow! Don't forget to follow us on Twitter where we have way more updates on big things we're working on. The future is bright https://x.com/RunDiffusion -RunDiffusion Team ![https://rundiffusion.com?utm_source=huggingface&utm_medium=referral&utm_campaign=juggernautv10hyper](https://i.imgur.com/fKPEqSu.jpg)

{"language": ["en"], "license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["art", "people", "diffusion", "Cinematic", "Photography", "Landscape", "Interior", "Food", "Car", "Wildlife", "Architecture"], "thumbnail": "https://storage.googleapis.com/run-diffusion-public-assets/juggernaut-x/juggernaut-x-hyper-0-256.webp", "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "pipeline_tag": "text-to-image"}

RunDiffusion/Juggernaut-X-Hyper

null

[ "diffusers", "art", "people", "diffusion", "Cinematic", "Photography", "Landscape", "Interior", "Food", "Car", "Wildlife", "Architecture", "text-to-image", "en", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

null

2024-05-03T16:14:58+00:00

automatic-speech-recognition

transformers

# Whisper Tiny chinese - VingeNie This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 16.1 dataset. It achieves the following results on the evaluation set: - Loss: 0.8984 - Cer Ortho: 82.5463 - Cer: 30.3139 ## 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-08 - train_batch_size: 32 - 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: 25 - training_steps: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer Ortho | Cer | |:-------------:|:------:|:----:|:---------------:|:---------:|:-------:| | 0.2618 | 0.0400 | 50 | 0.8984 | 82.5463 | 30.3139 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.0.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1

{"language": ["zh"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_16_1"], "base_model": "openai/whisper-tiny", "model-index": [{"name": "Whisper Tiny chinese - VingeNie", "results": []}]}

VingeNie/whisper-tiny-zh_CN_lr4_3600

null

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "zh", "dataset:mozilla-foundation/common_voice_16_1", "base_model:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:15:03+00:00

null

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama-2-ko-7b - GGUF - Model creator: https://huggingface.co/beomi/ - Original model: https://huggingface.co/beomi/llama-2-ko-7b/ | Name | Quant method | Size | | ---- | ---- | ---- | | [llama-2-ko-7b.Q2_K.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q2_K.gguf) | Q2_K | 2.42GB | | [llama-2-ko-7b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.IQ3_XS.gguf) | IQ3_XS | 2.67GB | | [llama-2-ko-7b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.IQ3_S.gguf) | IQ3_S | 2.81GB | | [llama-2-ko-7b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q3_K_S.gguf) | Q3_K_S | 2.81GB | | [llama-2-ko-7b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.IQ3_M.gguf) | IQ3_M | 2.97GB | | [llama-2-ko-7b.Q3_K.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q3_K.gguf) | Q3_K | 3.14GB | | [llama-2-ko-7b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q3_K_M.gguf) | Q3_K_M | 3.14GB | | [llama-2-ko-7b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q3_K_L.gguf) | Q3_K_L | 3.42GB | | [llama-2-ko-7b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.IQ4_XS.gguf) | IQ4_XS | 3.47GB | | [llama-2-ko-7b.Q4_0.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q4_0.gguf) | Q4_0 | 3.64GB | | [llama-2-ko-7b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.IQ4_NL.gguf) | IQ4_NL | 3.66GB | | [llama-2-ko-7b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q4_K_S.gguf) | Q4_K_S | 3.67GB | | [llama-2-ko-7b.Q4_K.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q4_K.gguf) | Q4_K | 3.88GB | | [llama-2-ko-7b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q4_K_M.gguf) | Q4_K_M | 3.88GB | | [llama-2-ko-7b.Q4_1.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q4_1.gguf) | Q4_1 | 4.03GB | | [llama-2-ko-7b.Q5_0.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q5_0.gguf) | Q5_0 | 4.42GB | | [llama-2-ko-7b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q5_K_S.gguf) | Q5_K_S | 4.42GB | | [llama-2-ko-7b.Q5_K.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q5_K.gguf) | Q5_K | 4.54GB | | [llama-2-ko-7b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q5_K_M.gguf) | Q5_K_M | 4.54GB | | [llama-2-ko-7b.Q5_1.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q5_1.gguf) | Q5_1 | 4.8GB | | [llama-2-ko-7b.Q6_K.gguf](https://huggingface.co/RichardErkhov/beomi_-_llama-2-ko-7b-gguf/blob/main/llama-2-ko-7b.Q6_K.gguf) | Q6_K | 5.24GB | Original model description: --- language: - en - ko pipeline_tag: text-generation inference: false tags: - facebook - meta - pytorch - llama - llama-2 - kollama - llama-2-ko --- **Update Log** - 2023.12.27 - New Model is here! Trained with only open-accessible Korean text corpus: https://huggingface.co/beomi/open-llama-2-ko-7b - 2023.10.19 - Fix Tokenizer bug(space not applied when decoding) after `transforemrs>=4.34.0` # **Llama-2-Ko** 🦙🇰🇷 Llama-2-Ko serves as an advanced iteration of Llama 2, benefiting from an expanded vocabulary and the inclusion of a Korean corpus in its further pretraining. Just like its predecessor, Llama-2-Ko operates within the broad range of generative text models that stretch from 7 billion to 70 billion parameters. This repository focuses on the 7B pretrained version, which is tailored to fit the Hugging Face Transformers format. For access to the other models, feel free to consult the index provided below. ## Model Details **Model Developers** Junbum Lee (Beomi) **Variations** Llama-2-Ko will come in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama-2-Ko is an auto-regressive language model that uses an optimized transformer architecture based on Llama-2. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of Korean online data*|7B|4k|✗|>40B*|1e<sup>-5</sup>| *Plan to train upto 200B tokens **Vocab Expansion** | Model Name | Vocabulary Size | Description | | --- | --- | --- | | Original Llama-2 | 32000 | Sentencepiece BPE | | **Expanded Llama-2-Ko** | 46336 | Sentencepiece BPE. Added Korean vocab and merges | **Tokenizing "안녕하세요, 오늘은 날씨가 좋네요."** | Model | Tokens | | --- | --- | | Llama-2 | `['▁', '안', '<0xEB>', '<0x85>', '<0x95>', '하', '세', '요', ',', '▁', '오', '<0xEB>', '<0x8A>', '<0x98>', '은', '▁', '<0xEB>', '<0x82>', '<0xA0>', '씨', '가', '▁', '<0xEC>', '<0xA2>', '<0x8B>', '<0xEB>', '<0x84>', '<0xA4>', '요']` | | Llama-2-Ko | `['▁안녕', '하세요', ',', '▁오늘은', '▁날', '씨가', '▁좋네요']` | **Tokenizing "Llama 2: Open Foundation and Fine-Tuned Chat Models"** | Model | Tokens | | --- | --- | | Llama-2 | `['▁L', 'l', 'ama', '▁', '2', ':', '▁Open', '▁Foundation', '▁and', '▁Fine', '-', 'T', 'un', 'ed', '▁Ch', 'at', '▁Mod', 'els']` | | Llama-2-Ko | `['▁L', 'l', 'ama', '▁', '2', ':', '▁Open', '▁Foundation', '▁and', '▁Fine', '-', 'T', 'un', 'ed', '▁Ch', 'at', '▁Mod', 'els']` | # **Model Benchmark** ## LM Eval Harness - Korean (polyglot branch) - Used EleutherAI's lm-evaluation-harness https://github.com/EleutherAI/lm-evaluation-harness/tree/polyglot ### NSMC (Acc) - 50000 full test TBD ### COPA (F1) <img src=https://user-images.githubusercontent.com/11323660/255575809-c037bc6e-0566-436a-a6c1-2329ac92187a.png style="max-width: 700px; width: 100%" /> | Model | 0-shot | 5-shot | 10-shot | 50-shot | | --- | --- | --- | --- | --- | | https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 | 0.6696 | 0.6477 | 0.6419 | 0.6514 | | https://huggingface.co/kakaobrain/kogpt | 0.7345 | 0.7287 | 0.7277 | 0.7479 | | https://huggingface.co/facebook/xglm-7.5B | 0.6723 | 0.6731 | 0.6769 | 0.7119 | | https://huggingface.co/EleutherAI/polyglot-ko-1.3b | 0.7196 | 0.7193 | 0.7204 | 0.7206 | | https://huggingface.co/EleutherAI/polyglot-ko-3.8b | 0.7595 | 0.7608 | 0.7638 | 0.7788 | | https://huggingface.co/EleutherAI/polyglot-ko-5.8b | 0.7745 | 0.7676 | 0.7775 | 0.7887 | | https://huggingface.co/EleutherAI/polyglot-ko-12.8b | 0.7937 | 0.8108 | 0.8037 | 0.8369 | | Llama-2 Original 7B* | 0.562033 | 0.575982 | 0.576216 | 0.595532 | | Llama-2-Ko-7b 20B (10k) | 0.738780 | 0.762639 | 0.780761 | 0.797863 | | Llama-2-Ko-7b 40B (20k) | 0.743630 | 0.792716 | 0.803746 | 0.825944 | *Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ### HellaSwag (F1) <img src=https://user-images.githubusercontent.com/11323660/255576090-a2bfc1ae-d117-44b7-9f7b-262e41179ec1.png style="max-width: 700px; width: 100%" /> | Model | 0-shot | 5-shot | 10-shot | 50-shot | | --- | --- | --- | --- | --- | | https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 | 0.5243 | 0.5272 | 0.5166 | 0.5352 | | https://huggingface.co/kakaobrain/kogpt | 0.5590 | 0.5833 | 0.5828 | 0.5907 | | https://huggingface.co/facebook/xglm-7.5B | 0.5665 | 0.5689 | 0.5565 | 0.5622 | | https://huggingface.co/EleutherAI/polyglot-ko-1.3b | 0.5247 | 0.5260 | 0.5278 | 0.5427 | | https://huggingface.co/EleutherAI/polyglot-ko-3.8b | 0.5707 | 0.5830 | 0.5670 | 0.5787 | | https://huggingface.co/EleutherAI/polyglot-ko-5.8b | 0.5976 | 0.5998 | 0.5979 | 0.6208 | | https://huggingface.co/EleutherAI/polyglot-ko-12.8b | 0.5954 | 0.6306 | 0.6098 | 0.6118 | | Llama-2 Original 7B* | 0.415390 | 0.431382 | 0.421342 | 0.442003 | | Llama-2-Ko-7b 20B (10k) | 0.451757 | 0.466751 | 0.472607 | 0.482776 | | Llama-2-Ko-7b 40B (20k) | 0.456246 | 0.465665 | 0.469810 | 0.477374 | *Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ### BoolQ (F1) <img src=https://user-images.githubusercontent.com/11323660/255576343-5d847a6f-3b6a-41a7-af37-0f11940a5ea4.png style="max-width: 700px; width: 100%" /> | Model | 0-shot | 5-shot | 10-shot | 50-shot | | --- | --- | --- | --- | --- | | https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 | 0.3356 | 0.4014 | 0.3640 | 0.3560 | | https://huggingface.co/kakaobrain/kogpt | 0.4514 | 0.5981 | 0.5499 | 0.5202 | | https://huggingface.co/facebook/xglm-7.5B | 0.4464 | 0.3324 | 0.3324 | 0.3324 | | https://huggingface.co/EleutherAI/polyglot-ko-1.3b | 0.3552 | 0.4751 | 0.4109 | 0.4038 | | https://huggingface.co/EleutherAI/polyglot-ko-3.8b | 0.4320 | 0.5263 | 0.4930 | 0.4038 | | https://huggingface.co/EleutherAI/polyglot-ko-5.8b | 0.4356 | 0.5698 | 0.5187 | 0.5236 | | https://huggingface.co/EleutherAI/polyglot-ko-12.8b | 0.4818 | 0.6041 | 0.6289 | 0.6448 | | Llama-2 Original 7B* | 0.352050 | 0.563238 | 0.474788 | 0.419222 | | Llama-2-Ko-7b 20B (10k) | 0.360656 | 0.679743 | 0.680109 | 0.662152 | | Llama-2-Ko-7b 40B (20k) | 0.578640 | 0.697747 | 0.708358 | 0.714423 | *Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ### SentiNeg (F1) <img src=https://user-images.githubusercontent.com/11323660/255576572-b005a81d-fa4d-4709-b48a-f0fe4eed17a3.png style="max-width: 700px; width: 100%" /> | Model | 0-shot | 5-shot | 10-shot | 50-shot | | --- | --- | --- | --- | --- | | https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 | 0.6065 | 0.6878 | 0.7280 | 0.8413 | | https://huggingface.co/kakaobrain/kogpt | 0.3747 | 0.8942 | 0.9294 | 0.9698 | | https://huggingface.co/facebook/xglm-7.5B | 0.3578 | 0.4471 | 0.3964 | 0.5271 | | https://huggingface.co/EleutherAI/polyglot-ko-1.3b | 0.6790 | 0.6257 | 0.5514 | 0.7851 | | https://huggingface.co/EleutherAI/polyglot-ko-3.8b | 0.4858 | 0.7950 | 0.7320 | 0.7851 | | https://huggingface.co/EleutherAI/polyglot-ko-5.8b | 0.3394 | 0.8841 | 0.8808 | 0.9521 | | https://huggingface.co/EleutherAI/polyglot-ko-12.8b | 0.9117 | 0.9015 | 0.9345 | 0.9723 | | Llama-2 Original 7B* | 0.347502 | 0.529124 | 0.480641 | 0.788457 | | Llama-2-Ko-7b 20B (10k) | 0.485546 | 0.829503 | 0.871141 | 0.851253 | | Llama-2-Ko-7b 40B (20k) | 0.459447 | 0.761079 | 0.727611 | 0.936988 | *Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ## Note for oobabooga/text-generation-webui Remove `ValueError` at `load_tokenizer` function(line 109 or near), in `modules/models.py`. ```python diff --git a/modules/models.py b/modules/models.py index 232d5fa..de5b7a0 100644 --- a/modules/models.py +++ b/modules/models.py @@ -106,7 +106,7 @@ def load_tokenizer(model_name, model): trust_remote_code=shared.args.trust_remote_code, use_fast=False ) - except ValueError: + except: tokenizer = AutoTokenizer.from_pretrained( path_to_model, trust_remote_code=shared.args.trust_remote_code, ``` Since Llama-2-Ko uses FastTokenizer provided by HF tokenizers NOT sentencepiece package, it is required to use `use_fast=True` option when initialize tokenizer. Apple Sillicon does not support BF16 computing, use CPU instead. (BF16 is supported when using NVIDIA GPU) ## Citation ``` @misc {l._junbum_2023, author = { {L. Junbum} }, title = { llama-2-ko-7b (Revision 4a9993e) }, year = 2023, url = { https://huggingface.co/beomi/llama-2-ko-7b }, doi = { 10.57967/hf/1098 }, publisher = { Hugging Face } } ``` ## Acknowledgement The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_beomi__llama-2-ko-7b) | Metric | Value | |-----------------------|---------------------------| | Avg. | 39.43 | | ARC (25-shot) | 48.46 | | HellaSwag (10-shot) | 75.28 | | MMLU (5-shot) | 39.56 | | TruthfulQA (0-shot) | 34.49 | | Winogrande (5-shot) | 72.14 | | GSM8K (5-shot) | 1.97 | | DROP (3-shot) | 4.1 |

{}

RichardErkhov/beomi_-_llama-2-ko-7b-gguf

null

[ "gguf", "region:us" ]

null

2024-05-03T16:15:23+00:00

null

{}

vup2p/model_sn25_44

null

[ "region:us" ]

null

2024-05-03T16:15:33+00:00

null

{}

Sebasvargas/CursoAI

null

[ "region:us" ]

null

2024-05-03T16:15:38+00:00

text-classification

transformers

{}

Paral1ax/vk_model_clf_distilbert

null

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:15:48+00:00

audio-to-audio

null

Jack from Genndy Tartakovsky's "Samurai Jack" Cartoon Network/Adult Swim show! For RVC/RVCv2 AI Covers Made with Weights.gg Feel free to use! Don't forget to credit if used Note: Jack's voice is naturally deep so don't forget to adjust your song's pitch so he can fully sound like himself Model by Radaverse | @samurairad

{"language": ["en"], "license": "openrail", "tags": ["RVC", "RVCv2", "AI", "Cover", "Voice", "Cartoon", "Samurai Jack"], "pipeline_tag": "audio-to-audio"}

Radaverse/SamuraiJack-RVCv2

null

[ "RVC", "RVCv2", "AI", "Cover", "Voice", "Cartoon", "Samurai Jack", "audio-to-audio", "en", "license:openrail", "region:us" ]

null

2024-05-03T16:15:49+00:00

null

{}

FanFierik/NoShaveNoHaircut

null

[ "region:us" ]

null

2024-05-03T16:15:57+00:00

null

{}

GeorgeImmanuel/autonomous_taxi_ride

null

[ "region:us" ]

null

2024-05-03T16:16:19+00:00

null

peft

# GUE_EMP_H3K79me3-seqsight_65536_512_47M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3K79me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K79me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.4367 - F1 Score: 0.8170 - Accuracy: 0.8173 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5342 | 1.1 | 200 | 0.4669 | 0.8027 | 0.8027 | | 0.4727 | 2.21 | 400 | 0.4585 | 0.7966 | 0.7982 | | 0.4678 | 3.31 | 600 | 0.4500 | 0.8050 | 0.8058 | | 0.4585 | 4.42 | 800 | 0.4482 | 0.8051 | 0.8062 | | 0.4586 | 5.52 | 1000 | 0.4469 | 0.8050 | 0.8062 | | 0.4519 | 6.63 | 1200 | 0.4499 | 0.8032 | 0.8048 | | 0.4567 | 7.73 | 1400 | 0.4412 | 0.8097 | 0.8103 | | 0.4482 | 8.84 | 1600 | 0.4460 | 0.8039 | 0.8051 | | 0.4492 | 9.94 | 1800 | 0.4426 | 0.8105 | 0.8103 | | 0.4476 | 11.05 | 2000 | 0.4397 | 0.8074 | 0.8083 | | 0.4472 | 12.15 | 2200 | 0.4359 | 0.8109 | 0.8114 | | 0.4424 | 13.26 | 2400 | 0.4347 | 0.8093 | 0.8100 | | 0.4412 | 14.36 | 2600 | 0.4350 | 0.8097 | 0.8100 | | 0.4441 | 15.47 | 2800 | 0.4438 | 0.8012 | 0.8031 | | 0.4389 | 16.57 | 3000 | 0.4347 | 0.8085 | 0.8089 | | 0.4408 | 17.68 | 3200 | 0.4338 | 0.8093 | 0.8100 | | 0.4352 | 18.78 | 3400 | 0.4318 | 0.8126 | 0.8128 | | 0.4363 | 19.89 | 3600 | 0.4363 | 0.8085 | 0.8096 | | 0.4377 | 20.99 | 3800 | 0.4340 | 0.8094 | 0.8100 | | 0.4367 | 22.1 | 4000 | 0.4326 | 0.8103 | 0.8110 | | 0.4356 | 23.2 | 4200 | 0.4325 | 0.8113 | 0.8121 | | 0.436 | 24.31 | 4400 | 0.4342 | 0.8125 | 0.8131 | | 0.4275 | 25.41 | 4600 | 0.4359 | 0.8140 | 0.8148 | | 0.4331 | 26.52 | 4800 | 0.4318 | 0.8132 | 0.8135 | | 0.4341 | 27.62 | 5000 | 0.4310 | 0.8130 | 0.8135 | | 0.4297 | 28.73 | 5200 | 0.4298 | 0.8112 | 0.8117 | | 0.428 | 29.83 | 5400 | 0.4309 | 0.8138 | 0.8141 | | 0.4299 | 30.94 | 5600 | 0.4318 | 0.8105 | 0.8107 | | 0.4299 | 32.04 | 5800 | 0.4303 | 0.8141 | 0.8141 | | 0.4309 | 33.15 | 6000 | 0.4284 | 0.8149 | 0.8152 | | 0.4284 | 34.25 | 6200 | 0.4307 | 0.8125 | 0.8128 | | 0.4275 | 35.36 | 6400 | 0.4322 | 0.8123 | 0.8131 | | 0.4272 | 36.46 | 6600 | 0.4292 | 0.8162 | 0.8162 | | 0.4286 | 37.57 | 6800 | 0.4303 | 0.8141 | 0.8145 | | 0.4263 | 38.67 | 7000 | 0.4320 | 0.8136 | 0.8141 | | 0.4246 | 39.78 | 7200 | 0.4304 | 0.8165 | 0.8166 | | 0.4268 | 40.88 | 7400 | 0.4290 | 0.8150 | 0.8152 | | 0.4263 | 41.99 | 7600 | 0.4290 | 0.8153 | 0.8155 | | 0.4243 | 43.09 | 7800 | 0.4303 | 0.8161 | 0.8166 | | 0.4262 | 44.2 | 8000 | 0.4295 | 0.8141 | 0.8145 | | 0.4233 | 45.3 | 8200 | 0.4301 | 0.8152 | 0.8155 | | 0.4256 | 46.41 | 8400 | 0.4286 | 0.8148 | 0.8152 | | 0.4238 | 47.51 | 8600 | 0.4293 | 0.8156 | 0.8159 | | 0.4236 | 48.62 | 8800 | 0.4312 | 0.8136 | 0.8141 | | 0.4221 | 49.72 | 9000 | 0.4301 | 0.8142 | 0.8145 | | 0.4283 | 50.83 | 9200 | 0.4296 | 0.8131 | 0.8135 | | 0.4232 | 51.93 | 9400 | 0.4299 | 0.8142 | 0.8145 | | 0.4238 | 53.04 | 9600 | 0.4297 | 0.8142 | 0.8145 | | 0.4218 | 54.14 | 9800 | 0.4295 | 0.8149 | 0.8152 | | 0.424 | 55.25 | 10000 | 0.4300 | 0.8145 | 0.8148 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3K79me3-seqsight_65536_512_47M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K79me3-seqsight_65536_512_47M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]

null

2024-05-03T16:16:34+00:00

text-generation

transformers

<img src="./llama-3-merges.webp" alt="Llama-3 DPO Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4 This model is a fine-tune (DPO) of `meta-llama/Meta-Llama-3-70B-Instruct` model. # Quantized GGUF All GGUF models are available here: [MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4-GGUF) # Prompt Template This model uses `ChatML` prompt template: ``` <|im_start|>system {System} <|im_end|> <|im_start|>user {User} <|im_end|> <|im_start|>assistant {Assistant} ```` # How to use You can use this model by using `MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4` as the model name in Hugging Face's transformers library. ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer from transformers import pipeline import torch model_id = "MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4" model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True, # attn_implementation="flash_attention_2" ) tokenizer = AutoTokenizer.from_pretrained( model_id, trust_remote_code=True ) streamer = TextStreamer(tokenizer) pipeline = pipeline( "text-generation", model=model, tokenizer=tokenizer, model_kwargs={"torch_dtype": torch.bfloat16}, streamer=streamer ) # Then you can use the pipeline to generate text. messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|im_end|>"), tokenizer.convert_tokens_to_ids("<|eot_id|>") # safer to have this too ] outputs = pipeline( prompt, max_new_tokens=2048, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.95, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## Sample Response ``` <|im_start|>system You are a helpful assistant.<|im_end|> <|im_start|>user describe about pros and cons of docker system.<|im_end|> <|im_start|>assistant ``` ``` Here are the pros and cons of the Docker system: **Pros:** 1. **Lightweight**: Docker containers are lightweight and use fewer resources compared to traditional virtual machines. 2. **Isolation**: Docker provides a high level of isolation between containers, ensuring that each application runs independently without interfering with others. 3. **Portability**: Docker containers are highly portable and can run on any system that supports Docker, without requiring specific dependencies or configurations. 4. **Efficient Use of Resources**: Docker containers can share the same kernel and use fewer resources, making them more efficient than traditional virtual machines. 5. **Fast Deployment**: Docker containers can be spun up quickly, making it ideal for rapid deployment and scaling of applications. 6. **Easy Maintenance**: Docker provides a simple way to manage and update applications, with features like rolling updates and rollbacks. 7. **Security**: Docker provides a secure environment for applications, with features like network isolation and encryption. 8. **Large Ecosystem**: Docker has a large and active ecosystem, with a wide range of tools and plugins available for various tasks. 9. **Version Control**: Docker provides an easy way to manage different versions of an application, with features like version tagging and rollbacks. 10. **Cost-Effective**: Docker can help reduce costs by minimizing the need for virtual machines and reducing the overhead of resource allocation. **Cons:** 1. **Steep Learning Curve**: Docker has a complex architecture and requires a good understanding of containerization, networking, and storage. 2. **Overhead**: While Docker containers are lightweight, they still require some overhead in terms of resources and management. 3. **Security Risks**: If not properly configured, Docker containers can introduce security risks, such as unauthorized access to the host system. 4. **Limited Support for GUI Applications**: Docker is primarily designed for running command-line applications, and running GUI applications can be challenging. 5. **Networking Complexity**: Docker's networking model can be complex, especially in large-scale deployments. 6. **Storage Complexity**: Docker's storage model can be complex, especially when dealing with persistent data. 7. **Limited Support for Legacy Applications**: Docker is designed for modern, cloud-native applications, and may not be suitable for legacy applications that rely on older technologies. 8. **Dependence on Docker Hub**: Docker relies heavily on Docker Hub, which can be a single point of failure if the hub is down or experiencing issues. 9. **Orchestration Complexity**: Docker provides a built-in orchestration tool, Docker Swarm, but it can be complex to set up and manage. 10. **Vendor Lock-in**: Docker is a proprietary technology, and while it has a large ecosystem, it can be difficult to switch to alternative containerization platforms. Overall, Docker provides a powerful and flexible way to deploy and manage applications, but it requires careful planning, configuration, and management to ensure optimal performance and security. ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__Llama-3-70B-Instruct-DPO-v0.4) | Metric |Value| |---------------------------------|----:| |Avg. |78.89| |AI2 Reasoning Challenge (25-Shot)|72.61| |HellaSwag (10-Shot) |86.03| |MMLU (5-Shot) |80.50| |TruthfulQA (0-shot) |63.26| |Winogrande (5-shot) |83.58| |GSM8k (5-shot) |87.34|

{"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["axolotl", "finetune", "dpo", "facebook", "meta", "pytorch", "llama", "llama-3", "chatml"], "datasets": ["argilla/ultrafeedback-binarized-preferences"], "base_model": "meta-llama/Meta-Llama-3-70B-Instruct", "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "inference": false, "model_creator": "MaziyarPanahi", "quantized_by": "MaziyarPanahi", "model-index": [{"name": "Llama-3-70B-Instruct-DPO-v0.4", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 72.61, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 86.03, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 80.5, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 63.26}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 83.58, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 87.34, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4", "name": "Open LLM Leaderboard"}}]}]}

blockblockblock/Llama-3-70B-Instruct-DPO-v0.4-bpw2.5-exl2

null

[ "transformers", "safetensors", "llama", "text-generation", "axolotl", "finetune", "dpo", "facebook", "meta", "pytorch", "llama-3", "chatml", "conversational", "en", "dataset:argilla/ultrafeedback-binarized-preferences", "base_model:meta-llama/Meta-Llama-3-70B-Instruct", "license:llama3", "model-index", "autotrain_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:17:38+00:00

null

{}

nzsadvdfgzv/test

null

[ "region:us" ]

null

2024-05-03T16:18:13+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

ar08/ar08

null

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:19:14+00:00

null

peft

# GUE_EMP_H3K79me3-seqsight_65536_512_47M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3K79me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K79me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.4309 - F1 Score: 0.8185 - Accuracy: 0.8187 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5148 | 1.1 | 200 | 0.4601 | 0.8090 | 0.8089 | | 0.4624 | 2.21 | 400 | 0.4479 | 0.8057 | 0.8065 | | 0.4592 | 3.31 | 600 | 0.4440 | 0.8066 | 0.8076 | | 0.4474 | 4.42 | 800 | 0.4400 | 0.8033 | 0.8044 | | 0.4463 | 5.52 | 1000 | 0.4429 | 0.8030 | 0.8048 | | 0.4386 | 6.63 | 1200 | 0.4471 | 0.8024 | 0.8048 | | 0.4403 | 7.73 | 1400 | 0.4353 | 0.8077 | 0.8089 | | 0.4328 | 8.84 | 1600 | 0.4478 | 0.8019 | 0.8041 | | 0.4305 | 9.94 | 1800 | 0.4266 | 0.8190 | 0.8190 | | 0.4265 | 11.05 | 2000 | 0.4371 | 0.8041 | 0.8051 | | 0.4265 | 12.15 | 2200 | 0.4269 | 0.8185 | 0.8183 | | 0.4207 | 13.26 | 2400 | 0.4243 | 0.8151 | 0.8155 | | 0.4176 | 14.36 | 2600 | 0.4245 | 0.8184 | 0.8183 | | 0.4192 | 15.47 | 2800 | 0.4285 | 0.8111 | 0.8117 | | 0.414 | 16.57 | 3000 | 0.4283 | 0.8175 | 0.8173 | | 0.4149 | 17.68 | 3200 | 0.4244 | 0.8161 | 0.8162 | | 0.4094 | 18.78 | 3400 | 0.4262 | 0.8172 | 0.8176 | | 0.4091 | 19.89 | 3600 | 0.4239 | 0.8140 | 0.8141 | | 0.4087 | 20.99 | 3800 | 0.4302 | 0.8091 | 0.8100 | | 0.4076 | 22.1 | 4000 | 0.4246 | 0.8108 | 0.8114 | | 0.4059 | 23.2 | 4200 | 0.4253 | 0.8144 | 0.8148 | | 0.4057 | 24.31 | 4400 | 0.4300 | 0.8124 | 0.8131 | | 0.3982 | 25.41 | 4600 | 0.4299 | 0.8159 | 0.8162 | | 0.4019 | 26.52 | 4800 | 0.4289 | 0.8184 | 0.8187 | | 0.4036 | 27.62 | 5000 | 0.4294 | 0.8112 | 0.8121 | | 0.3975 | 28.73 | 5200 | 0.4243 | 0.8114 | 0.8121 | | 0.3938 | 29.83 | 5400 | 0.4255 | 0.8134 | 0.8138 | | 0.3966 | 30.94 | 5600 | 0.4280 | 0.8160 | 0.8162 | | 0.3953 | 32.04 | 5800 | 0.4275 | 0.8214 | 0.8214 | | 0.3972 | 33.15 | 6000 | 0.4261 | 0.8150 | 0.8155 | | 0.3931 | 34.25 | 6200 | 0.4297 | 0.8170 | 0.8173 | | 0.3914 | 35.36 | 6400 | 0.4287 | 0.8140 | 0.8145 | | 0.393 | 36.46 | 6600 | 0.4275 | 0.8181 | 0.8183 | | 0.3901 | 37.57 | 6800 | 0.4299 | 0.8136 | 0.8141 | | 0.3893 | 38.67 | 7000 | 0.4314 | 0.8153 | 0.8159 | | 0.3881 | 39.78 | 7200 | 0.4304 | 0.8184 | 0.8187 | | 0.3886 | 40.88 | 7400 | 0.4277 | 0.8189 | 0.8190 | | 0.3859 | 41.99 | 7600 | 0.4314 | 0.8162 | 0.8166 | | 0.3869 | 43.09 | 7800 | 0.4308 | 0.8169 | 0.8173 | | 0.3859 | 44.2 | 8000 | 0.4329 | 0.8149 | 0.8155 | | 0.3839 | 45.3 | 8200 | 0.4341 | 0.8159 | 0.8162 | | 0.3871 | 46.41 | 8400 | 0.4291 | 0.8184 | 0.8187 | | 0.3848 | 47.51 | 8600 | 0.4327 | 0.8172 | 0.8176 | | 0.3837 | 48.62 | 8800 | 0.4334 | 0.8164 | 0.8169 | | 0.383 | 49.72 | 9000 | 0.4334 | 0.8158 | 0.8162 | | 0.388 | 50.83 | 9200 | 0.4328 | 0.8160 | 0.8166 | | 0.3826 | 51.93 | 9400 | 0.4316 | 0.8169 | 0.8173 | | 0.3819 | 53.04 | 9600 | 0.4315 | 0.8166 | 0.8169 | | 0.3815 | 54.14 | 9800 | 0.4318 | 0.8170 | 0.8173 | | 0.3831 | 55.25 | 10000 | 0.4325 | 0.8166 | 0.8169 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3K79me3-seqsight_65536_512_47M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K79me3-seqsight_65536_512_47M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]

null

2024-05-03T16:19:22+00:00

null

peft

# GUE_EMP_H3K79me3-seqsight_65536_512_47M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3K79me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K79me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.4374 - F1 Score: 0.8193 - Accuracy: 0.8193 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5042 | 1.1 | 200 | 0.4523 | 0.8134 | 0.8135 | | 0.4575 | 2.21 | 400 | 0.4432 | 0.8135 | 0.8135 | | 0.453 | 3.31 | 600 | 0.4381 | 0.8068 | 0.8079 | | 0.4367 | 4.42 | 800 | 0.4333 | 0.8096 | 0.8107 | | 0.4327 | 5.52 | 1000 | 0.4302 | 0.8134 | 0.8145 | | 0.423 | 6.63 | 1200 | 0.4528 | 0.8043 | 0.8065 | | 0.4233 | 7.73 | 1400 | 0.4418 | 0.8010 | 0.8031 | | 0.4154 | 8.84 | 1600 | 0.4534 | 0.7936 | 0.7961 | | 0.4116 | 9.94 | 1800 | 0.4231 | 0.8144 | 0.8145 | | 0.4052 | 11.05 | 2000 | 0.4394 | 0.8028 | 0.8037 | | 0.4028 | 12.15 | 2200 | 0.4245 | 0.8196 | 0.8197 | | 0.397 | 13.26 | 2400 | 0.4251 | 0.8144 | 0.8148 | | 0.3917 | 14.36 | 2600 | 0.4285 | 0.8201 | 0.8200 | | 0.3907 | 15.47 | 2800 | 0.4296 | 0.8129 | 0.8131 | | 0.3827 | 16.57 | 3000 | 0.4302 | 0.8171 | 0.8169 | | 0.3821 | 17.68 | 3200 | 0.4380 | 0.8186 | 0.8187 | | 0.3754 | 18.78 | 3400 | 0.4418 | 0.8105 | 0.8110 | | 0.371 | 19.89 | 3600 | 0.4367 | 0.8177 | 0.8176 | | 0.3684 | 20.99 | 3800 | 0.4477 | 0.8107 | 0.8110 | | 0.3639 | 22.1 | 4000 | 0.4422 | 0.8158 | 0.8159 | | 0.3605 | 23.2 | 4200 | 0.4480 | 0.8144 | 0.8145 | | 0.3561 | 24.31 | 4400 | 0.4502 | 0.8163 | 0.8166 | | 0.3478 | 25.41 | 4600 | 0.4584 | 0.8175 | 0.8173 | | 0.3503 | 26.52 | 4800 | 0.4596 | 0.8121 | 0.8121 | | 0.3491 | 27.62 | 5000 | 0.4524 | 0.8113 | 0.8117 | | 0.3407 | 28.73 | 5200 | 0.4644 | 0.8110 | 0.8117 | | 0.3349 | 29.83 | 5400 | 0.4509 | 0.8151 | 0.8152 | | 0.3364 | 30.94 | 5600 | 0.4585 | 0.8171 | 0.8169 | | 0.3328 | 32.04 | 5800 | 0.4492 | 0.8199 | 0.8197 | | 0.3307 | 33.15 | 6000 | 0.4530 | 0.8164 | 0.8166 | | 0.3277 | 34.25 | 6200 | 0.4746 | 0.8175 | 0.8173 | | 0.3223 | 35.36 | 6400 | 0.4711 | 0.8181 | 0.8183 | | 0.3192 | 36.46 | 6600 | 0.4757 | 0.8187 | 0.8187 | | 0.3178 | 37.57 | 6800 | 0.4753 | 0.8139 | 0.8141 | | 0.3153 | 38.67 | 7000 | 0.4703 | 0.8165 | 0.8169 | | 0.3129 | 39.78 | 7200 | 0.4812 | 0.8196 | 0.8197 | | 0.3105 | 40.88 | 7400 | 0.4763 | 0.8143 | 0.8141 | | 0.3064 | 41.99 | 7600 | 0.4652 | 0.8180 | 0.8180 | | 0.306 | 43.09 | 7800 | 0.4787 | 0.8145 | 0.8145 | | 0.3041 | 44.2 | 8000 | 0.4898 | 0.8150 | 0.8152 | | 0.3014 | 45.3 | 8200 | 0.4882 | 0.8173 | 0.8173 | | 0.3005 | 46.41 | 8400 | 0.4859 | 0.8173 | 0.8173 | | 0.3006 | 47.51 | 8600 | 0.4895 | 0.8143 | 0.8145 | | 0.2973 | 48.62 | 8800 | 0.4882 | 0.8124 | 0.8124 | | 0.2961 | 49.72 | 9000 | 0.4937 | 0.8140 | 0.8141 | | 0.3008 | 50.83 | 9200 | 0.4829 | 0.8128 | 0.8131 | | 0.2934 | 51.93 | 9400 | 0.4918 | 0.8133 | 0.8135 | | 0.2928 | 53.04 | 9600 | 0.4910 | 0.8149 | 0.8148 | | 0.2936 | 54.14 | 9800 | 0.4936 | 0.8156 | 0.8155 | | 0.2934 | 55.25 | 10000 | 0.4941 | 0.8135 | 0.8135 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3K79me3-seqsight_65536_512_47M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K79me3-seqsight_65536_512_47M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]

null

2024-05-03T16:19:50+00:00

null

{}

barrybadpak/agfdrvgb

null

[ "region:us" ]

null

2024-05-03T16:21:59+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

InayaKripa/gemma-2b-toxic-ConvoV1

null

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:23:48+00:00

text-classification

transformers

This model is a finetuned version of ```distilbert/distilbert-base-multilingual-cased``` model in the **Bengali** and **Hindi** languages. The dataset used is a Kaggle Dataset - [Modified-hate-speech-bengali-hindi](https://www.kaggle.com/datasets/abirmondal/modified-hate-speech-bengali-hindi) This model can classify Bengali and Hindi texts into the following 5 classes: - defamation - hate - non-hate - violence - vulgar

{"language": ["bn", "hi"], "license": "apache-2.0"}

kingshukroy/distilbert-base-multilingual-cased-hate-speech-ben-hin

null

[ "transformers", "safetensors", "distilbert", "text-classification", "bn", "hi", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:23:54+00:00

null

{}

vinven7/Llama2-ft-MatKG

null

[ "region:us" ]

null

2024-05-03T16:24:37+00:00

null

peft

# mistral_finetued_on_scigen_server This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) 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: 1e-06 - train_batch_size: 256 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 256 - total_train_batch_size: 65536 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 30 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "mistral_finetued_on_scigen_server", "results": []}]}

moetezsa/mistral_finetued_on_scigen_server

null

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]

null

2024-05-03T16:24:44+00:00

feature-extraction

transformers

{}

MahmoudTaktak/LEGAL_E2

null

[ "transformers", "pytorch", "bert", "feature-extraction", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:25:14+00:00

text-classification

transformers

# Prototipo_5_EMI This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4215 - Accuracy: 0.538 ## 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: 20 - eval_batch_size: 20 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 1.2459 | 0.1481 | 200 | 1.2168 | 0.4493 | | 1.1445 | 0.2963 | 400 | 1.0823 | 0.512 | | 1.1117 | 0.4444 | 600 | 1.0979 | 0.5053 | | 1.0618 | 0.5926 | 800 | 1.0457 | 0.5273 | | 1.0343 | 0.7407 | 1000 | 1.0219 | 0.537 | | 1.1239 | 0.8889 | 1200 | 1.0353 | 0.5257 | | 0.9012 | 1.0370 | 1400 | 1.0637 | 0.5383 | | 0.86 | 1.1852 | 1600 | 1.0682 | 0.5333 | | 0.898 | 1.3333 | 1800 | 1.0341 | 0.5483 | | 0.929 | 1.4815 | 2000 | 1.0437 | 0.5363 | | 0.9921 | 1.6296 | 2200 | 0.9968 | 0.5473 | | 0.9776 | 1.7778 | 2400 | 1.0418 | 0.5553 | | 0.9166 | 1.9259 | 2600 | 0.9874 | 0.5573 | | 0.703 | 2.0741 | 2800 | 1.0564 | 0.556 | | 0.8123 | 2.2222 | 3000 | 1.0582 | 0.561 | | 0.6727 | 2.3704 | 3200 | 1.0942 | 0.5483 | | 0.6843 | 2.5185 | 3400 | 1.1128 | 0.558 | | 0.7528 | 2.6667 | 3600 | 1.0823 | 0.5547 | | 0.7747 | 2.8148 | 3800 | 1.0744 | 0.5497 | | 0.7471 | 2.9630 | 4000 | 1.0749 | 0.5527 | | 0.5774 | 3.1111 | 4200 | 1.1422 | 0.552 | | 0.6105 | 3.2593 | 4400 | 1.2226 | 0.543 | | 0.573 | 3.4074 | 4600 | 1.2427 | 0.5417 | | 0.6047 | 3.5556 | 4800 | 1.2403 | 0.537 | | 0.5334 | 3.7037 | 5000 | 1.2470 | 0.5413 | | 0.5688 | 3.8519 | 5200 | 1.2585 | 0.5507 | | 0.4928 | 4.0 | 5400 | 1.2653 | 0.5437 | | 0.4314 | 4.1481 | 5600 | 1.3419 | 0.541 | | 0.4556 | 4.2963 | 5800 | 1.3677 | 0.5413 | | 0.4815 | 4.4444 | 6000 | 1.3912 | 0.5407 | | 0.4431 | 4.5926 | 6200 | 1.4004 | 0.5347 | | 0.4312 | 4.7407 | 6400 | 1.4161 | 0.5397 | | 0.459 | 4.8889 | 6600 | 1.4215 | 0.538 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "dccuchile/bert-base-spanish-wwm-uncased", "model-index": [{"name": "Prototipo_5_EMI", "results": []}]}

Armandodelca/Prototipo_5_EMI

null

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:25:41+00:00

text-generation

transformers

Barcenas 3.8b Based on the Phi-3-Mini-4K-Boost from DeepMount00 and trained with the pinzhenchen/alpaca-cleaned-es dataset, to improve Spanish conversations. The goal of this model is to have a small LLM that can express itself correctly and fluently in the Spanish language. Made with ❤️ in Guadalupe, Nuevo Leon, Mexico 🇲🇽

{"language": ["es", "en"], "license": "mit", "tags": ["phi"]}

Danielbrdz/Barcenas-3.8b

null

[ "transformers", "safetensors", "mistral", "text-generation", "phi", "conversational", "es", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:25:51+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

OwOpeepeepoopoo/herewegoagain15c

null

[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:25:57+00:00

null

{"license": "unknown"}

charaatasnim/chekpointzip

null

[ "license:unknown", "region:us" ]

null

2024-05-03T16:27:42+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

golf2248/l9w0l2v

null

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:27:49+00:00

text-generation

transformers

{"license": "mit"}

sheikhfoysal/chatEDU-v2

null

[ "transformers", "safetensors", "llama", "text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:28:49+00:00

null

peft

# GUE_EMP_H3K4me1-seqsight_65536_512_47M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me1) dataset. It achieves the following results on the evaluation set: - Loss: 0.5417 - F1 Score: 0.7453 - Accuracy: 0.7465 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6298 | 1.01 | 200 | 0.6057 | 0.6909 | 0.6929 | | 0.6045 | 2.02 | 400 | 0.6003 | 0.6998 | 0.7004 | | 0.5948 | 3.03 | 600 | 0.5930 | 0.7061 | 0.7074 | | 0.5909 | 4.04 | 800 | 0.5868 | 0.7121 | 0.7146 | | 0.5809 | 5.05 | 1000 | 0.5821 | 0.7155 | 0.7172 | | 0.578 | 6.06 | 1200 | 0.5811 | 0.7148 | 0.7191 | | 0.573 | 7.07 | 1400 | 0.5783 | 0.7180 | 0.7213 | | 0.5737 | 8.08 | 1600 | 0.5748 | 0.7206 | 0.7235 | | 0.5703 | 9.09 | 1800 | 0.5703 | 0.7262 | 0.7279 | | 0.5664 | 10.1 | 2000 | 0.5725 | 0.7213 | 0.7222 | | 0.5643 | 11.11 | 2200 | 0.5712 | 0.7248 | 0.7270 | | 0.5647 | 12.12 | 2400 | 0.5695 | 0.7278 | 0.7292 | | 0.563 | 13.13 | 2600 | 0.5682 | 0.7251 | 0.7270 | | 0.5629 | 14.14 | 2800 | 0.5641 | 0.7292 | 0.7314 | | 0.5582 | 15.15 | 3000 | 0.5625 | 0.7289 | 0.7307 | | 0.5586 | 16.16 | 3200 | 0.5639 | 0.7267 | 0.7295 | | 0.5564 | 17.17 | 3400 | 0.5630 | 0.7295 | 0.7323 | | 0.5565 | 18.18 | 3600 | 0.5582 | 0.7325 | 0.7336 | | 0.5531 | 19.19 | 3800 | 0.5613 | 0.7311 | 0.7336 | | 0.5546 | 20.2 | 4000 | 0.5590 | 0.7314 | 0.7330 | | 0.5507 | 21.21 | 4200 | 0.5631 | 0.7337 | 0.7367 | | 0.552 | 22.22 | 4400 | 0.5596 | 0.7344 | 0.7358 | | 0.5501 | 23.23 | 4600 | 0.5615 | 0.7342 | 0.7367 | | 0.5545 | 24.24 | 4800 | 0.5566 | 0.7385 | 0.7405 | | 0.5478 | 25.25 | 5000 | 0.5563 | 0.7372 | 0.7386 | | 0.5501 | 26.26 | 5200 | 0.5585 | 0.7345 | 0.7355 | | 0.5459 | 27.27 | 5400 | 0.5563 | 0.7350 | 0.7367 | | 0.5483 | 28.28 | 5600 | 0.5585 | 0.7332 | 0.7339 | | 0.5521 | 29.29 | 5800 | 0.5566 | 0.7362 | 0.7386 | | 0.5451 | 30.3 | 6000 | 0.5552 | 0.7358 | 0.7371 | | 0.5469 | 31.31 | 6200 | 0.5547 | 0.7378 | 0.7396 | | 0.5478 | 32.32 | 6400 | 0.5564 | 0.7350 | 0.7380 | | 0.5417 | 33.33 | 6600 | 0.5552 | 0.7365 | 0.7390 | | 0.5433 | 34.34 | 6800 | 0.5562 | 0.7347 | 0.7377 | | 0.5425 | 35.35 | 7000 | 0.5530 | 0.7407 | 0.7421 | | 0.5473 | 36.36 | 7200 | 0.5529 | 0.7363 | 0.7380 | | 0.5431 | 37.37 | 7400 | 0.5531 | 0.7360 | 0.7374 | | 0.542 | 38.38 | 7600 | 0.5538 | 0.7373 | 0.7383 | | 0.5421 | 39.39 | 7800 | 0.5536 | 0.7358 | 0.7371 | | 0.544 | 40.4 | 8000 | 0.5536 | 0.7371 | 0.7386 | | 0.5428 | 41.41 | 8200 | 0.5535 | 0.7369 | 0.7393 | | 0.5474 | 42.42 | 8400 | 0.5529 | 0.7382 | 0.7405 | | 0.5415 | 43.43 | 8600 | 0.5530 | 0.7345 | 0.7364 | | 0.54 | 44.44 | 8800 | 0.5529 | 0.7367 | 0.7383 | | 0.5416 | 45.45 | 9000 | 0.5526 | 0.7380 | 0.7396 | | 0.5424 | 46.46 | 9200 | 0.5527 | 0.7364 | 0.7383 | | 0.5451 | 47.47 | 9400 | 0.5525 | 0.7361 | 0.7380 | | 0.5367 | 48.48 | 9600 | 0.5528 | 0.7359 | 0.7377 | | 0.5461 | 49.49 | 9800 | 0.5524 | 0.7362 | 0.7380 | | 0.5409 | 50.51 | 10000 | 0.5525 | 0.7369 | 0.7386 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3K4me1-seqsight_65536_512_47M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me1-seqsight_65536_512_47M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]

null

2024-05-03T16:28:56+00:00

null

peft

# GUE_EMP_H3K4me1-seqsight_65536_512_47M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me1) dataset. It achieves the following results on the evaluation set: - Loss: 0.5342 - F1 Score: 0.7479 - Accuracy: 0.7503 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6119 | 1.01 | 200 | 0.5885 | 0.7076 | 0.7118 | | 0.5758 | 2.02 | 400 | 0.5650 | 0.7279 | 0.7295 | | 0.5634 | 3.03 | 600 | 0.5565 | 0.7319 | 0.7342 | | 0.5565 | 4.04 | 800 | 0.5557 | 0.7387 | 0.7405 | | 0.5482 | 5.05 | 1000 | 0.5473 | 0.7424 | 0.7434 | | 0.543 | 6.06 | 1200 | 0.5546 | 0.7293 | 0.7345 | | 0.5323 | 7.07 | 1400 | 0.5504 | 0.7414 | 0.7440 | | 0.531 | 8.08 | 1600 | 0.5479 | 0.7375 | 0.7402 | | 0.5291 | 9.09 | 1800 | 0.5429 | 0.7434 | 0.7459 | | 0.5196 | 10.1 | 2000 | 0.5444 | 0.7473 | 0.7494 | | 0.5147 | 11.11 | 2200 | 0.5500 | 0.7482 | 0.7513 | | 0.5142 | 12.12 | 2400 | 0.5434 | 0.7444 | 0.7462 | | 0.5088 | 13.13 | 2600 | 0.5517 | 0.7404 | 0.7431 | | 0.5083 | 14.14 | 2800 | 0.5411 | 0.7478 | 0.7494 | | 0.4974 | 15.15 | 3000 | 0.5450 | 0.7427 | 0.7449 | | 0.4994 | 16.16 | 3200 | 0.5419 | 0.7409 | 0.7440 | | 0.4928 | 17.17 | 3400 | 0.5453 | 0.7485 | 0.7503 | | 0.4909 | 18.18 | 3600 | 0.5473 | 0.7428 | 0.7443 | | 0.4837 | 19.19 | 3800 | 0.5471 | 0.7458 | 0.7478 | | 0.4813 | 20.2 | 4000 | 0.5473 | 0.7402 | 0.7421 | | 0.4778 | 21.21 | 4200 | 0.5463 | 0.7410 | 0.7440 | | 0.473 | 22.22 | 4400 | 0.5585 | 0.7418 | 0.7434 | | 0.4706 | 23.23 | 4600 | 0.5573 | 0.7435 | 0.7446 | | 0.4706 | 24.24 | 4800 | 0.5544 | 0.7398 | 0.7424 | | 0.4626 | 25.25 | 5000 | 0.5588 | 0.7457 | 0.7465 | | 0.463 | 26.26 | 5200 | 0.5579 | 0.7399 | 0.7408 | | 0.4582 | 27.27 | 5400 | 0.5557 | 0.7385 | 0.7415 | | 0.4599 | 28.28 | 5600 | 0.5634 | 0.7389 | 0.7399 | | 0.4575 | 29.29 | 5800 | 0.5552 | 0.7401 | 0.7431 | | 0.453 | 30.3 | 6000 | 0.5668 | 0.7389 | 0.7405 | | 0.4525 | 31.31 | 6200 | 0.5550 | 0.7407 | 0.7421 | | 0.4521 | 32.32 | 6400 | 0.5617 | 0.7406 | 0.7434 | | 0.4442 | 33.33 | 6600 | 0.5689 | 0.7395 | 0.7424 | | 0.442 | 34.34 | 6800 | 0.5624 | 0.7455 | 0.7472 | | 0.4384 | 35.35 | 7000 | 0.5687 | 0.7431 | 0.7449 | | 0.4474 | 36.36 | 7200 | 0.5577 | 0.7393 | 0.7412 | | 0.4343 | 37.37 | 7400 | 0.5663 | 0.7425 | 0.7443 | | 0.4362 | 38.38 | 7600 | 0.5668 | 0.7413 | 0.7424 | | 0.4359 | 39.39 | 7800 | 0.5707 | 0.7376 | 0.7386 | | 0.4341 | 40.4 | 8000 | 0.5797 | 0.7384 | 0.7396 | | 0.4292 | 41.41 | 8200 | 0.5780 | 0.7390 | 0.7421 | | 0.4375 | 42.42 | 8400 | 0.5716 | 0.7386 | 0.7405 | | 0.4293 | 43.43 | 8600 | 0.5735 | 0.7406 | 0.7424 | | 0.4273 | 44.44 | 8800 | 0.5755 | 0.7391 | 0.7412 | | 0.4243 | 45.45 | 9000 | 0.5755 | 0.7413 | 0.7427 | | 0.426 | 46.46 | 9200 | 0.5778 | 0.7400 | 0.7418 | | 0.4296 | 47.47 | 9400 | 0.5708 | 0.7387 | 0.7408 | | 0.4172 | 48.48 | 9600 | 0.5781 | 0.7385 | 0.7408 | | 0.4309 | 49.49 | 9800 | 0.5737 | 0.7396 | 0.7418 | | 0.4227 | 50.51 | 10000 | 0.5749 | 0.7402 | 0.7421 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3K4me1-seqsight_65536_512_47M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me1-seqsight_65536_512_47M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]

null

2024-05-03T16:29:06+00:00

null

{}

lngalmsyr/indobert-hoaxClass

null

[ "region:us" ]

null

2024-05-03T16:29:10+00:00

null

peft

# GUE_EMP_H3K4me1-seqsight_65536_512_47M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me1) dataset. It achieves the following results on the evaluation set: - Loss: 0.5308 - F1 Score: 0.7509 - Accuracy: 0.7532 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6204 | 1.01 | 200 | 0.5983 | 0.6993 | 0.7027 | | 0.5905 | 2.02 | 400 | 0.5808 | 0.7134 | 0.7165 | | 0.5754 | 3.03 | 600 | 0.5707 | 0.7255 | 0.7279 | | 0.5712 | 4.04 | 800 | 0.5692 | 0.7207 | 0.7235 | | 0.5627 | 5.05 | 1000 | 0.5617 | 0.7320 | 0.7339 | | 0.5601 | 6.06 | 1200 | 0.5642 | 0.7224 | 0.7266 | | 0.5519 | 7.07 | 1400 | 0.5591 | 0.7345 | 0.7371 | | 0.5513 | 8.08 | 1600 | 0.5567 | 0.7322 | 0.7352 | | 0.5475 | 9.09 | 1800 | 0.5500 | 0.7391 | 0.7412 | | 0.5416 | 10.1 | 2000 | 0.5502 | 0.7406 | 0.7424 | | 0.5385 | 11.11 | 2200 | 0.5543 | 0.7401 | 0.7427 | | 0.5387 | 12.12 | 2400 | 0.5486 | 0.7430 | 0.7443 | | 0.5355 | 13.13 | 2600 | 0.5536 | 0.7396 | 0.7418 | | 0.5353 | 14.14 | 2800 | 0.5485 | 0.7441 | 0.7462 | | 0.5297 | 15.15 | 3000 | 0.5485 | 0.7435 | 0.7456 | | 0.5287 | 16.16 | 3200 | 0.5456 | 0.7401 | 0.7431 | | 0.5269 | 17.17 | 3400 | 0.5484 | 0.7426 | 0.7453 | | 0.5257 | 18.18 | 3600 | 0.5428 | 0.7473 | 0.7487 | | 0.5213 | 19.19 | 3800 | 0.5433 | 0.7402 | 0.7431 | | 0.5215 | 20.2 | 4000 | 0.5433 | 0.7452 | 0.7472 | | 0.5196 | 21.21 | 4200 | 0.5514 | 0.7434 | 0.7465 | | 0.5188 | 22.22 | 4400 | 0.5465 | 0.7448 | 0.7472 | | 0.5182 | 23.23 | 4600 | 0.5449 | 0.7456 | 0.7481 | | 0.5198 | 24.24 | 4800 | 0.5446 | 0.7427 | 0.7456 | | 0.514 | 25.25 | 5000 | 0.5429 | 0.7467 | 0.7481 | | 0.5147 | 26.26 | 5200 | 0.5465 | 0.7484 | 0.7491 | | 0.5109 | 27.27 | 5400 | 0.5419 | 0.7468 | 0.7487 | | 0.514 | 28.28 | 5600 | 0.5464 | 0.7447 | 0.7453 | | 0.5148 | 29.29 | 5800 | 0.5456 | 0.7457 | 0.7487 | | 0.5083 | 30.3 | 6000 | 0.5455 | 0.7457 | 0.7472 | | 0.509 | 31.31 | 6200 | 0.5444 | 0.7466 | 0.7481 | | 0.5105 | 32.32 | 6400 | 0.5460 | 0.7460 | 0.7484 | | 0.5053 | 33.33 | 6600 | 0.5483 | 0.7447 | 0.7475 | | 0.5054 | 34.34 | 6800 | 0.5445 | 0.7463 | 0.7487 | | 0.5027 | 35.35 | 7000 | 0.5424 | 0.7487 | 0.7503 | | 0.5086 | 36.36 | 7200 | 0.5405 | 0.7464 | 0.7481 | | 0.5017 | 37.37 | 7400 | 0.5435 | 0.7440 | 0.7456 | | 0.4999 | 38.38 | 7600 | 0.5433 | 0.7486 | 0.7497 | | 0.5025 | 39.39 | 7800 | 0.5442 | 0.7475 | 0.7484 | | 0.5038 | 40.4 | 8000 | 0.5467 | 0.7470 | 0.7484 | | 0.501 | 41.41 | 8200 | 0.5445 | 0.7416 | 0.7443 | | 0.5077 | 42.42 | 8400 | 0.5422 | 0.7472 | 0.7494 | | 0.4974 | 43.43 | 8600 | 0.5435 | 0.7464 | 0.7481 | | 0.4987 | 44.44 | 8800 | 0.5445 | 0.7446 | 0.7462 | | 0.4971 | 45.45 | 9000 | 0.5449 | 0.7468 | 0.7484 | | 0.499 | 46.46 | 9200 | 0.5438 | 0.7470 | 0.7487 | | 0.503 | 47.47 | 9400 | 0.5428 | 0.7437 | 0.7456 | | 0.4927 | 48.48 | 9600 | 0.5450 | 0.7463 | 0.7481 | | 0.5038 | 49.49 | 9800 | 0.5438 | 0.7460 | 0.7478 | | 0.4975 | 50.51 | 10000 | 0.5441 | 0.7454 | 0.7472 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3K4me1-seqsight_65536_512_47M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me1-seqsight_65536_512_47M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]

null

2024-05-03T16:29:21+00:00

text-generation

transformers

{}

Zekunli/Llama-2-7b-mlp2x-mmncphysical-text-only-v2

null

[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:29:25+00:00

null

peft

# GUE_EMP_H3K36me3-seqsight_65536_512_47M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3K36me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K36me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5000 - F1 Score: 0.7751 - Accuracy: 0.7769 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5805 | 0.92 | 200 | 0.5475 | 0.7329 | 0.7351 | | 0.533 | 1.83 | 400 | 0.5360 | 0.7384 | 0.7411 | | 0.5243 | 2.75 | 600 | 0.5261 | 0.7465 | 0.7483 | | 0.5216 | 3.67 | 800 | 0.5199 | 0.7555 | 0.7563 | | 0.5103 | 4.59 | 1000 | 0.5198 | 0.7563 | 0.7583 | | 0.5074 | 5.5 | 1200 | 0.5137 | 0.7593 | 0.7615 | | 0.5047 | 6.42 | 1400 | 0.5086 | 0.7731 | 0.7738 | | 0.5017 | 7.34 | 1600 | 0.5109 | 0.7695 | 0.7712 | | 0.4951 | 8.26 | 1800 | 0.5114 | 0.7696 | 0.7718 | | 0.499 | 9.17 | 2000 | 0.5101 | 0.7674 | 0.7701 | | 0.4968 | 10.09 | 2200 | 0.5107 | 0.7670 | 0.7704 | | 0.4928 | 11.01 | 2400 | 0.5085 | 0.7655 | 0.7689 | | 0.4914 | 11.93 | 2600 | 0.5024 | 0.7741 | 0.7764 | | 0.4898 | 12.84 | 2800 | 0.5021 | 0.7707 | 0.7732 | | 0.4886 | 13.76 | 3000 | 0.5087 | 0.7676 | 0.7709 | | 0.4853 | 14.68 | 3200 | 0.4988 | 0.7759 | 0.7775 | | 0.489 | 15.6 | 3400 | 0.5080 | 0.7675 | 0.7712 | | 0.4866 | 16.51 | 3600 | 0.5003 | 0.7750 | 0.7769 | | 0.4851 | 17.43 | 3800 | 0.4924 | 0.7816 | 0.7830 | | 0.4856 | 18.35 | 4000 | 0.4995 | 0.7763 | 0.7787 | | 0.4816 | 19.27 | 4200 | 0.4990 | 0.7754 | 0.7775 | | 0.4845 | 20.18 | 4400 | 0.5034 | 0.7717 | 0.7749 | | 0.4832 | 21.1 | 4600 | 0.4975 | 0.7765 | 0.7787 | | 0.4828 | 22.02 | 4800 | 0.5014 | 0.7756 | 0.7778 | | 0.4829 | 22.94 | 5000 | 0.4969 | 0.7744 | 0.7769 | | 0.4803 | 23.85 | 5200 | 0.4996 | 0.7732 | 0.7761 | | 0.4788 | 24.77 | 5400 | 0.5065 | 0.7725 | 0.7758 | | 0.4817 | 25.69 | 5600 | 0.5004 | 0.7760 | 0.7784 | | 0.4796 | 26.61 | 5800 | 0.4973 | 0.7755 | 0.7778 | | 0.4758 | 27.52 | 6000 | 0.5100 | 0.7729 | 0.7764 | | 0.4787 | 28.44 | 6200 | 0.5018 | 0.7717 | 0.7747 | | 0.4762 | 29.36 | 6400 | 0.5042 | 0.7713 | 0.7747 | | 0.4794 | 30.28 | 6600 | 0.5040 | 0.7725 | 0.7758 | | 0.4762 | 31.19 | 6800 | 0.4930 | 0.7812 | 0.7827 | | 0.476 | 32.11 | 7000 | 0.4992 | 0.7733 | 0.7764 | | 0.4767 | 33.03 | 7200 | 0.5005 | 0.7742 | 0.7769 | | 0.4753 | 33.94 | 7400 | 0.5002 | 0.7756 | 0.7781 | | 0.4756 | 34.86 | 7600 | 0.4983 | 0.7750 | 0.7778 | | 0.4743 | 35.78 | 7800 | 0.4978 | 0.7738 | 0.7767 | | 0.476 | 36.7 | 8000 | 0.4983 | 0.7744 | 0.7772 | | 0.4736 | 37.61 | 8200 | 0.5032 | 0.7712 | 0.7747 | | 0.4758 | 38.53 | 8400 | 0.4928 | 0.7799 | 0.7818 | | 0.4734 | 39.45 | 8600 | 0.4986 | 0.7745 | 0.7772 | | 0.4725 | 40.37 | 8800 | 0.5023 | 0.7729 | 0.7761 | | 0.4773 | 41.28 | 9000 | 0.4986 | 0.7734 | 0.7764 | | 0.4743 | 42.2 | 9200 | 0.4955 | 0.7774 | 0.7798 | | 0.4721 | 43.12 | 9400 | 0.4984 | 0.7755 | 0.7781 | | 0.4744 | 44.04 | 9600 | 0.4979 | 0.7750 | 0.7778 | | 0.4732 | 44.95 | 9800 | 0.5005 | 0.7721 | 0.7752 | | 0.4742 | 45.87 | 10000 | 0.4987 | 0.7755 | 0.7784 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3K36me3-seqsight_65536_512_47M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K36me3-seqsight_65536_512_47M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]

null

2024-05-03T16:29:45+00:00

text-generation

transformers

# dareties 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 [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using ./models2/Meta-Llama-3-8B-instruct as a base. ### Models Merged The following models were included in the merge: * ./models2/Llama-3-Kafka-8B-v0.1 * [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B) * ./models2/Llama3_DiscoLM_German_8b_v0.1_experimental * ./models2/Llama-3-SauerkrautLM-8b-Instruct ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: ./models2/Meta-Llama-3-8B-instruct # No parameters necessary for base model - model: ./models2/Llama-3-SauerkrautLM-8b-Instruct parameters: density: 0.6 weight: 0.25 - model: ./models2/Llama3_DiscoLM_German_8b_v0.1_experimental parameters: density: 0.6 weight: 0.25 - model: ./models2/Llama-3-Kafka-8B-v0.1 parameters: density: 0.6 weight: 0.25 - model: NousResearch/Hermes-2-Pro-Llama-3-8B parameters: density: 0.6 weight: 0.25 merge_method: dare_ties base_model: ./models2/Meta-Llama-3-8B-instruct tokenizer_source: model:NousResearch/Hermes-2-Pro-Llama-3-8B parameters: int8_mask: true dtype: bfloat16 ```

{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["NousResearch/Hermes-2-Pro-Llama-3-8B"]}

johannhartmann/llama8_dt_b

null

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:NousResearch/Hermes-2-Pro-Llama-3-8B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:30:05+00:00

null

{}

Gokulapriyan9677/videomae-base-finetuned-ucf101-subset

null

[ "region:us" ]

null

2024-05-03T16:30:15+00:00

object-detection

transformers

{}

nsugianto/tabletransstructrecog_finetuned_pubt1m_lstabletransstrucrecogv1_session6

null

[ "transformers", "tensorboard", "safetensors", "table-transformer", "object-detection", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:30:25+00:00

null

transformers

# Uploaded model - **Developed by:** johannoriel - **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)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}

johannoriel/medllama_model

null

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:30:46+00:00

null

This model is finetuned with an ML Q&A dataset - hanyueshf/ml-arxiv-papers-qa. It outperforms both its base Llama-2-7B-Chat and Llama-3-8B-Instruct, as shown in below figure. Note: improvement = (finetuned_llama2_scores - base_llama2_scores) / base_llama2_scores. The finetuning code is available on Github at https://github.com/hanyuesgithub/QA-ml-arxiv-papers. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65ab7449356bf23b4ac0f556/vx-VNfMq3q4iNRqmFz1iw.png)

{}

hanyueshf/llama-2-7b-chat-ml-qa

null

[ "region:us" ]

null

2024-05-03T16:31:05+00:00

text-classification

transformers

# fine_tuned_boolq_bert This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5736 - Accuracy: 0.7222 - F1: 0.7325 ## 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 | |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:| | 0.6443 | 4.1667 | 50 | 0.5606 | 0.7778 | 0.6806 | | 0.3932 | 8.3333 | 100 | 0.6016 | 0.6111 | 0.6255 | | 0.126 | 12.5 | 150 | 1.0887 | 0.5 | 0.5418 | | 0.0166 | 16.6667 | 200 | 1.5543 | 0.5556 | 0.5829 | | 0.0041 | 20.8333 | 250 | 1.5032 | 0.7222 | 0.7325 | | 0.0022 | 25.0 | 300 | 1.7354 | 0.6667 | 0.6872 | | 0.0018 | 29.1667 | 350 | 1.5756 | 0.6667 | 0.6667 | | 0.0016 | 33.3333 | 400 | 1.5736 | 0.7222 | 0.7325 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "google-bert/bert-base-uncased", "model-index": [{"name": "fine_tuned_boolq_bert", "results": []}]}

lenatr99/fine_tuned_boolq_bert

null

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:31:44+00:00

reinforcement-learning

null

# **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="TeoGal/q-FrozenLake-v1-4x4-noSlippery", 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"]) ```

{"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}

TeoGal/q-FrozenLake-v1-4x4-noSlippery

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

null

2024-05-03T16:32:44+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

golf2248/mdxmtky

null

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:32:49+00:00

null

transformers

# LLOROv2 - GGUF VERSION Este repositório contém o modelo Llorov2 de 7B de parâmetros em formato GGUF, na versão 16 bits e também nas versão quantizada de 8 bits. Lloro, desenvolvido pelos Laboratórios de Pesquisa Semantix, é um Modelo de Linguagem que foi treinado para realizar efetivamente Análise de Dados em Português no Python. É uma versão aprimorada de codellama/CodeLlama-7b-Instruct-hf, que foi treinado em conjuntos de dados sintéticos. O processo de aprimoramento foi realizado usando a metodologia QLORA em uma GPU V100 com 16 GB de RAM. Acesse o [site](https://semantix.ai/conheca-o-lloro-o-primeiro-modelo-de-ia-expert-em-analise-de-dados-100-brasileiro/) para mais informações sobre o Lloro. # Sobre o formato GGUF O modelo no formato GGUF permite seu uso para inferência usando o llama.cpp, permitindo tanto o uso de CPU como de GPU, e outras bibliotecas e ferramentas compatíveis, como: * [text-generation-webui](https://github.com/oobabooga/text-generation-webui) * [KoboldCpp](https://github.com/LostRuins/koboldcpp) * [LM Studio](https://lmstudio.ai/) * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) * [ctransformers](https://github.com/marella/ctransformers) * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) ## Detalhes do Modelo - **Modelo Base:** CodeLlama-7b-Instruct-hf - **Dataset de Treinamento:** Synthetic - **Idioma:** Português ## Contribuições Contribuições para a melhoria deste modelo são bem-vindas. Sinta-se à vontade para abrir problemas e solicitações pull.

{"language": ["pt"], "license": "llama2", "library_name": "transformers", "tags": ["LLM", "Portuguese", "Lloro", "Llama 2", "Q&A"], "datasets": ["semantixai/Test-Dataset-Lloro"], "base_model": "codellama/CodeLlama-7b-Instruct-hf"}

anaxsouza/llorov2-gguf

null

[ "transformers", "gguf", "LLM", "Portuguese", "Lloro", "Llama 2", "Q&A", "pt", "dataset:semantixai/Test-Dataset-Lloro", "base_model:codellama/CodeLlama-7b-Instruct-hf", "license:llama2", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:33:25+00:00

null

{"license": "cc-by-4.0"}

unileon-robotics/home-objects

null

[ "license:cc-by-4.0", "region:us" ]

null

2024-05-03T16:34:43+00:00

text-generation

transformers

{}

Yeobin/llama-3-8b-math

null

[ "transformers", "pytorch", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:34:49+00:00

text-generation

transformers

# sft-fsi This model is a fine-tuned version of [dynamofl/dynamo-1.6B-v0.4-mosaic-dynamoDPO-iter0-2978](https://huggingface.co/dynamofl/dynamo-1.6B-v0.4-mosaic-dynamoDPO-iter0-2978) on the dynamofl/train-default-FSI-PersonalFinancialAdvice-input-formatted-chatml dataset. It achieves the following results on the evaluation set: - Loss: 0.7006 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-------:|:----:|:---------------:| | 5.8867 | 0.5333 | 1 | 5.7356 | | 5.8867 | 1.6 | 3 | 2.7050 | | 3.7066 | 2.6667 | 5 | 1.9496 | | 3.7066 | 3.7333 | 7 | 1.5918 | | 3.7066 | 4.8 | 9 | 1.3194 | | 1.6243 | 5.8667 | 11 | 1.0055 | | 1.6243 | 6.9333 | 13 | 0.8459 | | 0.9667 | 8.0 | 15 | 0.7559 | | 0.9667 | 8.5333 | 16 | 0.7331 | | 0.9667 | 9.6 | 18 | 0.7034 | | 0.7508 | 10.6667 | 20 | 0.7006 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer", "trl", "sft", "generated_from_trainer"], "datasets": ["dynamofl/train-default-FSI-PersonalFinancialAdvice-input-formatted-chatml"], "base_model": "dynamofl/dynamo-1.6B-v0.4-mosaic-dynamoDPO-iter0-2978", "model-index": [{"name": "sft-fsi", "results": []}]}

jamesoneill12/sft-fsi

null

[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "dataset:dynamofl/train-default-FSI-PersonalFinancialAdvice-input-formatted-chatml", "base_model:dynamofl/dynamo-1.6B-v0.4-mosaic-dynamoDPO-iter0-2978", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:34:49+00:00

null

{}

vinven7/Llama2-ft-CrystalStructure

null

[ "region:us" ]

null

2024-05-03T16:35:07+00:00

null

peft

# GUE_EMP_H3K36me3-seqsight_65536_512_47M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3K36me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K36me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.4908 - F1 Score: 0.7777 - Accuracy: 0.7790 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5667 | 0.92 | 200 | 0.5382 | 0.7367 | 0.7397 | | 0.5207 | 1.83 | 400 | 0.5229 | 0.7512 | 0.7537 | | 0.5071 | 2.75 | 600 | 0.5121 | 0.7597 | 0.7618 | | 0.505 | 3.67 | 800 | 0.5060 | 0.7706 | 0.7724 | | 0.4938 | 4.59 | 1000 | 0.5047 | 0.7715 | 0.7735 | | 0.4908 | 5.5 | 1200 | 0.5033 | 0.7695 | 0.7724 | | 0.4881 | 6.42 | 1400 | 0.4938 | 0.7790 | 0.7801 | | 0.4832 | 7.34 | 1600 | 0.5067 | 0.7749 | 0.7775 | | 0.4775 | 8.26 | 1800 | 0.4963 | 0.7822 | 0.7838 | | 0.4815 | 9.17 | 2000 | 0.4922 | 0.7798 | 0.7815 | | 0.478 | 10.09 | 2200 | 0.5153 | 0.7620 | 0.7675 | | 0.4733 | 11.01 | 2400 | 0.4963 | 0.7765 | 0.7801 | | 0.4707 | 11.93 | 2600 | 0.4824 | 0.7864 | 0.7873 | | 0.4677 | 12.84 | 2800 | 0.4894 | 0.7764 | 0.7792 | | 0.4668 | 13.76 | 3000 | 0.5010 | 0.7719 | 0.7761 | | 0.4613 | 14.68 | 3200 | 0.4897 | 0.7811 | 0.7833 | | 0.4644 | 15.6 | 3400 | 0.4857 | 0.7769 | 0.7795 | | 0.463 | 16.51 | 3600 | 0.4989 | 0.7776 | 0.7807 | | 0.4594 | 17.43 | 3800 | 0.4825 | 0.7863 | 0.7878 | | 0.4594 | 18.35 | 4000 | 0.4870 | 0.7809 | 0.7833 | | 0.4559 | 19.27 | 4200 | 0.4896 | 0.7829 | 0.7850 | | 0.4579 | 20.18 | 4400 | 0.4996 | 0.7734 | 0.7772 | | 0.4552 | 21.1 | 4600 | 0.4861 | 0.7824 | 0.7847 | | 0.4564 | 22.02 | 4800 | 0.4899 | 0.7840 | 0.7861 | | 0.4525 | 22.94 | 5000 | 0.4892 | 0.7759 | 0.7792 | | 0.4504 | 23.85 | 5200 | 0.4890 | 0.7818 | 0.7847 | | 0.4467 | 24.77 | 5400 | 0.5002 | 0.7733 | 0.7775 | | 0.4512 | 25.69 | 5600 | 0.4926 | 0.7807 | 0.7835 | | 0.4492 | 26.61 | 5800 | 0.4851 | 0.7833 | 0.7856 | | 0.4436 | 27.52 | 6000 | 0.5050 | 0.7786 | 0.7821 | | 0.4465 | 28.44 | 6200 | 0.4897 | 0.7824 | 0.7853 | | 0.4451 | 29.36 | 6400 | 0.4890 | 0.7758 | 0.7792 | | 0.4446 | 30.28 | 6600 | 0.4969 | 0.7771 | 0.7810 | | 0.4429 | 31.19 | 6800 | 0.4843 | 0.7854 | 0.7876 | | 0.441 | 32.11 | 7000 | 0.4919 | 0.7806 | 0.7838 | | 0.4424 | 33.03 | 7200 | 0.4934 | 0.7819 | 0.7850 | | 0.4413 | 33.94 | 7400 | 0.4864 | 0.7825 | 0.7850 | | 0.4409 | 34.86 | 7600 | 0.4901 | 0.7825 | 0.7853 | | 0.4398 | 35.78 | 7800 | 0.4866 | 0.7823 | 0.7847 | | 0.4412 | 36.7 | 8000 | 0.4897 | 0.7805 | 0.7835 | | 0.4369 | 37.61 | 8200 | 0.4985 | 0.7776 | 0.7815 | | 0.4408 | 38.53 | 8400 | 0.4874 | 0.7825 | 0.7853 | | 0.4359 | 39.45 | 8600 | 0.4935 | 0.7800 | 0.7833 | | 0.4366 | 40.37 | 8800 | 0.4989 | 0.7804 | 0.7838 | | 0.4396 | 41.28 | 9000 | 0.4934 | 0.7810 | 0.7844 | | 0.4359 | 42.2 | 9200 | 0.4899 | 0.7829 | 0.7858 | | 0.4332 | 43.12 | 9400 | 0.4930 | 0.7831 | 0.7861 | | 0.4371 | 44.04 | 9600 | 0.4909 | 0.7831 | 0.7861 | | 0.4348 | 44.95 | 9800 | 0.4961 | 0.7813 | 0.7847 | | 0.4362 | 45.87 | 10000 | 0.4933 | 0.7830 | 0.7861 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3K36me3-seqsight_65536_512_47M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K36me3-seqsight_65536_512_47M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]

null

2024-05-03T16:35:35+00:00

null

peft

# GUE_mouse_0-seqsight_65536_512_47M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_mouse_0](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_0) dataset. It achieves the following results on the evaluation set: - Loss: 0.5918 - F1 Score: 0.6924 - Accuracy: 0.6926 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.6506 | 3.92 | 200 | 0.6065 | 0.6652 | 0.6691 | | 0.6137 | 7.84 | 400 | 0.5919 | 0.6697 | 0.6716 | | 0.6024 | 11.76 | 600 | 0.5871 | 0.6856 | 0.6864 | | 0.5927 | 15.69 | 800 | 0.5826 | 0.6963 | 0.6963 | | 0.5819 | 19.61 | 1000 | 0.5840 | 0.6903 | 0.6914 | | 0.5778 | 23.53 | 1200 | 0.5791 | 0.7016 | 0.7025 | | 0.5674 | 27.45 | 1400 | 0.5823 | 0.6957 | 0.6963 | | 0.5655 | 31.37 | 1600 | 0.5750 | 0.7000 | 0.7 | | 0.5593 | 35.29 | 1800 | 0.5737 | 0.7037 | 0.7037 | | 0.5532 | 39.22 | 2000 | 0.5797 | 0.6943 | 0.6951 | | 0.5519 | 43.14 | 2200 | 0.5756 | 0.6985 | 0.6988 | | 0.5508 | 47.06 | 2400 | 0.5696 | 0.7145 | 0.7148 | | 0.5412 | 50.98 | 2600 | 0.5818 | 0.6816 | 0.6827 | | 0.5392 | 54.9 | 2800 | 0.5716 | 0.7124 | 0.7136 | | 0.5385 | 58.82 | 3000 | 0.5700 | 0.7047 | 0.7049 | | 0.5367 | 62.75 | 3200 | 0.5681 | 0.7013 | 0.7012 | | 0.5329 | 66.67 | 3400 | 0.5713 | 0.6976 | 0.6975 | | 0.5304 | 70.59 | 3600 | 0.5742 | 0.7010 | 0.7012 | | 0.5282 | 74.51 | 3800 | 0.5724 | 0.6975 | 0.6975 | | 0.5279 | 78.43 | 4000 | 0.5690 | 0.6988 | 0.6988 | | 0.5261 | 82.35 | 4200 | 0.5696 | 0.6998 | 0.7 | | 0.5231 | 86.27 | 4400 | 0.5752 | 0.6985 | 0.6988 | | 0.5214 | 90.2 | 4600 | 0.5722 | 0.7013 | 0.7012 | | 0.5186 | 94.12 | 4800 | 0.5787 | 0.6983 | 0.6988 | | 0.5183 | 98.04 | 5000 | 0.5680 | 0.7085 | 0.7086 | | 0.5154 | 101.96 | 5200 | 0.5731 | 0.7036 | 0.7037 | | 0.514 | 105.88 | 5400 | 0.5663 | 0.7121 | 0.7123 | | 0.5163 | 109.8 | 5600 | 0.5668 | 0.7148 | 0.7148 | | 0.5144 | 113.73 | 5800 | 0.5673 | 0.7184 | 0.7185 | | 0.5153 | 117.65 | 6000 | 0.5702 | 0.7037 | 0.7037 | | 0.5117 | 121.57 | 6200 | 0.5666 | 0.7161 | 0.7160 | | 0.5099 | 125.49 | 6400 | 0.5759 | 0.7047 | 0.7049 | | 0.5131 | 129.41 | 6600 | 0.5691 | 0.7112 | 0.7111 | | 0.5109 | 133.33 | 6800 | 0.5681 | 0.7124 | 0.7123 | | 0.5097 | 137.25 | 7000 | 0.5711 | 0.7086 | 0.7086 | | 0.5056 | 141.18 | 7200 | 0.5727 | 0.7112 | 0.7111 | | 0.5074 | 145.1 | 7400 | 0.5751 | 0.7060 | 0.7062 | | 0.5065 | 149.02 | 7600 | 0.5696 | 0.7136 | 0.7136 | | 0.5063 | 152.94 | 7800 | 0.5720 | 0.7099 | 0.7099 | | 0.5041 | 156.86 | 8000 | 0.5691 | 0.7112 | 0.7111 | | 0.5059 | 160.78 | 8200 | 0.5727 | 0.7099 | 0.7099 | | 0.5064 | 164.71 | 8400 | 0.5724 | 0.7111 | 0.7111 | | 0.5049 | 168.63 | 8600 | 0.5717 | 0.7099 | 0.7099 | | 0.5042 | 172.55 | 8800 | 0.5697 | 0.7149 | 0.7148 | | 0.5008 | 176.47 | 9000 | 0.5716 | 0.7149 | 0.7148 | | 0.4982 | 180.39 | 9200 | 0.5729 | 0.7136 | 0.7136 | | 0.4989 | 184.31 | 9400 | 0.5742 | 0.7099 | 0.7099 | | 0.4989 | 188.24 | 9600 | 0.5733 | 0.7111 | 0.7111 | | 0.504 | 192.16 | 9800 | 0.5722 | 0.7124 | 0.7123 | | 0.4969 | 196.08 | 10000 | 0.5727 | 0.7111 | 0.7111 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_mouse_0-seqsight_65536_512_47M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_mouse_0-seqsight_65536_512_47M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]

null

2024-05-03T16:35:35+00:00

null

peft

# GUE_EMP_H3K36me3-seqsight_65536_512_47M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_EMP_H3K36me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K36me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5059 - F1 Score: 0.7826 - Accuracy: 0.7850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5563 | 0.92 | 200 | 0.5396 | 0.7412 | 0.7451 | | 0.5106 | 1.83 | 400 | 0.5126 | 0.7627 | 0.7655 | | 0.4971 | 2.75 | 600 | 0.5045 | 0.7687 | 0.7709 | | 0.4962 | 3.67 | 800 | 0.4940 | 0.7739 | 0.7752 | | 0.4826 | 4.59 | 1000 | 0.4927 | 0.7786 | 0.7801 | | 0.4764 | 5.5 | 1200 | 0.4988 | 0.7700 | 0.7738 | | 0.4737 | 6.42 | 1400 | 0.4823 | 0.7788 | 0.7798 | | 0.4665 | 7.34 | 1600 | 0.4999 | 0.7720 | 0.7752 | | 0.4582 | 8.26 | 1800 | 0.4894 | 0.7817 | 0.7830 | | 0.4631 | 9.17 | 2000 | 0.4824 | 0.7819 | 0.7833 | | 0.4574 | 10.09 | 2200 | 0.5118 | 0.7611 | 0.7663 | | 0.4517 | 11.01 | 2400 | 0.4879 | 0.7811 | 0.7841 | | 0.4455 | 11.93 | 2600 | 0.4735 | 0.7886 | 0.7893 | | 0.4428 | 12.84 | 2800 | 0.4840 | 0.7766 | 0.7798 | | 0.4382 | 13.76 | 3000 | 0.5024 | 0.7712 | 0.7761 | | 0.4323 | 14.68 | 3200 | 0.4920 | 0.7870 | 0.7893 | | 0.4336 | 15.6 | 3400 | 0.4818 | 0.7847 | 0.7876 | | 0.4307 | 16.51 | 3600 | 0.4985 | 0.7775 | 0.7807 | | 0.4247 | 17.43 | 3800 | 0.4830 | 0.7893 | 0.7910 | | 0.4217 | 18.35 | 4000 | 0.4860 | 0.7895 | 0.7913 | | 0.4195 | 19.27 | 4200 | 0.5007 | 0.7898 | 0.7919 | | 0.4169 | 20.18 | 4400 | 0.5000 | 0.7856 | 0.7884 | | 0.4126 | 21.1 | 4600 | 0.4923 | 0.7923 | 0.7939 | | 0.4123 | 22.02 | 4800 | 0.4984 | 0.7824 | 0.7850 | | 0.4059 | 22.94 | 5000 | 0.4944 | 0.7830 | 0.7861 | | 0.4024 | 23.85 | 5200 | 0.4952 | 0.7799 | 0.7830 | | 0.399 | 24.77 | 5400 | 0.5144 | 0.7755 | 0.7798 | | 0.3994 | 25.69 | 5600 | 0.5067 | 0.7887 | 0.7913 | | 0.3975 | 26.61 | 5800 | 0.4957 | 0.7832 | 0.7858 | | 0.3908 | 27.52 | 6000 | 0.5261 | 0.7785 | 0.7818 | | 0.392 | 28.44 | 6200 | 0.4996 | 0.7841 | 0.7867 | | 0.3887 | 29.36 | 6400 | 0.5025 | 0.7812 | 0.7844 | | 0.3849 | 30.28 | 6600 | 0.5128 | 0.7742 | 0.7781 | | 0.3824 | 31.19 | 6800 | 0.5134 | 0.7831 | 0.7858 | | 0.3811 | 32.11 | 7000 | 0.5071 | 0.7820 | 0.7847 | | 0.3815 | 33.03 | 7200 | 0.5100 | 0.7833 | 0.7858 | | 0.3787 | 33.94 | 7400 | 0.5030 | 0.7860 | 0.7881 | | 0.3754 | 34.86 | 7600 | 0.5084 | 0.7831 | 0.7858 | | 0.373 | 35.78 | 7800 | 0.5119 | 0.7851 | 0.7876 | | 0.3724 | 36.7 | 8000 | 0.5201 | 0.7838 | 0.7870 | | 0.3728 | 37.61 | 8200 | 0.5250 | 0.7716 | 0.7758 | | 0.3755 | 38.53 | 8400 | 0.5147 | 0.7827 | 0.7853 | | 0.3679 | 39.45 | 8600 | 0.5214 | 0.7767 | 0.7804 | | 0.3668 | 40.37 | 8800 | 0.5325 | 0.7759 | 0.7795 | | 0.3704 | 41.28 | 9000 | 0.5190 | 0.7773 | 0.7807 | | 0.3628 | 42.2 | 9200 | 0.5186 | 0.7821 | 0.7850 | | 0.3624 | 43.12 | 9400 | 0.5233 | 0.7824 | 0.7853 | | 0.3623 | 44.04 | 9600 | 0.5235 | 0.7788 | 0.7821 | | 0.3628 | 44.95 | 9800 | 0.5291 | 0.7771 | 0.7807 | | 0.3665 | 45.87 | 10000 | 0.5250 | 0.7809 | 0.7841 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_EMP_H3K36me3-seqsight_65536_512_47M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K36me3-seqsight_65536_512_47M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_65536_512_47M", "region:us" ]

null

2024-05-03T16:35:35+00:00

null

peft

# GUE_prom_prom_300_notata-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.1181 - F1 Score: 0.9561 - Accuracy: 0.9561 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.2897 | 0.6 | 200 | 0.1532 | 0.9391 | 0.9391 | | 0.1588 | 1.2 | 400 | 0.1377 | 0.9472 | 0.9472 | | 0.1482 | 1.81 | 600 | 0.1222 | 0.9521 | 0.9521 | | 0.1344 | 2.41 | 800 | 0.1194 | 0.9534 | 0.9535 | | 0.1341 | 3.01 | 1000 | 0.1171 | 0.9550 | 0.9550 | | 0.1298 | 3.61 | 1200 | 0.1143 | 0.9559 | 0.9559 | | 0.1276 | 4.22 | 1400 | 0.1130 | 0.9550 | 0.9550 | | 0.1226 | 4.82 | 1600 | 0.1121 | 0.9572 | 0.9572 | | 0.1228 | 5.42 | 1800 | 0.1124 | 0.9565 | 0.9565 | | 0.1177 | 6.02 | 2000 | 0.1118 | 0.9540 | 0.9540 | | 0.1176 | 6.63 | 2200 | 0.1085 | 0.9582 | 0.9582 | | 0.1165 | 7.23 | 2400 | 0.1094 | 0.9585 | 0.9585 | | 0.1131 | 7.83 | 2600 | 0.1084 | 0.9582 | 0.9582 | | 0.1148 | 8.43 | 2800 | 0.1112 | 0.9576 | 0.9576 | | 0.1169 | 9.04 | 3000 | 0.1116 | 0.9580 | 0.9580 | | 0.1117 | 9.64 | 3200 | 0.1169 | 0.9550 | 0.9550 | | 0.1118 | 10.24 | 3400 | 0.1079 | 0.9593 | 0.9593 | | 0.1096 | 10.84 | 3600 | 0.1060 | 0.9582 | 0.9582 | | 0.1065 | 11.45 | 3800 | 0.1081 | 0.9597 | 0.9597 | | 0.1082 | 12.05 | 4000 | 0.1082 | 0.9585 | 0.9585 | | 0.1083 | 12.65 | 4200 | 0.1047 | 0.9606 | 0.9606 | | 0.1077 | 13.25 | 4400 | 0.1059 | 0.9600 | 0.9601 | | 0.1055 | 13.86 | 4600 | 0.1057 | 0.9597 | 0.9597 | | 0.1076 | 14.46 | 4800 | 0.1057 | 0.9587 | 0.9587 | | 0.1028 | 15.06 | 5000 | 0.1041 | 0.9585 | 0.9585 | | 0.1048 | 15.66 | 5200 | 0.1030 | 0.9597 | 0.9597 | | 0.1058 | 16.27 | 5400 | 0.1035 | 0.9612 | 0.9612 | | 0.1016 | 16.87 | 5600 | 0.1028 | 0.9608 | 0.9608 | | 0.1014 | 17.47 | 5800 | 0.1027 | 0.9604 | 0.9604 | | 0.1062 | 18.07 | 6000 | 0.1010 | 0.9616 | 0.9616 | | 0.1016 | 18.67 | 6200 | 0.1018 | 0.9595 | 0.9595 | | 0.1031 | 19.28 | 6400 | 0.1016 | 0.9606 | 0.9606 | | 0.097 | 19.88 | 6600 | 0.1047 | 0.9615 | 0.9616 | | 0.1034 | 20.48 | 6800 | 0.1034 | 0.9608 | 0.9608 | | 0.0985 | 21.08 | 7000 | 0.1016 | 0.9614 | 0.9614 | | 0.096 | 21.69 | 7200 | 0.1030 | 0.9612 | 0.9612 | | 0.098 | 22.29 | 7400 | 0.1037 | 0.9601 | 0.9601 | | 0.0999 | 22.89 | 7600 | 0.1004 | 0.9616 | 0.9616 | | 0.097 | 23.49 | 7800 | 0.1019 | 0.9616 | 0.9616 | | 0.0988 | 24.1 | 8000 | 0.1010 | 0.9614 | 0.9614 | | 0.0962 | 24.7 | 8200 | 0.1023 | 0.9608 | 0.9608 | | 0.0973 | 25.3 | 8400 | 0.1015 | 0.9612 | 0.9612 | | 0.0944 | 25.9 | 8600 | 0.1014 | 0.9614 | 0.9614 | | 0.0974 | 26.51 | 8800 | 0.1006 | 0.9619 | 0.9619 | | 0.0968 | 27.11 | 9000 | 0.1001 | 0.9616 | 0.9616 | | 0.0971 | 27.71 | 9200 | 0.1002 | 0.9614 | 0.9614 | | 0.0929 | 28.31 | 9400 | 0.1005 | 0.9612 | 0.9612 | | 0.0997 | 28.92 | 9600 | 0.1000 | 0.9619 | 0.9619 | | 0.0931 | 29.52 | 9800 | 0.1003 | 0.9617 | 0.9617 | | 0.0973 | 30.12 | 10000 | 0.1002 | 0.9614 | 0.9614 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_300_notata-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_300_notata-seqsight_4096_512_15M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T16:35:47+00:00

null

peft

# GUE_prom_prom_300_notata-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.1217 - F1 Score: 0.9582 - Accuracy: 0.9582 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.2508 | 0.6 | 200 | 0.1359 | 0.9487 | 0.9487 | | 0.1422 | 1.2 | 400 | 0.1312 | 0.9479 | 0.9480 | | 0.1383 | 1.81 | 600 | 0.1152 | 0.9548 | 0.9548 | | 0.1266 | 2.41 | 800 | 0.1144 | 0.9572 | 0.9572 | | 0.1273 | 3.01 | 1000 | 0.1097 | 0.9589 | 0.9589 | | 0.1216 | 3.61 | 1200 | 0.1115 | 0.9591 | 0.9591 | | 0.1195 | 4.22 | 1400 | 0.1070 | 0.9599 | 0.9599 | | 0.1141 | 4.82 | 1600 | 0.1052 | 0.9597 | 0.9597 | | 0.1124 | 5.42 | 1800 | 0.1060 | 0.9589 | 0.9589 | | 0.1089 | 6.02 | 2000 | 0.1047 | 0.9585 | 0.9585 | | 0.1082 | 6.63 | 2200 | 0.1061 | 0.9589 | 0.9589 | | 0.1055 | 7.23 | 2400 | 0.1037 | 0.9591 | 0.9591 | | 0.1022 | 7.83 | 2600 | 0.1004 | 0.9591 | 0.9591 | | 0.1037 | 8.43 | 2800 | 0.1056 | 0.9617 | 0.9617 | | 0.1061 | 9.04 | 3000 | 0.1053 | 0.9600 | 0.9601 | | 0.0992 | 9.64 | 3200 | 0.1105 | 0.9584 | 0.9584 | | 0.0982 | 10.24 | 3400 | 0.1002 | 0.9631 | 0.9631 | | 0.0983 | 10.84 | 3600 | 0.0987 | 0.9608 | 0.9608 | | 0.0929 | 11.45 | 3800 | 0.1052 | 0.9584 | 0.9584 | | 0.0951 | 12.05 | 4000 | 0.1039 | 0.9608 | 0.9608 | | 0.0946 | 12.65 | 4200 | 0.0979 | 0.9625 | 0.9625 | | 0.0942 | 13.25 | 4400 | 0.1007 | 0.9625 | 0.9625 | | 0.0916 | 13.86 | 4600 | 0.1005 | 0.9627 | 0.9627 | | 0.0927 | 14.46 | 4800 | 0.1026 | 0.9612 | 0.9612 | | 0.09 | 15.06 | 5000 | 0.1008 | 0.9623 | 0.9623 | | 0.0889 | 15.66 | 5200 | 0.1002 | 0.9631 | 0.9631 | | 0.0896 | 16.27 | 5400 | 0.1003 | 0.9631 | 0.9631 | | 0.0856 | 16.87 | 5600 | 0.0988 | 0.9625 | 0.9625 | | 0.0853 | 17.47 | 5800 | 0.1013 | 0.9606 | 0.9606 | | 0.089 | 18.07 | 6000 | 0.0973 | 0.9631 | 0.9631 | | 0.0842 | 18.67 | 6200 | 0.0977 | 0.9644 | 0.9644 | | 0.0855 | 19.28 | 6400 | 0.1026 | 0.9627 | 0.9627 | | 0.0804 | 19.88 | 6600 | 0.1034 | 0.9612 | 0.9612 | | 0.0855 | 20.48 | 6800 | 0.1006 | 0.9640 | 0.9640 | | 0.0813 | 21.08 | 7000 | 0.1014 | 0.9634 | 0.9634 | | 0.0791 | 21.69 | 7200 | 0.1038 | 0.9606 | 0.9606 | | 0.0798 | 22.29 | 7400 | 0.1026 | 0.9623 | 0.9623 | | 0.0816 | 22.89 | 7600 | 0.0986 | 0.9629 | 0.9629 | | 0.0788 | 23.49 | 7800 | 0.1031 | 0.9633 | 0.9633 | | 0.0791 | 24.1 | 8000 | 0.1003 | 0.9636 | 0.9636 | | 0.078 | 24.7 | 8200 | 0.1035 | 0.9632 | 0.9633 | | 0.0779 | 25.3 | 8400 | 0.1004 | 0.9646 | 0.9646 | | 0.0763 | 25.9 | 8600 | 0.1020 | 0.9631 | 0.9631 | | 0.0785 | 26.51 | 8800 | 0.1004 | 0.9633 | 0.9633 | | 0.0777 | 27.11 | 9000 | 0.0999 | 0.9631 | 0.9631 | | 0.0782 | 27.71 | 9200 | 0.0999 | 0.9638 | 0.9638 | | 0.0731 | 28.31 | 9400 | 0.1000 | 0.9634 | 0.9634 | | 0.0791 | 28.92 | 9600 | 0.1005 | 0.9633 | 0.9633 | | 0.0733 | 29.52 | 9800 | 0.1013 | 0.9631 | 0.9631 | | 0.0759 | 30.12 | 10000 | 0.1009 | 0.9631 | 0.9631 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_300_notata-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_300_notata-seqsight_4096_512_15M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T16:36:08+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

jeongmi/SOLAR_TG

null

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

null

2024-05-03T16:36:28+00:00

text-classification

transformers

# results This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3439 ## 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: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.3017 | 1.0 | 1563 | 0.3571 | | 0.2894 | 2.0 | 3126 | 0.3516 | | 0.2804 | 3.0 | 4689 | 0.3439 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "prajjwal1/bert-tiny", "model-index": [{"name": "results", "results": []}]}

soheill/results

null

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:prajjwal1/bert-tiny", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:36:31+00:00

text-generation

transformers

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "llama"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct"}

predibase/Meta-Llama-3-8B-Instruct-dequantized

null

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "conversational", "en", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:36:39+00:00

reinforcement-learning

null

# **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="TeoGal/q-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"]) ```

{"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.50 +/- 2.76", "name": "mean_reward", "verified": false}]}]}]}

TeoGal/q-Taxi-v3

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

null

2024-05-03T16:36:48+00:00

automatic-speech-recognition

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

ezyyeah/murix-large-v3-1k-MERGED

null

[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:37:32+00:00

text-classification

transformers

# fine_tuned_boolq_bert_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3270 - Accuracy: 0.8333 - F1: 0.8243 ## 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 | |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:| | 0.5567 | 4.1667 | 50 | 0.5262 | 0.7222 | 0.6523 | | 0.1098 | 8.3333 | 100 | 0.8949 | 0.8333 | 0.8243 | | 0.0031 | 12.5 | 150 | 1.2237 | 0.7778 | 0.7778 | | 0.0011 | 16.6667 | 200 | 1.2641 | 0.7778 | 0.7778 | | 0.0008 | 20.8333 | 250 | 1.2343 | 0.8333 | 0.8243 | | 0.0007 | 25.0 | 300 | 1.2852 | 0.8333 | 0.8243 | | 0.0005 | 29.1667 | 350 | 1.3133 | 0.8333 | 0.8243 | | 0.0005 | 33.3333 | 400 | 1.3270 | 0.8333 | 0.8243 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "fine_tuned_boolq_bert_croslo", "results": []}]}

lenatr99/fine_tuned_boolq_bert_croslo

null

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:EMBEDDIA/crosloengual-bert", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:40:39+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

cilantro9246/lji9v56

null

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T16:41:20+00:00

null

{}

ZurabDz/albert-geo-culturax-v2

null

[ "region:us" ]

null

2024-05-03T16:41:29+00:00

null

peft

# GUE_tf_3-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_tf_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5549 - F1 Score: 0.7132 - Accuracy: 0.716 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.634 | 0.93 | 200 | 0.5711 | 0.7179 | 0.718 | | 0.5965 | 1.87 | 400 | 0.5680 | 0.6977 | 0.698 | | 0.5884 | 2.8 | 600 | 0.5541 | 0.7093 | 0.712 | | 0.5816 | 3.74 | 800 | 0.5569 | 0.7101 | 0.71 | | 0.5801 | 4.67 | 1000 | 0.5477 | 0.7180 | 0.719 | | 0.572 | 5.61 | 1200 | 0.5466 | 0.7244 | 0.725 | | 0.5667 | 6.54 | 1400 | 0.5492 | 0.7268 | 0.727 | | 0.5625 | 7.48 | 1600 | 0.5510 | 0.7201 | 0.721 | | 0.5596 | 8.41 | 1800 | 0.5482 | 0.7151 | 0.715 | | 0.5585 | 9.35 | 2000 | 0.5531 | 0.7097 | 0.712 | | 0.5525 | 10.28 | 2200 | 0.5576 | 0.7141 | 0.714 | | 0.5513 | 11.21 | 2400 | 0.5523 | 0.7223 | 0.723 | | 0.5457 | 12.15 | 2600 | 0.5493 | 0.7081 | 0.711 | | 0.5403 | 13.08 | 2800 | 0.5559 | 0.7157 | 0.72 | | 0.5398 | 14.02 | 3000 | 0.5553 | 0.7123 | 0.713 | | 0.5364 | 14.95 | 3200 | 0.5487 | 0.7163 | 0.718 | | 0.533 | 15.89 | 3400 | 0.5521 | 0.7217 | 0.722 | | 0.5299 | 16.82 | 3600 | 0.5576 | 0.7101 | 0.711 | | 0.5268 | 17.76 | 3800 | 0.5548 | 0.7180 | 0.719 | | 0.5263 | 18.69 | 4000 | 0.5564 | 0.7150 | 0.715 | | 0.525 | 19.63 | 4200 | 0.5584 | 0.7231 | 0.725 | | 0.5235 | 20.56 | 4400 | 0.5605 | 0.7337 | 0.734 | | 0.5209 | 21.5 | 4600 | 0.5643 | 0.7158 | 0.716 | | 0.5176 | 22.43 | 4800 | 0.5569 | 0.7192 | 0.721 | | 0.517 | 23.36 | 5000 | 0.5774 | 0.7150 | 0.715 | | 0.5139 | 24.3 | 5200 | 0.5743 | 0.7049 | 0.706 | | 0.5084 | 25.23 | 5400 | 0.5705 | 0.7313 | 0.732 | | 0.5141 | 26.17 | 5600 | 0.5550 | 0.7319 | 0.732 | | 0.5062 | 27.1 | 5800 | 0.5617 | 0.7247 | 0.725 | | 0.5043 | 28.04 | 6000 | 0.5665 | 0.7436 | 0.744 | | 0.5019 | 28.97 | 6200 | 0.5656 | 0.7231 | 0.723 | | 0.5054 | 29.91 | 6400 | 0.5654 | 0.7313 | 0.732 | | 0.5034 | 30.84 | 6600 | 0.5657 | 0.7329 | 0.733 | | 0.5017 | 31.78 | 6800 | 0.5651 | 0.7250 | 0.725 | | 0.4963 | 32.71 | 7000 | 0.5708 | 0.7240 | 0.724 | | 0.502 | 33.64 | 7200 | 0.5654 | 0.7260 | 0.726 | | 0.4912 | 34.58 | 7400 | 0.5763 | 0.7117 | 0.712 | | 0.495 | 35.51 | 7600 | 0.5726 | 0.7140 | 0.714 | | 0.495 | 36.45 | 7800 | 0.5827 | 0.7230 | 0.723 | | 0.4953 | 37.38 | 8000 | 0.5693 | 0.7211 | 0.721 | | 0.4878 | 38.32 | 8200 | 0.5775 | 0.7261 | 0.726 | | 0.4885 | 39.25 | 8400 | 0.5817 | 0.7281 | 0.728 | | 0.4866 | 40.19 | 8600 | 0.5802 | 0.7191 | 0.719 | | 0.4858 | 41.12 | 8800 | 0.5821 | 0.7221 | 0.722 | | 0.4915 | 42.06 | 9000 | 0.5763 | 0.7201 | 0.72 | | 0.4871 | 42.99 | 9200 | 0.5790 | 0.7130 | 0.713 | | 0.4845 | 43.93 | 9400 | 0.5827 | 0.7120 | 0.712 | | 0.4834 | 44.86 | 9600 | 0.5823 | 0.7141 | 0.714 | | 0.4872 | 45.79 | 9800 | 0.5791 | 0.7201 | 0.72 | | 0.4824 | 46.73 | 10000 | 0.5811 | 0.7141 | 0.714 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_tf_3-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_3-seqsight_4096_512_15M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T16:41:31+00:00

null

peft

# Mistral-7B-Instruct-v0.2-finetune-SWE_90_10_EN This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0346 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.0356 | 0.9995 | 1855 | 1.4789 | | 0.9073 | 1.9989 | 3710 | 1.4895 | | 0.349 | 2.9984 | 5565 | 1.6255 | | 0.2672 | 3.9978 | 7420 | 1.8033 | | 0.341 | 4.9973 | 9275 | 2.0346 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "Mistral-7B-Instruct-v0.2-finetune-SWE_90_10_EN", "results": []}]}

JuanjoLopez19/Mistral-7B-Instruct-v0.2-finetune-SWE_90_10_EN

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]

null

2024-05-03T16:43:05+00:00

null

{}

mdiamore/mistral_instruct_coni_form_base_retrain

null

[ "region:us" ]

null

2024-05-03T16:43:34+00:00

text-classification

transformers

# fine_tuned_cb_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3249 - Accuracy: 0.6818 - F1: 0.6390 ## 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 | |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:| | 0.7054 | 3.5714 | 50 | 1.4024 | 0.3182 | 0.1536 | | 0.3117 | 7.1429 | 100 | 1.0030 | 0.6818 | 0.6383 | | 0.0286 | 10.7143 | 150 | 1.0108 | 0.7273 | 0.6791 | | 0.0038 | 14.2857 | 200 | 1.1886 | 0.6818 | 0.6390 | | 0.0025 | 17.8571 | 250 | 1.2342 | 0.6818 | 0.6390 | | 0.0019 | 21.4286 | 300 | 1.2576 | 0.7273 | 0.6791 | | 0.0015 | 25.0 | 350 | 1.2963 | 0.6818 | 0.6390 | | 0.0015 | 28.5714 | 400 | 1.3249 | 0.6818 | 0.6390 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "fine_tuned_cb_croslo", "results": []}]}

lenatr99/fine_tuned_cb_croslo

null

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:EMBEDDIA/crosloengual-bert", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T16:44:34+00:00