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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-26 12:28:48	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 498 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-26 12:28:16	card stringlengths 11 1.01M
liuxiong332/bge-m3-financial-mixed	liuxiong332	2024-05-30T08:26:01Z	11	0	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "license:mit", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-30T07:31:42Z	--- license: mit pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # liuxiong332/bge-m3-financial-mixed This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. Base on BAAI/bge-m3, finetuned with financial data ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('liuxiong332/bge-m3-financial-mixed') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ```
kamdong777/Viego_club_project	kamdong777	2024-05-30T08:25:55Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T08:25:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Leremi13/GreyCardReader	Leremi13	2024-05-30T08:25:35Z	0	0	null	[ "image-to-text", "fr", "dataset:google/imageinwords", "arxiv:1910.09700", "license:apache-2.0", "region:us" ]	image-to-text	2024-05-29T10:07:40Z	--- license: apache-2.0 datasets: - google/imageinwords language: - fr metrics: - accuracy pipeline_tag: image-to-text --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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liminerity/tesla2-lex	liminerity	2024-05-30T08:24:43Z	151	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:gate369/tesla6x6passthrough", "base_model:finetune:gate369/tesla6x6passthrough", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T09:53:04Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: gate369/tesla6x6passthrough --- # Uploaded model - Developed by: liminerity - License: apache-2.0 - Finetuned from model : gate369/tesla6x6passthrough This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Bagus/hubert_large_emodb	Bagus	2024-05-30T08:20:55Z	7	0	transformers	[ "transformers", "pytorch", "hubert", "generated_from_trainer", "base_model:facebook/hubert-large-ll60k", "base_model:finetune:facebook/hubert-large-ll60k", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T04:18:34Z	--- license: apache-2.0 base_model: facebook/hubert-large-ll60k tags: - generated_from_trainer model-index: - name: hubert_large_emodb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hubert_large_emodb This model is a fine-tuned version of [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9789 - Uar: 0.8800 - Acc: 0.8897 For the test Set: - UAR: 0.805 - 0.845 FI scores: labels: ['anger', 'happiness', 'sadness', 'neutral'] Result per class (F1 score): [0.84, 0.364, 1.0, 1.0] ## Model description This model is to predict one of four emotion categories: 'anger', 'happiness', 'sadness', 'neutral' ## Intended uses & limitations How to use: ``` from transformers import pipeline pipe = pipeline("audio-classification", model="Bagus/hubert_large_emodb") pipe('file.wav') ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Uar \| Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| No log \| 0.15 \| 1 \| 1.3865 \| 0.25 \| 0.1985 \| \| No log \| 0.31 \| 2 \| 1.3794 \| 0.25 \| 0.1985 \| \| No log \| 0.46 \| 3 \| 1.3745 \| 0.25 \| 0.1985 \| \| No log \| 0.62 \| 4 \| 1.3684 \| 0.3227 \| 0.3162 \| \| No log \| 0.77 \| 5 \| 1.3592 \| 0.4722 \| 0.5809 \| \| No log \| 0.92 \| 6 \| 1.3487 \| 0.3981 \| 0.5221 \| \| 1.4402 \| 1.08 \| 7 \| 1.3406 \| 0.4444 \| 0.5588 \| \| 1.4402 \| 1.23 \| 8 \| 1.3359 \| 0.5278 \| 0.625 \| \| 1.4402 \| 1.38 \| 9 \| 1.3305 \| 0.5418 \| 0.6324 \| \| 1.4402 \| 1.54 \| 10 \| 1.3228 \| 0.5790 \| 0.6544 \| \| 1.4402 \| 1.69 \| 11 \| 1.3078 \| 0.6392 \| 0.6985 \| \| 1.4402 \| 1.85 \| 12 \| 1.2832 \| 0.6577 \| 0.7132 \| \| 1.4402 \| 2.0 \| 13 \| 1.2445 \| 0.6670 \| 0.7206 \| \| 1.0783 \| 2.15 \| 14 \| 1.2087 \| 0.6715 \| 0.7279 \| \| 1.0783 \| 2.31 \| 15 \| 1.1857 \| 0.6579 \| 0.7059 \| \| 1.0783 \| 2.46 \| 16 \| 1.1746 \| 0.6488 \| 0.6912 \| \| 1.0783 \| 2.62 \| 17 \| 1.1666 \| 0.6397 \| 0.6765 \| \| 1.0783 \| 2.77 \| 18 \| 1.1393 \| 0.6443 \| 0.6838 \| \| 1.0783 \| 2.92 \| 19 \| 1.1079 \| 0.6810 \| 0.7279 \| \| 0.9255 \| 3.08 \| 20 \| 1.0908 \| 0.7271 \| 0.7721 \| \| 0.9255 \| 3.23 \| 21 \| 1.0786 \| 0.7131 \| 0.7647 \| \| 0.9255 \| 3.38 \| 22 \| 1.0697 \| 0.6574 \| 0.7279 \| \| 0.9255 \| 3.54 \| 23 \| 1.0711 \| 0.6111 \| 0.6912 \| \| 0.9255 \| 3.69 \| 24 \| 1.0651 \| 0.6389 \| 0.7132 \| \| 0.9255 \| 3.85 \| 25 \| 1.0596 \| 0.6481 \| 0.7206 \| \| 0.9255 \| 4.0 \| 26 \| 1.0566 \| 0.6667 \| 0.7353 \| \| 0.6547 \| 4.15 \| 27 \| 1.0562 \| 0.6667 \| 0.7353 \| \| 0.6547 \| 4.31 \| 28 \| 1.0553 \| 0.7222 \| 0.7794 \| \| 0.6547 \| 4.46 \| 29 \| 1.0549 \| 0.7316 \| 0.7794 \| \| 0.6547 \| 4.62 \| 30 \| 1.0546 \| 0.7456 \| 0.7868 \| \| 0.6547 \| 4.77 \| 31 \| 1.0516 \| 0.7549 \| 0.7941 \| \| 0.6547 \| 4.92 \| 32 \| 1.0428 \| 0.7456 \| 0.7868 \| \| 0.7058 \| 5.08 \| 33 \| 1.0312 \| 0.7502 \| 0.7941 \| \| 0.7058 \| 5.23 \| 34 \| 1.0235 \| 0.7594 \| 0.8015 \| \| 0.7058 \| 5.38 \| 35 \| 1.0143 \| 0.7732 \| 0.8162 \| \| 0.7058 \| 5.54 \| 36 \| 1.0079 \| 0.7963 \| 0.8382 \| \| 0.7058 \| 5.69 \| 37 \| 1.0049 \| 0.7963 \| 0.8382 \| \| 0.7058 \| 5.85 \| 38 \| 1.0051 \| 0.7778 \| 0.8235 \| \| 0.7058 \| 6.0 \| 39 \| 1.0066 \| 0.7593 \| 0.8088 \| \| 0.4919 \| 6.15 \| 40 \| 1.0119 \| 0.7407 \| 0.7941 \| \| 0.4919 \| 6.31 \| 41 \| 1.0172 \| 0.7222 \| 0.7794 \| \| 0.4919 \| 6.46 \| 42 \| 1.0191 \| 0.7130 \| 0.7721 \| \| 0.4919 \| 6.62 \| 43 \| 1.0175 \| 0.7130 \| 0.7721 \| \| 0.4919 \| 6.77 \| 44 \| 1.0144 \| 0.7222 \| 0.7794 \| \| 0.4919 \| 6.92 \| 45 \| 1.0094 \| 0.7222 \| 0.7794 \| \| 0.5048 \| 7.08 \| 46 \| 1.0050 \| 0.7593 \| 0.8088 \| \| 0.5048 \| 7.23 \| 47 \| 0.9984 \| 0.7870 \| 0.8309 \| \| 0.5048 \| 7.38 \| 48 \| 0.9948 \| 0.7778 \| 0.8235 \| \| 0.5048 \| 7.54 \| 49 \| 0.9917 \| 0.7825 \| 0.8235 \| \| 0.5048 \| 7.69 \| 50 \| 0.9884 \| 0.8195 \| 0.8529 \| \| 0.5048 \| 7.85 \| 51 \| 0.9846 \| 0.8242 \| 0.8529 \| \| 0.5048 \| 8.0 \| 52 \| 0.9827 \| 0.8152 \| 0.8382 \| \| 0.4133 \| 8.15 \| 53 \| 0.9816 \| 0.8337 \| 0.8529 \| \| 0.4133 \| 8.31 \| 54 \| 0.9812 \| 0.8522 \| 0.8676 \| \| 0.4133 \| 8.46 \| 55 \| 0.9810 \| 0.8522 \| 0.8676 \| \| 0.4133 \| 8.62 \| 56 \| 0.9810 \| 0.8707 \| 0.8824 \| \| 0.4133 \| 8.77 \| 57 \| 0.9806 \| 0.8800 \| 0.8897 \| \| 0.4133 \| 8.92 \| 58 \| 0.9796 \| 0.8800 \| 0.8897 \| \| 0.4717 \| 9.08 \| 59 \| 0.9793 \| 0.8800 \| 0.8897 \| \| 0.4717 \| 9.23 \| 60 \| 0.9789 \| 0.8800 \| 0.8897 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.13.3
Netta1994/setfit_baai_2k_best_hp_search	Netta1994	2024-05-30T08:19:48Z	8	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2024-05-30T08:19:11Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # Netta1994/setfit_baai_2k_best_hp_search This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("Netta1994/setfit_baai_2k_best_hp_search") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
John6666/pthno2-v1-sdxl	John6666	2024-05-30T08:19:43Z	227	3	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-30T08:15:06Z	--- license: other tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime --- Original model is [here](https://civitai.com/models/484016/pthno2?modelVersionId=538314).
IEITYuan/Yuan2-M32-hf	IEITYuan	2024-05-30T08:17:20Z	13	60	transformers	[ "transformers", "pytorch", "yuan", "text-generation", "custom_code", "arxiv:2405.17976", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-generation	2024-05-27T08:17:17Z	--- license: apache-2.0 --- <div align="center"> <h1> Yuan2.0-M32: Mixture of Experts with Attention Router </h1> </div> <p align="center"> 🌎 <a href="https://github.com/IEIT-Yuan/Yuan2.0-M32" target="_blank">GitHub</a> • 🤗 <a href="https://huggingface.co/IEITYuan" target="_blank">Hugging Face</a> • 💬 <a href="https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/images/%E6%BA%90%E5%85%AC%E4%BC%97%E5%8F%B7%E4%BA%8C%E7%BB%B4%E7%A0%81.png" target="_blank">WeChat</a>• 📎 <a href="https://arxiv.org/abs/2405.17976" target="_blank">Yuan2.0-M32 Paper</a> </p> <div align="center"> <a href="code_license"> <img alt="Code License" src="https://img.shields.io/badge/Apache%202.0%20-green?style=flat&label=Code%20License&link=https%3A%2F%2Fgithub.com%2FIEIT-Yuan%2FYuan-2.0-MoE%3Ftab%3DApache-2.0-1-ov-file"/> </a> <a href="model_license"> <img alt="Model License" src="https://img.shields.io/badge/Yuan2.0%20License-blue?style=flat&logoColor=blue&label=Model%20License&color=blue&link=https%3A%2F%2Fgithub.com%2FIEIT-Yuan%2FYuan-2.0%2Fblob%2Fmain%2FLICENSE-Yuan" /> </a> </div> ----- ## 1. Introduction Yuan2.0-M32 is a Mixture-of-Experts (MoE) language model with 32 experts, of which 2 are active. A new router network, Attention Router, is proposed and has been adopted for more efficient expert selection, boosting accuracy by 3.8% over models using a classical router network. Yuan 2.0-M32 is trained from scratch with 2000B tokens, and its training computation is only 9.25% of that required by a dense model of the same parameter scale. Demonstrating competitive capabilities in coding, math, and various specialized fields, Yuan2.0-M32 operates with only 3.7B active parameters out of a total 40B, and a forward computation of 7.4 GFLOPS per token, which is just 1/19th of Llama3-70B's requirement. Yuan 2.0-M32 has surpassed Llama3-70B on the MATH and ARC-Challenge benchmarks, achieving accuracies of 55.9% and 95.8%, respectively. The basic information of the Yuan2.0-M32 model is as follows: + Total Parameters ： 40B <br> + Experts： 32 <br> + Active Experts： 2 <br> + Active Parameters： 3.7B <br> + Training Tokens： 2000B tokens <br> + Sequence Length： 16K <br> The technical report for the Yuan2.0-M32 model has been released, and you can find more detailed technical information and evaluation results by referring to the <a href="https://arxiv.org/abs/2405.17976" target="_blank">paper</a>. ## 2. Model Downloads \| Model \| Sequence Length \| Type \| Download \| \| :----------: \| :------: \| :-------: \|:---------------------------: \| \| Yuan2.0-M32 \| 16K \| Megatron \| [HuggingFace](https://huggingface.co/IEITYuan/Yuan2-M32) \| Yuan2.0-M32-HF \| 16K \| HuggingFace \| [HuggingFace](https://huggingface.co/IEITYuan/Yuan2-M32-hf) \| Yuan2.0-M32-GGUF \| 16K \| GGUF \| [HuggingFace](https://huggingface.co/IEITYuan/Yuan2-M32-gguf) \| Yuan2.0-M32-GGUF-INT4 \| 16K \| GGUF \| [HuggingFace](https://huggingface.co/IEITYuan/Yuan2-M32-gguf-int4) ## 3. Evaluation 3.1 Benchmarks 🏆 We conducted a thorough evaluation of the Yuan2.0-M32 model across a range of benchmarks, including HumanEval, GSM8K, MMLU, Math, and ARC-Challenge. These benchmarks are designed to test the model's proficiency in key areas such as natural language understanding, knowledge acquisition, mathematical computation and reasoning, and code generation. The Yuan2.0-M32 has shown a consistent and significant advantage over other models like Llama3-8B and Mistral-8×7B, excelling in all evaluated tasks. Remarkably, its overall performance is on par with the more substantial Llama3-70B model.The detailed evaluation results are outlined in the subsequent table. \| Model \| HumanEval \| GSM8K \| MMLU \| Math \| ARC-C\* \| \| ------------------ \| :---------------: \| :------------: \| :---------------: \| :---------------: \| :---------------:\| \| Llama3-70B \| 81.7% \| 93% \| 80.3 \| 50.4% \| 93.3% \| \| Llama3-8B \| 62.2% \| 79.6% \| 68.4% \| 30% \| 78.6% \| \| Phi-3-medium \| 62.2% \| 91.0% \| 78.0% \| - \| 91.6% \| \| Phi-3-small \| 61% \| 89.6% \| 75.7% \| - \| 90.7% \| \| Phi-3-mini \| 58.5% \| 82.5% \| 68.8% \| - \| 84.9% \| \| Mistral-822B \| 45.1% \| 78.6% \| 77.8% \| 41,8% \| 91.3% \| \| Mistral-87B \| 40.2% \| 58.4% \| 70.86% \| 28.4% \| 85.9% \| \| Yuan2.0-M32 \| 74.4% \| 92.7% \| 72.2% \| 55.9% \| 95.8% \| \* __ARC-C__: AI2 Reasoning Challenge (ARC) benchmark contains more complex parts that need further reasoning. ----- 3.2 Computational Utilization for Model \| Model \| Params (B) \| Active Params (B) \| GFLOPs/token (Inference) \| GFLOPS/token (Fine-tune) \| Mean Accuracy \| Average Accuracy/GFLOPSs per token (Inference) \| \| ------------------ \| :---------------: \| :------------: \| :---------------: \| :---------------: \| :---------------:\|:---------------:\| \| Llama3-70B \| 70 \| 70 \| 140 \| 420 \| 79.25 \| 0.57 \| \| Llama3-8B \| 8 \| 8 \| 16 \| 48 \| 64.15 \| 4.00 \| \| Mistral-822B \| 141 \| 39 \| 78 \| 234 \| 72.38 \| 0.93 \| \| Mistral-87B \| 47 \| 12.9 \| 25.8 \| 77.3 \| 60.83 \| 2.36 \| \| Yuan2.0-M32 \| 40 \| 3.7 \| 7.4 \| 22.2 \| 79.15 \| 10.69 \| ## 4. Quick Start 4.1 Environment Config We strongly recommend using the latest release of docker images of Yuan2.0-M32.You can launch an instance of the Yuan 2.0 container with the following Docker commands: ```bash docker pull yuanmodel/yuan2.0:m32 docker run --gpus all --privileged --ulimit stack=68719476736 --shm-size=1000G -itd -v /path/to/yuan_2.0:/workspace/yuan_2.0 -v /path/to/dataset:/workspace/dataset -v /path/to/checkpoints:/workspace/checkpoints --name your_name yuanmodel/yuan2.0:m32 docker exec -it your_name bash ``` 4.2 Data Preprocess We have provided the data preprocess script. See documentation [here](https://github.com/IEIT-Yuan/Yuan2.0-M32/blob/main/docs/data_process.md ). 4.3 Model Pretrain We've provided several scripts for pretraining in the [`example`](https://github.com/IEIT-Yuan/Yuan2.0-M32/blob/main/examples). The details can be seen from documentation [here](https://github.com/IEIT-Yuan/Yuan2.0-M32/blob/main/docs/pretrain.md). 4.4 Inference Service For a detailed deployment plan, please refer to [vllm](https://github.com/IEIT-Yuan/Yuan2.0-M32/edit/main/vllm/README_Yuan_vllm.md). - For more information, please refer to [GitHub](https://github.com/IEIT-Yuan/Yuan2.0-M32) repository. ## 5. Statement of Agreement The use of the source code in this repository requires compliance with the open source license agreement Apache 2.0. The Yuan2.0 model supports commercial use and does not require authorization. Please understand and comply with the [《Yuan2.0 Model License Agreement》](./LICENSE-Yuan). Do not use the open source model and code, as well as derivatives generated from open source projects, for any purposes that may cause harm to the country and society, or for any services that have not undergone security assessment and filing. Although we have taken measures to ensure the compliance and accuracy of the data during training, the model has a huge number of parameters and is affected by probability and randomness factors. We cannot guarantee the accuracy of the output content, and the model is easily misled by input instructions. This project does not assume any data security, public opinion risks, or any model misleading, abusing, spreading caused by open-source models and code Risks and responsibilities arising from improper utilization You will be solely responsible for the risks and consequences arising from the use, copying, distribution, and modification of the model in this open source project ## 6. Contact Us If you have any questions, please raise an issue or contact us at [email protected] paper:arxiv.org/abs/2405.17976
nishantkumar1/lora_model_POC_1	nishantkumar1	2024-05-30T08:16:26Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T08:16:12Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: nishantkumar1 - 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)
bdsaglam/llama-3-8b-jerx-peft-t1ivk5r8	bdsaglam	2024-05-30T08:16:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T08:15:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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bdsaglam/llama-3-8b-jerx-peft-e8jpms8d	bdsaglam	2024-05-30T08:16:11Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T08:15:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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vsrohit-nuronics/OrpoLlama-3-8B	vsrohit-nuronics	2024-05-30T08:09:54Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T08:05:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree	DaveGergern	2024-05-30T08:07:13Z	10	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "base_model:KoboldAI/LLaMA2-13B-Erebus-v3", "base_model:merge:KoboldAI/LLaMA2-13B-Erebus-v3", "base_model:KoboldAI/LLaMA2-13B-Psyfighter2", "base_model:merge:KoboldAI/LLaMA2-13B-Psyfighter2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T07:55:49Z	--- base_model: - KoboldAI/LLaMA2-13B-Psyfighter2 - KoboldAI/LLaMA2-13B-Erebus-v3 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [KoboldAI/LLaMA2-13B-Psyfighter2](https://huggingface.co/KoboldAI/LLaMA2-13B-Psyfighter2) * [KoboldAI/LLaMA2-13B-Erebus-v3](https://huggingface.co/KoboldAI/LLaMA2-13B-Erebus-v3) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: KoboldAI/LLaMA2-13B-Psyfighter2 layer_range: - 0 - 32 - model: KoboldAI/LLaMA2-13B-Erebus-v3 layer_range: - 0 - 32 merge_method: slerp base_model: KoboldAI/LLaMA2-13B-Psyfighter2 parameters: t: - filter: self_attn value: - 0 - 0.5 - 0.3 - 0.7 - 1 - filter: mlp value: - 1 - 0.5 - 0.7 - 0.3 - 0 - value: 0.5 dtype: bfloat16 ```
Hgkang00/FT-label-aug-consent-10	Hgkang00	2024-05-30T08:04:39Z	10	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "dataset_size:100K<n<1M", "loss:CoSENTLoss", "arxiv:1908.10084", "base_model:sentence-transformers/all-MiniLM-L6-v2", "base_model:finetune:sentence-transformers/all-MiniLM-L6-v2", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-30T08:04:23Z	--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - dataset_size:100K<n<1M - loss:CoSENTLoss base_model: sentence-transformers/all-MiniLM-L6-v2 metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max widget: - source_sentence: Frequent headaches and muscle soreness are a result of my insomnia. sentences: - My frequent headaches and muscle soreness are a direct result of my insomnia. - A manic episode often prevents me from sitting still or relaxing as I constantly need to be on the move. - The fear of being away from familiar places during a panic attack is why I have refused job opportunities with travel obligations. - source_sentence: My insomnia results in frequent headaches and muscle soreness for me. sentences: - Due to my insomnia, I have frequent headaches and muscle soreness. - Thoughts of life not being worth living and feelings of hopelessness create a difficult challenge for me. - The fear of being away from familiar places during a panic attack is why I have refused job opportunities with travel obligations. - source_sentence: Faced with a snake, fear takes over and I stay frozen until it passes. sentences: - Whenever I encounter a snake, I freeze in fear and cannot move until it is gone. - Due to a sense of unworthiness of happiness, I struggle to enjoy activities that were once my favorites. - The fear of being away from familiar places during a panic attack is why I have refused job opportunities with travel obligations. - source_sentence: The idea of overdosing on medication crosses my mind when overwhelmed. sentences: - Thoughts of overdosing on medication often occur to me when I'm overwhelmed. - I, almost like being stuck in a loop, repeat certain actions or words without any clear purpose at times. - The fear of being away from familiar places during a panic attack is why I have refused job opportunities with travel obligations. - source_sentence: Insomnia has led me to experience frequent headaches and muscle soreness. sentences: - My insomnia has caused me to experience frequent headaches and muscle soreness. - I struggle with distinguishing between reality and illusions when I feel detached from reality at times. - The fear of being away from familiar places during a panic attack is why I have refused job opportunities with travel obligations. pipeline_tag: sentence-similarity model-index: - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: FT label aug type: FT_label_aug metrics: - type: pearson_cosine value: 0.42561450628852554 name: Pearson Cosine - type: spearman_cosine value: 0.23253817395631948 name: Spearman Cosine - type: pearson_manhattan value: 0.5095430319125491 name: Pearson Manhattan - type: spearman_manhattan value: 0.23187290173483613 name: Spearman Manhattan - type: pearson_euclidean value: 0.5153981915417447 name: Pearson Euclidean - type: spearman_euclidean value: 0.232538168642362 name: Spearman Euclidean - type: pearson_dot value: 0.4256145064012167 name: Pearson Dot - type: spearman_dot value: 0.23253817993475548 name: Spearman Dot - type: pearson_max value: 0.5153981915417447 name: Pearson Max - type: spearman_max value: 0.23253817993475548 name: Spearman Max --- # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a --> - Maximum Sequence Length: 256 tokens - Output Dimensionality: 384 tokens - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("Hgkang00/FT-label-aug-consent-10") # Run inference sentences = [ 'Insomnia has led me to experience frequent headaches and muscle soreness.', 'My insomnia has caused me to experience frequent headaches and muscle soreness.', 'I struggle with distinguishing between reality and illusions when I feel detached from reality at times.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `FT_label_aug` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.4256 \| \| spearman_cosine \| 0.2325 \| \| pearson_manhattan \| 0.5095 \| \| spearman_manhattan \| 0.2319 \| \| pearson_euclidean \| 0.5154 \| \| spearman_euclidean \| 0.2325 \| \| pearson_dot \| 0.4256 \| \| spearman_dot \| 0.2325 \| \| pearson_max \| 0.5154 \| \| spearman_max \| 0.2325 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 133,800 training samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: \| \| sentence1 \| sentence2 \| score \| \|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------\| \| type \| string \| string \| float \| \| details \| <ul><li>min: 11 tokens</li><li>mean: 31.63 tokens</li><li>max: 63 tokens</li></ul> \| <ul><li>min: 14 tokens</li><li>mean: 25.22 tokens</li><li>max: 41 tokens</li></ul> \| <ul><li>min: -1.0</li><li>mean: -0.92</li><li>max: 1.0</li></ul> \| * Samples: \| sentence1 \| sentence2 \| score \| \|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------\| \| <code>Presence of one or more of the following intrusion symptoms associated with the traumatic event: recurrent distressing memories, dreams, flashbacks, psychological distress, or physiological reactions to cues of the traumatic event.</code> \| <code>I avoid making phone calls, even to close friends or family, because I'm afraid of saying something wrong or sounding awkward.</code> \| <code>0.0</code> \| \| <code>The phobic object or situation almost always provokes immediate fear or anxiety.</code> \| <code>I find it hard to stick to a consistent eating schedule, sometimes going days without feeling the need to eat at all.</code> \| <code>-1.0</code> \| \| <code>The fear or anxiety is out of proportion to the actual danger posed by the specific object or situation and to the sociocultural context.</code> \| <code>I have difficulty going to places where I feel there are no immediate exits, such as cinemas or auditoriums, as the fear of being stuck or unable to escape escalates my anxiety.</code> \| <code>-1.0</code> \| * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 104,225 evaluation samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: \| \| sentence1 \| sentence2 \| score \| \|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------\| \| type \| string \| string \| float \| \| details \| <ul><li>min: 11 tokens</li><li>mean: 31.24 tokens</li><li>max: 63 tokens</li></ul> \| <ul><li>min: 15 tokens</li><li>mean: 24.86 tokens</li><li>max: 41 tokens</li></ul> \| <ul><li>min: -1.0</li><li>mean: -0.93</li><li>max: 1.0</li></ul> \| * Samples: \| sentence1 \| sentence2 \| score \| \|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------\|:------------------\| \| <code>Excessive anxiety and worry occurring more days than not for at least 6 months, about a number of events or activities such as work or school performance.</code> \| <code>Simple activities like going for a walk or doing household chores feel like daunting tasks due to my low energy levels.</code> \| <code>-1.0</code> \| \| <code>The individual fears acting in a way or showing anxiety symptoms that will be negatively evaluated, leading to humiliation, embarrassment, rejection, or offense to others.</code> \| <code>I often find myself mindlessly snacking throughout the day due to changes in my appetite.</code> \| <code>-1.0</code> \| \| <code>Persistent avoidance of stimuli associated with the trauma, evidenced by avoiding distressing memories, thoughts, or feelings, or external reminders of the event.</code> \| <code>Simple activities like going for a walk or doing household chores feel like daunting tasks due to my low energy levels.</code> \| <code>-1.0</code> \| * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 256 - `per_device_eval_batch_size`: 128 - `num_train_epochs`: 10 - `warmup_ratio`: 0.1 - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 256 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| loss \| FT_label_aug_spearman_cosine \| \|:------:\|:----:\|:-------------:\|:-------:\|:----------------------------:\| \| 1.0 \| 523 \| 7.773 \| - \| - \| \| 2.0 \| 1046 \| 0.0004 \| - \| - \| \| 2.9828 \| 1560 \| - \| 11.8818 \| 0.2184 \| \| 1.0172 \| 1569 \| 0.1169 \| - \| - \| \| 2.0172 \| 2092 \| 5.4076 \| - \| - \| \| 3.0172 \| 2615 \| 0.0002 \| - \| - \| \| 3.9828 \| 3120 \| - \| 11.8669 \| 0.2054 \| \| 2.0344 \| 3138 \| 0.1571 \| - \| - \| \| 3.0344 \| 3661 \| 4.0179 \| - \| - \| \| 4.0344 \| 4184 \| 0.0001 \| - \| - \| \| 4.9828 \| 4680 \| - \| 12.8814 \| 0.2291 \| \| 3.0516 \| 4707 \| 0.1592 \| - \| - \| \| 4.0516 \| 5230 \| 2.835 \| 13.5336 \| 0.2325 \| ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.0 - Transformers: 4.41.1 - PyTorch: 2.3.0+cu121 - Accelerate: 0.30.1 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
samim2024/llama2test	samim2024	2024-05-30T07:56:48Z	0	0	peft	[ "peft", "region:us" ]	null	2024-05-30T04:42:42Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
cj94/git-base-naruto	cj94	2024-05-30T07:56:40Z	65	0	transformers	[ "transformers", "tensorboard", "safetensors", "git", "image-text-to-text", "generated_from_trainer", "base_model:microsoft/git-base", "base_model:finetune:microsoft/git-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-30T07:45:23Z	--- license: mit base_model: microsoft/git-base tags: - generated_from_trainer model-index: - name: git-base-naruto results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # git-base-naruto This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0613 - Wer Score: 4.6462 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Score \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:---------:\| \| 7.3247 \| 3.7037 \| 50 \| 4.4756 \| 6.1692 \| \| 2.2782 \| 7.4074 \| 100 \| 0.4117 \| 0.4308 \| \| 0.1182 \| 11.1111 \| 150 \| 0.0433 \| 0.4462 \| \| 0.0162 \| 14.8148 \| 200 \| 0.0483 \| 0.5231 \| \| 0.0105 \| 18.5185 \| 250 \| 0.0527 \| 0.5231 \| \| 0.0085 \| 22.2222 \| 300 \| 0.0548 \| 0.4769 \| \| 0.007 \| 25.9259 \| 350 \| 0.0578 \| 0.8923 \| \| 0.006 \| 29.6296 \| 400 \| 0.0599 \| 0.8462 \| \| 0.0051 \| 33.3333 \| 450 \| 0.0598 \| 6.0 \| \| 0.004 \| 37.0370 \| 500 \| 0.0608 \| 5.5538 \| \| 0.0035 \| 40.7407 \| 550 \| 0.0606 \| 7.7077 \| \| 0.0028 \| 44.4444 \| 600 \| 0.0611 \| 5.4308 \| \| 0.0023 \| 48.1481 \| 650 \| 0.0613 \| 4.6462 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
redav/model	redav	2024-05-30T07:55:46Z	4	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T07:43:48Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: redav - 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)
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_2-Node_MvWZsdnR	MoTHer-VTHR	2024-05-30T07:55:43Z	170	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-30T07:55:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_2-Node_TGqofb6m	MoTHer-VTHR	2024-05-30T07:55:24Z	169	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-30T07:55:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Niggendar/datassRev3Pony_rev2	Niggendar	2024-05-30T07:54:44Z	85	1	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-30T07:43:38Z	--- library_name: diffusers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_1-Node_AVpZMbEo	MoTHer-VTHR	2024-05-30T07:54:40Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-30T07:54:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_2-Node_g5EB3Yu7	MoTHer-VTHR	2024-05-30T07:54:00Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-30T07:53:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_2-Node_mkcvpDd9	MoTHer-VTHR	2024-05-30T07:53:17Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-30T07:53:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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jimjakdiend/distil_whisper_til	jimjakdiend	2024-05-30T07:52:00Z	13	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:distil-whisper/distil-large-v2", "base_model:finetune:distil-whisper/distil-large-v2", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-29T13:43:00Z	--- license: mit base_model: distil-whisper/distil-large-v2 tags: - generated_from_trainer model-index: - name: distil_whisper_til results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distil_whisper_til This model is a fine-tuned version of [distil-whisper/distil-large-v2](https://huggingface.co/distil-whisper/distil-large-v2) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0001 - eval_wer: 0.0083 - eval_runtime: 1661.951 - eval_samples_per_second: 2.106 - eval_steps_per_second: 0.264 - epoch: 1.5982 - step: 700 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 4000 ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
kchilala/ppo-LunarLander-v2	kchilala	2024-05-30T07:51:33Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-29T21:04:07Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 253.41 +/- 14.34 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_2-Node_cUADKV7n	MoTHer-VTHR	2024-05-30T07:51:31Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-30T07:51:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_1-Node_fNVnisHH	MoTHer-VTHR	2024-05-30T07:51:10Z	169	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-30T07:50:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_2-Node_HVUUpUar	MoTHer-VTHR	2024-05-30T07:50:44Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:46:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_2-Node_m29nu62T	MoTHer-VTHR	2024-05-30T07:50:23Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:46:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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QuantFactory/Llama3-German-8B-GGUF	QuantFactory	2024-05-30T07:50:11Z	226	0	transformers	[ "transformers", "gguf", "text-generation", "de", "arxiv:2404.10830", "base_model:DiscoResearch/Llama3-German-8B", "base_model:quantized:DiscoResearch/Llama3-German-8B", "license:llama3", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T06:43:13Z	--- license: llama3 language: - de library_name: transformers base_model: DiscoResearch/Llama3-German-8B pipeline_tag: text-generation --- # Llama3-German-8B-GGUF This is quantized version of [DiscoResearch/Llama3-German-8B](https://huggingface.co/DiscoResearch/Llama3-German-8B) created using llama.cpp ## Model Description Llama3-German-8B-v0.1 is a large language model based on [Meta's Llama3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B). It is specialized for the German language through continuous pretraining on 65 billion high-quality tokens, similar to previous [LeoLM](https://huggingface.co/LeoLM) or [Occiglot](https://huggingface.co/collections/occiglot/occiglot-eu5-7b-v01-65dbed502a6348b052695e01) models. Llama3 itself was trained on 15T tokens, of which only <1T were multilingual, resulting in suboptimal performance in German with reduced linguistic capabilities and frequent grammatical errors, motivating the necessity for continued pretraining. Benchmark results on our model show minimal degradation in English performance, despite the absence of replay during training. Importantly, Llama3-German-8B-v0.1 demonstrates strong improvements in German, particularly on the Hellaswag benchmark, which measures linguistic understanding and general reasoning. [DiscoResearch/Llama3-German-8B-v0.1](https://huggingface.co/collections/DiscoResearch/discoleo-8b-llama3-for-german-6650527496c0fafefd4c9729) is the result of a joint effort between [DiscoResearch](https://huggingface.co/DiscoResearch) and [Occiglot](https://huggingface.co/occiglot) with support from the [DFKI](https://www.dfki.de/web/) (German Research Center for Artificial Intelligence) and [hessian.Ai](https://hessian.ai). Occiglot kindly handled data preprocessing, filtering, and deduplication as part of their latest [dataset release](https://huggingface.co/datasets/occiglot/occiglot-fineweb-v0.5), as well as sharing their compute allocation at hessian.Ai's 42 Supercomputer. ## How to use This is a base model and should probably be subject to finetuning before use. See our [collection](https://huggingface.co/collections/DiscoResearch/discoleo-8b-llama3-for-german-6650527496c0fafefd4c9729) for various finetuned and long-context versions. ## Model Training and Hyperparameters The model was trained on 128 GPUs on [hessian.Ai 42](hessian.ai) for ~60 hours. See detailed hyperparameters below. \| Parameter \| Value \| \|-------------------\|-----------------------------------\| \| Sequence Length \| 8192 tokens \| \| Learning Rate \| 1.5e-5 to 1.5e-6 (cosine schedule)\| \| Batch Size \| 4194304 (5128192) tokens \| \| Micro Batch Size \| 48192 tokens \| \| Training Steps \| 15500 \| \| Warmup Steps \| 155 (1%) \| \| Weight Decay \| 0.05 \| \| Optimizer \| AdamW \| ## Data Collection and Preprocessing For pre-training, we used 65B German tokens from the [occiglot-fineweb-0.5](https://huggingface.co/datasets/occiglot/occiglot-fineweb-v0.5) dataset. The data comprises multiple curated datasets from [LLM-Datasets](https://github.com/malteos/llm-datasets) as well as 12 [Common-Crawl](https://commoncrawl.org) releases that were processed with [OSCAR's Ungoliant pipeline](https://github.com/oscar-project/ungoliant). All data was further filtered with a set of language-specific filters based on [Huggingface's fine-web](https://github.com/huggingface/datatrove/blob/main/examples/fineweb.py) and globally deduplicated. For more information please refer to the [dataset card](https://huggingface.co/datasets/occiglot/occiglot-fineweb-v0.5) and corresponding [blog-post](https://occiglot.eu/posts/occiglot-fineweb/). ## Evaluation and Results We evaluated the model using a suite of common English Benchmarks and their German counterparts with [GermanBench](https://github.com/bjoernpl/GermanBenchmark). The following figure shows the benchmark results in comparison to the base model [meta-llama/Meta-Llama3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) and two different hyperparameter configurations. We swept different learning rates to identify a well-working setup. The final released model is the 1.5e-5 lr version. ![alt text](base_model_evals.png) Find the detailed benchmark scores for the base and long-context models in this table. \| Model \| truthful_qa_de \| truthfulqa_mc \| arc_challenge \| arc_challenge_de \| hellaswag \| hellaswag_de \| MMLU \| MMLU-DE \| mean \| \|--------------------------------------\|----------------\|---------------\|---------------\|------------------\|-----------\|--------------\|--------\|---------\|------------\| \| DiscoResearch/Llama3-German-8B \| 0.49499 \| 0.44838 \| 0.55802 \| 0.49829 \| 0.79924 \| 0.65395 \| 0.62240\| 0.54413 \| 0.57743 \| \| DiscoResearch/Llama3-German-8B-32k \| 0.48920 \| 0.45138 \| 0.54437 \| 0.49232 \| 0.79078 \| 0.64310 \| 0.58774\| 0.47971 \| 0.55982 \| \| meta-llama/Meta-Llama-3-8B-Instruct \| 0.47498 \| 0.43923 \| 0.59642 \| 0.47952 \| 0.82025\| 0.60008 \| 0.66658\| 0.53541 \| 0.57656 \| ## Long-Context Extension In addition to the base model, we release a long-context version of Llama3-German-8B ([DiscoResearch/Llama3-German-8B-32k](https://huggingface.co/DiscoResearch/Llama3-German-8B-32k) capable of processing context lengths up to 65k tokens. This variant was trained on an additional 100 million tokens at 32k context length, using a rope_theta value of `1.5e6` and a learning rate of `1.5e-5` with a batch size of `256*8192` tokens and otherwise equal hyperparameters to the base model. ## Instruction Tuning We also provide an instruction-tuned version: [DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1](https://huggingface.co/DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1), utilizing the DiscoLM German dataset for fine-tuning (also available as a long-context model at [DiscoResearch/Llama3-DiscoLeo-Instruct-8B-32k-v0.1](https://huggingface.co/DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1)). Find more details in the respective model cards. Also check out our experimental merge ([DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental](https://huggingface.co/DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental)) between [meta-llama/Meta-Llama3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) and our finetuned model in an attempt to keep the extraordinary capabilities of Llama3-Instruct and add exceptional German skills. ## Document Packing We employed a more intelligent document packing strategy based on the ["Fewer Truncations Improve Language Modeling" paper by Ding et al.](https://arxiv.org/abs/2404.10830v2), using the first-fit-decreasing algorithm to pack documents into batches without truncation. We packed our data in chunks of 10000 documents for more efficient processing while maintaining >99% packing efficiency. Documents longer than the sequence length are split into chunks of sequence length. This approach results in overall higher benchmark scores when training on the same data with equal hyperparameters. The following numbers are from initial experiments with `3e-5 lr` and 12k steps and show improvements comparable to those shown in the original paper. \| Task \| Naive Packing \| Fewer Truncations Packing \| Percentage Increase \| \|-------------------\|---------------\|---------------------------\|---------------------\| \| truthfulqa_mc \| 0.452648 \| 0.467687 \| 3.32% \| \| arc_challenge \| 0.517918 \| 0.528157 \| 1.98% \| \| truthful_qa_de \| 0.485529 \| 0.492979 \| 1.53% \| \| arc_challenge_de \| 0.480375 \| 0.493174 \| 2.66% \| \| hellaswag \| 0.776041 \| 0.773352 \| -0.35% \| \| hellaswag_de \| 0.655248 \| 0.653356 \| -0.29% \| \| MMLU \| 0.573719 \| 0.579802 \| 1.06% \| \| MMLU-DE \| 0.504509 \| 0.503863 \| -0.13% \| The following is our simple implementation of the first-fit-decreasing algorithm described in the paper. ```python def pack_documents(tokenized_documents): # Sort documents by their length in descending order sorted_docs = sorted(tokenized_documents, key=len, reverse=True) # Initialize bins bins = [] # Function to find the first bin that can accommodate the document def find_bin(doc): for b in bins: if sum(len(d) for d in b) + len(doc) <= 8192: return b return None # Place each document in the first available bin or create a new bin for doc in sorted_docs: target_bin = find_bin(doc) if target_bin is not None: target_bin.append(doc) else: # Create a new bin with this document if no suitable bin is found bins.append([doc]) # Return results return bins ``` ## Model Configurations We release DiscoLeo-8B in the following configurations: 1. [Base model with continued pretraining](https://huggingface.co/DiscoResearch/Llama3-German-8B) 2. [Long-context version (32k context length)](https://huggingface.co/DiscoResearch/Llama3-German-8B-32k) 3. [Instruction-tuned version of the base model](https://huggingface.co/DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1) 4. [Instruction-tuned version of the long-context model](https://huggingface.co/DiscoResearch/Llama3-DiscoLeo-Instruct-8B-32k-v0.1) 5. [Experimental `DARE-TIES` Merge with Llama3-Instruct](https://huggingface.co/DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental) 6. [Collection of Quantized versions](https://huggingface.co/collections/DiscoResearch/discoleo-8b-quants-6651bcf8f72c9a37ce485d42) ## How to use: Here's how to use the model with transformers: ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch device="cuda" model = AutoModelForCausalLM.from_pretrained( "DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1") prompt = "Schreibe ein Essay über die Bedeutung der Energiewende für Deutschlands Wirtschaft" messages = [ {"role": "system", "content": "Du bist ein hilfreicher Assistent."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## Acknowledgements The model was trained and evaluated by [Björn Plüster](https://huggingface.co/bjoernp) ([DiscoResearch](https://huggingface.co/DiscoResearch), [ellamind](https://ellamind.com)) with data preparation and project supervision by [Manuel Brack](http://manuel-brack.eu) ([DFKI](https://www.dfki.de/web/), [TU-Darmstadt](https://www.tu-darmstadt.de/)). Initial work on dataset collection and curation was performed by [Malte Ostendorff](https://ostendorff.org) and [Pedro Ortiz Suarez](https://portizs.eu). Instruction tuning was done with the DiscoLM German dataset created by [Jan-Philipp Harries](https://huggingface.co/jphme) and [Daniel Auras](https://huggingface.co/rasdani) ([DiscoResearch](https://huggingface.co/DiscoResearch), [ellamind](https://ellamind.com)). We extend our gratitude to [LAION](https://laion.ai/) and friends, especially [Christoph Schuhmann](https://entwickler.de/experten/christoph-schuhmann) and [Jenia Jitsev](https://huggingface.co/JJitsev), for initiating this collaboration. The model training was supported by a compute grant at the [42 supercomputer](https://hessian.ai/) which is a central component in the development of [hessian AI](https://hessian.ai/), the [AI Innovation Lab](https://hessian.ai/infrastructure/ai-innovationlab/) (funded by the [Hessian Ministry of Higher Education, Research and the Art (HMWK)](https://wissenschaft.hessen.de) & the [Hessian Ministry of the Interior, for Security and Homeland Security (HMinD)](https://innen.hessen.de)) and the [AI Service Centers](https://hessian.ai/infrastructure/ai-service-centre/) (funded by the [German Federal Ministry for Economic Affairs and Climate Action (BMWK)](https://www.bmwk.de/Navigation/EN/Home/home.html)). The curation of the training data is partially funded by the [German Federal Ministry for Economic Affairs and Climate Action (BMWK)](https://www.bmwk.de/Navigation/EN/Home/home.html) through the project [OpenGPT-X](https://opengpt-x.de/en/) (project no. 68GX21007D).
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_1-Node_BZz93ey6	MoTHer-VTHR	2024-05-30T07:50:04Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:45:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_0-Node_XrgQfakQ	MoTHer-VTHR	2024-05-30T07:49:54Z	122	0	transformers	[ "transformers", "safetensors", "vit_msn", "image-feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-feature-extraction	2024-05-28T16:44:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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leosaros/autotrain-ysdhw-yy5ar	leosaros	2024-05-30T07:49:45Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T07:48:42Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_Ch5K29UH	MoTHer-VTHR	2024-05-30T07:49:38Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:44:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_rxjL5iGb	MoTHer-VTHR	2024-05-30T07:49:11Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:43:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_dP52EnQd	MoTHer-VTHR	2024-05-30T07:48:37Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:42:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_7y54jSeg	MoTHer-VTHR	2024-05-30T07:48:27Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:41:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_N9XUxrSc	MoTHer-VTHR	2024-05-30T07:47:52Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:40:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_Lz898uTP	MoTHer-VTHR	2024-05-30T07:47:44Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:40:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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DaveGergern/PsyfighterTwo-ErebusThree-Three	DaveGergern	2024-05-30T07:47:38Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "arxiv:2306.01708", "base_model:KoboldAI/LLaMA2-13B-Erebus-v3", "base_model:merge:KoboldAI/LLaMA2-13B-Erebus-v3", "base_model:KoboldAI/LLaMA2-13B-Psyfighter2", "base_model:merge:KoboldAI/LLaMA2-13B-Psyfighter2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T07:42:19Z	--- base_model: - KoboldAI/LLaMA2-13B-Psyfighter2 - KoboldAI/LLaMA2-13B-Erebus-v3 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [KoboldAI/LLaMA2-13B-Psyfighter2](https://huggingface.co/KoboldAI/LLaMA2-13B-Psyfighter2) as a base. ### Models Merged The following models were included in the merge: * [KoboldAI/LLaMA2-13B-Erebus-v3](https://huggingface.co/KoboldAI/LLaMA2-13B-Erebus-v3) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: KoboldAI/LLaMA2-13B-Psyfighter2 - model: KoboldAI/LLaMA2-13B-Erebus-v3 parameters: density: 0.10 weight: [0, 0.3, 0.7, 1] merge_method: ties base_model: KoboldAI/LLaMA2-13B-Psyfighter2 parameters: normalize: true int8_mask: true dtype: float16 ```
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_1-Node_QVQtLHre	MoTHer-VTHR	2024-05-30T07:47:33Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:39:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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pantelnm/Llama-3-8b-Alpaca-Finetuned	pantelnm	2024-05-30T07:47:05Z	0	0	peft	[ "peft", "safetensors", "llama", "assistant", "text-generation", "en", "dataset:yahma/alpaca-cleaned", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:adapter:meta-llama/Meta-Llama-3-8B", "license:apache-2.0", "region:us" ]	text-generation	2024-05-27T18:40:11Z	--- library_name: peft base_model: meta-llama/Meta-Llama-3-8B license: apache-2.0 datasets: - yahma/alpaca-cleaned language: - en metrics: - accuracy - code_eval pipeline_tag: text-generation tags: - assistant --- # Model Card for Llama-3-8b-Alpaca-Finetuned <!-- Provide a quick summary of what the model is/does. --> Llama-3-8b-Alpaca-Finetuned is a large language model based on the Llama 3 architecture, fine-tuned using the Alpaca dataset. This model is designed to enhance natural language understanding and generation tasks by leveraging the strengths of both the Llama 3 architecture and the comprehensive training examples provided in the Alpaca dataset. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Llama-3-8b-Alpaca-Finetuned is a state-of-the-art NLP model finetuned on the Llama 3 architecture, with 8 billion parameters. The finetuning process utilized the Alpaca dataset, which is designed to improve the model's ability to understand and generate natural language instructions. This model is capable of handling a wide range of language tasks, including text generation, question answering, summarization, and more. - Developed by: Meta - Model type: Llama 3 8b - Language(s) (NLP): English - License: Apache License 2.0 - Finetuned from model [optional]: Llama 3 ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: pantelnm/Llama-3-8b-Alpaca-Finetuned ## 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. --> Llama-3-8b-Alpaca-Finetuned can be used directly for various NLP tasks, including: - Text generation for creative writing. - Question answering for customer support. - Summarization of long documents. - Conversational agents and chatbots. ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> When integrated into larger systems, Llama-3-8b-Alpaca-Finetuned can be used for: - Personalized content recommendation. - Advanced data analysis and report generation. - Enhanced user interaction in applications and services. ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> The model should not be used for: - Generating harmful or offensive content. - Automated decision-making without human oversight. - Any application intended to deceive or manipulate individuals. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Llama-3-8b-Alpaca-Finetuned may inherit biases present in the training data. The model's responses can be influenced by cultural and societal biases reflected in the data it was trained on. Additionally, the model may produce incorrect or misleading information, especially on topics requiring specialized knowledge. ### 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. ```py from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("pantelnm/Llama-3-8b-Alpaca-Finetuned") model = AutoModelForCausalLM.from_pretrained("pantelnm/Llama-3-8b-Alpaca-Finetuned") input_text = "Provide a summary of the latest research in AI." inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(inputs, max_length=150) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## 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. --> The Alpaca dataset consists of diverse text data specifically curated for instruction-following tasks. The data includes a wide range of examples designed to improve the model's performance in generating relevant and accurate responses to various prompts. [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. --> #### 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 --> The training data was preprocessed to ensure consistency and quality. Steps included tokenization, normalization, and filtering of inappropriate content. Training Hyperparameters Training regime: Mixed precision (fp16) to balance performance and efficiency. Batch size: 512 Learning rate: 3e-5 Epochs: 10 #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> Training throughput: 1000 tokens/second Total training time: 72 hours Checkpoint size: 16 GB ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics The model was evaluated using a separate validation set derived from the Alpaca dataset, containing diverse examples for a robust assessment of performance. #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> The evaluation considered factors such as response accuracy, relevance, coherence, and bias. [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> Key metrics included BLEU score, ROUGE score, and human evaluation for qualitative assessment. [More Information Needed] ### Results BLEU score: 28.5 ROUGE-L score: 35.2 Human evaluation: 90% accuracy in generating contextually appropriate responses. [More Information Needed] #### Summary The model demonstrated strong performance across various metrics, indicating its effectiveness in generating high-quality text. However, continuous monitoring and updates are recommended to maintain and improve performance. ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> Examinations included attention weight analysis and saliency maps to understand how the model processes input and generates output. [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: NVIDIA A100 GPUs - Hours used: 72 hours - Cloud Provider: Mircosoft Azure - Compute Region: US-West - Carbon Emitted:** 150 kg CO2eq ## Technical Specifications [optional] ### Model Architecture and Objective Llama-3-8b-Alpaca-Finetuned is based on the transformer architecture, designed for efficient processing of natural language tasks. The model's objective is to generate tex
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_8wr8xj4H	MoTHer-VTHR	2024-05-30T07:47:03Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:38:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_1-Node_crNiHCdg	MoTHer-VTHR	2024-05-30T07:46:41Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:37:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_CGVgaCAU	MoTHer-VTHR	2024-05-30T07:45:58Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:37:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_ge3wsm7v	MoTHer-VTHR	2024-05-30T07:45:50Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:36:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_pXcKSLSH	MoTHer-VTHR	2024-05-30T07:45:42Z	169	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:36:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_1-Node_4GE54fBw	MoTHer-VTHR	2024-05-30T07:45:26Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:35:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_uW5P2d4L	MoTHer-VTHR	2024-05-30T07:45:04Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:35:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_SEEVHMjS	MoTHer-VTHR	2024-05-30T07:44:56Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:34:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_1-Node_rVz2hcDW	MoTHer-VTHR	2024-05-30T07:44:38Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:34:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_psSHZ8Rz	MoTHer-VTHR	2024-05-30T07:44:31Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:33:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_c9K8GNmz	MoTHer-VTHR	2024-05-30T07:44:10Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:32:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Wellyowo/GIT-naruto	Wellyowo	2024-05-30T07:43:43Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "git", "image-text-to-text", "generated_from_trainer", "base_model:microsoft/git-base", "base_model:finetune:microsoft/git-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-30T06:49:39Z	--- license: mit base_model: microsoft/git-base tags: - generated_from_trainer model-index: - name: GIT-naruto results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GIT-naruto This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0774 - Wer Score: 16.0923 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\| \| 7.4722 \| 0.93 \| 50 \| 4.5072 \| 21.6154 \| \| 2.1729 \| 1.85 \| 100 \| 0.3006 \| 0.5077 \| \| 0.0896 \| 2.78 \| 150 \| 0.0626 \| 0.6154 \| \| 0.0296 \| 3.7 \| 200 \| 0.0647 \| 21.7538 \| \| 0.0228 \| 4.63 \| 250 \| 0.0599 \| 21.7077 \| \| 0.0169 \| 5.56 \| 300 \| 0.0627 \| 3.5846 \| \| 0.0162 \| 6.48 \| 350 \| 0.0611 \| 17.0769 \| \| 0.0147 \| 7.41 \| 400 \| 0.0649 \| 21.6769 \| \| 0.0131 \| 8.33 \| 450 \| 0.0631 \| 15.0154 \| \| 0.0119 \| 9.26 \| 500 \| 0.0668 \| 19.3231 \| \| 0.0117 \| 10.19 \| 550 \| 0.0645 \| 20.3231 \| \| 0.0106 \| 11.11 \| 600 \| 0.0631 \| 21.6308 \| \| 0.0099 \| 12.04 \| 650 \| 0.0655 \| 17.6923 \| \| 0.0098 \| 12.96 \| 700 \| 0.0662 \| 18.0615 \| \| 0.0092 \| 13.89 \| 750 \| 0.0656 \| 18.1385 \| \| 0.0089 \| 14.81 \| 800 \| 0.0658 \| 21.6615 \| \| 0.0086 \| 15.74 \| 850 \| 0.0677 \| 20.4 \| \| 0.0079 \| 16.67 \| 900 \| 0.0684 \| 21.6462 \| \| 0.0085 \| 17.59 \| 950 \| 0.0701 \| 21.6615 \| \| 0.0089 \| 18.52 \| 1000 \| 0.0716 \| 16.8923 \| \| 0.0083 \| 19.44 \| 1050 \| 0.0685 \| 21.6769 \| \| 0.0079 \| 20.37 \| 1100 \| 0.0665 \| 21.7077 \| \| 0.0075 \| 21.3 \| 1150 \| 0.0685 \| 19.5231 \| \| 0.0078 \| 22.22 \| 1200 \| 0.0669 \| 20.7385 \| \| 0.0078 \| 23.15 \| 1250 \| 0.0677 \| 18.6923 \| \| 0.007 \| 24.07 \| 1300 \| 0.0698 \| 19.7231 \| \| 0.008 \| 25.0 \| 1350 \| 0.0682 \| 20.4769 \| \| 0.0073 \| 25.93 \| 1400 \| 0.0705 \| 19.3231 \| \| 0.008 \| 26.85 \| 1450 \| 0.0738 \| 21.6615 \| \| 0.0071 \| 27.78 \| 1500 \| 0.0722 \| 19.9231 \| \| 0.0064 \| 28.7 \| 1550 \| 0.0731 \| 21.6923 \| \| 0.0063 \| 29.63 \| 1600 \| 0.0741 \| 20.5385 \| \| 0.0069 \| 30.56 \| 1650 \| 0.0780 \| 19.8462 \| \| 0.0063 \| 31.48 \| 1700 \| 0.0763 \| 16.9538 \| \| 0.0061 \| 32.41 \| 1750 \| 0.0775 \| 19.7846 \| \| 0.0062 \| 33.33 \| 1800 \| 0.0772 \| 19.1077 \| \| 0.0065 \| 34.26 \| 1850 \| 0.0737 \| 17.7231 \| \| 0.0062 \| 35.19 \| 1900 \| 0.0752 \| 19.5385 \| \| 0.0058 \| 36.11 \| 1950 \| 0.0748 \| 19.4 \| \| 0.006 \| 37.04 \| 2000 \| 0.0752 \| 18.4154 \| \| 0.0053 \| 37.96 \| 2050 \| 0.0746 \| 17.1385 \| \| 0.0053 \| 38.89 \| 2100 \| 0.0766 \| 15.8154 \| \| 0.0052 \| 39.81 \| 2150 \| 0.0770 \| 17.2 \| \| 0.0049 \| 40.74 \| 2200 \| 0.0763 \| 19.3538 \| \| 0.0051 \| 41.67 \| 2250 \| 0.0766 \| 19.9692 \| \| 0.0046 \| 42.59 \| 2300 \| 0.0768 \| 19.9846 \| \| 0.0045 \| 43.52 \| 2350 \| 0.0773 \| 16.3692 \| \| 0.0044 \| 44.44 \| 2400 \| 0.0771 \| 16.7846 \| \| 0.0041 \| 45.37 \| 2450 \| 0.0773 \| 17.6308 \| \| 0.0042 \| 46.3 \| 2500 \| 0.0774 \| 16.0615 \| \| 0.0041 \| 47.22 \| 2550 \| 0.0767 \| 16.3231 \| \| 0.004 \| 48.15 \| 2600 \| 0.0771 \| 16.1846 \| \| 0.0037 \| 49.07 \| 2650 \| 0.0772 \| 16.0462 \| \| 0.0035 \| 50.0 \| 2700 \| 0.0774 \| 16.0923 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.1
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_tqxvVZeD	MoTHer-VTHR	2024-05-30T07:43:32Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:31:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_YjeJvMd4	MoTHer-VTHR	2024-05-30T07:43:22Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:31:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_0-Node_zA4aeZqL	MoTHer-VTHR	2024-05-30T07:42:53Z	123	0	transformers	[ "transformers", "safetensors", "vit_mae", "image-feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-feature-extraction	2024-05-28T16:30:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_YAXn6cPa	MoTHer-VTHR	2024-05-30T07:42:41Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:29:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AbdelrahmanRagab/news	AbdelrahmanRagab	2024-05-30T07:42:16Z	0	0	null	[ "arxiv:1910.09700", "region:us" ]	null	2024-05-30T07:37:37Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_YEFPBgYj	MoTHer-VTHR	2024-05-30T07:41:59Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:27:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_dGtYYtjr	MoTHer-VTHR	2024-05-30T07:41:41Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:27:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_N9NGjrzj	MoTHer-VTHR	2024-05-30T07:41:33Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:26:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_1-Node_9w6g388s	MoTHer-VTHR	2024-05-30T07:41:22Z	170	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:26:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_2jDdcs5Y	MoTHer-VTHR	2024-05-30T07:40:51Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:25:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_1-Node_63g6pEt9	MoTHer-VTHR	2024-05-30T07:40:32Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:24:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_dDs7JsxP	MoTHer-VTHR	2024-05-30T07:40:01Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:23:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_ZQejZsZn	MoTHer-VTHR	2024-05-30T07:39:50Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:23:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_0-Node_zMet6y4c	MoTHer-VTHR	2024-05-30T07:39:29Z	163	0	transformers	[ "transformers", "safetensors", "vit", "image-feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-feature-extraction	2024-05-28T16:22:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_MNwpKQQL	MoTHer-VTHR	2024-05-30T07:39:18Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:22:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_YYY3hYbS	MoTHer-VTHR	2024-05-30T07:38:59Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:21:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_PDCcfhoH	MoTHer-VTHR	2024-05-30T07:38:28Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:20:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_EZ6KezwF	MoTHer-VTHR	2024-05-30T07:38:06Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:19:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_jEgCc229	MoTHer-VTHR	2024-05-30T07:37:29Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:17:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_yjivEgny	MoTHer-VTHR	2024-05-30T07:37:21Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:17:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_1-Node_qMNtZfhn	MoTHer-VTHR	2024-05-30T07:37:10Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:17:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_ZemwcrUG	MoTHer-VTHR	2024-05-30T07:36:49Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:16:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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karan451/whisper-tiny-hindi-100steps	karan451	2024-05-30T07:36:32Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T07:36:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_XydcPvT5	MoTHer-VTHR	2024-05-30T07:36:26Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:15:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_0-Node_Cpdh8UjZ	MoTHer-VTHR	2024-05-30T07:36:03Z	162	0	transformers	[ "transformers", "safetensors", "vit", "image-feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-feature-extraction	2024-05-28T16:14:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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tarikcelik/Meta-Llama-3-8B-Q8_0-GGUF	tarikcelik	2024-05-30T07:32:07Z	1	0	null	[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo", "text-generation", "en", "license:llama3", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T07:31:44Z	--- language: - en license: llama3 tags: - facebook - meta - pytorch - llama - llama-3 - llama-cpp - gguf-my-repo pipeline_tag: text-generation extra_gated_prompt: "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version\ \ Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for\ \ use, reproduction, distribution and modification of the Llama Materials set forth\ \ herein.\n\"Documentation\" means the specifications, manuals and documentation\ \ accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\ \"Licensee\" or \"you\" means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf), of\ \ the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama\ \ 3\" means the foundational large language models and software and algorithms,\ \ including machine-learning model code, trained model weights, inference-enabling\ \ code, training-enabling code, fine-tuning enabling code and other elements of\ \ the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\ \"Llama Materials\" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation\ \ (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"\ we\" means Meta Platforms Ireland Limited (if you are located in or, if you are\ \ an entity, your principal place of business is in the EEA or Switzerland) and\ \ Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).\n\ \ \n1. License Rights and Redistribution.\na. Grant of Rights. You are granted\ \ a non-exclusive, worldwide, non-transferable and royalty-free limited license\ \ under Meta’s intellectual property or other rights owned by Meta embodied in the\ \ Llama Materials to use, reproduce, distribute, copy, create derivative works of,\ \ and make modifications to the Llama Materials.\nb. Redistribution and Use.\ni.\ \ If you distribute or make available the Llama Materials (or any derivative works\ \ thereof), or a product or service that uses any of them, including another AI\ \ model, you shall (A) provide a copy of this Agreement with any such Llama Materials;\ \ and (B) prominently display “Built with Meta Llama 3” on a related website, user\ \ interface, blogpost, about page, or product documentation. If you use the Llama\ \ Materials to create, train, fine tune, or otherwise improve an AI model, which\ \ is distributed or made available, you shall also include “Llama 3” at the beginning\ \ of any such AI model name.\nii. If you receive Llama Materials, or any derivative\ \ works thereof, from a Licensee as part of an integrated end user product, then\ \ Section 2 of this Agreement will not apply to you.\niii. You must retain in all\ \ copies of the Llama Materials that you distribute the following attribution notice\ \ within a “Notice” text file distributed as a part of such copies: “Meta Llama\ \ 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms,\ \ Inc. All Rights Reserved.”\niv. 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No trademark licenses are granted under this Agreement,\ \ and in connection with the Llama Materials, neither Meta nor Licensee may use\ \ any name or mark owned by or associated with the other or any of its affiliates,\ \ except as required for reasonable and customary use in describing and redistributing\ \ the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you\ \ a license to use “Llama 3” (the “Mark”) solely as required to comply with the\ \ last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently\ \ accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All\ \ goodwill arising out of your use of the Mark will inure to the benefit of Meta.\n\ b. 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The term of this\ \ Agreement will commence upon your acceptance of this Agreement or access to the\ \ Llama Materials and will continue in full force and effect until terminated in\ \ accordance with the terms and conditions herein. Meta may terminate this Agreement\ \ if you are in breach of any term or condition of this Agreement. Upon termination\ \ of this Agreement, you shall delete and cease use of the Llama Materials. Sections\ \ 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law\ \ and Jurisdiction. This Agreement will be governed and construed under the laws\ \ of the State of California without regard to choice of law principles, and the\ \ UN Convention on Contracts for the International Sale of Goods does not apply\ \ to this Agreement. The courts of California shall have exclusive jurisdiction\ \ of any dispute arising out of this Agreement.\n### Meta Llama 3 Acceptable Use\ \ Policy\nMeta is committed to promoting safe and fair use of its tools and features,\ \ including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable\ \ Use Policy (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)\n\ #### Prohibited Uses\nWe want everyone to use Meta Llama 3 safely and responsibly.\ \ You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate\ \ the law or others’ rights, including to:\n 1. Engage in, promote, generate,\ \ contribute to, encourage, plan, incite, or further illegal or unlawful activity\ \ or content, such as:\n 1. Violence or terrorism\n 2. Exploitation\ \ or harm to children, including the solicitation, creation, acquisition, or dissemination\ \ of child exploitative content or failure to report Child Sexual Abuse Material\n\ \ 3. Human trafficking, exploitation, and sexual violence\n 4. The\ \ illegal distribution of information or materials to minors, including obscene\ \ materials, or failure to employ legally required age-gating in connection with\ \ such information or materials.\n 5. Sexual solicitation\n 6. Any\ \ other criminal activity\n 2. Engage in, promote, incite, or facilitate the\ \ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\ \ 3. Engage in, promote, incite, or facilitate discrimination or other unlawful\ \ or harmful conduct in the provision of employment, employment benefits, credit,\ \ housing, other economic benefits, or other essential goods and services\n 4.\ \ Engage in the unauthorized or unlicensed practice of any profession including,\ \ but not limited to, financial, legal, medical/health, or related professional\ \ practices\n 5. Collect, process, disclose, generate, or infer health, demographic,\ \ or other sensitive personal or private information about individuals without rights\ \ and consents required by applicable laws\n 6. Engage in or facilitate any action\ \ or generate any content that infringes, misappropriates, or otherwise violates\ \ any third-party rights, including the outputs or results of any products or services\ \ using the Llama Materials\n 7. Create, generate, or facilitate the creation\ \ of malicious code, malware, computer viruses or do anything else that could disable,\ \ overburden, interfere with or impair the proper working, integrity, operation\ \ or appearance of a website or computer system\n2. Engage in, promote, incite,\ \ facilitate, or assist in the planning or development of activities that present\ \ a risk of death or bodily harm to individuals, including use of Meta Llama 3 related\ \ to the following:\n 1. Military, warfare, nuclear industries or applications,\ \ espionage, use for materials or activities that are subject to the International\ \ Traffic Arms Regulations (ITAR) maintained by the United States Department of\ \ State\n 2. Guns and illegal weapons (including weapon development)\n 3.\ \ Illegal drugs and regulated/controlled substances\n 4. Operation of critical\ \ infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm\ \ or harm to others, including suicide, cutting, and eating disorders\n 6. Any\ \ content intended to incite or promote violence, abuse, or any infliction of bodily\ \ harm to an individual\n3. Intentionally deceive or mislead others, including use\ \ of Meta Llama 3 related to the following:\n 1. Generating, promoting, or furthering\ \ fraud or the creation or promotion of disinformation\n 2. Generating, promoting,\ \ or furthering defamatory content, including the creation of defamatory statements,\ \ images, or other content\n 3. Generating, promoting, or further distributing\ \ spam\n 4. Impersonating another individual without consent, authorization,\ \ or legal right\n 5. Representing that the use of Meta Llama 3 or outputs are\ \ human-generated\n 6. Generating or facilitating false online engagement, including\ \ fake reviews and other means of fake online engagement\n4. Fail to appropriately\ \ disclose to end users any known dangers of your AI system\nPlease report any violation\ \ of this Policy, software “bug,” or other problems that could lead to a violation\ \ of this Policy through one of the following means:\n * Reporting issues with\ \ the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n\ \ * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n\ \ * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting\ \ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]" extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location ? By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- # tarikkral/Meta-Llama-3-8B-Q8_0-GGUF This model was converted to GGUF format from [`meta-llama/Meta-Llama-3-8B`](https://huggingface.co/meta-llama/Meta-Llama-3-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo tarikkral/Meta-Llama-3-8B-Q8_0-GGUF --model meta-llama-3-8b-q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo tarikkral/Meta-Llama-3-8B-Q8_0-GGUF --model meta-llama-3-8b-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m meta-llama-3-8b-q8_0.gguf -n 128 ```
eeeyounglee/EEVE-10.8B-mean-4096-1	eeeyounglee	2024-05-30T07:31:43Z	10	0	sentence-transformers	[ "sentence-transformers", "safetensors", "llama", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-30T07:29:07Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # eeeyounglee/EEVE-10.8B-mean-4096-1 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 4096 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('eeeyounglee/EEVE-10.8B-mean-4096-1') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=eeeyounglee/EEVE-10.8B-mean-4096-1) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 224 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `__main__.MultipleNegativesRankingLoss_with_logging` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 112, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 4096, 'do_lower_case': False}) with Transformer model: LlamaModel (1): Pooling({'word_embedding_dimension': 4096, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Dense({'in_features': 4096, 'out_features': 4096, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
Shengkun/Llama3-8B-Structural-Pruning-1.25	Shengkun	2024-05-30T07:24:31Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-27T17:33:29Z	--- license: apache-2.0 --- ## 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]
ping990579/jjk_LoRA	ping990579	2024-05-30T07:19:26Z	1	1	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-30T07:17:49Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of TOK jjk widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - ping990579/jjk_LoRA <Gallery /> ## Model description These are ping990579/jjk_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK jjk to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](ping990579/jjk_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
uwwee/git-base-naruto	uwwee	2024-05-30T07:17:10Z	65	0	transformers	[ "transformers", "tensorboard", "safetensors", "git", "image-text-to-text", "generated_from_trainer", "base_model:microsoft/git-base", "base_model:finetune:microsoft/git-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-30T07:02:31Z	--- license: mit base_model: microsoft/git-base tags: - generated_from_trainer model-index: - name: git-base-naruto results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # git-base-naruto This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0400 - Wer Score: 0.3529 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Score \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:---------:\| \| 7.3756 \| 5.8824 \| 50 \| 4.6183 \| 7.9118 \| \| 2.5329 \| 11.7647 \| 100 \| 0.6340 \| 7.0 \| \| 0.199 \| 17.6471 \| 150 \| 0.0438 \| 0.7941 \| \| 0.0155 \| 23.5294 \| 200 \| 0.0390 \| 0.8529 \| \| 0.0051 \| 29.4118 \| 250 \| 0.0385 \| 0.3529 \| \| 0.0025 \| 35.2941 \| 300 \| 0.0392 \| 0.3235 \| \| 0.0018 \| 41.1765 \| 350 \| 0.0397 \| 0.3529 \| \| 0.0016 \| 47.0588 \| 400 \| 0.0400 \| 0.3529 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
RichardErkhov/Yukang_-_LongAlpaca-7B-gguf	RichardErkhov	2024-05-30T07:16:00Z	45	0	null	[ "gguf", "arxiv:2309.12307", "endpoints_compatible", "region:us" ]	null	2024-05-30T04:33:50Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) LongAlpaca-7B - GGUF - Model creator: https://huggingface.co/Yukang/ - Original model: https://huggingface.co/Yukang/LongAlpaca-7B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [LongAlpaca-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.Q2_K.gguf) \| Q2_K \| 2.36GB \| \| [LongAlpaca-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.6GB \| \| [LongAlpaca-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.IQ3_S.gguf) \| IQ3_S \| 2.75GB \| \| [LongAlpaca-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.Q3_K_S.gguf) \| Q3_K_S \| 2.75GB \| \| [LongAlpaca-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.IQ3_M.gguf) \| IQ3_M \| 2.9GB \| \| [LongAlpaca-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.Q3_K.gguf) \| Q3_K \| 3.07GB \| \| [LongAlpaca-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.07GB \| \| [LongAlpaca-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.35GB \| \| [LongAlpaca-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.4GB \| \| [LongAlpaca-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.Q4_0.gguf) \| Q4_0 \| 3.56GB \| \| [LongAlpaca-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.IQ4_NL.gguf) \| IQ4_NL \| 3.58GB \| \| [LongAlpaca-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.Q4_K_S.gguf) \| Q4_K_S \| 3.59GB \| \| [LongAlpaca-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.Q4_K.gguf) \| Q4_K \| 3.8GB \| \| [LongAlpaca-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.Q4_K_M.gguf) \| Q4_K_M \| 3.8GB \| \| [LongAlpaca-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.Q4_1.gguf) \| Q4_1 \| 3.95GB \| \| [LongAlpaca-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.Q5_0.gguf) \| Q5_0 \| 4.33GB \| \| [LongAlpaca-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.33GB \| \| [LongAlpaca-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.Q5_K.gguf) \| Q5_K \| 4.45GB \| \| [LongAlpaca-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.45GB \| \| [LongAlpaca-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.Q5_1.gguf) \| Q5_1 \| 4.72GB \| \| [LongAlpaca-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.Q6_K.gguf) \| Q6_K \| 5.15GB \| \| [LongAlpaca-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Yukang_-_LongAlpaca-7B-gguf/blob/main/LongAlpaca-7B.Q8_0.gguf) \| Q8_0 \| 6.67GB \| Original model description: # LongLoRA and LongAlpaca for Long-context LLMs [![Huggingface Models](https://img.shields.io/badge/Models-Huggingface%20Models-bron)](https://huggingface.co/Yukang) [![Github](https://img.shields.io/badge/Github-Repo-cyan)](https://github.com/dvlab-research/LongLoRA) [![Data](https://img.shields.io/badge/Data-LongAlpaca%2012k-light)](https://huggingface.co/datasets/Yukang/LongAlpaca-12k) [![Paper](https://img.shields.io/badge/Paper-Arvix-blue)](https://arxiv.org/abs/2309.12307) [![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-yellow.svg)](https://github.com/dvlab-research/LongLoRA/blob/main/LICENSE) [![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-orange.svg)](https://github.com/dvlab-research/LongLoRA/blob/main/DATA_LICENSE) [![Weight License](https://img.shields.io/badge/Weight%20License-CC%20By%20NC%204.0-red)](https://github.com/dvlab-research/LongLoRA/blob/main/WEIGHT_LICENSE) For detailed usage and codes, please visit the [Github project](https://github.com/dvlab-research/LongLoRA). ## TABLE OF CONTENTS 1. [News](#news) 2. [Examples](#examples) 3. [Highlights](#highlights) 4. [How to contribute](#how-to-contribute) 5. [Requirements](#usage-requirements) 6. [Installation and quick guide](#installation-and-quick-guide) 7. [LongAlpaca Data](#longalpaca-data) 8. [Models](#models) 9. [Training](#training) 10. [Evaluation](#evaluation) 11. [Demo](#demo) 12. [Data Generation via Pdf2Text](#data-generation-via-pdf2text) 13. [Citation](#citation) 14. [Acknowledgement](#acknowledgement) 15. [License](#license) ## News - [x] [2023.10.8] We release the long instruction-following dataset, [LongAlpaca-12k](https://huggingface.co/datasets/Yukang/LongAlpaca-12k) and the corresponding models, [LongAlpaca-7B](https://huggingface.co/Yukang/LongAlpaca-7B), [LongAlpaca-13B](https://huggingface.co/Yukang/LongAlpaca-13B), and [LongAlpaca-70B](https://huggingface.co/Yukang/LongAlpaca-70B). - (The previous sft models, [Llama-2-13b-chat-longlora-32k-sft](https://huggingface.co/Yukang/Llama-2-13b-chat-longlora-32k-sft) and [Llama-2-70b-chat-longlora-32k-sft](https://huggingface.co/Yukang/Llama-2-70b-chat-longlora-32k-sft), have been depreciated.) - [x] [2023.10.3] We add support GPTNeoX models. Please refer to this [PR](https://github.com/dvlab-research/LongLoRA/pull/32) for usage. Thanks for @naubull2 for this contribution. - [x] [2023.9.22] We release all our fine-tuned [models](https://huggingface.co/Yukang), including 70B-32k models, [LLaMA2-LongLoRA-70B-32k](https://huggingface.co/Yukang/Llama-2-70b-longlora-32k), [LLaMA2-LongLoRA-7B-100k](https://huggingface.co/Yukang/Llama-2-7b-longlora-100k-ft). Welcome to check them out! - [x] [2023.9.22] We release [Paper](http://arxiv.org/abs/2309.12307) and this GitHub repo, including training and evaluation code. LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models [[Paper](http://arxiv.org/abs/2309.12307)] <br /> [Yukang Chen](https://scholar.google.com/citations?user=6p0ygKUAAAAJ&hl=en), [Shengju Qian](https://scholar.google.com/citations?user=QNnWmasAAAAJ), [Haotian Tang](https://scholar.google.com/citations?user=WxL13BAAAAAJ&hl), [Xin Lai](https://scholar.google.com/citations?user=tqNDPA4AAAAJ&hl=zh-CN), [Zhijian Liu](https://scholar.google.com/citations?user=3coYSTUAAAAJ&hl=en), [Song Han](https://scholar.google.com/citations?user=E0iCaa4AAAAJ&hl=zh-CN), [Jiaya Jia](https://scholar.google.com/citations?user=XPAkzTEAAAAJ&hl=en)<br /> ## Highlights 1. In LongLoRA approach, The proposed shifted short attention is easy to implement, compatible with Flash-Attention, and is not required during inference. 2. We released all our models, including models from 7B to 70B, context length from 8k to 100k, including [LLaMA2-LongLoRA-7B-100k](https://huggingface.co/Yukang/Llama-2-7b-longlora-100k-ft), [LLaMA2-LongLoRA-13B-64k](https://huggingface.co/Yukang/Llama-2-13b-longlora-64k), and [LLaMA2-LongLoRA-70B-32k](https://huggingface.co/Yukang/Llama-2-70b-longlora-32k). 3. We built up a long-context instruction-following dataset, [LongAlpaca-12k](#longalpaca-data). We released the corresponding [LongAlpaca-7B](https://huggingface.co/Yukang/LongAlpaca-7B), [LongAlpaca-13B](https://huggingface.co/Yukang/LongAlpaca-13B) and [LongAlpaca-70B](https://huggingface.co/Yukang/LongAlpaca-70B) models. To our best knowledge, this is the first open-sourced long-context 70B model. ## How to Contribute - Make sure to have git installed. - Create your own [fork](https://github.com/dvlab-research/LongLoRA/fork) of the project. - Clone the repository on your local machine, using git clone and pasting the url of this project. - Read both the `Requirements` and `Installation and Quick Guide` sections below. - Commit and push your changes. - Make a pull request when finished modifying the project. ## Usage Requirements To download and use the [pre-trained weights](#pre-trained-weights) you will need: 1. Hugging Face (HF) account with valid email. Note, the email used for HF must alse be used for the license agreement. 2. Accept the Meta [license and acceptable use policy](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) ## Installation and Quick Guide To install and run the application: 1. [Fork this repo](https://github.com/dvlab-research/LongLoRA/fork) on github 2. Clone the repository on your local machine, using git clone and pasting the url of this project. 3. Run the following code: ``` pip install -r requirements.txt pip install flash-attn --no-build-isolation ``` 4. Use either a [Released model](#released-models) or [Fine tune](#fine-tuning) a model to fit your preferences. 5. Test your model by chat. 6. Deploy your own demo. ## LongAlpaca Data LongAlpaca-12k contains 9k long QA data that we collected and 3k short QA sampled from the original [Alpaca data](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json). This is to avoid the case that the model might degrade at short instruction following. The data we collect contains various types and amounts as the following figure. \| Data \| Short QA \| Long QA \| Total \| Download \| \|:---------------\|----------\|----------\|----------\|----------\| \| LongAlpaca-12k \| 3k \| 9k \| 12k \| [Link](https://huggingface.co/datasets/Yukang/LongAlpaca-12k) \| Following the original Alpaca format, our Long QA data uses the following prompts for fine-tuning: - `instruction`: `str`, describes the task the model should perform. For example, to answer a question after reading a book section or paper. We vary the contents and questions to make instructions diverse. - `output`: `str`, the answer to the instruction. We did not use the `input` format in the Alpaca format for simplicity. ## Models ### Models with supervised fine-tuning \| Model \| Size \| Context \| Train \| Link \| \|:---------------\|------\|---------\|---------\|-----------------------------------------------------------------------------------------------------------------------\| \| LongAlpaca-7B \| 7B \| 32768 \| Full FT \| [Model](https://huggingface.co/Yukang/LongAlpaca-7B) \| \| LongAlpaca-13B \| 13B \| 32768 \| Full FT \| [Model](https://huggingface.co/Yukang/LongAlpaca-13B) \| \| LongAlpaca-70B \| 70B \| 32768 \| LoRA+ \| [Model](https://huggingface.co/Yukang/LongAlpaca-70B) [(LoRA-weight)](https://huggingface.co/Yukang/LongAlpaca-70B-lora) \| ### Models with context extension via fully fine-tuning \| Model \| Size \| Context \| Train \| Link \| \|:----------------------------\|------\|---------\|-------\|-------------------------------------------------------------------\| \| Llama-2-7b-longlora-8k-ft \| 7B \| 8192 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-7b-longlora-8k-ft) \| \| Llama-2-7b-longlora-16k-ft \| 7B \| 16384 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-7b-longlora-16k-ft) \| \| Llama-2-7b-longlora-32k-ft \| 7B \| 32768 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-7b-longlora-32k-ft) \| \| Llama-2-7b-longlora-100k-ft \| 7B \| 100000 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-7b-longlora-100k-ft) \| \| Llama-2-13b-longlora-8k-ft \| 13B \| 8192 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-13b-longlora-8k-ft) \| \| Llama-2-13b-longlora-16k-ft \| 13B \| 16384 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-13b-longlora-16k-ft) \| \| Llama-2-13b-longlora-32k-ft \| 13B \| 32768 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-13b-longlora-32k-ft) \| ### Models with context extension via improved LoRA fine-tuning \| Model \| Size \| Context \| Train \| Link \| \|:----------------------------\|------\|---------\|-------\|---------------------------------------------------------------------\| \| Llama-2-7b-longlora-8k \| 7B \| 8192 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-7b-longlora-8k) \| \| Llama-2-7b-longlora-16k \| 7B \| 16384 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-7b-longlora-16k) \| \| Llama-2-7b-longlora-32k \| 7B \| 32768 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-7b-longlora-32k) \| \| Llama-2-13b-longlora-8k \| 13B \| 8192 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-13b-longlora-8k) \| \| Llama-2-13b-longlora-16k \| 13B \| 16384 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-13b-longlora-16k) \| \| Llama-2-13b-longlora-32k \| 13B \| 32768 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-13b-longlora-32k) \| \| Llama-2-13b-longlora-64k \| 13B \| 65536 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-13b-longlora-64k) \| \| Llama-2-70b-longlora-32k \| 70B \| 32768 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-70b-longlora-32k) \| \| Llama-2-70b-chat-longlora-32k \| 70B \| 32768 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-70b-chat-longlora-32k) \| ## Training ### Pre-trained weights We use LLaMA2 models as the pre-trained weights and fine-tune them to long context window sizes. Download based on your choices. \| Pre-trained weights \| \|:-------------------------------------------------------------------------------------\| \| [Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) \| \|[Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) \| \| [Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf) \| \| [Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) \| \| [Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) \| \| [Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) \| This project also supports GPTNeoX models as the base model architecture. Some candidate pre-trained weights may include [GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b), [Polyglot-ko-12.8B](https://huggingface.co/EleutherAI/polyglot-ko-12.8b) and other variants. ### Fine-tuning ``` torchrun --nproc_per_node=8 fine-tune.py \ --model_name_or_path path_to/Llama-2-7b-hf \ --bf16 True \ --output_dir path_to_saving_checkpoints \ --cache_dir path_to_cache \ --model_max_length 8192 \ --use_flash_attn True \ --low_rank_training False \ --num_train_epochs 1 \ --per_device_train_batch_size 1 \ --per_device_eval_batch_size 2 \ --gradient_accumulation_steps 8 \ --evaluation_strategy "no" \ --save_strategy "steps" \ --save_steps 1000 \ --save_total_limit 2 \ --learning_rate 2e-5 \ --weight_decay 0.0 \ --warmup_steps 20 \ --lr_scheduler_type "constant_with_warmup" \ --logging_steps 1 \ --deepspeed "ds_configs/stage2.json" \ --tf32 True \ --max_steps 1000 ``` - Please remember to change `path_to/Llama-2-7b-hf`, `path_to_saving_checkpoints`, `path_to_cache` to your own directory. - Note that you can change `model_max_length` to other values. - You could change `ds_configs/stage2.json` to `ds_configs/stage3.json` if you want. - Please set `use_flash_attn` as `False` if you use V100 machines or do not install flash attention. - You can set `low_rank_training` as `False` if you want to use fully fine-tuning. It will cost more GPU memory and slower, but the performance will be a bit better. - When training is finished, to get the full model weight: ``` cd path_to_saving_checkpoints && python zero_to_fp32.py . pytorch_model.bin ``` ### Supervised Fine-tuning ``` torchrun --nproc_per_node=8 supervised-fine-tune.py \ --model_name_or_path path_to_Llama2_chat_models \ --bf16 True \ --output_dir path_to_saving_checkpoints \ --model_max_length 32768 \ --use_flash_attn True \ --data_path LongAlpaca-12k.json \ --low_rank_training True \ --num_train_epochs 3 \ --per_device_train_batch_size 1 \ --per_device_eval_batch_size 2 \ --gradient_accumulation_steps 1 \ --evaluation_strategy "no" \ --save_strategy "steps" \ --save_steps 1000 \ --save_total_limit 2 \ --learning_rate 2e-5 \ --weight_decay 0.0 \ --warmup_steps 20 \ --lr_scheduler_type "constant_with_warmup" \ --logging_steps 1 \ --deepspeed "ds_configs/stage2.json" \ --tf32 True ``` - There is no need to make supervised fine-tuning upon the fine-tuned context extended models. It is all right to directly use base model as Llama2-chat models, as the amount of long instruction following data is enough for SFT. - Our long instruction following data can be found in [LongAlpaca-12k.json](https://huggingface.co/datasets/Yukang/LongAlpaca-12k). ### Get trainable weights in low-rank training In low-rank training, we set embedding and normalization layers as trainable. Please use the following line to extract the trainable weights `trainable_params.bin` from `pytorch_model.bin` ``` python3 get_trainable_weights.py --checkpoint_path path_to_saving_checkpoints --trainable_params "embed,norm" ``` ### Merge LoRA Weight Merge the LoRA weights of `pytorch_model.bin` and trainable parameters `trainable_params.bin`, save the resulting model into your desired path in the Hugging Face format: ``` python3 merge_lora_weights_and_save_hf_model.py \ --base_model path_to/Llama-2-7b-hf \ --peft_model path_to_saving_checkpoints \ --context_size 8192 \ --save_path path_to_saving_merged_model ``` For example, ``` python3 merge_lora_weights_and_save_hf_model.py \ --base_model /dataset/pretrained-models/Llama-2-7b-hf \ --peft_model /dataset/yukangchen/hf_models/lora-models/Llama-2-7b-longlora-8k \ --context_size 8192 \ --save_path /dataset/yukangchen/models/Llama-2-7b-longlora-8k-merged ``` ## Evaluation ### Perplexity Validation To evaluate a model that is trained in the low-rank setting, please set both `base_model` and `peft_model`. `base_model` is the pre-trained weight. `peft_model` is the path to the saved checkpoint, which should contain `trainable_params.bin`, `adapter_model.bin` and `adapter_config.json`. For example, ``` python3 eval.py --seq_len 8192 --context_size 8192 --batch_size 1 --base_model path_to/Llama-2-7b-hf --peft_model path_to_saving_checkpoints --data_path pg19/test.bin ``` To evaluate a model that is fully fine-tuned, you only need to set `base_model` as the path to the saved checkpoint, which should contain `pytorch_model.bin` and `config.json`. `peft_model` should be ignored. ``` python3 eval.py --seq_len 8192 --context_size 8192 --batch_size 1 --base_model path_to_saving_checkpoints --data_path pg19/test.bin ``` - Note that `--seq_len` is to set the sequence length for evaluation. `--context_size` is to set the context length of the model during fine-tuning. `--seq_len` should not be larger than `--context_size`. - We have already tokenized the validation and test splits of PG19 and proof-pile dataset into `pg19/validation.bin`, `pg19/test.bin`, and `proof-pile/test_sampled_data.bin`, with the tokenizer of LLaMA. `proof-pile/test_sampled_data.bin` contains 128 documents that are randomly sampled from the total proof-pile test split. For each document, it has at least 32768 tokens. We also release the sampled ids in [proof-pile/test_sampled_ids.bin](https://drive.google.com/file/d/1cnzWODLRQYAd7HeugzLCIhaqzaLZv7J5/view?usp=share_link). You can download them from the links below. \| Dataset \| Split \| Link \| \|:-----------\|------------\|--------------------------------------------------------------------------------------------------------------\| \| PG19 \| validation \| [pg19/validation.bin](https://drive.google.com/file/d/1rbJvb0qRIf2mQoN2ON7S93TbTzMnlrN6/view?usp=share_link) \| \| PG19 \| test \| [pg19/test.bin](https://drive.google.com/file/d/1QANDMdctpacPAYgS04adDXqByGEq-Ret/view?usp=share_link) \| \| Proof-pile \| test \| [proof-pile/test_sampled_data.bin](https://drive.google.com/file/d/1bUI5lPDvrqzY_XXJJ2sSuvZx0Y9AZClE/view?usp=share_link) \| ### Passkey Retrieval We provide a manner to test the passkey retrieval accuracy. For example, ``` python3 passkey_retrivial.py \ --context_size 32768 \ --base_model path_to/Llama-2-7b-longlora-32k \ --max_tokens 32768 \ --interval 1000 ``` - Note that the `context_size` is the context length during fine-tuning. - `max_tokens` is maximum length for the document in passkey retrieval evaluation. - `interval` is the interval during the document length increasing. It is a rough number because the document increases by sentences. ## Demo ### Local Inference To chat with [Llama-2-13b-chat-longlora-32k-sft](https://huggingface.co/Yukang/Llama-2-13b-chat-longlora-32k-sft) or [Llama-2-70b-chat-longlora-32k-sft](https://huggingface.co/Yukang/Llama-2-70b-chat-longlora-32k-sft), you need to run `merge_lora_weights_and_save_hf_model.py` first, and then: ``` python3 inference.py \ --base_model path_to_model \ --question $question \ --context_size $context_length \ --max_gen_len $max_gen_len \ --flash_attn True \ --material $material_content \ --material_type $material_type \ --material_title $material_title ``` To ask a question related to a book: ``` python3 inference.py \ --base_model /data/models/Llama-2-13b-chat-longlora-32k-sft \ --question "Why doesn't Professor Snape seem to like Harry?" \ --context_size 32768 \ --max_gen_len 512 \ --flash_attn True \ --material "materials/Harry Potter and the Philosophers Stone_section2.txt" \ --material_type "book" \ --material_title "Harry Potter and the Philosophers Stone" ``` Note that you can ignore `material_type` or `material_title`. To ask a question related to a paper: ``` python3 inference.py \ --base_model /data/models/Llama-2-13b-chat-longlora-32k-sft \ --question "What are the main contributions and novelties of this work?" \ --context_size 32768 \ --max_gen_len 512 \ --flash_attn True \ --material "materials/paper1.txt" \ --material_type "paper" ``` ### Online Demo To deploy your own demo run ``` python3 demo.py \ --base_model path_to_model \ --context_size $context_size \ --max_gen_len $max_gen_len \ --flash_attn True ``` Example ``` python3 demo.py \ --base_model /data/models/Llama-2-13b-chat-longlora-32k-sft \ --context_size 32768 \ --max_gen_len 512 \ --flash_attn True ``` - Note that `flash_attn=True` will make the generation slow but save much GPU memory. ## Data Generation via Pdf2text During our dataset collection, we convert paper and books from pdf to text. The conversion quality has a large influence on the final model quality. We think that this step is non-trivial. We release the tool for the pdf2txt conversion, in the folder `pdf2txt`. It is built upon `pdf2image`, `easyocr`, `ditod` and `detectron2`. Please refer to the [README.md](pdf2txt/README.md) in `pdf2txt` for more details. ## Citation If you find this project useful in your research, please consider citing: ``` @article{longlora, title={LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models}, author={Yukang Chen and Shengju Qian and Haotian Tang and Xin Lai and Zhijian Liu and Song Han and Jiaya Jia}, journal={arXiv:2309.12307}, year={2023} } ``` ``` @misc{long-alpaca, author = {Yukang Chen and Shaozuo Yu and Shengju Qian and Haotian Tang and Xin Lai and Zhijian Liu and Song Han and Jiaya Jia}, title = {Long Alpaca: Long-context Instruction-following models}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/dvlab-research/LongLoRA}}, } ``` ## Acknowledgement - This work is built upon the [LLaMA2](https://ai.meta.com/llama) as the pre-trained models. - This work can also be built upon the [GPTNeoX-HF](https://huggingface.co/docs/transformers/model_doc/gpt_neox) which is based upon [EleutherAI/GPTNeoX](https://github.com/EleutherAI/gpt-neox) as the pre-trained model architecture. - This work is based on [DeepSpeed](https://github.com/microsoft/DeepSpeed), [peft](https://github.com/huggingface/peft), and [Flash-Attention2](https://github.com/Dao-AILab/flash-attention) for acceleration. - Some evaluation code is modified upon [Landmark Attention](https://github.com/epfml/landmark-attention). - We use [LongChat](https://github.com/DachengLi1/LongChat) for the retrieval evaluation. ## License - LongLoRA is licensed under the Apache License 2.0. This means that it requires the preservation of copyright and license notices. - Data and weights are under CC-BY-NC 4.0 License. They are licensed for research use only, and allowed only non-commercial. Models trained using the dataset should not be used outside of research purposes.
2xionger/bert-base-banking77-pt2	2xionger	2024-05-30T07:11:30Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-30T06:55:02Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - f1 model-index: - name: bert-base-banking77-pt2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-banking77-pt2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2976 - F1: 0.9292 ## 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 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.0141 \| 1.0 \| 626 \| 0.7477 \| 0.8569 \| \| 0.3561 \| 2.0 \| 1252 \| 0.3632 \| 0.9168 \| \| 0.1645 \| 3.0 \| 1878 \| 0.2976 \| 0.9292 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.1.1 - Datasets 2.19.1 - Tokenizers 0.19.1
ClearLove7777/CVPR2024_OccFlow_Sub	ClearLove7777	2024-05-30T07:11:24Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-30T06:35:38Z	--- license: apache-2.0 ---
krittin-ch/dummy-model	krittin-ch	2024-05-30T07:11:01Z	108	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-30T07:08:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
leowang707/git-base-naruto	leowang707	2024-05-30T07:10:29Z	65	0	transformers	[ "transformers", "tensorboard", "safetensors", "git", "image-text-to-text", "generated_from_trainer", "base_model:microsoft/git-base", "base_model:finetune:microsoft/git-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-30T07:00:48Z	--- license: mit base_model: microsoft/git-base tags: - generated_from_trainer model-index: - name: git-base-naruto results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # git-base-naruto This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0600 - Wer Score: 2.1639 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Score \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:---------:\| \| 7.2846 \| 3.7037 \| 50 \| 4.4712 \| 22.9180 \| \| 2.2861 \| 7.4074 \| 100 \| 0.4239 \| 10.6230 \| \| 0.1211 \| 11.1111 \| 150 \| 0.0471 \| 0.4754 \| \| 0.0161 \| 14.8148 \| 200 \| 0.0453 \| 0.4098 \| \| 0.0114 \| 18.5185 \| 250 \| 0.0474 \| 0.4426 \| \| 0.0093 \| 22.2222 \| 300 \| 0.0501 \| 1.4754 \| \| 0.0084 \| 25.9259 \| 350 \| 0.0503 \| 0.7049 \| \| 0.0068 \| 29.6296 \| 400 \| 0.0534 \| 0.4590 \| \| 0.0058 \| 33.3333 \| 450 \| 0.0562 \| 0.4426 \| \| 0.0048 \| 37.0370 \| 500 \| 0.0572 \| 0.4590 \| \| 0.0035 \| 40.7407 \| 550 \| 0.0597 \| 0.7869 \| \| 0.0025 \| 44.4444 \| 600 \| 0.0602 \| 2.0164 \| \| 0.0017 \| 48.1481 \| 650 \| 0.0600 \| 2.1639 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
zhuchi76/git-base-naruto	zhuchi76	2024-05-30T07:10:15Z	64	0	transformers	[ "transformers", "tensorboard", "safetensors", "git", "image-text-to-text", "generated_from_trainer", "base_model:microsoft/git-base", "base_model:finetune:microsoft/git-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-30T06:50:37Z	--- license: mit base_model: microsoft/git-base tags: - generated_from_trainer model-index: - name: git-base-naruto results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # git-base-naruto This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0481 - Wer Score: 3.1327 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Score \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:---------:\| \| 0.0029 \| 3.5714 \| 50 \| 0.0447 \| 3.9381 \| \| 0.0007 \| 7.1429 \| 100 \| 0.0473 \| 3.3097 \| \| 0.0002 \| 10.7143 \| 150 \| 0.0476 \| 4.3274 \| \| 0.0001 \| 14.2857 \| 200 \| 0.0481 \| 3.1327 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half-gguf	lightblue	2024-05-30T07:05:40Z	29	10	null	[ "gguf", "arxiv:2405.18952", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-26T02:40:57Z	--- license: cc-by-nc-4.0 --- # Suzume ORPO <p align="center"> <img width=500 src="https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/kWQSu02YfgYdUQqv4s5lq.png" alt="Suzume with Mitsu - a Japanese tree sparrow with honey on it"/> </p> [[Paper]](https://arxiv.org/abs/2405.18952) [[Dataset]](https://huggingface.co/datasets/lightblue/mitsu) This is Suzume ORPO, an ORPO trained fine-tune of the [lightblue/suzume-llama-3-8B-multilingual](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual) model using our [lightblue/mitsu](https://huggingface.co/datasets/lightblue/mitsu) dataset. We have trained several versions of this model using ORPO and so recommend that you use the best performing model from our tests, [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half). Note that this model has a non-commerical license as we used the Command R and Command R+ models to generate our training data for this model ([lightblue/mitsu](https://huggingface.co/datasets/lightblue/mitsu)). We are currently working on a developing a commerically usable model, so stay tuned for that! # Model list We have ORPO trained the following models using different proportions of the [lightblue/mitsu](https://huggingface.co/datasets/lightblue/mitsu) dataset: * Trained on the top/bottom responses of all prompts in the dataset: [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full) * Trained on the top/bottom responses of the prompts of the 75\% most consistently ranked responses in the dataset: [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75) * Trained on the top/bottom responses of the prompts of the 50\% most consistently ranked responses in the dataset: [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half) * Trained on the top/bottom responses of the prompts of the 25\% most consistently ranked responses in the dataset: [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25) # Model results We compare the MT-Bench scores across 6 languages for our 4 ORPO trained models, as well as some baselines: * [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) - The foundation model that our models are ultimately built upon * [Nexusflow/Starling-LM-7B-beta](https://huggingface.co/Nexusflow/Starling-LM-7B-beta) - The highest performing open model on the Chatbot arena that is of a similar size to ours * gpt-3.5-turbo - A fairly high quality (although not state-of-the-art) proprietary LLM * [lightblue/suzume-llama-3-8B-multilingual](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual) - The base model which we train our ORPO finetunes from \| MT-Bench language \| meta-llama/Meta-Llama-3-8B-Instruct \| Nexusflow/Starling-LM-7B-beta \| gpt-3.5-turbo \| lightblue/suzume-llama-3-8B-multilingual \| lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full \| lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75 \| lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half \| lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25 \| \|-----------------------\|-----------------------------------------\|-----------------------------------\|-------------------\|----------------------------------------------\|--------------------------------------------------------------\|---------------------------------------------------------------\|--------------------------------------------------------------\|---------------------------------------------------------------\| \| Chinese 🇨🇳 \| NaN \| 6.97 \| 7.55 \| 7.11 \| 7.65 \| 7.77 \| 7.74 \| 7.44 \| \| English 🇺🇸 \| 7.98 \| 7.92 \| 8.26 \| 7.73 \| 7.98 \| 7.94 \| 7.98 \| 8.22 \| \| French 🇫🇷 \| NaN \| 7.29 \| 7.74 \| 7.66 \| 7.84 \| 7.46 \| 7.78 \| 7.81 \| \| German 🇩🇪 \| NaN \| 6.99 \| 7.68 \| 7.26 \| 7.28 \| 7.64 \| 7.7 \| 7.71 \| \| Japanese 🇯🇵 \| NaN \| 6.22 \| 7.84 \| 6.56 \| 7.2 \| 7.12 \| 7.34 \| 7.04 \| \| Russian 🇷🇺 \| NaN \| 8.28 \| 7.94 \| 8.19 \| 8.3 \| 8.74 \| 8.94 \| 8.81 \| We can see noticable improvement on most languages compared to the base model. We also find that our ORPO models achieve the highest score out of all the models we evaluated for a number of languages. # Training data We trained this model using the [lightblue/mitsu_full_borda](https://huggingface.co/datasets/lightblue/mitsu_full_borda) dataset. # Training configuration <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: lightblue/suzume-llama-3-8B-multilingual model_type: LlamaForCausalLM tokenizer_type: AutoTokenizer # PreTrainedTokenizerFast load_in_8bit: false load_in_4bit: false strict: false rl: orpo orpo_alpha: 0.1 remove_unused_columns: false chat_template: chatml datasets: - path: lightblue/mitsu_tophalf_borda type: orpo.chat_template conversation: llama-3 dataset_prepared_path: /workspace/llm_training/axolotl/llama3-multilingual-orpo/prepared_mitsu_half_borda val_set_size: 0.02 output_dir: /workspace/llm_training/axolotl/llama3-multilingual-orpo/output_mitsu_half_borda sequence_len: 8192 sample_packing: false pad_to_sequence_len: true use_wandb: true wandb_project: axolotl wandb_entity: peterd wandb_name: mitsu_half_borda gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 1 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 8e-6 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: resume_from_checkpoint: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 20 eval_table_size: saves_per_epoch: 1 debug: deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json weight_decay: 0.0 special_tokens: pad_token: <\|end_of_text\|> ``` </details><br> # workspace/llm_training/axolotl/llama3-multilingual-orpo/output_mitsu_half_borda This model is a fine-tuned version of [lightblue/suzume-llama-3-8B-multilingual](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0935 ## 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: 8e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 7.6299 \| 0.02 \| 1 \| 7.7014 \| \| 7.041 \| 0.07 \| 3 \| 3.9786 \| \| 0.6089 \| 0.15 \| 6 \| 0.1393 \| \| 0.1308 \| 0.22 \| 9 \| 0.1244 \| \| 0.1051 \| 0.29 \| 12 \| 0.1112 \| \| 0.1021 \| 0.36 \| 15 \| 0.1063 \| \| 0.0861 \| 0.44 \| 18 \| 0.1026 \| \| 0.1031 \| 0.51 \| 21 \| 0.0979 \| \| 0.0996 \| 0.58 \| 24 \| 0.0967 \| \| 0.0923 \| 0.65 \| 27 \| 0.0960 \| \| 0.1025 \| 0.73 \| 30 \| 0.0944 \| \| 0.1103 \| 0.8 \| 33 \| 0.0939 \| \| 0.0919 \| 0.87 \| 36 \| 0.0937 \| \| 0.104 \| 0.94 \| 39 \| 0.0935 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.0 # How to cite ```tex @article{devine2024sure, title={Are You Sure? Rank Them Again: Repeated Ranking For Better Preference Datasets}, author={Devine, Peter}, journal={arXiv preprint arXiv:2405.18952}, year={2024} } ``` # Developer Peter Devine - ([ptrdvn](https://huggingface.co/ptrdvn))
NTQAI/Nxcode-CQ-7B-orpo	NTQAI	2024-05-30T07:04:52Z	10,197	117	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "code", "conversational", "arxiv:2403.07691", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-24T04:56:38Z	--- license_name: tongyi-qianwen-research license_link: https://huggingface.co/Qwen/CodeQwen1.5-7B/blob/main/LICENSE tags: - code pipeline_tag: text-generation license: other --- <a href="https://ntq.com.vn" target="_blank"><img src="https://cdn-uploads.huggingface.co/production/uploads/5ee1b417636bdb3834e2da19/etbfTJuVdAub2evNP_E4g.png" width="200"/></a> ## Introduction Nxcode-CQ-7B-orpo is an [Monolithic Preference Optimization without Reference Model](https://arxiv.org/abs/2403.07691) fine-tune of Qwen/CodeQwen1.5-7B on 100k samples of high-quality ranking data. ## [Evalplus](https://github.com/evalplus/evalplus) \| EvalPlus \| pass@1 \| \| --- \| --- \| \| HumanEval \| 86.6 \| \| HumanEval+ \| 83.5 \| \| MBPP(v0.2.0) \| 82.3 \| \| MBPP+(v0.2.0) \| 70.4 \| We use a simple template to generate the solution for evalplus: ```python "Complete the following Python function:\n{prompt}" ``` [Evalplus Leaderboard](https://evalplus.github.io/leaderboard.html) \| Models \| HumanEval \| HumanEval+\| \|------ \| ------ \| ------ \| \| GPT-4-Turbo (April 2024)\| 90.2\| 86.6\| \| GPT-4 (May 2023)\| 88.4\| 81.17\| \| GPT-4-Turbo (Nov 2023)\| 85.4\| 79.3\| \| CodeQwen1.5-7B-Chat\| 83.5\| 78.7\| \| claude-3-opus (Mar 2024)\| 82.9\| 76.8\| \| DeepSeek-Coder-33B-instruct\| 81.1\| 75.0\| \| WizardCoder-33B-V1.1\| 79.9\| 73.2\| \| OpenCodeInterpreter-DS-33B\| 79.3\| 73.8\| \| speechless-codellama-34B-v2.0\| 77.4\| 72\| \| GPT-3.5-Turbo (Nov 2023)\| 76.8\| 70.7\| \| Llama3-70B-instruct\| 76.2\| 70.7\| ## Bigcode Leaderboard [Bigcode Leaderboard](https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard) 09/05/2024 Top 1 average score. Top 2 winrate. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5ee1b417636bdb3834e2da19/OQonD6a7aNjnN9SsTkFp-.png) ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. You should upgrade the transformers if you receive an error when loading the tokenizer ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "NTQAI/Nxcode-CQ-7B-orpo", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("NTQAI/Nxcode-CQ-7B-orpo") prompt = """Complete the following Python function: from typing import List def has_close_elements(numbers: List[float], threshold: float) -> bool: """ Check if in given list of numbers, are any two numbers closer to each other than given threshold. >>> has_close_elements([1.0, 2.0, 3.0], 0.5) False >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) True """ """ messages = [ {"role": "user", "content": prompt} ] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) res = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True) ``` ### Contact information For personal communication related to this project, please contact Nha Nguyen Van ([email protected]).
Niggendar/darksealSDXL10_v60	Niggendar	2024-05-30T07:04:18Z	83	2	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-30T06:56:37Z	--- library_name: diffusers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. 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Ksgk-fy/ecoach_philippine_v1_merge	Ksgk-fy	2024-05-30T07:03:57Z	78	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-30T06:58:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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zzunyang/law_dpo4	zzunyang	2024-05-30T07:03:21Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/open-llama-2-ko-7b", "base_model:adapter:beomi/open-llama-2-ko-7b", "region:us" ]	null	2024-05-30T07:02:43Z	--- library_name: peft base_model: beomi/open-llama-2-ko-7b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
nguyennghia0902/electra-small-discriminator_0.0005_32_15e	nguyennghia0902	2024-05-30T07:01:58Z	60	0	transformers	[ "transformers", "tf", "electra", "question-answering", "generated_from_keras_callback", "base_model:google/electra-small-discriminator", "base_model:finetune:google/electra-small-discriminator", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-05-30T01:03:56Z	--- license: apache-2.0 base_model: google/electra-small-discriminator tags: - generated_from_keras_callback model-index: - name: nguyennghia0902/electra-small-discriminator_0.0005_32_15e results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # nguyennghia0902/electra-small-discriminator_0.0005_32_15e This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5725 - Train End Logits Accuracy: 0.8401 - Train Start Logits Accuracy: 0.8151 - Validation Loss: 0.2404 - Validation End Logits Accuracy: 0.9316 - Validation Start Logits Accuracy: 0.9222 - Epoch: 14 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0005, 'decay_steps': 23445, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 3.3489 \| 0.2750 \| 0.2432 \| 2.6409 \| 0.3858 \| 0.3668 \| 0 \| \| 2.7567 \| 0.3772 \| 0.3444 \| 2.3037 \| 0.4607 \| 0.4455 \| 1 \| \| 2.5118 \| 0.4254 \| 0.3927 \| 2.0684 \| 0.5046 \| 0.4834 \| 2 \| \| 2.3234 \| 0.4624 \| 0.4283 \| 1.8489 \| 0.5461 \| 0.5257 \| 3 \| \| 2.1433 \| 0.4977 \| 0.4608 \| 1.6848 \| 0.5907 \| 0.5742 \| 4 \| \| 1.9832 \| 0.5289 \| 0.4980 \| 1.4704 \| 0.6378 \| 0.6177 \| 5 \| \| 1.8204 \| 0.5619 \| 0.5290 \| 1.2837 \| 0.6769 \| 0.6665 \| 6 \| \| 1.6387 \| 0.5991 \| 0.5696 \| 1.0838 \| 0.7217 \| 0.7115 \| 7 \| \| 1.4657 \| 0.6379 \| 0.6048 \| 0.9057 \| 0.7589 \| 0.7562 \| 8 \| \| 1.2902 \| 0.6729 \| 0.6458 \| 0.7410 \| 0.8034 \| 0.7975 \| 9 \| \| 1.1103 \| 0.7149 \| 0.6867 \| 0.5707 \| 0.8407 \| 0.8374 \| 10 \| \| 0.9500 \| 0.7493 \| 0.7214 \| 0.4523 \| 0.8761 \| 0.8660 \| 11 \| \| 0.7931 \| 0.7855 \| 0.7606 \| 0.3483 \| 0.9018 \| 0.8924 \| 12 \| \| 0.6702 \| 0.8166 \| 0.7889 \| 0.2710 \| 0.9236 \| 0.9152 \| 13 \| \| 0.5725 \| 0.8401 \| 0.8151 \| 0.2404 \| 0.9316 \| 0.9222 \| 14 \| ### Framework versions - Transformers 4.39.3 - TensorFlow 2.15.0 - Datasets 2.18.0 - Tokenizers 0.15.2
zjoe99/outputs	zjoe99	2024-05-30T06:54:06Z	2	0	peft	[ "peft", "safetensors", "trl", "sft", "unsloth", "generated_from_trainer", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:adapter:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "region:us" ]	null	2024-05-23T03:40:09Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - unsloth - generated_from_trainer base_model: unsloth/llama-3-8b-Instruct-bnb-4bit model-index: - name: outputs results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # outputs This model is a fine-tuned version of [unsloth/llama-3-8b-Instruct-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 3407 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.2.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Llamarider222/Mixtral-8x7b-Instruct-GPTQ	Llamarider222	2024-05-30T06:53:37Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T06:53:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
AmilaUvaz/autotrain-awz6e-4wcl0	AmilaUvaz	2024-05-30T06:53:35Z	3	0	diffusers	[ "diffusers", "autotrain", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:openrail++", "region:us" ]	text-to-image	2024-05-30T06:53:30Z	--- tags: - autotrain - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: runwayml/stable-diffusion-v1-5 instance_prompt: <A man Shenil> license: openrail++ --- # AutoTrain LoRA DreamBooth - AmilaUvaz/autotrain-awz6e-4wcl0 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on <A man Shenil> using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False.
ReySajju742/QuranAi	ReySajju742	2024-05-30T06:53:07Z	0	0	transformers	[ "transformers", "Koran", "quran", "quranai", "en", "ur", "ar", "es", "ru", "hi", "license:cc", "endpoints_compatible", "region:us" ]	null	2024-05-30T06:51:48Z	--- license: cc language: - en - ur - ar - es - ru - hi library_name: transformers tags: - Koran - quran - quranai ---

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