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train · 838k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-23 12:29:03	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 492 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-23 12:24:08	card stringlengths 11 1.01M
azharsultan/Meta-Llama-3-8B-orpo	azharsultan	2024-05-21T00:52:36Z	9	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:mlabonne/orpo-dpo-mix-40k", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T09:49:47Z	--- library_name: transformers license: apache-2.0 datasets: - mlabonne/orpo-dpo-mix-40k --- # 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: Azhar Sultan - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: Llama-3-8B - 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]
abdiharyadi/opus-mt-ft-4	abdiharyadi	2024-05-21T00:51:56Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "base_model:Helsinki-NLP/opus-mt-en-id", "base_model:finetune:Helsinki-NLP/opus-mt-en-id", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-21T00:51:08Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: Helsinki-NLP/opus-mt-en-id model-index: - name: opus-mt-ft-4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-ft-4 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-id](https://huggingface.co/Helsinki-NLP/opus-mt-en-id) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2295 - eval_exact_match: 1.0 - eval_runtime: 24.6982 - eval_samples_per_second: 6.802 - eval_steps_per_second: 0.85 - epoch: 7.0 - step: 147 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
reynaldhavard/xlm-roberta-base-finetuned-panx-all	reynaldhavard	2024-05-21T00:48:44Z	104	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-21T00:33:35Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all 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. --> # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1758 - F1: 0.8558 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.299 \| 1.0 \| 835 \| 0.2074 \| 0.8078 \| \| 0.1587 \| 2.0 \| 1670 \| 0.1705 \| 0.8461 \| \| 0.1012 \| 3.0 \| 2505 \| 0.1758 \| 0.8558 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Zoyd/01-ai_Yi-1.5-34B-Chat-16K-6_5bpw_exl2	Zoyd	2024-05-21T00:48:30Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:2403.04652", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-21T00:11:26Z	--- license: apache-2.0 --- Exllamav2 quant (exl2 / 6.5 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_2bpw_exl2)</center> \| <center>10049 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2)</center> \| <center>11195 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_0bpw_exl2)</center> \| <center>13193 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_5bpw_exl2)</center> \| <center>15187 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_75bpw_exl2)</center> \| <center>16186 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-4_0bpw_exl2)</center> \| <center>17183 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-4_25bpw_exl2)</center> \| <center>18179 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-5_0bpw_exl2)</center> \| <center>21171 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-6_0bpw_exl2)</center> \| <center>25231 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-6_5bpw_exl2)</center> \| <center>27111 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-8_0bpw_exl2)</center> \| <center>29540 MB</center> \| <center>8</center> \| <div align="center"> <picture> <img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px"> </picture> </div> <p align="center"> <a href="https://github.com/01-ai">🐙 GitHub</a> • <a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> • <a href="https://twitter.com/01ai_yi">🐤 Twitter</a> • <a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a> <br/> <a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a> </p> # Intro Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. <div align="center"> Model \| Context Length \| Pre-trained Tokens \| :------------: \| :------------: \| :------------: \| \| Yi-1.5 \| 4K, 16K, 32K \| 3.6T </div> # Models - Chat models <div align="center"> \| Name \| Download \| \| --------------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-34B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-6B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| </div> - Base models <div align="center"> \| Name \| Download \| \| ---------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-34B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-6B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| </div> # Benchmarks - Chat models Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/KcsJ9Oc1VnEmfCDEJc5cd.png) Yi-1.5-9B-Chat is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/xf6pLg5jqRCwjlh6m3t6_.png) - Base models Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/BwU7QM-03dZvZzwdIE1xY.png) Yi-1.5-9B is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/y-EYSYPT-3aWLJ0x8R94F.png) # Quick Start For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
Zoyd/01-ai_Yi-1.5-34B-Chat-16K-6_0bpw_exl2	Zoyd	2024-05-21T00:48:23Z	6	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:2403.04652", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2024-05-20T23:38:38Z	--- license: apache-2.0 --- Exllamav2 quant (exl2 / 6.0 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_2bpw_exl2)</center> \| <center>10049 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2)</center> \| <center>11195 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_0bpw_exl2)</center> \| <center>13193 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_5bpw_exl2)</center> \| <center>15187 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_75bpw_exl2)</center> \| <center>16186 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-4_0bpw_exl2)</center> \| <center>17183 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-4_25bpw_exl2)</center> \| <center>18179 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-5_0bpw_exl2)</center> \| <center>21171 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-6_0bpw_exl2)</center> \| <center>25231 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-6_5bpw_exl2)</center> \| <center>27111 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-8_0bpw_exl2)</center> \| <center>29540 MB</center> \| <center>8</center> \| <div align="center"> <picture> <img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px"> </picture> </div> <p align="center"> <a href="https://github.com/01-ai">🐙 GitHub</a> • <a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> • <a href="https://twitter.com/01ai_yi">🐤 Twitter</a> • <a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a> <br/> <a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a> </p> # Intro Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. <div align="center"> Model \| Context Length \| Pre-trained Tokens \| :------------: \| :------------: \| :------------: \| \| Yi-1.5 \| 4K, 16K, 32K \| 3.6T </div> # Models - Chat models <div align="center"> \| Name \| Download \| \| --------------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-34B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-6B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| </div> - Base models <div align="center"> \| Name \| Download \| \| ---------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-34B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-6B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| </div> # Benchmarks - Chat models Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/KcsJ9Oc1VnEmfCDEJc5cd.png) Yi-1.5-9B-Chat is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/xf6pLg5jqRCwjlh6m3t6_.png) - Base models Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/BwU7QM-03dZvZzwdIE1xY.png) Yi-1.5-9B is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/y-EYSYPT-3aWLJ0x8R94F.png) # Quick Start For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
Zoyd/01-ai_Yi-1.5-34B-Chat-16K-5_0bpw_exl2	Zoyd	2024-05-21T00:48:13Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:2403.04652", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]	text-generation	2024-05-20T23:06:00Z	--- license: apache-2.0 --- Exllamav2 quant (exl2 / 5.0 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_2bpw_exl2)</center> \| <center>10049 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2)</center> \| <center>11195 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_0bpw_exl2)</center> \| <center>13193 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_5bpw_exl2)</center> \| <center>15187 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_75bpw_exl2)</center> \| <center>16186 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-4_0bpw_exl2)</center> \| <center>17183 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-4_25bpw_exl2)</center> \| <center>18179 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-5_0bpw_exl2)</center> \| <center>21171 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-6_0bpw_exl2)</center> \| <center>25231 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-6_5bpw_exl2)</center> \| <center>27111 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-8_0bpw_exl2)</center> \| <center>29540 MB</center> \| <center>8</center> \| <div align="center"> <picture> <img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px"> </picture> </div> <p align="center"> <a href="https://github.com/01-ai">🐙 GitHub</a> • <a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> • <a href="https://twitter.com/01ai_yi">🐤 Twitter</a> • <a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a> <br/> <a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a> </p> # Intro Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. <div align="center"> Model \| Context Length \| Pre-trained Tokens \| :------------: \| :------------: \| :------------: \| \| Yi-1.5 \| 4K, 16K, 32K \| 3.6T </div> # Models - Chat models <div align="center"> \| Name \| Download \| \| --------------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-34B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-6B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| </div> - Base models <div align="center"> \| Name \| Download \| \| ---------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-34B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-6B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| </div> # Benchmarks - Chat models Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/KcsJ9Oc1VnEmfCDEJc5cd.png) Yi-1.5-9B-Chat is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/xf6pLg5jqRCwjlh6m3t6_.png) - Base models Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/BwU7QM-03dZvZzwdIE1xY.png) Yi-1.5-9B is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/y-EYSYPT-3aWLJ0x8R94F.png) # Quick Start For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
Zoyd/01-ai_Yi-1.5-34B-Chat-16K-4_25bpw_exl2	Zoyd	2024-05-21T00:48:06Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:2403.04652", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-20T22:32:44Z	--- license: apache-2.0 --- Exllamav2 quant (exl2 / 4.25 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_2bpw_exl2)</center> \| <center>10049 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2)</center> \| <center>11195 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_0bpw_exl2)</center> \| <center>13193 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_5bpw_exl2)</center> \| <center>15187 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_75bpw_exl2)</center> \| <center>16186 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-4_0bpw_exl2)</center> \| <center>17183 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-4_25bpw_exl2)</center> \| <center>18179 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-5_0bpw_exl2)</center> \| <center>21171 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-6_0bpw_exl2)</center> \| <center>25231 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-6_5bpw_exl2)</center> \| <center>27111 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-8_0bpw_exl2)</center> \| <center>29540 MB</center> \| <center>8</center> \| <div align="center"> <picture> <img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px"> </picture> </div> <p align="center"> <a href="https://github.com/01-ai">🐙 GitHub</a> • <a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> • <a href="https://twitter.com/01ai_yi">🐤 Twitter</a> • <a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a> <br/> <a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a> </p> # Intro Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. <div align="center"> Model \| Context Length \| Pre-trained Tokens \| :------------: \| :------------: \| :------------: \| \| Yi-1.5 \| 4K, 16K, 32K \| 3.6T </div> # Models - Chat models <div align="center"> \| Name \| Download \| \| --------------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-34B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-6B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| </div> - Base models <div align="center"> \| Name \| Download \| \| ---------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-34B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-6B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| </div> # Benchmarks - Chat models Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/KcsJ9Oc1VnEmfCDEJc5cd.png) Yi-1.5-9B-Chat is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/xf6pLg5jqRCwjlh6m3t6_.png) - Base models Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/BwU7QM-03dZvZzwdIE1xY.png) Yi-1.5-9B is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/y-EYSYPT-3aWLJ0x8R94F.png) # Quick Start For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_0bpw_exl2	Zoyd	2024-05-21T00:47:38Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:2403.04652", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "exl2", "region:us" ]	text-generation	2024-05-20T20:21:20Z	--- license: apache-2.0 --- Exllamav2 quant (exl2 / 3.0 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_2bpw_exl2)</center> \| <center>10049 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2)</center> \| <center>11195 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_0bpw_exl2)</center> \| <center>13193 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_5bpw_exl2)</center> \| <center>15187 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_75bpw_exl2)</center> \| <center>16186 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-4_0bpw_exl2)</center> \| <center>17183 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-4_25bpw_exl2)</center> \| <center>18179 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-5_0bpw_exl2)</center> \| <center>21171 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-6_0bpw_exl2)</center> \| <center>25231 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-6_5bpw_exl2)</center> \| <center>27111 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-8_0bpw_exl2)</center> \| <center>29540 MB</center> \| <center>8</center> \| <div align="center"> <picture> <img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px"> </picture> </div> <p align="center"> <a href="https://github.com/01-ai">🐙 GitHub</a> • <a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> • <a href="https://twitter.com/01ai_yi">🐤 Twitter</a> • <a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a> <br/> <a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a> </p> # Intro Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. <div align="center"> Model \| Context Length \| Pre-trained Tokens \| :------------: \| :------------: \| :------------: \| \| Yi-1.5 \| 4K, 16K, 32K \| 3.6T </div> # Models - Chat models <div align="center"> \| Name \| Download \| \| --------------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-34B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-6B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| </div> - Base models <div align="center"> \| Name \| Download \| \| ---------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-34B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-6B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| </div> # Benchmarks - Chat models Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/KcsJ9Oc1VnEmfCDEJc5cd.png) Yi-1.5-9B-Chat is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/xf6pLg5jqRCwjlh6m3t6_.png) - Base models Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/BwU7QM-03dZvZzwdIE1xY.png) Yi-1.5-9B is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/y-EYSYPT-3aWLJ0x8R94F.png) # Quick Start For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_2bpw_exl2	Zoyd	2024-05-21T00:47:18Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:2403.04652", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-20T19:17:17Z	--- license: apache-2.0 --- Exllamav2 quant (exl2 / 2.2 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_2bpw_exl2)</center> \| <center>10049 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2)</center> \| <center>11195 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_0bpw_exl2)</center> \| <center>13193 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_5bpw_exl2)</center> \| <center>15187 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-3_75bpw_exl2)</center> \| <center>16186 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-4_0bpw_exl2)</center> \| <center>17183 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-4_25bpw_exl2)</center> \| <center>18179 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-5_0bpw_exl2)</center> \| <center>21171 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-6_0bpw_exl2)</center> \| <center>25231 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-6_5bpw_exl2)</center> \| <center>27111 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-8_0bpw_exl2)</center> \| <center>29540 MB</center> \| <center>8</center> \| <div align="center"> <picture> <img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px"> </picture> </div> <p align="center"> <a href="https://github.com/01-ai">🐙 GitHub</a> • <a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> • <a href="https://twitter.com/01ai_yi">🐤 Twitter</a> • <a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a> <br/> <a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a> </p> # Intro Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. <div align="center"> Model \| Context Length \| Pre-trained Tokens \| :------------: \| :------------: \| :------------: \| \| Yi-1.5 \| 4K, 16K, 32K \| 3.6T </div> # Models - Chat models <div align="center"> \| Name \| Download \| \| --------------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-34B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-6B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| </div> - Base models <div align="center"> \| Name \| Download \| \| ---------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-34B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-6B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| </div> # Benchmarks - Chat models Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/KcsJ9Oc1VnEmfCDEJc5cd.png) Yi-1.5-9B-Chat is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/xf6pLg5jqRCwjlh6m3t6_.png) - Base models Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/BwU7QM-03dZvZzwdIE1xY.png) Yi-1.5-9B is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/y-EYSYPT-3aWLJ0x8R94F.png) # Quick Start For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
MaziyarPanahi/Shadowm7expMeliodaspercival_01_experiment26t3q-7B-GGUF	MaziyarPanahi	2024-05-21T00:39:03Z	85	0	transformers	[ "transformers", "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "safetensors", "text-generation", "merge", "mergekit", "lazymergekit", "automerger", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "base_model:automerger/Shadowm7expMeliodaspercival_01_experiment26t3q-7B", "base_model:quantized:automerger/Shadowm7expMeliodaspercival_01_experiment26t3q-7B" ]	text-generation	2024-05-21T00:07:34Z	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - transformers - safetensors - mistral - text-generation - merge - mergekit - lazymergekit - automerger - license:apache-2.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us - text-generation model_name: Shadowm7expMeliodaspercival_01_experiment26t3q-7B-GGUF base_model: automerger/Shadowm7expMeliodaspercival_01_experiment26t3q-7B inference: false model_creator: automerger pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/Shadowm7expMeliodaspercival_01_experiment26t3q-7B-GGUF](https://huggingface.co/MaziyarPanahi/Shadowm7expMeliodaspercival_01_experiment26t3q-7B-GGUF) - Model creator: [automerger](https://huggingface.co/automerger) - Original model: [automerger/Shadowm7expMeliodaspercival_01_experiment26t3q-7B](https://huggingface.co/automerger/Shadowm7expMeliodaspercival_01_experiment26t3q-7B) ## Description [MaziyarPanahi/Shadowm7expMeliodaspercival_01_experiment26t3q-7B-GGUF](https://huggingface.co/MaziyarPanahi/Shadowm7expMeliodaspercival_01_experiment26t3q-7B-GGUF) contains GGUF format model files for [automerger/Shadowm7expMeliodaspercival_01_experiment26t3q-7B](https://huggingface.co/automerger/Shadowm7expMeliodaspercival_01_experiment26t3q-7B). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
soumi-maiti/ParallelWaveGAN_VoxtLM	soumi-maiti	2024-05-21T00:37:19Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-21T00:10:12Z	--- license: apache-2.0 ---
JianKim3293/llama3-KoEn-8B-Instruct-mergelaw	JianKim3293	2024-05-21T00:37:08Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-21T00:32:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
DUAL-GPO-2/phi-2-irepo-chatml-v16-i1	DUAL-GPO-2	2024-05-21T00:33:49Z	4	0	peft	[ "peft", "tensorboard", "safetensors", "phi", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "custom_code", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:DUAL-GPO/phi-2-irepo-chatml-merged-i0", "base_model:adapter:DUAL-GPO/phi-2-irepo-chatml-merged-i0", "region:us" ]	null	2024-05-20T23:49:20Z	--- library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer base_model: DUAL-GPO/phi-2-irepo-chatml-merged-i0 datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: phi-2-irepo-chatml-v16-i1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-2-irepo-chatml-v16-i1 This model is a fine-tuned version of [DUAL-GPO/phi-2-irepo-chatml-merged-i0](https://huggingface.co/DUAL-GPO/phi-2-irepo-chatml-merged-i0) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
reynaldhavard/xlm-roberta-base-finetuned-panx-en	reynaldhavard	2024-05-21T00:33:32Z	104	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-21T00:31:11Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en 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. --> # xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3905 - F1: 0.6861 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.0479 \| 1.0 \| 50 \| 0.4854 \| 0.5857 \| \| 0.4604 \| 2.0 \| 100 \| 0.3995 \| 0.6605 \| \| 0.3797 \| 3.0 \| 150 \| 0.3905 \| 0.6861 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
LampOfSocrates/bert-cased-plodcw-sourav	LampOfSocrates	2024-05-21T00:32:30Z	121	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-19T23:38:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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HaitameLaframe/Llama-3-8B-StoryGenerator	HaitameLaframe	2024-05-21T00:31:59Z	47	2	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T23:46:49Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: HaitameLaf - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Skoba777/Skoba	Skoba777	2024-05-21T00:22:53Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-21T00:20:15Z	--- license: apache-2.0 ---
reynaldhavard/xlm-roberta-base-finetuned-panx-de-fr	reynaldhavard	2024-05-21T00:22:38Z	104	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-21T00:09:09Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr 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. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1639 - F1: 0.8591 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2836 \| 1.0 \| 715 \| 0.1859 \| 0.8212 \| \| 0.1484 \| 2.0 \| 1430 \| 0.1632 \| 0.8487 \| \| 0.0953 \| 3.0 \| 2145 \| 0.1639 \| 0.8591 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
HaitameLaframe/Meta-Llama-3-8B-LoRA	HaitameLaframe	2024-05-21T00:14:11Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-20T23:44:14Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: HaitameLaf - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
aztro/sdxl-maba	aztro	2024-05-21T00:13:57Z	4	0	diffusers	[ "diffusers", "autotrain", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "template:sd-lora", "en", "base_model:Yntec/DreamPhotoGASM", "base_model:adapter:Yntec/DreamPhotoGASM", "license:openrail++", "region:us" ]	text-to-image	2024-05-15T11:53:35Z	--- tags: - autotrain - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: Yntec/DreamPhotoGASM instance_prompt: tessy license: openrail++ language: - en --- # AutoTrain LoRA DreamBooth - ovieyra21/autotrain-begg7-ozit5 These are LoRA adaption weights for Yntec/DreamPhotoGASM. The weights were trained on tessy using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False.
marua15/mistral_fine_tune	marua15	2024-05-21T00:08:53Z	4	0	transformers	[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T22:47:33Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Habaznya/p_model	Habaznya	2024-05-21T00:01:07Z	109	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	2024-05-04T21:51:51Z	--- license: apache-2.0 base_model: google-bert/bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: p_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # p_model This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3591 - Accuracy: 0.9066 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7455 \| 1.0 \| 832 \| 0.4333 \| 0.8517 \| \| 0.3497 \| 2.0 \| 1664 \| 0.3821 \| 0.8785 \| \| 0.2924 \| 3.0 \| 2496 \| 0.3402 \| 0.8955 \| \| 0.2017 \| 4.0 \| 3328 \| 0.3417 \| 0.9066 \| \| 0.1476 \| 5.0 \| 4160 \| 0.3591 \| 0.9066 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
worldboss/idefics-9b-doodles-v1	worldboss	2024-05-20T23:56:21Z	64	0	transformers	[ "transformers", "safetensors", "idefics", "image-text-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	image-text-to-text	2024-05-20T23:29:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Prolux/FFire	Prolux	2024-05-20T23:49:37Z	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	2024-05-20T23:49:30Z	--- 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: Prolux - 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)
damgomz/ft_bs32_lr6_mlm	damgomz	2024-05-20T23:47:25Z	106	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-20T20:39:31Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-21T01:47:18' project_name: ft_bs32_lr6_mlm_emissions_tracker run_id: 67fdf341-7766-43ef-b8f2-0ca8fd67de3e duration: 14551.246319770811 emissions: 0.0088051864348589 emissions_rate: 6.051156197456002e-07 cpu_power: 42.5 gpu_power: 0.0 ram_power: 3.75 cpu_energy: 0.1717852426302099 gpu_energy: 0 ram_energy: 0.015157421747595 energy_consumed: 0.1869426643778051 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 2 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 10 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 14551.246319770811 \| \| Emissions (Co2eq in kg) \| 0.0088051864348589 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 3.75 \| \| CPU energy (kWh) \| 0.1717852426302099 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.015157421747595 \| \| Consumed energy (kWh) \| 0.1869426643778051 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.028011149165558812 \| \| Emissions (Co2eq in kg) \| 0.005699238141910234 \| ## Note 20 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| damgomz/ThunBERT_bs16_lr5_MLM \| \| model_name \| ft_bs32_lr6_mlm \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 5e-06 \| \| batch_size \| 32 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 32580 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| Accuracy \| Recall ---\|---\|---\|---\|--- \| 0 \| 0.481592 \| 0.361436 \| 0.838733 \| 0.880368 \| \| 1 \| 0.339060 \| 0.335583 \| 0.852725 \| 0.881902 \| \| 2 \| 0.298181 \| 0.333528 \| 0.855670 \| 0.881902 \| \| 3 \| 0.259961 \| 0.338860 \| 0.855670 \| 0.875767 \| \| 4 \| 0.214407 \| 0.369720 \| 0.849043 \| 0.904908 \| \| 5 \| 0.144635 \| 0.418665 \| 0.837261 \| 0.815951 \|
lleticiasilvaa/TinyLlama1B-spider-v3-25500steps	lleticiasilvaa	2024-05-20T23:46:26Z	136	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-16T18:40:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> Fine Tuning using Spider Some passing the schema with examples and some, if no longer than 2048 tokens, with sql examples, like: Below are some sample questions and their corresponding SQL queries that might help you answer the user's question: [QUESTION]What is the number of cars with a greater accelerate than the one with the most horsepower?[/QUESTION] [SQL]SELECT COUNT() FROM CARS_DATA WHERE Accelerate > (SELECT Accelerate FROM CARS_DATA ORDER BY Horsepower DESC LIMIT 1);[/SQL] #<\|ddl\|> The query will run on a database with the following schema: CREATE TABLE car_makers ( "Id" INTEGER, "Maker" TEXT, "FullName" TEXT, "Country" TEXT, PRIMARY KEY ("Id"), FOREIGN KEY("Country") REFERENCES countries ("CountryId") ) / 2 rows from car_makers table: Id Maker FullName Country 1 amc American Motor Company 1 2 volkswagen Volkswagen 2 / Max sequence length: 2048 After filtering the dataset, there are 7665 rows remaining Prompt: ``` """# <\|system\|> You are a chatbot tasked with responding to questions about an SQLite database. Your responses must always consist of valid SQL code, and only that. If you are unable to generate SQL for a question, respond with 'I do not know'.{sql_example} # <\|ddl\|> The query will run on a database with the following schema: {ddl} # <\|user\|> {question} # <\|assistant\|> [SQL]""" ``` ## 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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lleticiasilvaa/TinyLlama1B-spider-v1-15epochs	lleticiasilvaa	2024-05-20T23:38:41Z	134	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-13T12:56:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> Fine Tuning using Spider (train_spider.join) without 'multiple SELECT' and 'multiple JOIN', so only with 'Easy' and 'Medium' complexity. Max sequence length: 1024 After filtering the dataset, there are 3940 rows remaining: * Train: 3546 rows * Eval: 394 rows (Evaluated every 0.5 epoch => 3546/2 = 1773 steps) Prompt: ``` # <\|system\|> You are a chatbot tasked with responding to questions about an SQL database. Your responses must always consist of valid SQL code, and only that. If you are unable to generate SQL for a question, respond with 'I do not know'. # <\|database schema\|> {schema} # <\|user\|> {question} # <\|assistant\|> [SQL] ``` ## 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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qwp4w3hyb/Yi-1.5-9B-Chat-16K-iMat-GGUF	qwp4w3hyb	2024-05-20T23:37:11Z	49	0	null	[ "gguf", "yi", "01-ai", "instruct", "finetune", "chatml", "imatrix", "importance matrix", "text-generation", "arxiv:2403.04652", "base_model:01-ai/Yi-1.5-9B-Chat-16K", "base_model:quantized:01-ai/Yi-1.5-9B-Chat-16K", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-20T21:02:44Z	--- license: apache-2.0 pipeline_tag: text-generation base_model: 01-ai/Yi-1.5-9B-Chat-16K tags: - yi - 01-ai - instruct - finetune - chatml - gguf - imatrix - importance matrix model-index: - name: 01-ai/Yi-1.5-9B-Chat-16K-iMat-GGUF results: [] --- # Quant Infos - quants done with an importance matrix for improved quantization loss - ggufs & imatrix generated from bf16 for "optimal" accuracy loss - Wide coverage of different gguf quant types from Q\_8\_0 down to IQ1\_S - Quantized with [llama.cpp](https://github.com/ggerganov/llama.cpp) commit [fabf30b4c4fca32e116009527180c252919ca922](https://github.com/ggerganov/llama.cpp/commit/fabf30b4c4fca32e116009527180c252919ca922) (master as of 2024-05-20) - Imatrix generated with [this](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384) multi-purpose dataset. ``` ./imatrix -c 512 -m $model_name-f16.gguf -f $llama_cpp_path/groups_merged.txt -o $out_path/imat-f16-gmerged.dat ``` # Original Model Card: <div align="center"> <picture> <img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px"> </picture> </div> <p align="center"> <a href="https://github.com/01-ai">🐙 GitHub</a> • <a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> • <a href="https://twitter.com/01ai_yi">🐤 Twitter</a> • <a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a> <br/> <a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a> </p> # Intro Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. <div align="center"> Model \| Context Length \| Pre-trained Tokens \| :------------: \| :------------: \| :------------: \| \| Yi-1.5 \| 4K \| 3.6T </div> # Models - Chat models <div align="center"> \| Name \| Download \| \| --------------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-6B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| </div> - Base models <div align="center"> \| Name \| Download \| \| ---------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-6B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| </div> # Benchmarks - Chat models Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/KcsJ9Oc1VnEmfCDEJc5cd.png) Yi-1.5-9B-Chat is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/xf6pLg5jqRCwjlh6m3t6_.png) - Base models Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/BwU7QM-03dZvZzwdIE1xY.png) Yi-1.5-9B is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/y-EYSYPT-3aWLJ0x8R94F.png) # Quick Start For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
DUAL-GPO-2/phi-2-irepo-chatml-v13-i1	DUAL-GPO-2	2024-05-20T23:36:47Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "phi", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "custom_code", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:DUAL-GPO/phi-2-irepo-chatml-merged-i0", "base_model:adapter:DUAL-GPO/phi-2-irepo-chatml-merged-i0", "region:us" ]	null	2024-05-20T21:25:10Z	--- library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer base_model: DUAL-GPO/phi-2-irepo-chatml-merged-i0 datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: phi-2-irepo-chatml-v13-i1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-2-irepo-chatml-v13-i1 This model is a fine-tuned version of [DUAL-GPO/phi-2-irepo-chatml-merged-i0](https://huggingface.co/DUAL-GPO/phi-2-irepo-chatml-merged-i0) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
baek26/all_9929_bart-all_rl	baek26	2024-05-20T23:29:57Z	50	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	reinforcement-learning	2024-05-20T23:29:16Z	--- license: apache-2.0 tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="baek26//tmp/tmpew_kovwt/baek26/all_9929_bart-all_rl") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("baek26//tmp/tmpew_kovwt/baek26/all_9929_bart-all_rl") model = AutoModelForCausalLMWithValueHead.from_pretrained("baek26//tmp/tmpew_kovwt/baek26/all_9929_bart-all_rl") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
kadirnar/lora-trained-xl	kadirnar	2024-05-20T23:27:25Z	30	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:SG161222/Realistic_Vision_V6.0_B1_noVAE", "base_model:finetune:SG161222/Realistic_Vision_V6.0_B1_noVAE", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-20T21:25:51Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers base_model: SG161222/Realistic_Vision_V6.0_B1_noVAE inference: true instance_prompt: a photo of try_on a model wearing --- <!-- 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. --> # DreamBooth - kadirnar/lora-trained-xl This is a dreambooth model derived from SG161222/Realistic_Vision_V6.0_B1_noVAE. The weights were trained on a photo of try_on a model wearing using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## 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]
damgomz/ft_bs16_lr6_mlm	damgomz	2024-05-20T23:24:45Z	106	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-20T20:26:35Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-21T01:24:30' project_name: ft_bs16_lr6_mlm_emissions_tracker run_id: 0ddfd27b-4fbf-416a-aefd-5bc661eddbd4 duration: 13016.97026515007 emissions: 0.0078767721168084 emissions_rate: 6.051156264754384e-07 cpu_power: 42.5 gpu_power: 0.0 ram_power: 3.75 cpu_energy: 0.1536722855205337 gpu_energy: 0 ram_energy: 0.0135592407062649 energy_consumed: 0.1672315262267985 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 2 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 10 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 13016.97026515007 \| \| Emissions (Co2eq in kg) \| 0.0078767721168084 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 3.75 \| \| CPU energy (kWh) \| 0.1536722855205337 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.0135592407062649 \| \| Consumed energy (kWh) \| 0.1672315262267985 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.025057667760413883 \| \| Emissions (Co2eq in kg) \| 0.005098313353850444 \| ## Note 20 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| damgomz/ThunBERT_bs16_lr5_MLM \| \| model_name \| ft_bs16_lr6_mlm \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 5e-06 \| \| batch_size \| 16 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 32580 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| Accuracy \| Recall ---\|---\|---\|---\|--- \| 0 \| 0.441067 \| 0.342969 \| 0.849779 \| 0.877301 \| \| 1 \| 0.324801 \| 0.346633 \| 0.851252 \| 0.918712 \| \| 2 \| 0.278107 \| 0.331949 \| 0.857143 \| 0.871166 \| \| 3 \| 0.216568 \| 0.365292 \| 0.849779 \| 0.878834 \| \| 4 \| 0.141970 \| 0.435924 \| 0.844624 \| 0.915644 \| \| 5 \| 0.062279 \| 0.516091 \| 0.843152 \| 0.889571 \|
beam-searchers/curry-dpo-easy-llama-lora-model	beam-searchers	2024-05-20T23:24:42Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-20T14:29:22Z	--- library_name: transformers tags: - unsloth --- # 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]
Anish13/junk	Anish13	2024-05-20T23:24:20Z	35	0	transformers	[ "transformers", "safetensors", "transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2024-05-20T22:52:33Z	--- tags: - generated_from_trainer model-index: - name: junk 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. --> # junk This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 8.1252 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 30 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 10.42 \| 1.25 \| 5 \| 10.1940 \| \| 10.1087 \| 2.5 \| 10 \| 9.7539 \| \| 9.7572 \| 3.75 \| 15 \| 9.4707 \| \| 9.5321 \| 5.0 \| 20 \| 9.2852 \| \| 9.13 \| 6.25 \| 25 \| 9.1155 \| \| 8.9989 \| 7.5 \| 30 \| 8.9138 \| \| 8.7422 \| 8.75 \| 35 \| 8.7181 \| \| 8.5133 \| 10.0 \| 40 \| 8.5220 \| \| 8.0836 \| 11.25 \| 45 \| 8.3687 \| \| 7.8212 \| 12.5 \| 50 \| 8.2344 \| \| 7.6616 \| 13.75 \| 55 \| 8.1437 \| \| 7.4743 \| 15.0 \| 60 \| 8.0750 \| \| 7.1668 \| 16.25 \| 65 \| 8.0275 \| \| 7.0485 \| 17.5 \| 70 \| 7.9937 \| \| 6.9619 \| 18.75 \| 75 \| 7.9525 \| \| 6.8705 \| 20.0 \| 80 \| 7.9584 \| \| 6.6232 \| 21.25 \| 85 \| 7.9238 \| \| 6.6423 \| 22.5 \| 90 \| 7.9155 \| \| 6.5876 \| 23.75 \| 95 \| 7.9088 \| \| 6.5075 \| 25.0 \| 100 \| 7.9154 \| \| 6.4218 \| 26.25 \| 105 \| 7.8957 \| \| 6.2857 \| 27.5 \| 110 \| 7.9040 \| \| 6.1833 \| 28.75 \| 115 \| 7.9092 \| \| 6.1263 \| 30.0 \| 120 \| 7.9198 \| \| 6.0123 \| 31.25 \| 125 \| 7.9103 \| \| 5.9111 \| 32.5 \| 130 \| 7.9150 \| \| 5.9157 \| 33.75 \| 135 \| 7.9178 \| \| 5.8237 \| 35.0 \| 140 \| 7.9479 \| \| 5.6626 \| 36.25 \| 145 \| 7.9358 \| \| 5.657 \| 37.5 \| 150 \| 7.9548 \| \| 5.5894 \| 38.75 \| 155 \| 7.9572 \| \| 5.5157 \| 40.0 \| 160 \| 7.9800 \| \| 5.4606 \| 41.25 \| 165 \| 7.9481 \| \| 5.2962 \| 42.5 \| 170 \| 7.9568 \| \| 5.2877 \| 43.75 \| 175 \| 7.9720 \| \| 5.2395 \| 45.0 \| 180 \| 7.9709 \| \| 5.1394 \| 46.25 \| 185 \| 7.9900 \| \| 5.0096 \| 47.5 \| 190 \| 8.0010 \| \| 4.9646 \| 48.75 \| 195 \| 8.0105 \| \| 4.973 \| 50.0 \| 200 \| 8.0182 \| \| 4.866 \| 51.25 \| 205 \| 8.0310 \| \| 4.8044 \| 52.5 \| 210 \| 8.0372 \| \| 4.7804 \| 53.75 \| 215 \| 8.0387 \| \| 4.7187 \| 55.0 \| 220 \| 8.0166 \| \| 4.6399 \| 56.25 \| 225 \| 8.0598 \| \| 4.6644 \| 57.5 \| 230 \| 8.0465 \| \| 4.5318 \| 58.75 \| 235 \| 8.0482 \| \| 4.4451 \| 60.0 \| 240 \| 8.0538 \| \| 4.4442 \| 61.25 \| 245 \| 8.0473 \| \| 4.3778 \| 62.5 \| 250 \| 8.0517 \| \| 4.4453 \| 63.75 \| 255 \| 8.0740 \| \| 4.3813 \| 65.0 \| 260 \| 8.0658 \| \| 4.2654 \| 66.25 \| 265 \| 8.0764 \| \| 4.2278 \| 67.5 \| 270 \| 8.0737 \| \| 4.2212 \| 68.75 \| 275 \| 8.0952 \| \| 4.1481 \| 70.0 \| 280 \| 8.0877 \| \| 4.162 \| 71.25 \| 285 \| 8.0882 \| \| 4.077 \| 72.5 \| 290 \| 8.0813 \| \| 4.0134 \| 73.75 \| 295 \| 8.0862 \| \| 3.9975 \| 75.0 \| 300 \| 8.0980 \| \| 3.9174 \| 76.25 \| 305 \| 8.0989 \| \| 3.9748 \| 77.5 \| 310 \| 8.0903 \| \| 3.9362 \| 78.75 \| 315 \| 8.1109 \| \| 3.8585 \| 80.0 \| 320 \| 8.1049 \| \| 3.8832 \| 81.25 \| 325 \| 8.1076 \| \| 3.8799 \| 82.5 \| 330 \| 8.1078 \| \| 3.8354 \| 83.75 \| 335 \| 8.1073 \| \| 3.8073 \| 85.0 \| 340 \| 8.1182 \| \| 3.8701 \| 86.25 \| 345 \| 8.1179 \| \| 3.7696 \| 87.5 \| 350 \| 8.1204 \| \| 3.7907 \| 88.75 \| 355 \| 8.1187 \| \| 3.7428 \| 90.0 \| 360 \| 8.1172 \| \| 3.7048 \| 91.25 \| 365 \| 8.1201 \| \| 3.724 \| 92.5 \| 370 \| 8.1205 \| \| 3.7308 \| 93.75 \| 375 \| 8.1191 \| \| 3.7665 \| 95.0 \| 380 \| 8.1211 \| \| 3.6804 \| 96.25 \| 385 \| 8.1244 \| \| 3.6001 \| 97.5 \| 390 \| 8.1220 \| \| 3.6411 \| 98.75 \| 395 \| 8.1245 \| \| 3.6321 \| 100.0 \| 400 \| 8.1252 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
MaziyarPanahi/Experiment26Mergerix-7B-GGUF	MaziyarPanahi	2024-05-20T23:17:30Z	44	0	transformers	[ "transformers", "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "safetensors", "text-generation", "merge", "mergekit", "lazymergekit", "automerger", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "base_model:automerger/Experiment26Mergerix-7B", "base_model:quantized:automerger/Experiment26Mergerix-7B" ]	text-generation	2024-05-20T22:47:40Z	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - transformers - safetensors - mistral - text-generation - merge - mergekit - lazymergekit - automerger - license:apache-2.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us - text-generation model_name: Experiment26Mergerix-7B-GGUF base_model: automerger/Experiment26Mergerix-7B inference: false model_creator: automerger pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/Experiment26Mergerix-7B-GGUF](https://huggingface.co/MaziyarPanahi/Experiment26Mergerix-7B-GGUF) - Model creator: [automerger](https://huggingface.co/automerger) - Original model: [automerger/Experiment26Mergerix-7B](https://huggingface.co/automerger/Experiment26Mergerix-7B) ## Description [MaziyarPanahi/Experiment26Mergerix-7B-GGUF](https://huggingface.co/MaziyarPanahi/Experiment26Mergerix-7B-GGUF) contains GGUF format model files for [automerger/Experiment26Mergerix-7B](https://huggingface.co/automerger/Experiment26Mergerix-7B). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
ZInnatRia/Model_Deploy	ZInnatRia	2024-05-20T23:14:13Z	182	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-20T23:13:55Z	--- 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. 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]
damgomz/ft_bs16_lr6	damgomz	2024-05-20T23:12:27Z	118	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-20T20:17:33Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-21T01:11:56' project_name: ft_bs16_lr6_emissions_tracker run_id: de899d70-200b-4886-a631-f7b1208dff83 duration: 12794.296389102936 emissions: 0.0077420295479005 emissions_rate: 6.0511569471648e-07 cpu_power: 42.5 gpu_power: 0.0 ram_power: 3.75 cpu_energy: 0.1510435175374149 gpu_energy: 0 ram_energy: 0.0133272930165131 energy_consumed: 0.1643708105539282 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 2 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 10.0 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 12794.296389102936 \| \| Emissions (Co2eq in kg) \| 0.0077420295479005 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 3.75 \| \| CPU energy (kWh) \| 0.1510435175374149 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.0133272930165131 \| \| Consumed energy (kWh) \| 0.1643708105539282 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.024629020549023148 \| \| Emissions (Co2eq in kg) \| 0.005011099419065316 \| ## Note 20 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| damgomz/ThunBERT_bs32_lr5 \| \| model_name \| ft_bs16_lr6 \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 5e-06 \| \| batch_size \| 16 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 32580 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| Accuracy \| Recall ---\|---\|---\|---\|--- \| 0 \| 0.470165 \| 0.347914 \| 0.852725 \| 0.901840 \| \| 1 \| 0.327644 \| 0.400890 \| 0.831370 \| 0.950920 \| \| 2 \| 0.270167 \| 0.349906 \| 0.852725 \| 0.877301 \| \| 3 \| 0.212724 \| 0.386609 \| 0.831370 \| 0.837423 \| \| 4 \| 0.132359 \| 0.448329 \| 0.832106 \| 0.898773 \| \| 5 \| 0.074659 \| 0.475184 \| 0.844624 \| 0.822086 \|
BecarIA/Longformer-SQuAD-becas-2	BecarIA	2024-05-20T23:07:46Z	121	0	transformers	[ "transformers", "safetensors", "roberta", "question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	question-answering	2024-05-20T22:15:53Z	--- 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. 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]
aimeeli/dummy	aimeeli	2024-05-20T23:04:58Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-20T23:04:57Z	--- license: apache-2.0 ---
PHVK1611/swin-tiny-patch4-window7-224-finetuned-eurosat	PHVK1611	2024-05-20T23:03:59Z	219	0	transformers	[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-09T09:59:40Z	--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.959752766997269 --- <!-- 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. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1659 - Accuracy: 0.9598 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.6782 \| 1.0 \| 31306 \| 0.3116 \| 0.9094 \| \| 0.2155 \| 2.0 \| 62612 \| 0.2301 \| 0.9399 \| \| 0.1997 \| 3.0 \| 93918 \| 0.1659 \| 0.9598 \| ### Framework versions - Transformers 4.39.0 - Pytorch 2.2.1+cpu - Datasets 2.18.0 - Tokenizers 0.15.2
bartowski/Yi-1.5-34B-Chat-16K-GGUF	bartowski	2024-05-20T22:57:42Z	247	6	null	[ "gguf", "text-generation", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	text-generation	2024-05-20T21:08:32Z	--- license: apache-2.0 quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp imatrix Quantizations of Yi-1.5-34B-Chat-16K Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2940">b2940</a> for quantization. Original model: https://huggingface.co/01-ai/Yi-1.5-34B-Chat-16K All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/b6ac44691e994344625687afe3263b3a) ## Prompt format ``` {system_prompt}<\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant <\|im_end\|> ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [Yi-1.5-34B-Chat-16K-Q8_0.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-Q8_0.gguf) \| Q8_0 \| 36.54GB \| Extremely high quality, generally unneeded but max available quant. \| \| [Yi-1.5-34B-Chat-16K-Q6_K.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-Q6_K.gguf) \| Q6_K \| 28.21GB \| Very high quality, near perfect, recommended. \| \| [Yi-1.5-34B-Chat-16K-Q5_K_M.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-Q5_K_M.gguf) \| Q5_K_M \| 24.32GB \| High quality, recommended. \| \| [Yi-1.5-34B-Chat-16K-Q5_K_S.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-Q5_K_S.gguf) \| Q5_K_S \| 23.70GB \| High quality, recommended. \| \| [Yi-1.5-34B-Chat-16K-Q4_K_M.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-Q4_K_M.gguf) \| Q4_K_M \| 20.65GB \| Good quality, uses about 4.83 bits per weight, recommended. \| \| [Yi-1.5-34B-Chat-16K-Q4_K_S.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-Q4_K_S.gguf) \| Q4_K_S \| 19.59GB \| Slightly lower quality with more space savings, recommended. \| \| [Yi-1.5-34B-Chat-16K-IQ4_NL.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-IQ4_NL.gguf) \| IQ4_NL \| 19.52GB \| Decent quality, slightly smaller than Q4_K_S with similar performance recommended. \| \| [Yi-1.5-34B-Chat-16K-IQ4_XS.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-IQ4_XS.gguf) \| IQ4_XS \| 18.47GB \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [Yi-1.5-34B-Chat-16K-Q3_K_L.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-Q3_K_L.gguf) \| Q3_K_L \| 18.13GB \| Lower quality but usable, good for low RAM availability. \| \| [Yi-1.5-34B-Chat-16K-Q3_K_M.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-Q3_K_M.gguf) \| Q3_K_M \| 16.65GB \| Even lower quality. \| \| [Yi-1.5-34B-Chat-16K-IQ3_M.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-IQ3_M.gguf) \| IQ3_M \| 15.56GB \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [Yi-1.5-34B-Chat-16K-IQ3_S.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-IQ3_S.gguf) \| IQ3_S \| 15.01GB \| Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. \| \| [Yi-1.5-34B-Chat-16K-Q3_K_S.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-Q3_K_S.gguf) \| Q3_K_S \| 14.96GB \| Low quality, not recommended. \| \| [Yi-1.5-34B-Chat-16K-IQ3_XS.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-IQ3_XS.gguf) \| IQ3_XS \| 14.23GB \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [Yi-1.5-34B-Chat-16K-IQ3_XXS.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-IQ3_XXS.gguf) \| IQ3_XXS \| 13.33GB \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [Yi-1.5-34B-Chat-16K-Q2_K.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-Q2_K.gguf) \| Q2_K \| 12.82GB \| Very low quality but surprisingly usable. \| \| [Yi-1.5-34B-Chat-16K-IQ2_M.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-IQ2_M.gguf) \| IQ2_M \| 11.79GB \| Very low quality, uses SOTA techniques to also be surprisingly usable. \| \| [Yi-1.5-34B-Chat-16K-IQ2_S.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-IQ2_S.gguf) \| IQ2_S \| 10.89GB \| Very low quality, uses SOTA techniques to be usable. \| \| [Yi-1.5-34B-Chat-16K-IQ2_XS.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-IQ2_XS.gguf) \| IQ2_XS \| 10.30GB \| Very low quality, uses SOTA techniques to be usable. \| \| [Yi-1.5-34B-Chat-16K-IQ2_XXS.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-IQ2_XXS.gguf) \| IQ2_XXS \| 9.30GB \| Lower quality, uses SOTA techniques to be usable. \| \| [Yi-1.5-34B-Chat-16K-IQ1_M.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-IQ1_M.gguf) \| IQ1_M \| 8.17GB \| Extremely low quality, not recommended. \| \| [Yi-1.5-34B-Chat-16K-IQ1_S.gguf](https://huggingface.co/bartowski/Yi-1.5-34B-Chat-16K-GGUF/blob/main/Yi-1.5-34B-Chat-16K-IQ1_S.gguf) \| IQ1_S \| 7.49GB \| Extremely low quality, not recommended. \| ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/Yi-1.5-34B-Chat-16K-GGUF --include "Yi-1.5-34B-Chat-16K-Q4_K_M.gguf" --local-dir ./ --local-dir-use-symlinks False ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/Yi-1.5-34B-Chat-16K-GGUF --include "Yi-1.5-34B-Chat-16K-Q8_0.gguf/" --local-dir Yi-1.5-34B-Chat-16K-Q8_0 --local-dir-use-symlinks False ``` You can either specify a new local-dir (Yi-1.5-34B-Chat-16K-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
stanrom/ShareGPT4V-7B	stanrom	2024-05-20T22:57:24Z	4	0	transformers	[ "transformers", "pytorch", "InternLMXComposer", "feature-extraction", "image-text-to-text", "custom_code", "arxiv:2311.12793", "region:us" ]	image-text-to-text	2024-05-20T20:25:52Z	--- inference: false pipeline_tag: image-text-to-text --- <br> <br> # ShareGPT4V-7B Model Card ## Model details Model type: ShareGPT4V-7B is an open-source chatbot trained by fine-tuning CLP vision tower and LLaMA/Vicuna on GPT4-Vision-assisted [ShareGPT4V](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V) data and LLaVA instruction-tuning data. Model date: ShareGPT4V-7B was trained in Nov 2023. Paper or resources for more information: [[Project](https://ShareGPT4V.github.io/)] [[Paper](https://huggingface.co/papers/2311.12793)] [[Code](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V)] ## Usage You can directly utilize this model as we provide in our [[repository](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V)]. Moreover, you can modify the architecture name from "Share4VLlamaForCausalLM" to "LLaVALlamaForCausalLM" and the model_type keyword from "share4v" to "llava" in our config file and seamlessly load our model in the [[LLaVA repository](https://github.com/haotian-liu/LLaVA)]. ## License Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. ## Intended use Primary intended uses: The primary use of ShareGPT4V-7B is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Training dataset - 1.2M high-quality image-text pairs, i.e., ShareGPT4V-PT data - 100K GPT4-Vision-generated image-text pairs - LLaVA instruction-tuning data ## Evaluation dataset A collection of 11 benchmarks
mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF	mradermacher	2024-05-20T22:56:18Z	158	8	transformers	[ "transformers", "gguf", "en", "dataset:aqua_rat", "dataset:microsoft/orca-math-word-problems-200k", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "base_model:abacusai/Smaug-Llama-3-70B-Instruct", "base_model:quantized:abacusai/Smaug-Llama-3-70B-Instruct", "license:llama3", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-05-19T14:29:22Z	--- base_model: abacusai/Smaug-Llama-3-70B-Instruct datasets: - aqua_rat - microsoft/orca-math-word-problems-200k - m-a-p/CodeFeedback-Filtered-Instruction language: - en library_name: transformers license: llama3 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hfhfix --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-IQ1_S.gguf) \| i1-IQ1_S \| 15.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 19.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 21.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-IQ2_S.gguf) \| i1-IQ2_S \| 22.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-IQ2_M.gguf) \| i1-IQ2_M \| 24.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-Q2_K.gguf) \| i1-Q2_K \| 26.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 27.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-IQ3_S.gguf) \| i1-IQ3_S \| 31.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 31.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-IQ3_M.gguf) \| i1-IQ3_M \| 32.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 34.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 37.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 38.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-Q4_0.gguf) \| i1-Q4_0 \| 40.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 40.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 58.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Deep-Miqu-103B-GGUF	mradermacher	2024-05-20T22:55:44Z	21	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "license:other", "endpoints_compatible", "region:us" ]	null	2024-05-20T08:05:00Z	--- base_model: jukofyork/Deep-Miqu-103B language: - en library_name: transformers license: other quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/jukofyork/Deep-Miqu-103B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.Q2_K.gguf) \| Q2_K \| 38.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.IQ3_XS.gguf) \| IQ3_XS \| 42.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.Q3_K_S.gguf) \| Q3_K_S \| 44.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.IQ3_S.gguf) \| IQ3_S \| 44.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.IQ3_M.gguf) \| IQ3_M \| 46.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.Q3_K_M.gguf) \| Q3_K_M \| 49.7 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.Q3_K_L.gguf.part2of2) \| Q3_K_L \| 54.2 \| \| \| [PART 1](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.IQ4_XS.gguf.part2of2) \| IQ4_XS \| 55.7 \| \| \| [PART 1](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.Q4_K_S.gguf.part2of2) \| Q4_K_S \| 58.7 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.Q4_K_M.gguf.part2of2) \| Q4_K_M \| 62.0 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.Q5_K_S.gguf.part2of2) \| Q5_K_S \| 71.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 73.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.Q6_K.gguf.part2of2) \| Q6_K \| 84.8 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Deep-Miqu-103B-GGUF/resolve/main/Deep-Miqu-103B.Q8_0.gguf.part3of3) \| Q8_0 \| 109.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mtarasovic/rel-rent-sk-spacy	mtarasovic	2024-05-20T22:54:01Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-20T22:52:39Z	--- license: apache-2.0 ---
mtarasovic/ner-rel-rent-sk-spacy	mtarasovic	2024-05-20T22:52:18Z	0	0	spacy	[ "spacy", "legal", "sk", "license:apache-2.0", "region:us" ]	null	2024-05-20T22:35:29Z	--- license: apache-2.0 language: - sk library_name: spacy tags: - legal ---
maneln/tinyllama-qa	maneln	2024-05-20T22:51:36Z	135	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T22:43:50Z	--- license: apache-2.0 ---
matthieuzone/VACHERINbis	matthieuzone	2024-05-20T22:49:07Z	1	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-20T22:40:59Z	--- 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 sks cheese 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 - matthieuzone/VACHERINbis <Gallery /> ## Model description These are matthieuzone/VACHERINbis 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 sks cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/VACHERINbis/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]
fzzhang/mistralv1_spectral_r8_4e5_e05_merged	fzzhang	2024-05-20T22:47:19Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T22:44:15Z	--- 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. 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]
fzzhang/mistralv1_spectral_r8_35e5_e05	fzzhang	2024-05-20T22:41:48Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-20T22:41:45Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistralv1_spectral_r8_35e5_e05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistralv1_spectral_r8_35e5_e05 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3.5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
matthieuzone/TETE_DE_MOINESbis	matthieuzone	2024-05-20T22:40:44Z	2	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-20T22:32:34Z	--- 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 sks cheese 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 - matthieuzone/TETE_DE_MOINESbis <Gallery /> ## Model description These are matthieuzone/TETE_DE_MOINESbis 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 sks cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/TETE_DE_MOINESbis/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]
EthanRhys/18-Volt-Current	EthanRhys	2024-05-20T22:36:12Z	0	0	null	[ "license:openrail++", "region:us" ]	null	2024-05-20T22:34:21Z	--- license: openrail++ ---
fzzhang/mistralv1_spectral_r8_3e5_e05_merged	fzzhang	2024-05-20T22:34:23Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T22:31:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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jinyuan22/RFamLlama-large	jinyuan22	2024-05-20T22:34:13Z	166	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "biology", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T15:44:17Z	--- license: cc-by-nc-4.0 library_name: transformers tags: - biology pipeline_tag: text-generation widget: - text: <\|bos\|> <\|tag_start\|> 00050 <\|tag_end\|> <\|5\|> --- # RFamLlama The ability to efficiently generate specific RNA sequences on demand has significant implications for both scientific research and therapeutic applications. In this context, we introduce RFamLlama, a conditional language model that is specifically optimized for generating RNA sequences across diverse families. This model was trained on RNA sequences representing over 4,000 distinct families, each augmented with control tags to denote the specific family. We have shown that the inclusion of family-specific tags substantially enhances the capabilities of our model in zero-shot fitness prediction of RNA molecules. Additionally, this model supports a conditional generation approach, allowing for the generation of RNA sequences by using Rfam IDs as input prompts, thereby eliminating the need for further functional-specific fine-tuning. Consequently, RFamLlama is poised to be an effective and widely applicable tool for the zero-shot fitness prediction and generation of RNA sequences, potentially pushing the boundaries of what can be achieved beyond natural evolutionary processes. ## Use RFamLlama-base ```python # generation from transformers import LlamaForCausalLM, AutoTokenizer, pipeline import torch import sys model_url = "jinyuan22/RFamLlama-large" model = LlamaForCausalLM.from_pretrained(model_url, torch_dtype=torch.float16) tokenizer = AutoTokenizer.from_pretrained(model_url) device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") pipe = pipeline("text-generation", model=model, device=device, tokenizer=tokenizer) tag = "RF00005" txt = f"<\|bos\|> <\|tag_start\|> {tag[2:]} <\|tag_end\|> <\|5\|> " all_outputs = [] outputs = pipe(txt, num_return_sequences=10, max_new_tokens=300, repetition_penalty=1, top_p=1,temperature=1, do_sample=True) for i, output in enumerate(outputs): seq = output["generated_text"] seq = seq.split("<\|5\|>")[1].split("<\|3\|>")[0] print(f">{i}\n{seq}") ```
bgspaditya/malurl-roberta-5000-95	bgspaditya	2024-05-20T22:33:01Z	108	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-20T22:28:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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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damgomz/ft_bs64_lr6_mlm	damgomz	2024-05-20T22:29:55Z	106	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-20T20:07:43Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-21T00:23:19' project_name: ft_bs64_lr6_mlm_emissions_tracker run_id: 744170a9-7abe-403e-97c9-b1d9cb1ff01e duration: 9463.653764724731 emissions: 0.006190923917876 emissions_rate: 6.541790382222564e-07 cpu_power: 42.5 gpu_power: 0.0 ram_power: 7.5 cpu_energy: 0.1117235191464424 gpu_energy: 0 ram_energy: 0.019715811608235 energy_consumed: 0.1314393307546774 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 3 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 20 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 9463.653764724731 \| \| Emissions (Co2eq in kg) \| 0.006190923917876 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 7.5 \| \| CPU energy (kWh) \| 0.1117235191464424 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.019715811608235 \| \| Consumed energy (kWh) \| 0.1314393307546774 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 3 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.01821753349709511 \| \| Emissions (Co2eq in kg) \| 0.003706597724517186 \| ## Note 20 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| damgomz/ThunBERT_bs16_lr5_MLM \| \| model_name \| ft_bs64_lr6_mlm \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 5e-06 \| \| batch_size \| 64 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 32580 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| Accuracy \| Recall ---\|---\|---\|---\|--- \| 0 \| 0.540673 \| 0.431631 \| 0.799705 \| 0.845092 \| \| 1 \| 0.384934 \| 0.352200 \| 0.850515 \| 0.861963 \| \| 2 \| 0.322558 \| 0.338670 \| 0.850515 \| 0.868098 \| \| 3 \| 0.295680 \| 0.333823 \| 0.849779 \| 0.863497 \| \| 4 \| 0.262446 \| 0.344478 \| 0.848306 \| 0.858896 \| \| 5 \| 0.221483 \| 0.376880 \| 0.849779 \| 0.895706 \|
macundin/entrega3	macundin	2024-05-20T22:26:21Z	0	0	fastai	[ "fastai", "region:us" ]	null	2024-05-20T22:26:14Z	--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
ssmits/Falcon2-5.5B-Polish-GGUF	ssmits	2024-05-20T22:26:13Z	7	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "lazymergekit", "tiiuae/falcon-11B", "llama-cpp", "gguf-my-repo", "pl", "base_model:tiiuae/falcon-11B", "base_model:quantized:tiiuae/falcon-11B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-20T22:07:26Z	--- language: - pl license: apache-2.0 library_name: transformers tags: - mergekit - merge - lazymergekit - tiiuae/falcon-11B - llama-cpp - gguf-my-repo base_model: - tiiuae/falcon-11B --- # ssmits/Falcon2-5.5B-Polish-Q5_K_M-GGUF This model was converted to GGUF format from [`ssmits/Falcon2-5.5B-Polish`](https://huggingface.co/ssmits/Falcon2-5.5B-Polish) 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/ssmits/Falcon2-5.5B-Polish) 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 ssmits/Falcon2-5.5B-Polish-Q5_K_M-GGUF --model falcon2-5.5b-polish.Q5_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo ssmits/Falcon2-5.5B-Polish-Q5_K_M-GGUF --model falcon2-5.5b-polish.Q5_K_M.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 falcon2-5.5b-polish.Q5_K_M.gguf -n 128 ```
tali1/LLMNIDS-t5base-1	tali1	2024-05-20T22:26:10Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "autotrain", "dataset:LLMNIDS-t5base-1/autotrain-data", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-20T22:25:18Z	--- tags: - autotrain - text2text-generation widget: - text: "I love AutoTrain" datasets: - LLMNIDS-t5base-1/autotrain-data --- # Model Trained Using AutoTrain - Problem type: Seq2Seq ## Validation Metrics loss: 0.020906077697873116 rouge1: 98.3836 rouge2: 44.4266 rougeL: 98.3836 rougeLsum: 98.3909 gen_len: 4.5541 runtime: 55.669 samples_per_second: 122.797 steps_per_second: 7.688 : 3.0
fzzhang/mistralv1_spectral_r8_3e5_e05	fzzhang	2024-05-20T22:24:20Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-20T22:24:18Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistralv1_spectral_r8_3e5_e05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistralv1_spectral_r8_3e5_e05 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
ClaudioItaly/Evolver-Q5_K_M-GGUF	ClaudioItaly	2024-05-20T22:24:16Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:SanjiWatsuki/Kunoichi-DPO-v2-7B", "base_model:merge:SanjiWatsuki/Kunoichi-DPO-v2-7B", "base_model:mergekit-community/mergekit-slerp-ebgdloh", "base_model:merge:mergekit-community/mergekit-slerp-ebgdloh", "endpoints_compatible", "region:us" ]	null	2024-05-20T22:24:02Z	--- library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo base_model: - SanjiWatsuki/Kunoichi-DPO-v2-7B - mergekit-community/mergekit-slerp-ebgdloh --- # ClaudioItaly/Evolver-Q5_K_M-GGUF This model was converted to GGUF format from [`mergekit-community/Evolver`](https://huggingface.co/mergekit-community/Evolver) 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/mergekit-community/Evolver) 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 ClaudioItaly/Evolver-Q5_K_M-GGUF --model evolver.Q5_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo ClaudioItaly/Evolver-Q5_K_M-GGUF --model evolver.Q5_K_M.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 evolver.Q5_K_M.gguf -n 128 ```
rafaelsandroni/LoRa-RAG-pt-br-gemma-7b	rafaelsandroni	2024-05-20T22:23:12Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-7b-bnb-4bit", "base_model:finetune:unsloth/gemma-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-20T22:22:53Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-7b-bnb-4bit --- # Uploaded model - Developed by: rafaelsandroni - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MaziyarPanahi/Experiment26Percival_01-7B-GGUF	MaziyarPanahi	2024-05-20T22:22:27Z	84	0	transformers	[ "transformers", "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "safetensors", "text-generation", "merge", "mergekit", "lazymergekit", "automerger", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "base_model:automerger/Experiment26Percival_01-7B", "base_model:quantized:automerger/Experiment26Percival_01-7B" ]	text-generation	2024-05-20T21:52:14Z	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - transformers - safetensors - mistral - text-generation - merge - mergekit - lazymergekit - automerger - license:apache-2.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us - text-generation model_name: Experiment26Percival_01-7B-GGUF base_model: automerger/Experiment26Percival_01-7B inference: false model_creator: automerger pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/Experiment26Percival_01-7B-GGUF](https://huggingface.co/MaziyarPanahi/Experiment26Percival_01-7B-GGUF) - Model creator: [automerger](https://huggingface.co/automerger) - Original model: [automerger/Experiment26Percival_01-7B](https://huggingface.co/automerger/Experiment26Percival_01-7B) ## Description [MaziyarPanahi/Experiment26Percival_01-7B-GGUF](https://huggingface.co/MaziyarPanahi/Experiment26Percival_01-7B-GGUF) contains GGUF format model files for [automerger/Experiment26Percival_01-7B](https://huggingface.co/automerger/Experiment26Percival_01-7B). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
diegozambrana/BV_symbols_model	diegozambrana	2024-05-20T22:19:27Z	217	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-26T12:44:40Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: BV_symbols_model results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9423191870890616 --- <!-- 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. --> # BV_symbols_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4636 - Accuracy: 0.9423 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.9683 \| 0.9988 \| 209 \| 0.9087 \| 0.9259 \| \| 0.5438 \| 1.9976 \| 418 \| 0.5415 \| 0.9381 \| \| 0.4768 \| 2.9964 \| 627 \| 0.4636 \| 0.9423 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
GenTrendGPT/TypeGEN	GenTrendGPT	2024-05-20T22:13:51Z	132	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T22:13:00Z	--- base_model: - TinyLlama/TinyLlama-1.1B-Chat-v1.0 library_name: transformers tags: - mergekit - merge --- # TypeGEN 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 passthrough merge method. ### Models Merged The following models were included in the merge: * [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) ### Configuration The following YAML configuration was used to produce this model: ```yaml dtype: float16 merge_method: passthrough slices: - sources: - layer_range: [0, 20] model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 - sources: - layer_range: [0, 20] model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 ```
yifanxie/jasper-cicada-1-1	yifanxie	2024-05-20T22:02:57Z	134	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "gpt", "llm", "large language model", "h2o-llmstudio", "conversational", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-05-20T22:00:30Z	--- language: - en library_name: transformers tags: - gpt - llm - large language model - h2o-llmstudio inference: false thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico --- # Model Card ## Summary This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). - Base model: [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed. ```bash pip install transformers==4.40.1 ``` Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo. - Either leave `token=True` in the `pipeline` and login to hugginface_hub by running ```python import huggingface_hub huggingface_hub.login(<ACCESS_TOKEN>) ``` - Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline` ```python from transformers import pipeline generate_text = pipeline( model="yifanxie/jasper-cicada-1-1", torch_dtype="auto", trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, token=True, ) # generate configuration can be modified to your needs # generate_text.model.generation_config.min_new_tokens = 2 # generate_text.model.generation_config.max_new_tokens = 256 # generate_text.model.generation_config.do_sample = False # generate_text.model.generation_config.num_beams = 1 # generate_text.model.generation_config.temperature = float(0.0) # generate_text.model.generation_config.repetition_penalty = float(1.0) res = generate_text( "Why is drinking water so healthy?", renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <\|prompt\|>Why is drinking water so healthy?<eos><\|answer\|> ``` Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`. ```python from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "yifanxie/jasper-cicada-1-1", use_fast=True, padding_side="left", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "yifanxie/jasper-cicada-1-1", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) # generate configuration can be modified to your needs # generate_text.model.generation_config.min_new_tokens = 2 # generate_text.model.generation_config.max_new_tokens = 256 # generate_text.model.generation_config.do_sample = False # generate_text.model.generation_config.num_beams = 1 # generate_text.model.generation_config.temperature = float(0.0) # generate_text.model.generation_config.repetition_penalty = float(1.0) res = generate_text( "Why is drinking water so healthy?", renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "yifanxie/jasper-cicada-1-1" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<\|prompt\|>How are you?<eos><\|answer\|>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=True, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs # model.generation_config.min_new_tokens = 2 # model.generation_config.max_new_tokens = 256 # model.generation_config.do_sample = False # model.generation_config.num_beams = 1 # model.generation_config.temperature = float(0.0) # model.generation_config.repetition_penalty = float(1.0) tokens = model.generate( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Quantization and sharding You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```. ## Model Architecture ``` GemmaForCausalLM( (model): GemmaModel( (embed_tokens): Embedding(256000, 2048, padding_idx=0) (layers): ModuleList( (0-17): 18 x GemmaDecoderLayer( (self_attn): GemmaSdpaAttention( (q_proj): Linear(in_features=2048, out_features=2048, bias=False) (k_proj): Linear(in_features=2048, out_features=256, bias=False) (v_proj): Linear(in_features=2048, out_features=256, bias=False) (o_proj): Linear(in_features=2048, out_features=2048, bias=False) (rotary_emb): GemmaRotaryEmbedding() ) (mlp): GemmaMLP( (gate_proj): Linear(in_features=2048, out_features=16384, bias=False) (up_proj): Linear(in_features=2048, out_features=16384, bias=False) (down_proj): Linear(in_features=16384, out_features=2048, bias=False) (act_fn): PytorchGELUTanh() ) (input_layernorm): GemmaRMSNorm() (post_attention_layernorm): GemmaRMSNorm() ) ) (norm): GemmaRMSNorm() ) (lm_head): Linear(in_features=2048, out_features=256000, bias=False) ) ``` ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
matthieuzone/SAINT-_FELICIENbis	matthieuzone	2024-05-20T21:58:30Z	1	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-20T21:50:22Z	--- 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 sks cheese 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 - matthieuzone/SAINT-_FELICIENbis <Gallery /> ## Model description These are matthieuzone/SAINT-_FELICIENbis 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 sks cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/SAINT-_FELICIENbis/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]
ghost613/phi3_on_korean_events	ghost613	2024-05-20T21:57:55Z	1	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:adapter:microsoft/Phi-3-mini-4k-instruct", "license:mit", "region:us" ]	null	2024-05-20T21:57:39Z	--- license: mit library_name: peft tags: - generated_from_trainer base_model: microsoft/Phi-3-mini-4k-instruct model-index: - name: phi3_on_korean_events 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. --> # phi3_on_korean_events This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5047 ## 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: 5 - total_train_batch_size: 10 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 760 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.2308 \| 0.26 \| 20 \| 1.1259 \| \| 1.0422 \| 0.53 \| 40 \| 0.9463 \| \| 0.8644 \| 0.79 \| 60 \| 0.7820 \| \| 0.7426 \| 1.05 \| 80 \| 0.7199 \| \| 0.6894 \| 1.32 \| 100 \| 0.6800 \| \| 0.6671 \| 1.58 \| 120 \| 0.6521 \| \| 0.6397 \| 1.84 \| 140 \| 0.6290 \| \| 0.6018 \| 2.11 \| 160 \| 0.6115 \| \| 0.5723 \| 2.37 \| 180 \| 0.5936 \| \| 0.5555 \| 2.63 \| 200 \| 0.5810 \| \| 0.5455 \| 2.89 \| 220 \| 0.5682 \| \| 0.5289 \| 3.16 \| 240 \| 0.5612 \| \| 0.4929 \| 3.42 \| 260 \| 0.5518 \| \| 0.4966 \| 3.68 \| 280 \| 0.5404 \| \| 0.4831 \| 3.95 \| 300 \| 0.5346 \| \| 0.4598 \| 4.21 \| 320 \| 0.5293 \| \| 0.4462 \| 4.47 \| 340 \| 0.5227 \| \| 0.4404 \| 4.74 \| 360 \| 0.5176 \| \| 0.433 \| 5.0 \| 380 \| 0.5079 \| \| 0.4036 \| 5.26 \| 400 \| 0.5146 \| \| 0.3933 \| 5.53 \| 420 \| 0.5070 \| \| 0.393 \| 5.79 \| 440 \| 0.5027 \| \| 0.3912 \| 6.05 \| 460 \| 0.5023 \| \| 0.3589 \| 6.32 \| 480 \| 0.5031 \| \| 0.3714 \| 6.58 \| 500 \| 0.5018 \| \| 0.362 \| 6.84 \| 520 \| 0.4935 \| \| 0.3474 \| 7.11 \| 540 \| 0.5052 \| \| 0.3355 \| 7.37 \| 560 \| 0.4990 \| \| 0.3333 \| 7.63 \| 580 \| 0.4992 \| \| 0.3436 \| 7.89 \| 600 \| 0.4974 \| \| 0.3175 \| 8.16 \| 620 \| 0.5024 \| \| 0.321 \| 8.42 \| 640 \| 0.5020 \| \| 0.3065 \| 8.68 \| 660 \| 0.5023 \| \| 0.3139 \| 8.95 \| 680 \| 0.5019 \| \| 0.313 \| 9.21 \| 700 \| 0.5071 \| \| 0.3011 \| 9.47 \| 720 \| 0.5045 \| \| 0.2905 \| 9.74 \| 740 \| 0.5061 \| \| 0.3035 \| 10.0 \| 760 \| 0.5047 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.2 - Pytorch 2.1.2+cu121 - Datasets 2.17.0 - Tokenizers 0.15.0
quirky-lats-at-mats/base_rmu_2	quirky-lats-at-mats	2024-05-20T21:46:16Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-13T22:06:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> On our tasks WMDP evals: wmdp-bio: 0.35 wmdp-cyber: 0.33 wmdp-chem: 0.44 mmlu: 0.61 steps to retraining 50% bio accuracy on 2 samples, batch size 2 (4 samples total): 8 (interpolated) ## Model Details Retraining on bio unlearn run path: quirky_lats_at_mats/pgd_rmu_evals/pdr9xikq ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
kalexa2/marian-finetuned-kde4-en-to-fr	kalexa2	2024-05-20T21:42:28Z	115	0	transformers	[ "transformers", "tensorboard", "safetensors", "marian", "text2text-generation", "translation", "generated_from_trainer", "dataset:kde4", "base_model:Helsinki-NLP/opus-mt-en-fr", "base_model:finetune:Helsinki-NLP/opus-mt-en-fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2024-05-20T15:42:32Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-fr tags: - translation - generated_from_trainer datasets: - kde4 model-index: - name: marian-finetuned-kde4-en-to-fr 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. --> # marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 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: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Mahanthesh0r/BipedalWalker-RL	Mahanthesh0r	2024-05-20T21:32:26Z	1	0	stable-baselines3	[ "stable-baselines3", "BipedalWalker-v3", "deep-reinforcement-learning", "reinforcement-learning", "Bipedal", "OpenAI", "model-index", "region:us" ]	reinforcement-learning	2024-05-20T20:19:51Z	--- library_name: stable-baselines3 tags: - BipedalWalker-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 - Bipedal - OpenAI model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: BipedalWalker-v3 type: BipedalWalker-v3 metrics: - type: mean_reward value: '-58.54 +/- 39.24' name: mean_reward verified: false --- # PPO Agent playing BipedalWalker-v3 This is a trained model of a PPO agent playing BipedalWalker-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub # 1. Setup ### Install Packages """ # Install necessary packages !apt install swig cmake ffmpeg xvfb python3-opengl !pip install stable-baselines3==2.0.0a5 gymnasium[box2d] huggingface_sb3 pyvirtualdisplay imageio[ffmpeg] """The Next Cell will force the notebook runtime to restart. This is to ensure all the new libraries installed will be used.""" import os os.kill(os.getpid(), 9) """### Start Virtual Display""" from pyvirtualdisplay import Display virtual_display = Display(visible=0, size=(1400, 900)) virtual_display.start() """### Setup Environment""" import gymnasium as gym env = gym.make("BipedalWalker-v3", hardcore=True) env.reset() """### Observation Space Observation Space Shape (24,) vector of size 24, where each value contains different information about the walker: - Hull Angle Speed: The speed at which the main body of the walker is rotating. - Angular Velocity: The rate of change of the angular position of the walker. - Horizontal Speed: The speed at which the walker is moving horizontally. - Vertical Speed: The speed at which the walker is moving vertically. - Position of Joints: The positions (angles) of the walker's joints. Given that the walker has 4 joints, this take up 4 values. - Joints Angular Speed: The rate of change of the angular position for each joint. Again, this would be 4 values for the 4 joints. - Legs Contact with Ground: Indicating whether each leg is in contact with the ground. Given two legs, this contains 2 values. - 10 Lidar Rangefinder Measurements: These are distance measurements to detect obstacles or terrain features around the walker. There are 10 of these values. """ print("_____OBSERVATION SPACE_____ \n") print("Observation Space Shape", env.observation_space.shape) print("Sample observation", env.observation_space.sample()) # Get a random observation """### Action Space Actions are motor speed values in the [-1, 1] range for each of the 4 joints at both hips and knees. """ print("\n _____ACTION SPACE_____ \n") print("Action Space Shape", env.action_space.shape) print("Action Space Sample", env.action_space.sample()) # Take a random action """### Vectorized Environment Create a vectorized environment (a method for stacking multiple independent environments into a single environment) of 16 environments to have more diverse experiences. """ from stable_baselines3.common.env_util import make_vec_env env = make_vec_env('BipedalWalker-v3', n_envs=16) """# 2. Building the Model""" from stable_baselines3 import PPO model = PPO( policy = 'MlpPolicy', env = env, n_steps = 2048, batch_size = 128, n_epochs = 6, gamma = 0.999, gae_lambda = 0.98, ent_coef = 0.01, verbose=1) """# 3.Video Generation""" from wasabi import Printer import numpy as np from stable_baselines3.common.base_class import BaseAlgorithm from pathlib import Path import tempfile from stable_baselines3.common.monitor import Monitor from stable_baselines3.common.vec_env import ( DummyVecEnv, VecEnv, VecVideoRecorder, ) msg = Printer() def generate_replay( model: BaseAlgorithm, eval_env: VecEnv, video_length: int, is_deterministic: bool, local_path: Path, ): """ Generate a replay video of the agent :param model: trained model :param eval_env: environment used to evaluate the agent :param video_length: length of the video (in timesteps) :param is_deterministic: use deterministic or stochastic actions :param local_path: path of the local repository """ # This is another temporary directory for video outputs # SB3 created a -step-0-to-... meta files as well as other # artifacts which we don't want in the repo. with tempfile.TemporaryDirectory() as tmpdirname: # Step 1: Create the VecVideoRecorder env = VecVideoRecorder( eval_env, tmpdirname, record_video_trigger=lambda x: x == 0, video_length=video_length, name_prefix="", ) obs = env.reset() lstm_states = None episode_starts = np.ones((env.num_envs,), dtype=bool) try: for _ in range(video_length): action, lstm_states = model.predict( obs, state=lstm_states, episode_start=episode_starts, deterministic=is_deterministic, ) obs, _, episode_starts, _ = env.step(action) # Save the video env.close() # Convert the video with x264 codec inp = env.video_recorder.path out = local_path os.system(f"ffmpeg -y -i {inp} -vcodec h264 {out}".format(inp, out)) print(f"Video saved to: {out}") except KeyboardInterrupt: pass except Exception as e: msg.fail(str(e)) # Add a message for video msg.fail( "We are unable to generate a replay of your agent" ) """# 4. Training, Saving and Record the Videos""" import os #create a directory to save the videos video_dir = "/content/videos" if not os.path.exists(video_dir): os.makedirs(video_dir) env_id = "BipedalWalker-v3" # Train and generate video at every 100000 steps, adjust the timesteps to your liking for i in range(0, 2000000, 100000): model.learn(total_timesteps=100000) # Save the model model_name = "ppo-BipedalWalker-v3" model.save(model_name) video_name = f"replay_{i + 100000}.mp4" generate_replay( model=model, eval_env=DummyVecEnv([lambda: Monitor(gym.make(env_id, hardcore=True, render_mode="rgb_array"))]), video_length=1000, is_deterministic=True, local_path=os.path.join(video_dir, video_name) ) model_name = "ppo-BipedalWalker-v3" model.save(model_name) with open(os.path.join(video_dir, "filelist.txt"), "w") as f: for i in range(0, 2000000, 100000): video_name = f"replay_{i + 100000}.mp4" f.write(f"file '{os.path.join(video_dir, video_name)}'\n") # Concatenate all the videos into one os.system(f"ffmpeg -f concat -safe 0 -i {os.path.join(video_dir, 'filelist.txt')} -c copy {os.path.join(video_dir, 'replay_all.mp4')}") """# 5. Visualize Final Video""" from IPython.display import HTML from base64 import b64encode mp4 = open('videos/replay_all.mp4','rb').read() data_url = "data:video/mp4;base64," + b64encode(mp4).decode() HTML(""" <video width=600 controls> <source src="%s" type="video/mp4"> </video> """ % data_url) """# 6. Evaluate the Model""" from stable_baselines3.common.evaluation import evaluate_policy eval_env = Monitor(gym.make("BipedalWalker-v3")) mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True) print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") """# 7. Upload to HuggingFace""" from huggingface_sb3 import load_from_hub, package_to_hub from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub. notebook_login() !git config --global credential.helper store env_id = "BipedalWalker-v3" model_name = "ppo-BipedalWalker-v3" model_architecture = "PPO" repo_id = "Mahanthesh0r/BipedalWalker-RL" # Change with your repo id ## Define the commit message commit_message = "Upload PPO BipedalWalker-v3 trained agent" # Create the evaluation env and set the render_mode="rgb_array" eval_env = DummyVecEnv([lambda: gym.make(env_id, hardcore=True, render_mode="rgb_array")]) package_to_hub(model=model, # trained model model_name=model_name, # The name of our trained model model_architecture=model_architecture, # The model architecture we used: in our case PPO env_id=env_id, # Name of the environment eval_env=eval_env, repo_id=repo_id, commit_message=commit_message) """# 8. Load Models from HuggingFace (Optional)""" from huggingface_sb3 import load_from_hub repo_id = "Mahanthesh0r/BipedalWalker-RL" # The repo_id filename = "ppo-BipedalWalker-v3.zip" # The model filename.zip checkpoint = load_from_hub(repo_id, filename) model = PPO.load(checkpoint, print_system_info=True) eval_env = Monitor(gym.make("BipedalWalker-v3", hardcore=True)) mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True) print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") ... ```
BilalMuftuoglu/beit-base-patch16-224-85-fold5	BilalMuftuoglu	2024-05-20T21:30:20Z	27	0	transformers	[ "transformers", "tensorboard", "safetensors", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-base-patch16-224", "base_model:finetune:microsoft/beit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-20T21:09:50Z	--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: beit-base-patch16-224-85-fold5 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9318181818181818 --- <!-- 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. --> # beit-base-patch16-224-85-fold5 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2499 - Accuracy: 0.9318 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 2 \| 1.0896 \| 0.2955 \| \| No log \| 2.0 \| 4 \| 0.6456 \| 0.7273 \| \| No log \| 3.0 \| 6 \| 1.0355 \| 0.7045 \| \| No log \| 4.0 \| 8 \| 0.9124 \| 0.7045 \| \| 0.7607 \| 5.0 \| 10 \| 0.5809 \| 0.7955 \| \| 0.7607 \| 6.0 \| 12 \| 0.6812 \| 0.75 \| \| 0.7607 \| 7.0 \| 14 \| 0.6529 \| 0.75 \| \| 0.7607 \| 8.0 \| 16 \| 0.7174 \| 0.7273 \| \| 0.7607 \| 9.0 \| 18 \| 0.6619 \| 0.6136 \| \| 0.4221 \| 10.0 \| 20 \| 0.8063 \| 0.75 \| \| 0.4221 \| 11.0 \| 22 \| 0.6372 \| 0.75 \| \| 0.4221 \| 12.0 \| 24 \| 0.5886 \| 0.75 \| \| 0.4221 \| 13.0 \| 26 \| 0.6359 \| 0.6364 \| \| 0.4221 \| 14.0 \| 28 \| 0.5585 \| 0.75 \| \| 0.3287 \| 15.0 \| 30 \| 0.4541 \| 0.7273 \| \| 0.3287 \| 16.0 \| 32 \| 0.7624 \| 0.5682 \| \| 0.3287 \| 17.0 \| 34 \| 0.6806 \| 0.75 \| \| 0.3287 \| 18.0 \| 36 \| 0.7708 \| 0.7273 \| \| 0.3287 \| 19.0 \| 38 \| 0.4170 \| 0.7273 \| \| 0.3663 \| 20.0 \| 40 \| 0.4282 \| 0.7727 \| \| 0.3663 \| 21.0 \| 42 \| 0.5613 \| 0.75 \| \| 0.3663 \| 22.0 \| 44 \| 0.4025 \| 0.8409 \| \| 0.3663 \| 23.0 \| 46 \| 0.4109 \| 0.7955 \| \| 0.3663 \| 24.0 \| 48 \| 0.4373 \| 0.8409 \| \| 0.2344 \| 25.0 \| 50 \| 0.3211 \| 0.8864 \| \| 0.2344 \| 26.0 \| 52 \| 0.5561 \| 0.75 \| \| 0.2344 \| 27.0 \| 54 \| 0.3149 \| 0.8636 \| \| 0.2344 \| 28.0 \| 56 \| 0.3166 \| 0.7955 \| \| 0.2344 \| 29.0 \| 58 \| 0.4164 \| 0.8636 \| \| 0.2051 \| 30.0 \| 60 \| 0.4345 \| 0.8636 \| \| 0.2051 \| 31.0 \| 62 \| 0.3180 \| 0.8636 \| \| 0.2051 \| 32.0 \| 64 \| 0.3673 \| 0.8409 \| \| 0.2051 \| 33.0 \| 66 \| 0.4313 \| 0.8409 \| \| 0.2051 \| 34.0 \| 68 \| 0.4359 \| 0.8409 \| \| 0.1694 \| 35.0 \| 70 \| 0.3700 \| 0.8182 \| \| 0.1694 \| 36.0 \| 72 \| 0.5843 \| 0.7955 \| \| 0.1694 \| 37.0 \| 74 \| 0.4064 \| 0.8636 \| \| 0.1694 \| 38.0 \| 76 \| 0.3992 \| 0.8182 \| \| 0.1694 \| 39.0 \| 78 \| 0.3153 \| 0.8636 \| \| 0.1566 \| 40.0 \| 80 \| 0.5581 \| 0.8182 \| \| 0.1566 \| 41.0 \| 82 \| 0.2921 \| 0.8636 \| \| 0.1566 \| 42.0 \| 84 \| 0.3217 \| 0.8864 \| \| 0.1566 \| 43.0 \| 86 \| 0.3255 \| 0.8864 \| \| 0.1566 \| 44.0 \| 88 \| 0.7238 \| 0.75 \| \| 0.1389 \| 45.0 \| 90 \| 0.4053 \| 0.8864 \| \| 0.1389 \| 46.0 \| 92 \| 0.2499 \| 0.9318 \| \| 0.1389 \| 47.0 \| 94 \| 0.2584 \| 0.8864 \| \| 0.1389 \| 48.0 \| 96 \| 0.4432 \| 0.8409 \| \| 0.1389 \| 49.0 \| 98 \| 0.6965 \| 0.7955 \| \| 0.1311 \| 50.0 \| 100 \| 0.3910 \| 0.8409 \| \| 0.1311 \| 51.0 \| 102 \| 0.3017 \| 0.8636 \| \| 0.1311 \| 52.0 \| 104 \| 0.3050 \| 0.8636 \| \| 0.1311 \| 53.0 \| 106 \| 0.2193 \| 0.8636 \| \| 0.1311 \| 54.0 \| 108 \| 0.2369 \| 0.8409 \| \| 0.1386 \| 55.0 \| 110 \| 0.3143 \| 0.8864 \| \| 0.1386 \| 56.0 \| 112 \| 0.2932 \| 0.8864 \| \| 0.1386 \| 57.0 \| 114 \| 0.2725 \| 0.9091 \| \| 0.1386 \| 58.0 \| 116 \| 0.5664 \| 0.8409 \| \| 0.1386 \| 59.0 \| 118 \| 0.5875 \| 0.8182 \| \| 0.1194 \| 60.0 \| 120 \| 0.4623 \| 0.8636 \| \| 0.1194 \| 61.0 \| 122 \| 0.4716 \| 0.8182 \| \| 0.1194 \| 62.0 \| 124 \| 0.5028 \| 0.8182 \| \| 0.1194 \| 63.0 \| 126 \| 0.4558 \| 0.8182 \| \| 0.1194 \| 64.0 \| 128 \| 0.4798 \| 0.8182 \| \| 0.1122 \| 65.0 \| 130 \| 0.3827 \| 0.8409 \| \| 0.1122 \| 66.0 \| 132 \| 0.3653 \| 0.8409 \| \| 0.1122 \| 67.0 \| 134 \| 0.3972 \| 0.8409 \| \| 0.1122 \| 68.0 \| 136 \| 0.5705 \| 0.7727 \| \| 0.1122 \| 69.0 \| 138 \| 0.5935 \| 0.7727 \| \| 0.1041 \| 70.0 \| 140 \| 0.3905 \| 0.8636 \| \| 0.1041 \| 71.0 \| 142 \| 0.2791 \| 0.8409 \| \| 0.1041 \| 72.0 \| 144 \| 0.2845 \| 0.9091 \| \| 0.1041 \| 73.0 \| 146 \| 0.2401 \| 0.8636 \| \| 0.1041 \| 74.0 \| 148 \| 0.2260 \| 0.8864 \| \| 0.0982 \| 75.0 \| 150 \| 0.2454 \| 0.8864 \| \| 0.0982 \| 76.0 \| 152 \| 0.3773 \| 0.8864 \| \| 0.0982 \| 77.0 \| 154 \| 0.6185 \| 0.8182 \| \| 0.0982 \| 78.0 \| 156 \| 0.7238 \| 0.7727 \| \| 0.0982 \| 79.0 \| 158 \| 0.5469 \| 0.8409 \| \| 0.1065 \| 80.0 \| 160 \| 0.4318 \| 0.8636 \| \| 0.1065 \| 81.0 \| 162 \| 0.3348 \| 0.8864 \| \| 0.1065 \| 82.0 \| 164 \| 0.3041 \| 0.8636 \| \| 0.1065 \| 83.0 \| 166 \| 0.3350 \| 0.8864 \| \| 0.1065 \| 84.0 \| 168 \| 0.3464 \| 0.8864 \| \| 0.0829 \| 85.0 \| 170 \| 0.3375 \| 0.8864 \| \| 0.0829 \| 86.0 \| 172 \| 0.3309 \| 0.8864 \| \| 0.0829 \| 87.0 \| 174 \| 0.3325 \| 0.8864 \| \| 0.0829 \| 88.0 \| 176 \| 0.3441 \| 0.8864 \| \| 0.0829 \| 89.0 \| 178 \| 0.3456 \| 0.8636 \| \| 0.0902 \| 90.0 \| 180 \| 0.3244 \| 0.8636 \| \| 0.0902 \| 91.0 \| 182 \| 0.3126 \| 0.8636 \| \| 0.0902 \| 92.0 \| 184 \| 0.3117 \| 0.8636 \| \| 0.0902 \| 93.0 \| 186 \| 0.2877 \| 0.8636 \| \| 0.0902 \| 94.0 \| 188 \| 0.2643 \| 0.8636 \| \| 0.0838 \| 95.0 \| 190 \| 0.2525 \| 0.8864 \| \| 0.0838 \| 96.0 \| 192 \| 0.2462 \| 0.9091 \| \| 0.0838 \| 97.0 \| 194 \| 0.2417 \| 0.9091 \| \| 0.0838 \| 98.0 \| 196 \| 0.2402 \| 0.9091 \| \| 0.0838 \| 99.0 \| 198 \| 0.2409 \| 0.9091 \| \| 0.0747 \| 100.0 \| 200 \| 0.2426 \| 0.9091 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
eventdata-utd/ConfliBERT-cont-uncased-BBC_News	eventdata-utd	2024-05-20T21:27:27Z	0	0	null	[ "en", "region:us" ]	null	2024-04-04T20:05:30Z	--- language: - en --- Political news dataset from BBC for relevance classification.
eventdata-utd/ConfliBERT-cont-cased-BBC_News	eventdata-utd	2024-05-20T21:26:26Z	0	0	null	[ "en", "region:us" ]	null	2024-04-04T20:05:47Z	--- language: - en --- Political news dataset from BBC for relevance classification.
sxg2520/tiny-chatbot-dpo	sxg2520	2024-05-20T21:25:59Z	2	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-20T21:23:56Z	--- license: apache-2.0 library_name: peft tags: - trl - dpo - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 model-index: - name: tiny-chatbot-dpo 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. --> # tiny-chatbot-dpo This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
beetlespinner/bloomer	beetlespinner	2024-05-20T21:22:05Z	0	0	null	[ "license:other", "region:us" ]	null	2024-05-20T21:22:05Z	--- license: other license_name: beetlespinner license_link: LICENSE ---
0xBeaverT/organize-breakfast-2	0xBeaverT	2024-05-20T21:15:18Z	134	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T21:11:46Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
sxg2520/sft-tiny-chatbot	sxg2520	2024-05-20T21:15:17Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-20T21:13:57Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 model-index: - name: sft-tiny-chatbot 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. --> # sft-tiny-chatbot This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-994439	fine-tuned	2024-05-20T21:11:20Z	7	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "Argumentation", "Corpus", "Research", "Dataset", "Quality", "custom_code", "en", "dataset:fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-994439", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-20T21:11:05Z	--- license: apache-2.0 datasets: - fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-994439 - allenai/c4 language: - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - Argumentation - Corpus - Research - Dataset - Quality --- This model is a fine-tuned version of [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case: academic research data retrieval ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-994439', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
BilalMuftuoglu/beit-base-patch16-224-85-fold4	BilalMuftuoglu	2024-05-20T21:09:42Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-base-patch16-224", "base_model:finetune:microsoft/beit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-20T20:49:03Z	--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: beit-base-patch16-224-85-fold4 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9545454545454546 --- <!-- 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. --> # beit-base-patch16-224-85-fold4 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1725 - Accuracy: 0.9545 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 2 \| 0.6874 \| 0.6818 \| \| No log \| 2.0 \| 4 \| 0.7083 \| 0.7045 \| \| No log \| 3.0 \| 6 \| 0.8871 \| 0.7045 \| \| No log \| 4.0 \| 8 \| 0.7305 \| 0.7045 \| \| 0.6246 \| 5.0 \| 10 \| 0.5791 \| 0.7045 \| \| 0.6246 \| 6.0 \| 12 \| 0.5888 \| 0.7045 \| \| 0.6246 \| 7.0 \| 14 \| 0.6050 \| 0.7045 \| \| 0.6246 \| 8.0 \| 16 \| 0.5468 \| 0.7045 \| \| 0.6246 \| 9.0 \| 18 \| 0.5351 \| 0.7045 \| \| 0.4453 \| 10.0 \| 20 \| 0.4155 \| 0.8409 \| \| 0.4453 \| 11.0 \| 22 \| 0.8266 \| 0.7045 \| \| 0.4453 \| 12.0 \| 24 \| 0.3905 \| 0.8409 \| \| 0.4453 \| 13.0 \| 26 \| 0.3942 \| 0.8409 \| \| 0.4453 \| 14.0 \| 28 \| 0.4015 \| 0.8409 \| \| 0.3613 \| 15.0 \| 30 \| 0.3474 \| 0.8182 \| \| 0.3613 \| 16.0 \| 32 \| 0.4763 \| 0.8182 \| \| 0.3613 \| 17.0 \| 34 \| 0.3894 \| 0.7955 \| \| 0.3613 \| 18.0 \| 36 \| 0.4290 \| 0.7955 \| \| 0.3613 \| 19.0 \| 38 \| 0.3525 \| 0.8636 \| \| 0.2928 \| 20.0 \| 40 \| 0.3426 \| 0.8864 \| \| 0.2928 \| 21.0 \| 42 \| 0.4060 \| 0.8182 \| \| 0.2928 \| 22.0 \| 44 \| 0.6962 \| 0.75 \| \| 0.2928 \| 23.0 \| 46 \| 0.3514 \| 0.8636 \| \| 0.2928 \| 24.0 \| 48 \| 0.5302 \| 0.8409 \| \| 0.2256 \| 25.0 \| 50 \| 0.3094 \| 0.8636 \| \| 0.2256 \| 26.0 \| 52 \| 0.2977 \| 0.8636 \| \| 0.2256 \| 27.0 \| 54 \| 0.4883 \| 0.8182 \| \| 0.2256 \| 28.0 \| 56 \| 0.3008 \| 0.8409 \| \| 0.2256 \| 29.0 \| 58 \| 0.3226 \| 0.8409 \| \| 0.2231 \| 30.0 \| 60 \| 0.4101 \| 0.8409 \| \| 0.2231 \| 31.0 \| 62 \| 0.3197 \| 0.8409 \| \| 0.2231 \| 32.0 \| 64 \| 0.4133 \| 0.7727 \| \| 0.2231 \| 33.0 \| 66 \| 0.2923 \| 0.8636 \| \| 0.2231 \| 34.0 \| 68 \| 0.4391 \| 0.8636 \| \| 0.1756 \| 35.0 \| 70 \| 0.3016 \| 0.8636 \| \| 0.1756 \| 36.0 \| 72 \| 0.2749 \| 0.9091 \| \| 0.1756 \| 37.0 \| 74 \| 0.3146 \| 0.8864 \| \| 0.1756 \| 38.0 \| 76 \| 0.3095 \| 0.8409 \| \| 0.1756 \| 39.0 \| 78 \| 0.3017 \| 0.8864 \| \| 0.1592 \| 40.0 \| 80 \| 0.2762 \| 0.8864 \| \| 0.1592 \| 41.0 \| 82 \| 0.4054 \| 0.8409 \| \| 0.1592 \| 42.0 \| 84 \| 0.2787 \| 0.8864 \| \| 0.1592 \| 43.0 \| 86 \| 0.3193 \| 0.8636 \| \| 0.1592 \| 44.0 \| 88 \| 0.2783 \| 0.9091 \| \| 0.1857 \| 45.0 \| 90 \| 0.2934 \| 0.9091 \| \| 0.1857 \| 46.0 \| 92 \| 0.3579 \| 0.8636 \| \| 0.1857 \| 47.0 \| 94 \| 0.3501 \| 0.8864 \| \| 0.1857 \| 48.0 \| 96 \| 0.3358 \| 0.8864 \| \| 0.1857 \| 49.0 \| 98 \| 0.2981 \| 0.9091 \| \| 0.1179 \| 50.0 \| 100 \| 0.3364 \| 0.8636 \| \| 0.1179 \| 51.0 \| 102 \| 0.3324 \| 0.8636 \| \| 0.1179 \| 52.0 \| 104 \| 0.1725 \| 0.9545 \| \| 0.1179 \| 53.0 \| 106 \| 0.1222 \| 0.9545 \| \| 0.1179 \| 54.0 \| 108 \| 0.1500 \| 0.9091 \| \| 0.1448 \| 55.0 \| 110 \| 0.2358 \| 0.9091 \| \| 0.1448 \| 56.0 \| 112 \| 0.2224 \| 0.9091 \| \| 0.1448 \| 57.0 \| 114 \| 0.1457 \| 0.9318 \| \| 0.1448 \| 58.0 \| 116 \| 0.1745 \| 0.9318 \| \| 0.1448 \| 59.0 \| 118 \| 0.1990 \| 0.9091 \| \| 0.1343 \| 60.0 \| 120 \| 0.2905 \| 0.8864 \| \| 0.1343 \| 61.0 \| 122 \| 0.3842 \| 0.8864 \| \| 0.1343 \| 62.0 \| 124 \| 0.3031 \| 0.8864 \| \| 0.1343 \| 63.0 \| 126 \| 0.2642 \| 0.8864 \| \| 0.1343 \| 64.0 \| 128 \| 0.2412 \| 0.9091 \| \| 0.1109 \| 65.0 \| 130 \| 0.3347 \| 0.8864 \| \| 0.1109 \| 66.0 \| 132 \| 0.4005 \| 0.8864 \| \| 0.1109 \| 67.0 \| 134 \| 0.2905 \| 0.8864 \| \| 0.1109 \| 68.0 \| 136 \| 0.3168 \| 0.9318 \| \| 0.1109 \| 69.0 \| 138 \| 0.3845 \| 0.8864 \| \| 0.1221 \| 70.0 \| 140 \| 0.3178 \| 0.9318 \| \| 0.1221 \| 71.0 \| 142 \| 0.2690 \| 0.9318 \| \| 0.1221 \| 72.0 \| 144 \| 0.2516 \| 0.8864 \| \| 0.1221 \| 73.0 \| 146 \| 0.2347 \| 0.9091 \| \| 0.1221 \| 74.0 \| 148 \| 0.2376 \| 0.9318 \| \| 0.1191 \| 75.0 \| 150 \| 0.2480 \| 0.9318 \| \| 0.1191 \| 76.0 \| 152 \| 0.2597 \| 0.9091 \| \| 0.1191 \| 77.0 \| 154 \| 0.3071 \| 0.9091 \| \| 0.1191 \| 78.0 \| 156 \| 0.3354 \| 0.9091 \| \| 0.1191 \| 79.0 \| 158 \| 0.2988 \| 0.8864 \| \| 0.1133 \| 80.0 \| 160 \| 0.2760 \| 0.9091 \| \| 0.1133 \| 81.0 \| 162 \| 0.2832 \| 0.9318 \| \| 0.1133 \| 82.0 \| 164 \| 0.2793 \| 0.9318 \| \| 0.1133 \| 83.0 \| 166 \| 0.2779 \| 0.9091 \| \| 0.1133 \| 84.0 \| 168 \| 0.3004 \| 0.8864 \| \| 0.098 \| 85.0 \| 170 \| 0.3275 \| 0.8864 \| \| 0.098 \| 86.0 \| 172 \| 0.3394 \| 0.8864 \| \| 0.098 \| 87.0 \| 174 \| 0.3257 \| 0.8864 \| \| 0.098 \| 88.0 \| 176 \| 0.3172 \| 0.8864 \| \| 0.098 \| 89.0 \| 178 \| 0.3122 \| 0.9318 \| \| 0.0917 \| 90.0 \| 180 \| 0.3208 \| 0.9318 \| \| 0.0917 \| 91.0 \| 182 \| 0.3236 \| 0.9318 \| \| 0.0917 \| 92.0 \| 184 \| 0.3274 \| 0.9318 \| \| 0.0917 \| 93.0 \| 186 \| 0.3331 \| 0.8864 \| \| 0.0917 \| 94.0 \| 188 \| 0.3379 \| 0.8864 \| \| 0.0989 \| 95.0 \| 190 \| 0.3404 \| 0.8864 \| \| 0.0989 \| 96.0 \| 192 \| 0.3452 \| 0.8864 \| \| 0.0989 \| 97.0 \| 194 \| 0.3491 \| 0.8864 \| \| 0.0989 \| 98.0 \| 196 \| 0.3489 \| 0.8864 \| \| 0.0989 \| 99.0 \| 198 \| 0.3482 \| 0.8864 \| \| 0.0916 \| 100.0 \| 200 \| 0.3467 \| 0.8864 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
ClaudioItaly/TopEvolution-Q5_K_M-GGUF	ClaudioItaly	2024-05-20T21:07:43Z	2	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:NousResearch/Hermes-2-Pro-Mistral-7B", "base_model:merge:NousResearch/Hermes-2-Pro-Mistral-7B", "base_model:mergekit-community/mergekit-slerp-ebgdloh", "base_model:merge:mergekit-community/mergekit-slerp-ebgdloh", "endpoints_compatible", "region:us" ]	null	2024-05-20T21:07:29Z	--- library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo base_model: - NousResearch/Hermes-2-Pro-Mistral-7B - mergekit-community/mergekit-slerp-ebgdloh --- # ClaudioItaly/TopEvolution-Q5_K_M-GGUF This model was converted to GGUF format from [`mergekit-community/TopEvolution`](https://huggingface.co/mergekit-community/TopEvolution) 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/mergekit-community/TopEvolution) 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 ClaudioItaly/TopEvolution-Q5_K_M-GGUF --model topevolution.Q5_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo ClaudioItaly/TopEvolution-Q5_K_M-GGUF --model topevolution.Q5_K_M.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 topevolution.Q5_K_M.gguf -n 128 ```
matthieuzone/PARMESANbis	matthieuzone	2024-05-20T21:07:23Z	3	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-20T20:59:12Z	--- 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 sks cheese 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 - matthieuzone/PARMESANbis <Gallery /> ## Model description These are matthieuzone/PARMESANbis 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 sks cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/PARMESANbis/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]
msbayindir/mt5-small-sample	msbayindir	2024-05-20T21:06:44Z	108	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "tr", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-19T00:29:37Z	--- library_name: transformers language: - tr metrics: - bleu --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details <p> This model(10-epoch) has been trained with the dataset <a href="https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering-Dataset">Turkish-Reading-Comprehension-Question-Answering-Dataset </a>for Turkish question-answering </p> <h1>Usage</h1> <h2>install package</h2> <ul> <li>!pip install transformers</li> <li>!pip install evaluate</li> <li>!pip install rouge</li> </ul> <h2>Codes</h2> <h3>Imports</h3> ```python import torch import nltk import string import evaluate # Bleu import pandas as pd import numpy as np from transformers import T5Tokenizer, MT5Model, MT5ForConditionalGeneration, MT5TokenizerFast import warnings ``` ```python TOKENIZER = MT5TokenizerFast.from_pretrained("msbayindir/mt5-small-sample") MODEL = MT5ForConditionalGeneration.from_pretrained("msbayindir/mt5-small-sample", return_dict=True) DEVICE = "cuda:0" MODEL = MODEL.to(device=DEVICE) Q_LEN = 256 # Question Length T_LEN = 32 ``` ```python def predict_answer(context, question, ref_answer=None): inputs = TOKENIZER(question, context, max_length=Q_LEN, padding="max_length", truncation=True, add_special_tokens=True) input_ids = torch.tensor(inputs["input_ids"], dtype=torch.long).to(DEVICE).unsqueeze(0) attention_mask = torch.tensor(inputs["attention_mask"], dtype=torch.long).to(DEVICE).unsqueeze(0) outputs = MODEL.generate(input_ids=input_ids, attention_mask=attention_mask) predicted_answer = TOKENIZER.decode(outputs.flatten(), skip_special_tokens=True) if ref_answer: # Load the Bleu metric bleu = evaluate.load("google_bleu") score = bleu.compute(predictions=[predicted_answer], references=[ref_answer]) print("Context: \n", context) print("\n") print("Question: \n", question) return { "Reference Answer: ": ref_answer, "Predicted Answer: ": predicted_answer, "BLEU Score: ": score } else: return predicted_answer ``` ###Samples ```python context = """Katmandu Büyükşehir Şehri (KMC), uluslararası ilişkileri teşvik etmek amacıyla Uluslararası İlişkiler Sekreterliği (IRC) kurmuştur. KMC'nin ilk uluslararası ilişkisi 1975 yılında Eugene, Oregon, Amerika Birleşik Devletleri ile kurulmuştur. Bu etkinlik, diğer 8 şehirle resmi ilişkiler kurarak daha da geliştirilmiştir: Motsumoto City of Japan, Rochester, Myanmar Yangon (eski adıyla Rangoon), Çin Halk Cumhuriyeti'nden Xi'an, Belarus Minsk ve Kore Demokratik Cumhuriyeti'nden Pyongyang. KMC'nin sürekli çabası, Katmandu için daha iyi kentsel yönetim ve gelişim programları elde etmek için SAARC ülkeleri, diğer Uluslararası ajanslar ve dünyanın diğer birçok büyük şehirleri ile etkileşimini geliştirmektir.""" answer = "Katmandu ilk uluslararası ilişkisini hangi yılda yarattı?" predict_answer(context,answer) '1975' ``` ```python context = """Yapay zeka (YZ), modern dünyada hızla gelişen bir teknoloji alanıdır. YZ, sağlık, finans ve eğitim gibi birçok sektörde kullanılmaktadır. Sağlık alanında, YZ algoritmaları hastalık teşhislerinde yardımcı olabilir. Finans sektöründe, YZ, piyasa analizleri yaparak yatırım kararlarını destekler. Eğitimde ise, öğrenci performansını izler ve bireyselleştirilmiş öğrenme programları oluşturur. Ancak, YZ'nin kullanımı etik ve gizlilik konularında endişeler doğurur. Veri güvenliği ve algoritmik önyargılar, dikkatle ele alınması gereken önemli meselelerdir.""" answer = "Yapay zeka hangi sektörlerde kullanılmaktadır?" predict_answer(context,answer) 'sağlık, finans ve eğitim' ```
hazyresearch/M2-BERT-128-Retrieval-Encoder-V1	hazyresearch	2024-05-20T21:06:23Z	126	1	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "custom_code", "en", "arxiv:2402.07440", "license:apache-2.0", "autotrain_compatible", "region:us" ]	fill-mask	2024-02-10T00:34:16Z	--- license: apache-2.0 language: - en pipeline_tag: fill-mask inference: false --- # Monarch Mixer-BERT The 80M checkpoint for M2-BERT-128 from the paper [Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT](https://arxiv.org/abs/2402.07440). Check out our [GitHub](https://github.com/HazyResearch/m2/tree/main) for instructions on how to download and fine-tune it! ## How to use You can load this model using Hugging Face `AutoModel`: ```python from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("jonsaadfalcon/M2-BERT-128-Retrieval-Encoder-V1", trust_remote_code=True) ``` This model uses the Hugging Face `bert-base-uncased tokenizer`: ``` from transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') ``` ## How to use This model generates embeddings for retrieval. The embeddings have a dimensionality of 768: ``` from transformers import AutoTokenizer, AutoModelForMaskedLM max_seq_length = 128 testing_string = "Every morning, I make a cup of coffee to start my day." model = AutoModelForMaskedLM.from_pretrained("jonsaadfalcon/M2-BERT-128-Retrieval-Encoder-V1", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", model_max_length=max_seq_length) input_ids = tokenizer([testing_string], return_tensors="pt", padding="max_length", return_token_type_ids=False, truncation=True, max_length=max_seq_length) outputs = model(**input_ids) embeddings = outputs['sentence_embedding'] ``` ### Remote Code This model requires `trust_remote_code=True` to be passed to the `from_pretrained` method. This is because we use custom PyTorch code (see our GitHub). You should consider passing a `revision` argument that specifies the exact git commit of the code, for example: ```python mlm = AutoModelForMaskedLM.from_pretrained( "jonsaadfalcon/M2-BERT-128-Retrieval-Encoder-V1", trust_remote_code=True, ) ``` ### Configuration Note `use_flash_mm` is false by default. Using FlashMM is currently not supported.
yifanxie/jasper-cicada-1	yifanxie	2024-05-20T21:03:38Z	135	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "gpt", "llm", "large language model", "h2o-llmstudio", "conversational", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-05-20T21:01:37Z	--- language: - en library_name: transformers tags: - gpt - llm - large language model - h2o-llmstudio inference: false thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico --- # Model Card ## Summary This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). - Base model: [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed. ```bash pip install transformers==4.40.1 ``` Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo. - Either leave `token=True` in the `pipeline` and login to hugginface_hub by running ```python import huggingface_hub huggingface_hub.login(<ACCESS_TOKEN>) ``` - Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline` ```python from transformers import pipeline generate_text = pipeline( model="yifanxie/jasper-cicada-1", torch_dtype="auto", trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, token=True, ) # generate configuration can be modified to your needs # generate_text.model.generation_config.min_new_tokens = 2 # generate_text.model.generation_config.max_new_tokens = 256 # generate_text.model.generation_config.do_sample = False # generate_text.model.generation_config.num_beams = 1 # generate_text.model.generation_config.temperature = float(0.0) # generate_text.model.generation_config.repetition_penalty = float(1.0) res = generate_text( "Why is drinking water so healthy?", renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <\|prompt\|>Why is drinking water so healthy?<eos><\|answer\|> ``` Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`. ```python from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "yifanxie/jasper-cicada-1", use_fast=True, padding_side="left", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "yifanxie/jasper-cicada-1", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) # generate configuration can be modified to your needs # generate_text.model.generation_config.min_new_tokens = 2 # generate_text.model.generation_config.max_new_tokens = 256 # generate_text.model.generation_config.do_sample = False # generate_text.model.generation_config.num_beams = 1 # generate_text.model.generation_config.temperature = float(0.0) # generate_text.model.generation_config.repetition_penalty = float(1.0) res = generate_text( "Why is drinking water so healthy?", renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "yifanxie/jasper-cicada-1" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<\|prompt\|>How are you?<eos><\|answer\|>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=True, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs # model.generation_config.min_new_tokens = 2 # model.generation_config.max_new_tokens = 256 # model.generation_config.do_sample = False # model.generation_config.num_beams = 1 # model.generation_config.temperature = float(0.0) # model.generation_config.repetition_penalty = float(1.0) tokens = model.generate( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Quantization and sharding You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```. ## Model Architecture ``` GemmaForCausalLM( (model): GemmaModel( (embed_tokens): Embedding(256000, 2048, padding_idx=0) (layers): ModuleList( (0-17): 18 x GemmaDecoderLayer( (self_attn): GemmaSdpaAttention( (q_proj): Linear(in_features=2048, out_features=2048, bias=False) (k_proj): Linear(in_features=2048, out_features=256, bias=False) (v_proj): Linear(in_features=2048, out_features=256, bias=False) (o_proj): Linear(in_features=2048, out_features=2048, bias=False) (rotary_emb): GemmaRotaryEmbedding() ) (mlp): GemmaMLP( (gate_proj): Linear(in_features=2048, out_features=16384, bias=False) (up_proj): Linear(in_features=2048, out_features=16384, bias=False) (down_proj): Linear(in_features=16384, out_features=2048, bias=False) (act_fn): PytorchGELUTanh() ) (input_layernorm): GemmaRMSNorm() (post_attention_layernorm): GemmaRMSNorm() ) ) (norm): GemmaRMSNorm() ) (lm_head): Linear(in_features=2048, out_features=256000, bias=False) ) ``` ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
mergekit-community/TopEvolution	mergekit-community	2024-05-20T21:01:09Z	12	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:NousResearch/Hermes-2-Pro-Mistral-7B", "base_model:merge:NousResearch/Hermes-2-Pro-Mistral-7B", "base_model:mergekit-community/mergekit-slerp-ebgdloh", "base_model:merge:mergekit-community/mergekit-slerp-ebgdloh", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T20:53:23Z	--- base_model: - NousResearch/Hermes-2-Pro-Mistral-7B - mergekit-community/mergekit-slerp-ebgdloh 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: * [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B) * [mergekit-community/mergekit-slerp-ebgdloh](https://huggingface.co/mergekit-community/mergekit-slerp-ebgdloh) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: NousResearch/Hermes-2-Pro-Mistral-7B - model: mergekit-community/mergekit-slerp-ebgdloh merge_method: slerp base_model: mergekit-community/mergekit-slerp-ebgdloh dtype: bfloat16 parameters: t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers ```
msbayindir/sample-mt5-small	msbayindir	2024-05-20T21:00:44Z	110	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "tr", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-16T16:01:54Z	--- library_name: transformers language: - tr metrics: - bleu --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details <p> This model has been trained with the dataset <a href="https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering-Dataset">Turkish-Reading-Comprehension-Question-Answering-Dataset </a>for Turkish question-answering </p> <h1>Usage</h1> <h2>install package</h2> <ul> <li>!pip install transformers</li> <li>!pip install evaluate</li> <li>!pip install rouge</li> </ul> <h2>Codes</h2> <h3>Imports</h3> ```python import torch import nltk import string import evaluate # Bleu import pandas as pd import numpy as np from transformers import T5Tokenizer, MT5Model, MT5ForConditionalGeneration, MT5TokenizerFast import warnings ``` ```python TOKENIZER = MT5TokenizerFast.from_pretrained("msbayindir/sample-mt5-small") MODEL = MT5ForConditionalGeneration.from_pretrained("msbayindir/sample-mt5-small", return_dict=True) DEVICE = "cuda:0" MODEL = MODEL.to(device=DEVICE) Q_LEN = 256 # Question Length T_LEN = 32 ``` ```python def predict_answer(context, question, ref_answer=None): inputs = TOKENIZER(question, context, max_length=Q_LEN, padding="max_length", truncation=True, add_special_tokens=True) input_ids = torch.tensor(inputs["input_ids"], dtype=torch.long).to(DEVICE).unsqueeze(0) attention_mask = torch.tensor(inputs["attention_mask"], dtype=torch.long).to(DEVICE).unsqueeze(0) outputs = MODEL.generate(input_ids=input_ids, attention_mask=attention_mask) predicted_answer = TOKENIZER.decode(outputs.flatten(), skip_special_tokens=True) if ref_answer: # Load the Bleu metric bleu = evaluate.load("google_bleu") score = bleu.compute(predictions=[predicted_answer], references=[ref_answer]) print("Context: \n", context) print("\n") print("Question: \n", question) return { "Reference Answer: ": ref_answer, "Predicted Answer: ": predicted_answer, "BLEU Score: ": score } else: return predicted_answer ``` ###Samples ```python context = """Katmandu Büyükşehir Şehri (KMC), uluslararası ilişkileri teşvik etmek amacıyla Uluslararası İlişkiler Sekreterliği (IRC) kurmuştur. KMC'nin ilk uluslararası ilişkisi 1975 yılında Eugene, Oregon, Amerika Birleşik Devletleri ile kurulmuştur. Bu etkinlik, diğer 8 şehirle resmi ilişkiler kurarak daha da geliştirilmiştir: Motsumoto City of Japan, Rochester, Myanmar Yangon (eski adıyla Rangoon), Çin Halk Cumhuriyeti'nden Xi'an, Belarus Minsk ve Kore Demokratik Cumhuriyeti'nden Pyongyang. KMC'nin sürekli çabası, Katmandu için daha iyi kentsel yönetim ve gelişim programları elde etmek için SAARC ülkeleri, diğer Uluslararası ajanslar ve dünyanın diğer birçok büyük şehirleri ile etkileşimini geliştirmektir.""" answer = "Katmandu ilk uluslararası ilişkisini hangi yılda yarattı?" predict_answer(context,answer) '1975' ``` ```python context = """Yapay zeka (YZ), modern dünyada hızla gelişen bir teknoloji alanıdır. YZ, sağlık, finans ve eğitim gibi birçok sektörde kullanılmaktadır. Sağlık alanında, YZ algoritmaları hastalık teşhislerinde yardımcı olabilir. Finans sektöründe, YZ, piyasa analizleri yaparak yatırım kararlarını destekler. Eğitimde ise, öğrenci performansını izler ve bireyselleştirilmiş öğrenme programları oluşturur. Ancak, YZ'nin kullanımı etik ve gizlilik konularında endişeler doğurur. Veri güvenliği ve algoritmik önyargılar, dikkatle ele alınması gereken önemli meselelerdir.""" answer = "Yapay zeka hangi sektörlerde kullanılmaktadır?" predict_answer(context,answer) 'sağlık, finans ve eğitim' ```
matthieuzone/OSSAU-_IRATYbis	matthieuzone	2024-05-20T20:58:56Z	4	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-20T20:50:46Z	--- 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 sks cheese 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 - matthieuzone/OSSAU-_IRATYbis <Gallery /> ## Model description These are matthieuzone/OSSAU-_IRATYbis 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 sks cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/OSSAU-_IRATYbis/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]
maneln/tinyllamaconvo	maneln	2024-05-20T20:54:45Z	132	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T19:57:50Z	--- license: apache-2.0 ---
rajiv-data-chef/smaug-8b-finetuned-entity-level-sentiment-analysis	rajiv-data-chef	2024-05-20T20:49:58Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-20T20:49:54Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
LoneStriker/Yi-1.5-34B-32K-8.0bpw-h8-exl2	LoneStriker	2024-05-20T20:49:49Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:2403.04652", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "exl2", "region:us" ]	text-generation	2024-05-20T20:37:34Z	--- license: apache-2.0 --- <div align="center"> <picture> <img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px"> </picture> </div> <p align="center"> <a href="https://github.com/01-ai">🐙 GitHub</a> • <a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> • <a href="https://twitter.com/01ai_yi">🐤 Twitter</a> • <a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a> <br/> <a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a> </p> # Intro Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. <div align="center"> Model \| Context Length \| Pre-trained Tokens \| :------------: \| :------------: \| :------------: \| \| Yi-1.5 \| 4K, 16K, 32K \| 3.6T </div> # Models - Chat models <div align="center"> \| Name \| Download \| \| --------------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-34B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-6B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| </div> - Base models <div align="center"> \| Name \| Download \| \| ---------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-34B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-6B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| </div> # Benchmarks - Chat models Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/KcsJ9Oc1VnEmfCDEJc5cd.png) Yi-1.5-9B-Chat is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/xf6pLg5jqRCwjlh6m3t6_.png) - Base models Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/BwU7QM-03dZvZzwdIE1xY.png) Yi-1.5-9B is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/y-EYSYPT-3aWLJ0x8R94F.png) # Quick Start For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
powermove72/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-Q4_K_M-GGUF	powermove72	2024-05-20T20:46:52Z	0	0	null	[ "gguf", "moe", "DPO", "RL-TUNED", "llama-cpp", "gguf-my-repo", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-05-20T20:38:09Z	--- license: mit tags: - moe - DPO - RL-TUNED - llama-cpp - gguf-my-repo --- # powermove72/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-Q4_K_M-GGUF This model was converted to GGUF format from [`yunconglong/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B`](https://huggingface.co/yunconglong/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B) 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/yunconglong/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B) 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 powermove72/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-Q4_K_M-GGUF --model truthful_dpo_tomgrc_fusionnet_7bx2_moe_13b.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo powermove72/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-Q4_K_M-GGUF --model truthful_dpo_tomgrc_fusionnet_7bx2_moe_13b.Q4_K_M.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 truthful_dpo_tomgrc_fusionnet_7bx2_moe_13b.Q4_K_M.gguf -n 128 ```
ulkaa/gemma-2b-dolly-qa	ulkaa	2024-05-20T20:46:32Z	13	1	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:other", "region:us" ]	null	2024-03-16T19:44:05Z	--- license: other library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b model-index: - name: gemma-2b-dolly-qa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gemma-2b-dolly-qa This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) 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: 1e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - training_steps: 296 ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0a0+cxx11.abi - Datasets 2.18.0 - Tokenizers 0.15.2
fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-264015	fine-tuned	2024-05-20T20:44:59Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "custom_code", "en", "dataset:fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-264015", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-20T20:44:44Z	--- license: apache-2.0 datasets: - fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-264015 - allenai/c4 language: - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case: custom ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-264015', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
seregadgl101/baii_v12_14ep	seregadgl101	2024-05-20T20:42:33Z	8	0	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-20T20:40:33Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # seregadgl101/baii_v12_14ep 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. <!--- 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('seregadgl101/baii_v12_14ep') 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=seregadgl101/baii_v12_14ep) ## 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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
matthieuzone/MUNSTERbis	matthieuzone	2024-05-20T20:42:11Z	1	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-20T20:33:59Z	--- 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 sks cheese 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 - matthieuzone/MUNSTERbis <Gallery /> ## Model description These are matthieuzone/MUNSTERbis 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 sks cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/MUNSTERbis/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]
NAMJOON/kisa-fine-tuned2	NAMJOON	2024-05-20T20:39:36Z	76	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	2024-05-20T20:28:06Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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LoneStriker/Yi-1.5-34B-32K-6.0bpw-h6-exl2	LoneStriker	2024-05-20T20:37:30Z	6	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:2403.04652", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2024-05-20T20:26:37Z	--- license: apache-2.0 --- <div align="center"> <picture> <img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px"> </picture> </div> <p align="center"> <a href="https://github.com/01-ai">🐙 GitHub</a> • <a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> • <a href="https://twitter.com/01ai_yi">🐤 Twitter</a> • <a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a> <br/> <a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a> </p> # Intro Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. <div align="center"> Model \| Context Length \| Pre-trained Tokens \| :------------: \| :------------: \| :------------: \| \| Yi-1.5 \| 4K, 16K, 32K \| 3.6T </div> # Models - Chat models <div align="center"> \| Name \| Download \| \| --------------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-34B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-6B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| </div> - Base models <div align="center"> \| Name \| Download \| \| ---------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-34B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-9B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| \| Yi-1.5-6B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) \| </div> # Benchmarks - Chat models Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/KcsJ9Oc1VnEmfCDEJc5cd.png) Yi-1.5-9B-Chat is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/xf6pLg5jqRCwjlh6m3t6_.png) - Base models Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/BwU7QM-03dZvZzwdIE1xY.png) Yi-1.5-9B is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/y-EYSYPT-3aWLJ0x8R94F.png) # Quick Start For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
OpenLLM-Ro/RoLlama2-7b-Instruct-GPTQ-4Bits-wikitext2	OpenLLM-Ro	2024-05-20T20:34:30Z	88	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-05-20T20:26:46Z	--- 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. --> GPTQ 4Bit quantised version of RoLlama2-7b-Instruct 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]
pbruna/distilbert-base-uncased-finetuned-emotion	pbruna	2024-05-20T20:30:40Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-20T20:11:30Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9375 - name: F1 type: f1 value: 0.9376104649098359 --- <!-- 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. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1518 - Accuracy: 0.9375 - F1: 0.9376 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.1058 \| 1.0 \| 250 \| 0.1741 \| 0.933 \| 0.9333 \| \| 0.1051 \| 2.0 \| 500 \| 0.1518 \| 0.9375 \| 0.9376 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

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