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train · 836k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-22 06:27:16	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 2025-06-22 06:26:41	card stringlengths 11 1.01M
T3Q-LLM/T3Q-LLM2-sft1.2	T3Q-LLM	2024-05-24T01:45:51Z	41	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T00:16:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ## Evaluation hf-causal-experimental (pretrained=T3Q-LLM/T3Q-LLM2-sft1.2,use_accelerate=true,trust_remote_code=true), limit: None, provide_description: False, num_fewshot: 0, batch_size: 8 \| Task \|Version\| Metric \|Value \| \|Stderr\| \|----------------\|------:\|--------\|-----:\|---\|-----:\| \|kobest_boolq \| 0\|acc \|0.9416\|± \|0.0063\| \| \| \|macro_f1\|0.9415\|± \|0.0063\| \|kobest_copa \| 0\|acc \|0.7730\|± \|0.0133\| \| \| \|macro_f1\|0.7725\|± \|0.0133\| \|kobest_hellaswag\| 0\|acc \|0.5100\|± \|0.0224\| \| \| \|acc_norm\|0.5740\|± \|0.0221\| \| \| \|macro_f1\|0.5074\|± \|0.0223\| \|kobest_sentineg \| 0\|acc \|0.7632\|± \|0.0214\| \| \| \|macro_f1\|0.7545\|± \|0.0220\|
Liuza1/test1111	Liuza1	2024-05-24T01:45:42Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-24T01:45:42Z	--- license: apache-2.0 ---
Timeshift/distilbert-base-uncased-finetuned-emotion	Timeshift	2024-05-24T01:45:25Z	107	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	2024-05-24T01:40:44Z	--- 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: 0.927 - name: F1 type: f1 value: 0.9268799638507115 --- <!-- 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: 0.2204 - Accuracy: 0.927 - F1: 0.9269 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8393 \| 1.0 \| 250 \| 0.3184 \| 0.904 \| 0.9027 \| \| 0.2532 \| 2.0 \| 500 \| 0.2204 \| 0.927 \| 0.9269 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
jspr/talosian_v3_merged	jspr	2024-05-24T01:43:06Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:mistralai/Mistral-7B-v0.3", "base_model:finetune:mistralai/Mistral-7B-v0.3", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T01:33:50Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft base_model: mistralai/Mistral-7B-v0.3 --- # Uploaded model - Developed by: jspr - License: apache-2.0 - Finetuned from model : mistralai/Mistral-7B-v0.3 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
gaianet/Llama-3-8B-Instruct-GGUF	gaianet	2024-05-24T01:39:13Z	450	1	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-11T12:24:42Z	--- license: apache-2.0 --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/GaiaNet-AI/.github/assets/45785633/d6976adc-f97d-4f86-a648-0f2f5c8e7eee" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Llama-3-8B-Instruct-GGUF ## Original Model [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) ## Run with Gaianet Prompt template: prompt template: `llama-3-chat` Context size: chat_ctx_size: `4096` Run with GaiaNet: - Quick start: https://docs.gaianet.ai/node-guide/quick-start - Customize your node: https://docs.gaianet.ai/node-guide/customize
AlignmentResearch/robust_llm_pythia-31m-imdb-gen-ian-nd	AlignmentResearch	2024-05-24T01:28:36Z	147	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T01:28:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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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AlignmentResearch/robust_llm_pythia-14m-imdb-gen-ian-nd	AlignmentResearch	2024-05-24T01:26:36Z	148	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T01:26:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
mradermacher/Mistral-C64Wizard-instruct-GGUF	mradermacher	2024-05-24T01:14:39Z	2	0	transformers	[ "transformers", "gguf", "en", "base_model:pechaut/Mistral-C64Wizard-instruct", "base_model:quantized:pechaut/Mistral-C64Wizard-instruct", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-23T23:51:33Z	--- base_model: pechaut/Mistral-C64Wizard-instruct language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/pechaut/Mistral-C64Wizard-instruct <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| 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 -->
junaidiqbalsyed/juanid-phi3-instruct-finetuned	junaidiqbalsyed	2024-05-24T01:10:30Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-24T01:10:08Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: junaidiqbalsyed - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
T3Q-LLM/T3Q-LLM2-FP-v2.0	T3Q-LLM	2024-05-24T01:08:46Z	45	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-08T04:54:06Z	--- library_name: transformers license: apache-2.0 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> 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. --> ## Evaluation hf-causal-experimental (pretrained=T3Q-LLM/T3Q-LLM2-FP-v2.0,use_accelerate=true,trust_remote_code=true), limit: None, provide_description: False, num_fewshot: 0, batch_size: 8 \| Task \|Version\| Metric \|Value \| \|Stderr\| \|----------------\|------:\|--------\|-----:\|---\|-----:\| \|kobest_boolq \| 0\|acc \|0.5085\|± \|0.0133\| \| \| \|macro_f1\|0.3496\|± \|0.0076\| \|kobest_copa \| 0\|acc \|0.7680\|± \|0.0134\| \| \| \|macro_f1\|0.7677\|± \|0.0134\| \|kobest_hellaswag\| 0\|acc \|0.4920\|± \|0.0224\| \| \| \|acc_norm\|0.5740\|± \|0.0221\| \| \| \|macro_f1\|0.4889\|± \|0.0223\| \|kobest_sentineg \| 0\|acc \|0.6826\|± \|0.0234\| \| \| \|macro_f1\|0.6616\|± \|0.0244\|
mradermacher/Athena-Mistral-7b-v0.2-GGUF	mradermacher	2024-05-24T01:07:25Z	16	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "mistral", "trl", "sft", "en", "dataset:NotAiLOL/Athena-v0.1", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-13T21:52:09Z	--- base_model: NotAiLOL/Athena-Mistral-7b-v0.2 datasets: - NotAiLOL/Athena-v0.1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/NotAiLOL/Athena-Mistral-7b-v0.2 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| 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 -->
hgnoi/4zorKjh2MTNaftD3	hgnoi	2024-05-24T01:05:26Z	139	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T01:03:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Augusto777/vit-base-patch16-224-RU3-40	Augusto777	2024-05-24T01:04:43Z	218	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-24T00:36:28Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-RU3-40 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.8333333333333334 --- <!-- 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. --> # vit-base-patch16-224-RU3-40 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5667 - Accuracy: 0.8333 ## 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: 5.5e-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: 0.05 - num_epochs: 40 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.3821 \| 0.99 \| 19 \| 1.3119 \| 0.4833 \| \| 1.2698 \| 1.97 \| 38 \| 1.0852 \| 0.6167 \| \| 0.9819 \| 2.96 \| 57 \| 0.8757 \| 0.7 \| \| 0.6671 \| 4.0 \| 77 \| 0.7689 \| 0.7333 \| \| 0.4248 \| 4.99 \| 96 \| 0.7294 \| 0.7167 \| \| 0.3005 \| 5.97 \| 115 \| 0.6518 \| 0.7833 \| \| 0.2035 \| 6.96 \| 134 \| 0.5667 \| 0.8333 \| \| 0.2195 \| 8.0 \| 154 \| 0.6646 \| 0.8333 \| \| 0.1654 \| 8.99 \| 173 \| 0.6294 \| 0.8167 \| \| 0.1581 \| 9.97 \| 192 \| 0.7211 \| 0.7833 \| \| 0.1338 \| 10.96 \| 211 \| 0.8129 \| 0.7833 \| \| 0.1188 \| 12.0 \| 231 \| 0.7925 \| 0.8167 \| \| 0.1179 \| 12.99 \| 250 \| 0.9588 \| 0.7667 \| \| 0.1017 \| 13.97 \| 269 \| 1.0875 \| 0.7167 \| \| 0.0845 \| 14.96 \| 288 \| 0.9355 \| 0.7 \| \| 0.1109 \| 16.0 \| 308 \| 0.9387 \| 0.8167 \| \| 0.0711 \| 16.99 \| 327 \| 1.1214 \| 0.7333 \| \| 0.0884 \| 17.97 \| 346 \| 0.9688 \| 0.7667 \| \| 0.0668 \| 18.96 \| 365 \| 1.0306 \| 0.8 \| \| 0.0716 \| 20.0 \| 385 \| 1.2653 \| 0.7167 \| \| 0.0643 \| 20.99 \| 404 \| 0.9894 \| 0.7833 \| \| 0.0517 \| 21.97 \| 423 \| 1.0439 \| 0.7667 \| \| 0.0597 \| 22.96 \| 442 \| 1.1470 \| 0.7667 \| \| 0.0533 \| 24.0 \| 462 \| 1.0848 \| 0.7833 \| \| 0.0529 \| 24.99 \| 481 \| 1.1481 \| 0.75 \| \| 0.0524 \| 25.97 \| 500 \| 1.1322 \| 0.7333 \| \| 0.0525 \| 26.96 \| 519 \| 1.1868 \| 0.7333 \| \| 0.0517 \| 28.0 \| 539 \| 1.1561 \| 0.7167 \| \| 0.0309 \| 28.99 \| 558 \| 1.0562 \| 0.7833 \| \| 0.0403 \| 29.97 \| 577 \| 1.2901 \| 0.7333 \| \| 0.0392 \| 30.96 \| 596 \| 1.1295 \| 0.7667 \| \| 0.0404 \| 32.0 \| 616 \| 1.1198 \| 0.7667 \| \| 0.0381 \| 32.99 \| 635 \| 1.2986 \| 0.7167 \| \| 0.0262 \| 33.97 \| 654 \| 1.1655 \| 0.75 \| \| 0.0354 \| 34.96 \| 673 \| 1.1223 \| 0.7833 \| \| 0.0224 \| 36.0 \| 693 \| 1.1679 \| 0.7833 \| \| 0.0244 \| 36.99 \| 712 \| 1.0999 \| 0.8167 \| \| 0.0368 \| 37.97 \| 731 \| 1.1213 \| 0.7833 \| \| 0.0199 \| 38.96 \| 750 \| 1.1003 \| 0.8 \| \| 0.028 \| 39.48 \| 760 \| 1.0989 \| 0.8 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
mradermacher/MAmmoTH2-8x7B-GGUF	mradermacher	2024-05-24T01:04:38Z	54	0	transformers	[ "transformers", "gguf", "en", "dataset:TIGER-Lab/WebInstructSub", "base_model:TIGER-Lab/MAmmoTH2-8x7B", "base_model:quantized:TIGER-Lab/MAmmoTH2-8x7B", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-20T20:30:53Z	--- base_model: TIGER-Lab/MAmmoTH2-8x7B datasets: - TIGER-Lab/WebInstructSub language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/MAmmoTH2-8x7B-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/MAmmoTH2-8x7B-GGUF/resolve/main/MAmmoTH2-8x7B.Q2_K.gguf) \| Q2_K \| 17.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-GGUF/resolve/main/MAmmoTH2-8x7B.IQ3_XS.gguf) \| IQ3_XS \| 19.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-GGUF/resolve/main/MAmmoTH2-8x7B.IQ3_S.gguf) \| IQ3_S \| 20.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-GGUF/resolve/main/MAmmoTH2-8x7B.Q3_K_S.gguf) \| Q3_K_S \| 20.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-GGUF/resolve/main/MAmmoTH2-8x7B.IQ3_M.gguf) \| IQ3_M \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-GGUF/resolve/main/MAmmoTH2-8x7B.Q3_K_M.gguf) \| Q3_K_M \| 22.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-GGUF/resolve/main/MAmmoTH2-8x7B.Q3_K_L.gguf) \| Q3_K_L \| 24.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-GGUF/resolve/main/MAmmoTH2-8x7B.IQ4_XS.gguf) \| IQ4_XS \| 25.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-GGUF/resolve/main/MAmmoTH2-8x7B.Q4_K_S.gguf) \| Q4_K_S \| 26.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-GGUF/resolve/main/MAmmoTH2-8x7B.Q4_K_M.gguf) \| Q4_K_M \| 28.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-GGUF/resolve/main/MAmmoTH2-8x7B.Q5_K_S.gguf) \| Q5_K_S \| 32.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-GGUF/resolve/main/MAmmoTH2-8x7B.Q5_K_M.gguf) \| Q5_K_M \| 33.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-GGUF/resolve/main/MAmmoTH2-8x7B.Q6_K.gguf) \| Q6_K \| 38.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-GGUF/resolve/main/MAmmoTH2-8x7B.Q8_0.gguf) \| Q8_0 \| 49.7 \| 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 -->
mradermacher/Deep-Miqu-103B-i1-GGUF	mradermacher	2024-05-24T01:04:26Z	42	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "license:other", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-05-20T21:35:00Z	--- 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: --> weighted/imatrix quants of https://huggingface.co/jukofyork/Deep-Miqu-103B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Deep-Miqu-103B-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-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 21.8 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 23.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 27.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 30.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 32.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 34.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-Q2_K.gguf) \| i1-Q2_K \| 38.0 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 39.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 42.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 44.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 44.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 46.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 49.7 \| IQ3_S probably better \| \| [PART 1](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-Q3_K_L.gguf.part2of2) \| i1-Q3_K_L \| 54.2 \| IQ3_M probably better \| \| [PART 1](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-IQ4_XS.gguf.part2of2) \| i1-IQ4_XS \| 55.2 \| \| \| [PART 1](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-Q4_0.gguf.part2of2) \| i1-Q4_0 \| 58.4 \| fast, low quality \| \| [PART 1](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-Q4_K_S.gguf.part2of2) \| i1-Q4_K_S \| 58.7 \| optimal size/speed/quality \| \| [PART 1](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-Q4_K_M.gguf.part2of2) \| i1-Q4_K_M \| 62.0 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-Q5_K_S.gguf.part2of2) \| i1-Q5_K_S \| 71.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 73.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Deep-Miqu-103B-i1-GGUF/resolve/main/Deep-Miqu-103B.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 84.8 \| 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/MAmmoTH2-8x7B-Plus-GGUF	mradermacher	2024-05-24T01:04:21Z	34	0	transformers	[ "transformers", "gguf", "en", "dataset:TIGER-Lab/WebInstructSub", "base_model:TIGER-Lab/MAmmoTH2-8x7B-Plus", "base_model:quantized:TIGER-Lab/MAmmoTH2-8x7B-Plus", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-20T22:42:53Z	--- base_model: TIGER-Lab/MAmmoTH2-8x7B-Plus datasets: - TIGER-Lab/WebInstructSub language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B-Plus <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/MAmmoTH2-8x7B-Plus-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/MAmmoTH2-8x7B-Plus-GGUF/resolve/main/MAmmoTH2-8x7B-Plus.Q2_K.gguf) \| Q2_K \| 17.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-Plus-GGUF/resolve/main/MAmmoTH2-8x7B-Plus.IQ3_XS.gguf) \| IQ3_XS \| 19.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-Plus-GGUF/resolve/main/MAmmoTH2-8x7B-Plus.IQ3_S.gguf) \| IQ3_S \| 20.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-Plus-GGUF/resolve/main/MAmmoTH2-8x7B-Plus.Q3_K_S.gguf) \| Q3_K_S \| 20.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-Plus-GGUF/resolve/main/MAmmoTH2-8x7B-Plus.IQ3_M.gguf) \| IQ3_M \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-Plus-GGUF/resolve/main/MAmmoTH2-8x7B-Plus.Q3_K_M.gguf) \| Q3_K_M \| 22.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-Plus-GGUF/resolve/main/MAmmoTH2-8x7B-Plus.Q3_K_L.gguf) \| Q3_K_L \| 24.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-Plus-GGUF/resolve/main/MAmmoTH2-8x7B-Plus.IQ4_XS.gguf) \| IQ4_XS \| 25.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-Plus-GGUF/resolve/main/MAmmoTH2-8x7B-Plus.Q4_K_S.gguf) \| Q4_K_S \| 26.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-Plus-GGUF/resolve/main/MAmmoTH2-8x7B-Plus.Q4_K_M.gguf) \| Q4_K_M \| 28.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-Plus-GGUF/resolve/main/MAmmoTH2-8x7B-Plus.Q5_K_S.gguf) \| Q5_K_S \| 32.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-Plus-GGUF/resolve/main/MAmmoTH2-8x7B-Plus.Q5_K_M.gguf) \| Q5_K_M \| 33.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-Plus-GGUF/resolve/main/MAmmoTH2-8x7B-Plus.Q6_K.gguf) \| Q6_K \| 38.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-Plus-GGUF/resolve/main/MAmmoTH2-8x7B-Plus.Q8_0.gguf) \| Q8_0 \| 49.7 \| 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 -->
mradermacher/L3_SnowStorm_4x8B-i1-GGUF	mradermacher	2024-05-24T01:04:17Z	130	4	transformers	[ "transformers", "gguf", "moe", "en", "base_model:xxx777xxxASD/L3_SnowStorm_4x8B", "base_model:quantized:xxx777xxxASD/L3_SnowStorm_4x8B", "license:llama3", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-05-20T23:25:51Z	--- base_model: xxx777xxxASD/L3_SnowStorm_4x8B language: - en library_name: transformers license: llama3 quantized_by: mradermacher tags: - moe --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hfhfix --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/xxx777xxxASD/L3_SnowStorm_4x8B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-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/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 5.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 6.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 7.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 7.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 8.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-Q2_K.gguf) \| i1-Q2_K \| 9.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 9.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 11.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 11.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 11.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 12.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 13.1 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 13.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-Q4_0.gguf) \| i1-Q4_0 \| 14.3 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 14.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 15.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3_SnowStorm_4x8B-i1-GGUF/resolve/main/L3_SnowStorm_4x8B.i1-Q6_K.gguf) \| i1-Q6_K \| 20.6 \| 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/MAmmoTH2-8x7B-i1-GGUF	mradermacher	2024-05-24T01:04:10Z	38	0	transformers	[ "transformers", "gguf", "en", "dataset:TIGER-Lab/WebInstructSub", "base_model:TIGER-Lab/MAmmoTH2-8x7B", "base_model:quantized:TIGER-Lab/MAmmoTH2-8x7B", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-05-21T09:28:35Z	--- base_model: TIGER-Lab/MAmmoTH2-8x7B datasets: - TIGER-Lab/WebInstructSub language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MAmmoTH2-8x7B-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/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 9.9 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 10.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 12.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 14.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 14.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 15.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-Q2_K.gguf) \| i1-Q2_K \| 17.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 18.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 19.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 20.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 20.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 22.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 24.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 25.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-Q4_0.gguf) \| i1-Q4_0 \| 26.7 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 26.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 28.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 32.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 33.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MAmmoTH2-8x7B-i1-GGUF/resolve/main/MAmmoTH2-8x7B.i1-Q6_K.gguf) \| i1-Q6_K \| 38.5 \| 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/Llama-3-70Bx2-MOE-i1-GGUF	mradermacher	2024-05-24T01:04:04Z	17	1	transformers	[ "transformers", "gguf", "en", "base_model:cloudyu/Llama-3-70Bx2-MOE", "base_model:quantized:cloudyu/Llama-3-70Bx2-MOE", "license:llama3", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-05-21T13:05:51Z	--- base_model: cloudyu/Llama-3-70Bx2-MOE language: - en library_name: transformers license: llama3 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/cloudyu/Llama-3-70Bx2-MOE <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-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/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-IQ1_S.gguf) \| i1-IQ1_S \| 26.8 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-IQ1_M.gguf) \| i1-IQ1_M \| 29.4 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 33.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 37.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-IQ2_S.gguf) \| i1-IQ2_S \| 39.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-IQ2_M.gguf) \| i1-IQ2_M \| 42.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q2_K.gguf) \| i1-Q2_K \| 46.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 49.2 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-IQ3_XS.gguf.part2of2) \| i1-IQ3_XS \| 52.3 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-IQ3_S.gguf.part2of2) \| i1-IQ3_S \| 55.2 \| beats Q3_K* \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q3_K_S.gguf.part2of2) \| i1-Q3_K_S \| 55.2 \| IQ3_XS probably better \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-IQ3_M.gguf.part2of2) \| i1-IQ3_M \| 56.6 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q3_K_M.gguf.part2of2) \| i1-Q3_K_M \| 61.2 \| IQ3_S probably better \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q3_K_L.gguf.part2of2) \| i1-Q3_K_L \| 66.3 \| IQ3_M probably better \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-IQ4_XS.gguf.part2of2) \| i1-IQ4_XS \| 68.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q4_0.gguf.part2of2) \| i1-Q4_0 \| 72.1 \| fast, low quality \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q4_K_S.gguf.part2of2) \| i1-Q4_K_S \| 72.5 \| optimal size/speed/quality \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q4_K_M.gguf.part2of2) \| i1-Q4_K_M \| 76.8 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q5_K_S.gguf.part2of2) \| i1-Q5_K_S \| 87.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 90.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF/resolve/main/Llama-3-70Bx2-MOE.i1-Q6_K.gguf.part3of3) \| i1-Q6_K \| 104.2 \| 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/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF	mradermacher	2024-05-24T01:03:40Z	136	5	transformers	[ "transformers", "gguf", "en", "base_model:failspy/Smaug-Llama-3-70B-Instruct-abliterated-v3", "base_model:quantized:failspy/Smaug-Llama-3-70B-Instruct-abliterated-v3", "license:llama3", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-05-22T17:20:38Z	--- base_model: failspy/Smaug-Llama-3-70B-Instruct-abliterated-v3 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/failspy/Smaug-Llama-3-70B-Instruct-abliterated-v3 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-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-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.i1-IQ1_S.gguf) \| i1-IQ1_S \| 15.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 19.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 21.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.i1-IQ2_S.gguf) \| i1-IQ2_S \| 22.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.i1-IQ2_M.gguf) \| i1-IQ2_M \| 24.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.i1-Q2_K.gguf) \| i1-Q2_K \| 26.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 27.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.i1-IQ3_S.gguf) \| i1-IQ3_S \| 31.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.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-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.i1-IQ3_M.gguf) \| i1-IQ3_M \| 32.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.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-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.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-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 38.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.i1-Q4_0.gguf) \| i1-Q4_0 \| 40.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.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-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.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 -->
RichardErkhov/beberik_-_Nyxene-v1-11B-gguf	RichardErkhov	2024-05-24T01:02:20Z	5	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-23T20:58:33Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Nyxene-v1-11B - GGUF - Model creator: https://huggingface.co/beberik/ - Original model: https://huggingface.co/beberik/Nyxene-v1-11B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Nyxene-v1-11B.Q2_K.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.Q2_K.gguf) \| Q2_K \| 3.73GB \| \| [Nyxene-v1-11B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.IQ3_XS.gguf) \| IQ3_XS \| 4.14GB \| \| [Nyxene-v1-11B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.IQ3_S.gguf) \| IQ3_S \| 4.37GB \| \| [Nyxene-v1-11B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.Q3_K_S.gguf) \| Q3_K_S \| 4.34GB \| \| [Nyxene-v1-11B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.IQ3_M.gguf) \| IQ3_M \| 4.51GB \| \| [Nyxene-v1-11B.Q3_K.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.Q3_K.gguf) \| Q3_K \| 4.84GB \| \| [Nyxene-v1-11B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.Q3_K_M.gguf) \| Q3_K_M \| 4.84GB \| \| [Nyxene-v1-11B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.Q3_K_L.gguf) \| Q3_K_L \| 5.26GB \| \| [Nyxene-v1-11B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.IQ4_XS.gguf) \| IQ4_XS \| 5.43GB \| \| [Nyxene-v1-11B.Q4_0.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.Q4_0.gguf) \| Q4_0 \| 5.66GB \| \| [Nyxene-v1-11B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.IQ4_NL.gguf) \| IQ4_NL \| 5.72GB \| \| [Nyxene-v1-11B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.Q4_K_S.gguf) \| Q4_K_S \| 5.7GB \| \| [Nyxene-v1-11B.Q4_K.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.Q4_K.gguf) \| Q4_K \| 6.02GB \| \| [Nyxene-v1-11B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.Q4_K_M.gguf) \| Q4_K_M \| 6.02GB \| \| [Nyxene-v1-11B.Q4_1.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.Q4_1.gguf) \| Q4_1 \| 6.27GB \| \| [Nyxene-v1-11B.Q5_0.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.Q5_0.gguf) \| Q5_0 \| 6.89GB \| \| [Nyxene-v1-11B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.Q5_K_S.gguf) \| Q5_K_S \| 6.89GB \| \| [Nyxene-v1-11B.Q5_K.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.Q5_K.gguf) \| Q5_K \| 7.08GB \| \| [Nyxene-v1-11B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.Q5_K_M.gguf) \| Q5_K_M \| 7.08GB \| \| [Nyxene-v1-11B.Q5_1.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.Q5_1.gguf) \| Q5_1 \| 7.51GB \| \| [Nyxene-v1-11B.Q6_K.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.Q6_K.gguf) \| Q6_K \| 8.2GB \| \| [Nyxene-v1-11B.Q8_0.gguf](https://huggingface.co/RichardErkhov/beberik_-_Nyxene-v1-11B-gguf/blob/main/Nyxene-v1-11B.Q8_0.gguf) \| Q8_0 \| 10.62GB \| Original model description: --- license: cc-by-nc-4.0 tags: - merge model-index: - name: Nyxene-v1-11B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 67.49 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Nyxene-v1-11B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 84.52 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Nyxene-v1-11B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 65.12 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Nyxene-v1-11B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 57.28 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Nyxene-v1-11B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 79.01 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Nyxene-v1-11B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 52.08 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Nyxene-v1-11B name: Open LLM Leaderboard --- ## Description This repo contains bf16 files of Nyxene-v1-11B. Same as the [previous version](https://huggingface.co/beberik/Nyxene-11B) but I used newer models and tried to repeat what I experimented with when there were older models. ## Model used - [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha) - [openaccess-ai-collective/DPOpenHermes-7B](https://huggingface.co/openaccess-ai-collective/DPOpenHermes-7B) - [fblgit/juanako-7b-UNA](https://huggingface.co/fblgit/juanako-7b-UNA) - [chargoddard/loyal-piano-m7](https://huggingface.co/chargoddard/loyal-piano-m7) - [argilla/notus-7b-v1](https://huggingface.co/argilla/notus-7b-v1) I added a new model because after the same action but using zephyr and dolphin the model turned out to be more creative. ## Prompt template The best one after further testing is this one: ``` <\|system\|> Below is an instruction that describes a task. Write a response that appropriately completes the request. <\|user\|> {prompt} <\|assistant\|> ``` ## The secret sauce loyal-piano with 1% of notus : ``` slices: - sources: - model: chargoddard/loyal-piano-m7 layer_range: [0, 48] - model: argilla/notus-7b-v1 layer_range: [0, 48] merge_method: slerp base_model: argilla/notus-7b-v1 parameters: t: - filter: lm_head value: [0.75] - filter: embed_tokens value: [0.75] - filter: self_attn value: [0.75, 0.25] - filter: mlp value: [0.25, 0.75] - filter: layernorm value: [0.5, 0.5] - filter: modelnorm value: [0.75] - value: 0.99 # fallback for rest of tensors dtype: bfloat16 ``` loyal-piano-juanako-11B : ``` slices: - sources: - model: fblgit/juanako-7b-UNA layer_range: [0, 24] - sources: - model: chargoddard/loyal-piano-m7 layer_range: [8, 32] merge_method: passthrough dtype: bfloat16 ``` Starling-DPOHermes-11B : ``` slices: - sources: - model: berkeley-nest/Starling-LM-7B-alpha layer_range: [0, 24] - sources: - model: openaccess-ai-collective/DPOpenHermes-7B layer_range: [8, 32] merge_method: passthrough dtype: bfloat16 ``` Nyxene-11B : ``` slices: - sources: - model: loyal-piano-juanako-11B layer_range: [0, 48] - model: Starling-NeuralHermes-11B layer_range: [0, 48] merge_method: slerp base_model: dolphin-juanako-11B parameters: t: - filter: lm_head value: [0.75] - filter: embed_tokens value: [0.75] - filter: self_attn value: [0.75, 0.25] - filter: mlp value: [0.25, 0.75] - filter: layernorm value: [0.5, 0.5] - filter: modelnorm value: [0.75] - value: 0.5 # fallback for rest of tensors dtype: bfloat16 ``` I use [mergekit](https://github.com/cg123/mergekit) for all the manipulation told here. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_beberik__Nyxene-v1-11B) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|67.58\| \|AI2 Reasoning Challenge (25-Shot)\|67.49\| \|HellaSwag (10-Shot) \|84.52\| \|MMLU (5-Shot) \|65.12\| \|TruthfulQA (0-shot) \|57.28\| \|Winogrande (5-shot) \|79.01\| \|GSM8k (5-shot) \|52.08\|
TwT-6/cr-model-v1	TwT-6	2024-05-24T01:02:20Z	44	2	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T07:41:31Z	--- license: cc-by-4.0 --- My model is a state-of-the-art language processing AI designed to understand and generate human-like text. It leverages deep learning algorithms to engage in a wide range of language tasks, providing users with information, recommendations, and even casual conversation. With a broad knowledge base and nuanced understanding of context, my capabilities enable me to assist with various inquiries and perform complex language-based tasks effectively.
Klarly/multilingual-MT_FR-ES-IT-PT-RO_CAS-NLP	Klarly	2024-05-24T00:48:23Z	31	0	transformers	[ "transformers", "tensorboard", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "base_model:Helsinki-NLP/opus-mt-en-roa", "base_model:finetune:Helsinki-NLP/opus-mt-en-roa", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-23T16:45:00Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-roa tags: - generated_from_trainer model-index: - name: multilingual-MT_FR-ES-IT-PT-RO_CAS-NLP 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. --> # multilingual-MT_FR-ES-IT-PT-RO_CAS-NLP This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-roa](https://huggingface.co/Helsinki-NLP/opus-mt-en-roa) 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: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
hgnoi/PNtvstHsvaxTN2DP	hgnoi	2024-05-24T00:42:03Z	133	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T00:38:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
Zoyd/CohereForAI_aya-23-35B-8_0bpw_exl2	Zoyd	2024-05-24T00:39:00Z	5	0	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "exl2", "region:us" ]	text-generation	2024-05-24T00:21:28Z	--- library_name: transformers language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 --- Exllamav2 quant (exl2 / 8.0 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-2_2bpw_exl2)</center> \| <center>14760 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-2_5bpw_exl2)</center> \| <center>16011 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-3_0bpw_exl2)</center> \| <center>18096 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-3_5bpw_exl2)</center> \| <center>20178 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-3_75bpw_exl2)</center> \| <center>21213 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-4_0bpw_exl2)</center> \| <center>22266 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-4_25bpw_exl2)</center> \| <center>23307 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-5_0bpw_exl2)</center> \| <center>26431 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-6_0bpw_exl2)</center> \| <center>31014 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-6_5bpw_exl2)</center> \| <center>33112 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-8_0bpw_exl2)</center> \| <center>37423 MB</center> \| <center>8</center> \| # Model Card for Aya-23-35B ## Model Summary Aya 23 is an open weights research release of an instruction fine-tuned model with highly advanced multilingual capabilities. Aya 23 focuses on pairing a highly performant pre-trained [Command family](https://huggingface.co/CohereForAI/c4ai-command-r-plus) of models with the recently released [Aya Collection](https://huggingface.co/datasets/CohereForAI/aya_collection). The result is a powerful multilingual large language model serving 23 languages. This model card corresponds to the 35-billion version of the Aya 23 model. We also released an 8-billion version which you can find [here](https://huggingface.co/CohereForAI/aya-23-8B). We cover 23 languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Developed by: [Cohere For AI](https://cohere.for.ai) and [Cohere](https://cohere.com/) - Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy) - Model: aya-23-8B - Model Size: 35 billion parameters Try Aya 23 You can try out Aya 23 (35B) before downloading the weights in our hosted Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Usage Please install transformers from the source repository that includes the necessary changes for this model ```python # pip install 'git+https://github.com/huggingface/transformers.git' from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/aya-23-35B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the command-r-plus chat template messages = [{"role": "user", "content": "Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` ### Example Notebook [This notebook](https://huggingface.co/CohereForAI/aya-23-35B/blob/main/Aya_23_notebook.ipynb) showcases a detailed use of Aya 23 (8B) including inference and fine-tuning with [QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes). ## Model Details Input: Models input text only. Output: Models generate text only. Model Architecture: Aya-23-35B is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model is fine-tuned (IFT) to follow human instructions. Languages covered: The model is particularly optimized for multilinguality and supports the following languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Context length: 8192 ### Evaluation <img src="benchmarks.png" alt="multilingual benchmarks" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> <img src="winrates.png" alt="average win rates" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Please refer to the [Aya 23 technical report](https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23) for further details about the base model, data, instruction tuning, and evaluation. ### Model Card Contact For errors or additional questions about details in this model card, contact [email protected]. ### Terms of Use We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant multilingual model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ### Try the model today You can try Aya 23 in the Cohere [playground](https://dashboard.cohere.com/playground/chat) here. You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Citation info ```bibtex @misc{aya23technicalreport, title={Aya 23: Open Weight Releases to Further Multilingual Progress}, author={Viraat Aryabumi, John Dang, Dwarak Talupuru, Saurabh Dash, David Cairuz, Hangyu Lin, Bharat Venkitesh, Madeline Smith, Kelly Marchisio, Sebastian Ruder, Acyr Locatelli, Julia Kreutzer, Nick Frosst, Phil Blunsom, Marzieh Fadaee, Ahmet Üstün, and Sara Hooker}, url={https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23}, year={2024} }
Zoyd/CohereForAI_aya-23-35B-5_0bpw_exl2	Zoyd	2024-05-24T00:37:55Z	7	1	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]	text-generation	2024-05-23T22:27:56Z	--- library_name: transformers language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 --- 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/CohereForAI_aya-23-35B-2_2bpw_exl2)</center> \| <center>14760 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-2_5bpw_exl2)</center> \| <center>16011 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-3_0bpw_exl2)</center> \| <center>18096 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-3_5bpw_exl2)</center> \| <center>20178 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-3_75bpw_exl2)</center> \| <center>21213 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-4_0bpw_exl2)</center> \| <center>22266 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-4_25bpw_exl2)</center> \| <center>23307 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-5_0bpw_exl2)</center> \| <center>26431 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-6_0bpw_exl2)</center> \| <center>31014 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-6_5bpw_exl2)</center> \| <center>33112 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-8_0bpw_exl2)</center> \| <center>37423 MB</center> \| <center>8</center> \| # Model Card for Aya-23-35B ## Model Summary Aya 23 is an open weights research release of an instruction fine-tuned model with highly advanced multilingual capabilities. Aya 23 focuses on pairing a highly performant pre-trained [Command family](https://huggingface.co/CohereForAI/c4ai-command-r-plus) of models with the recently released [Aya Collection](https://huggingface.co/datasets/CohereForAI/aya_collection). The result is a powerful multilingual large language model serving 23 languages. This model card corresponds to the 35-billion version of the Aya 23 model. We also released an 8-billion version which you can find [here](https://huggingface.co/CohereForAI/aya-23-8B). We cover 23 languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Developed by: [Cohere For AI](https://cohere.for.ai) and [Cohere](https://cohere.com/) - Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy) - Model: aya-23-8B - Model Size: 35 billion parameters Try Aya 23 You can try out Aya 23 (35B) before downloading the weights in our hosted Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Usage Please install transformers from the source repository that includes the necessary changes for this model ```python # pip install 'git+https://github.com/huggingface/transformers.git' from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/aya-23-35B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the command-r-plus chat template messages = [{"role": "user", "content": "Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` ### Example Notebook [This notebook](https://huggingface.co/CohereForAI/aya-23-35B/blob/main/Aya_23_notebook.ipynb) showcases a detailed use of Aya 23 (8B) including inference and fine-tuning with [QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes). ## Model Details Input: Models input text only. Output: Models generate text only. Model Architecture: Aya-23-35B is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model is fine-tuned (IFT) to follow human instructions. Languages covered: The model is particularly optimized for multilinguality and supports the following languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Context length: 8192 ### Evaluation <img src="benchmarks.png" alt="multilingual benchmarks" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> <img src="winrates.png" alt="average win rates" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Please refer to the [Aya 23 technical report](https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23) for further details about the base model, data, instruction tuning, and evaluation. ### Model Card Contact For errors or additional questions about details in this model card, contact [email protected]. ### Terms of Use We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant multilingual model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ### Try the model today You can try Aya 23 in the Cohere [playground](https://dashboard.cohere.com/playground/chat) here. You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Citation info ```bibtex @misc{aya23technicalreport, title={Aya 23: Open Weight Releases to Further Multilingual Progress}, author={Viraat Aryabumi, John Dang, Dwarak Talupuru, Saurabh Dash, David Cairuz, Hangyu Lin, Bharat Venkitesh, Madeline Smith, Kelly Marchisio, Sebastian Ruder, Acyr Locatelli, Julia Kreutzer, Nick Frosst, Phil Blunsom, Marzieh Fadaee, Ahmet Üstün, and Sara Hooker}, url={https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23}, year={2024} }
GTsuya/magic_moonarts_pony	GTsuya	2024-05-24T00:34:49Z	1	1	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:GraydientPlatformAPI/autism-pony", "base_model:adapter:GraydientPlatformAPI/autism-pony", "license:mit", "region:us" ]	text-to-image	2024-05-24T00:33:27Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, Bustier, from_above , cowboy_shot, rating_safe, <lora:magic_moonarts_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00005-2600470274.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, Shorts, from_side , cowboy_shot, rating_safe, <lora:magic_moonarts_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00007-2393379456.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, dark-skinned_female, Bodysuit, sideways , lower_body, rating_safe, <lora:magic_moonarts_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00017-2795844222.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, very_dark_skin, Culottes, from_side , upper_body, rating_questionable, <lora:magic_moonarts_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00084-3581396895.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, very_dark_skin, Halter Top, from_above , feet_out_of_frame, rating_questionable, <lora:magic_moonarts_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00090-1426675186.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, very_dark_skin, Scarves, dutch_angle , cowboy_shot, rating_explicit, <lora:magic_moonarts_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00164-1578228206.png base_model: GraydientPlatformAPI/autism-pony instance_prompt: null license: mit --- # magic_moonarts_pony <Gallery /> ## Model description This LoRA model has been trained with Kohya SS using Magic Moon Arts's artworks on Autism Mix SDXL checkpoint. Obtained graphics could be really close the original art style. This LoRA model could be use for cartoon representation of sexy women ## Download model Weights for this model are available in Safetensors format. [Download](/GTsuya/magic_moonarts_pony/tree/main) them in the Files & versions tab.
Augusto777/vit-base-patch16-224-RU3-10	Augusto777	2024-05-24T00:33:34Z	218	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-24T00:24:24Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-RU3-10 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.7833333333333333 --- <!-- 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. --> # vit-base-patch16-224-RU3-10 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6241 - Accuracy: 0.7833 ## 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: 5.5e-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: 0.05 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.3698 \| 0.99 \| 19 \| 1.1845 \| 0.65 \| \| 1.1232 \| 1.97 \| 38 \| 0.9393 \| 0.65 \| \| 0.8168 \| 2.96 \| 57 \| 0.9117 \| 0.6333 \| \| 0.5992 \| 4.0 \| 77 \| 0.8330 \| 0.7333 \| \| 0.4258 \| 4.99 \| 96 \| 0.7471 \| 0.7 \| \| 0.3283 \| 5.97 \| 115 \| 0.6241 \| 0.7833 \| \| 0.2543 \| 6.96 \| 134 \| 0.5916 \| 0.7833 \| \| 0.2345 \| 8.0 \| 154 \| 0.6783 \| 0.7833 \| \| 0.2027 \| 8.99 \| 173 \| 0.6577 \| 0.7833 \| \| 0.1733 \| 9.87 \| 190 \| 0.6589 \| 0.7833 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
mayabedge/wav2vec2-large-xls-r-300m-amharic-maya2	mayabedge	2024-05-24T00:31:16Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-24T00:18:47Z	--- 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. (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]
leafspark/SFR-Iterative-DPO-LLaMA-3-8B-R-lora	leafspark	2024-05-24T00:28:22Z	0	2	transformers	[ "transformers", "safetensors", "mergekit", "peft", "license:llama3", "endpoints_compatible", "region:us" ]	null	2024-05-24T00:27:04Z	--- base_model: [] library_name: transformers tags: - mergekit - peft license: llama3 --- # SFR-Iterative-DPO-LLaMA-3-8B-R LoRA Model This is a LoRA extracted from a language model. It was extracted using [mergekit](https://github.com/arcee-ai/mergekit). ## LoRA Details This LoRA adapter was extracted from SFR-Iterative-DPO-LLaMA-3-8B-R and uses Meta-Llama-3-8B as a base. ### Parameters The following command was used to extract this LoRA adapter: ```sh mergekit-extract-lora Meta-Llama-3-8B SFR-Iterative-DPO-LLaMA-3-8B-R OUTPUT_PATH --rank=32 ```
Zoyd/CohereForAI_aya-23-35B-6_5bpw_exl2	Zoyd	2024-05-24T00:26:04Z	7	0	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-23T23:42:12Z	--- library_name: transformers language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 --- 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/CohereForAI_aya-23-35B-2_2bpw_exl2)</center> \| <center>14760 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-2_5bpw_exl2)</center> \| <center>16011 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-3_0bpw_exl2)</center> \| <center>18096 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-3_5bpw_exl2)</center> \| <center>20178 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-3_75bpw_exl2)</center> \| <center>21213 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-4_0bpw_exl2)</center> \| <center>22266 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-4_25bpw_exl2)</center> \| <center>23307 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-5_0bpw_exl2)</center> \| <center>26431 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-6_0bpw_exl2)</center> \| <center>31014 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-6_5bpw_exl2)</center> \| <center>33112 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-8_0bpw_exl2)</center> \| <center>37423 MB</center> \| <center>8</center> \| # Model Card for Aya-23-35B ## Model Summary Aya 23 is an open weights research release of an instruction fine-tuned model with highly advanced multilingual capabilities. Aya 23 focuses on pairing a highly performant pre-trained [Command family](https://huggingface.co/CohereForAI/c4ai-command-r-plus) of models with the recently released [Aya Collection](https://huggingface.co/datasets/CohereForAI/aya_collection). The result is a powerful multilingual large language model serving 23 languages. This model card corresponds to the 35-billion version of the Aya 23 model. We also released an 8-billion version which you can find [here](https://huggingface.co/CohereForAI/aya-23-8B). We cover 23 languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Developed by: [Cohere For AI](https://cohere.for.ai) and [Cohere](https://cohere.com/) - Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy) - Model: aya-23-8B - Model Size: 35 billion parameters Try Aya 23 You can try out Aya 23 (35B) before downloading the weights in our hosted Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Usage Please install transformers from the source repository that includes the necessary changes for this model ```python # pip install 'git+https://github.com/huggingface/transformers.git' from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/aya-23-35B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the command-r-plus chat template messages = [{"role": "user", "content": "Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` ### Example Notebook [This notebook](https://huggingface.co/CohereForAI/aya-23-35B/blob/main/Aya_23_notebook.ipynb) showcases a detailed use of Aya 23 (8B) including inference and fine-tuning with [QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes). ## Model Details Input: Models input text only. Output: Models generate text only. Model Architecture: Aya-23-35B is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model is fine-tuned (IFT) to follow human instructions. Languages covered: The model is particularly optimized for multilinguality and supports the following languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Context length: 8192 ### Evaluation <img src="benchmarks.png" alt="multilingual benchmarks" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> <img src="winrates.png" alt="average win rates" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Please refer to the [Aya 23 technical report](https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23) for further details about the base model, data, instruction tuning, and evaluation. ### Model Card Contact For errors or additional questions about details in this model card, contact [email protected]. ### Terms of Use We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant multilingual model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ### Try the model today You can try Aya 23 in the Cohere [playground](https://dashboard.cohere.com/playground/chat) here. You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Citation info ```bibtex @misc{aya23technicalreport, title={Aya 23: Open Weight Releases to Further Multilingual Progress}, author={Viraat Aryabumi, John Dang, Dwarak Talupuru, Saurabh Dash, David Cairuz, Hangyu Lin, Bharat Venkitesh, Madeline Smith, Kelly Marchisio, Sebastian Ruder, Acyr Locatelli, Julia Kreutzer, Nick Frosst, Phil Blunsom, Marzieh Fadaee, Ahmet Üstün, and Sara Hooker}, url={https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23}, year={2024} }
Zoyd/CohereForAI_aya-23-35B-6_0bpw_exl2	Zoyd	2024-05-24T00:26:01Z	5	0	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2024-05-23T23:05:09Z	--- library_name: transformers language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 --- 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/CohereForAI_aya-23-35B-2_2bpw_exl2)</center> \| <center>14760 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-2_5bpw_exl2)</center> \| <center>16011 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-3_0bpw_exl2)</center> \| <center>18096 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-3_5bpw_exl2)</center> \| <center>20178 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-3_75bpw_exl2)</center> \| <center>21213 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-4_0bpw_exl2)</center> \| <center>22266 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-4_25bpw_exl2)</center> \| <center>23307 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-5_0bpw_exl2)</center> \| <center>26431 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-6_0bpw_exl2)</center> \| <center>31014 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-6_5bpw_exl2)</center> \| <center>33112 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-8_0bpw_exl2)</center> \| <center>37423 MB</center> \| <center>8</center> \| # Model Card for Aya-23-35B ## Model Summary Aya 23 is an open weights research release of an instruction fine-tuned model with highly advanced multilingual capabilities. Aya 23 focuses on pairing a highly performant pre-trained [Command family](https://huggingface.co/CohereForAI/c4ai-command-r-plus) of models with the recently released [Aya Collection](https://huggingface.co/datasets/CohereForAI/aya_collection). The result is a powerful multilingual large language model serving 23 languages. This model card corresponds to the 35-billion version of the Aya 23 model. We also released an 8-billion version which you can find [here](https://huggingface.co/CohereForAI/aya-23-8B). We cover 23 languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Developed by: [Cohere For AI](https://cohere.for.ai) and [Cohere](https://cohere.com/) - Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy) - Model: aya-23-8B - Model Size: 35 billion parameters Try Aya 23 You can try out Aya 23 (35B) before downloading the weights in our hosted Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Usage Please install transformers from the source repository that includes the necessary changes for this model ```python # pip install 'git+https://github.com/huggingface/transformers.git' from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/aya-23-35B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the command-r-plus chat template messages = [{"role": "user", "content": "Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` ### Example Notebook [This notebook](https://huggingface.co/CohereForAI/aya-23-35B/blob/main/Aya_23_notebook.ipynb) showcases a detailed use of Aya 23 (8B) including inference and fine-tuning with [QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes). ## Model Details Input: Models input text only. Output: Models generate text only. Model Architecture: Aya-23-35B is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model is fine-tuned (IFT) to follow human instructions. Languages covered: The model is particularly optimized for multilinguality and supports the following languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Context length: 8192 ### Evaluation <img src="benchmarks.png" alt="multilingual benchmarks" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> <img src="winrates.png" alt="average win rates" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Please refer to the [Aya 23 technical report](https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23) for further details about the base model, data, instruction tuning, and evaluation. ### Model Card Contact For errors or additional questions about details in this model card, contact [email protected]. ### Terms of Use We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant multilingual model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ### Try the model today You can try Aya 23 in the Cohere [playground](https://dashboard.cohere.com/playground/chat) here. You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Citation info ```bibtex @misc{aya23technicalreport, title={Aya 23: Open Weight Releases to Further Multilingual Progress}, author={Viraat Aryabumi, John Dang, Dwarak Talupuru, Saurabh Dash, David Cairuz, Hangyu Lin, Bharat Venkitesh, Madeline Smith, Kelly Marchisio, Sebastian Ruder, Acyr Locatelli, Julia Kreutzer, Nick Frosst, Phil Blunsom, Marzieh Fadaee, Ahmet Üstün, and Sara Hooker}, url={https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23}, year={2024} }
Zoyd/CohereForAI_aya-23-35B-2_5bpw_exl2	Zoyd	2024-05-24T00:25:05Z	5	0	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-23T19:00:37Z	--- library_name: transformers language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 --- Exllamav2 quant (exl2 / 2.5 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-2_2bpw_exl2)</center> \| <center>14760 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-2_5bpw_exl2)</center> \| <center>16011 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-3_0bpw_exl2)</center> \| <center>18096 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-3_5bpw_exl2)</center> \| <center>20178 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-3_75bpw_exl2)</center> \| <center>21213 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-4_0bpw_exl2)</center> \| <center>22266 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-4_25bpw_exl2)</center> \| <center>23307 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-5_0bpw_exl2)</center> \| <center>26431 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-6_0bpw_exl2)</center> \| <center>31014 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-6_5bpw_exl2)</center> \| <center>33112 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-8_0bpw_exl2)</center> \| <center>37423 MB</center> \| <center>8</center> \| # Model Card for Aya-23-35B ## Model Summary Aya 23 is an open weights research release of an instruction fine-tuned model with highly advanced multilingual capabilities. Aya 23 focuses on pairing a highly performant pre-trained [Command family](https://huggingface.co/CohereForAI/c4ai-command-r-plus) of models with the recently released [Aya Collection](https://huggingface.co/datasets/CohereForAI/aya_collection). The result is a powerful multilingual large language model serving 23 languages. This model card corresponds to the 35-billion version of the Aya 23 model. We also released an 8-billion version which you can find [here](https://huggingface.co/CohereForAI/aya-23-8B). We cover 23 languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Developed by: [Cohere For AI](https://cohere.for.ai) and [Cohere](https://cohere.com/) - Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy) - Model: aya-23-8B - Model Size: 35 billion parameters Try Aya 23 You can try out Aya 23 (35B) before downloading the weights in our hosted Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Usage Please install transformers from the source repository that includes the necessary changes for this model ```python # pip install 'git+https://github.com/huggingface/transformers.git' from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/aya-23-35B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the command-r-plus chat template messages = [{"role": "user", "content": "Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` ### Example Notebook [This notebook](https://huggingface.co/CohereForAI/aya-23-35B/blob/main/Aya_23_notebook.ipynb) showcases a detailed use of Aya 23 (8B) including inference and fine-tuning with [QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes). ## Model Details Input: Models input text only. Output: Models generate text only. Model Architecture: Aya-23-35B is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model is fine-tuned (IFT) to follow human instructions. Languages covered: The model is particularly optimized for multilinguality and supports the following languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Context length: 8192 ### Evaluation <img src="benchmarks.png" alt="multilingual benchmarks" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> <img src="winrates.png" alt="average win rates" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Please refer to the [Aya 23 technical report](https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23) for further details about the base model, data, instruction tuning, and evaluation. ### Model Card Contact For errors or additional questions about details in this model card, contact [email protected]. ### Terms of Use We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant multilingual model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ### Try the model today You can try Aya 23 in the Cohere [playground](https://dashboard.cohere.com/playground/chat) here. You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Citation info ```bibtex @misc{aya23technicalreport, title={Aya 23: Open Weight Releases to Further Multilingual Progress}, author={Viraat Aryabumi, John Dang, Dwarak Talupuru, Saurabh Dash, David Cairuz, Hangyu Lin, Bharat Venkitesh, Madeline Smith, Kelly Marchisio, Sebastian Ruder, Acyr Locatelli, Julia Kreutzer, Nick Frosst, Phil Blunsom, Marzieh Fadaee, Ahmet Üstün, and Sara Hooker}, url={https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23}, year={2024} }
Zoyd/CohereForAI_aya-23-35B-2_2bpw_exl2	Zoyd	2024-05-24T00:24:56Z	6	0	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-23T18:28:13Z	--- library_name: transformers language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 --- 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/CohereForAI_aya-23-35B-2_2bpw_exl2)</center> \| <center>14760 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-2_5bpw_exl2)</center> \| <center>16011 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-3_0bpw_exl2)</center> \| <center>18096 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-3_5bpw_exl2)</center> \| <center>20178 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-3_75bpw_exl2)</center> \| <center>21213 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-4_0bpw_exl2)</center> \| <center>22266 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-4_25bpw_exl2)</center> \| <center>23307 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-5_0bpw_exl2)</center> \| <center>26431 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-6_0bpw_exl2)</center> \| <center>31014 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-6_5bpw_exl2)</center> \| <center>33112 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-35B-8_0bpw_exl2)</center> \| <center>37423 MB</center> \| <center>8</center> \| # Model Card for Aya-23-35B ## Model Summary Aya 23 is an open weights research release of an instruction fine-tuned model with highly advanced multilingual capabilities. Aya 23 focuses on pairing a highly performant pre-trained [Command family](https://huggingface.co/CohereForAI/c4ai-command-r-plus) of models with the recently released [Aya Collection](https://huggingface.co/datasets/CohereForAI/aya_collection). The result is a powerful multilingual large language model serving 23 languages. This model card corresponds to the 35-billion version of the Aya 23 model. We also released an 8-billion version which you can find [here](https://huggingface.co/CohereForAI/aya-23-8B). We cover 23 languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Developed by: [Cohere For AI](https://cohere.for.ai) and [Cohere](https://cohere.com/) - Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy) - Model: aya-23-8B - Model Size: 35 billion parameters Try Aya 23 You can try out Aya 23 (35B) before downloading the weights in our hosted Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Usage Please install transformers from the source repository that includes the necessary changes for this model ```python # pip install 'git+https://github.com/huggingface/transformers.git' from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/aya-23-35B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the command-r-plus chat template messages = [{"role": "user", "content": "Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` ### Example Notebook [This notebook](https://huggingface.co/CohereForAI/aya-23-35B/blob/main/Aya_23_notebook.ipynb) showcases a detailed use of Aya 23 (8B) including inference and fine-tuning with [QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes). ## Model Details Input: Models input text only. Output: Models generate text only. Model Architecture: Aya-23-35B is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model is fine-tuned (IFT) to follow human instructions. Languages covered: The model is particularly optimized for multilinguality and supports the following languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Context length: 8192 ### Evaluation <img src="benchmarks.png" alt="multilingual benchmarks" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> <img src="winrates.png" alt="average win rates" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Please refer to the [Aya 23 technical report](https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23) for further details about the base model, data, instruction tuning, and evaluation. ### Model Card Contact For errors or additional questions about details in this model card, contact [email protected]. ### Terms of Use We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant multilingual model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ### Try the model today You can try Aya 23 in the Cohere [playground](https://dashboard.cohere.com/playground/chat) here. You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Citation info ```bibtex @misc{aya23technicalreport, title={Aya 23: Open Weight Releases to Further Multilingual Progress}, author={Viraat Aryabumi, John Dang, Dwarak Talupuru, Saurabh Dash, David Cairuz, Hangyu Lin, Bharat Venkitesh, Madeline Smith, Kelly Marchisio, Sebastian Ruder, Acyr Locatelli, Julia Kreutzer, Nick Frosst, Phil Blunsom, Marzieh Fadaee, Ahmet Üstün, and Sara Hooker}, url={https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23}, year={2024} }
T3Q-LLM/T3Q-LLM2-FP-v1.0	T3Q-LLM	2024-05-24T00:23:29Z	45	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-07T23:58:37Z	--- library_name: transformers license: apache-2.0 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> 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. --> ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> hf-causal-experimental (pretrained=T3Q-LLM/T3Q-LLM2-FP-v1.0,use_accelerate=true,trust_remote_code=true), limit: None, provide_description: False, num_fewshot: 0, batch_size: 8 \| Task \|Version\| Metric \|Value \| \|Stderr\| \|----------------\|------:\|--------\|-----:\|---\|-----:\| \|kobest_boolq \| 0\|acc \|0.5976\|± \|0.0131\| \| \| \|macro_f1\|0.5224\|± \|0.0136\| \|kobest_copa \| 0\|acc \|0.8190\|± \|0.0122\| \| \| \|macro_f1\|0.8189\|± \|0.0122\| \|kobest_hellaswag\| 0\|acc \|0.5240\|± \|0.0224\| \| \| \|acc_norm\|0.5740\|± \|0.0221\| \| \| \|macro_f1\|0.5214\|± \|0.0224\| \|kobest_sentineg \| 0\|acc \|0.7809\|± \|0.0208\| \| \| \|macro_f1\|0.7786\|± \|0.0211\|
crazydevlegend/Qwen-Qwen1.5-0.5B-1716509910	crazydevlegend	2024-05-24T00:19:09Z	147	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T00:18:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hgnoi/svb7DgXPcZi51zzj	hgnoi	2024-05-24T00:16:25Z	133	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T00:14:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf	RichardErkhov	2024-05-24T00:12:52Z	9	0	null	[ "gguf", "arxiv:2404.17733", "endpoints_compatible", "region:us" ]	null	2024-05-23T21:51:16Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Swallow-MS-7b-v0.1 - GGUF - Model creator: https://huggingface.co/tokyotech-llm/ - Original model: https://huggingface.co/tokyotech-llm/Swallow-MS-7b-v0.1/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Swallow-MS-7b-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q2_K.gguf) \| Q2_K \| 2.58GB \| \| [Swallow-MS-7b-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.IQ3_XS.gguf) \| IQ3_XS \| 2.86GB \| \| [Swallow-MS-7b-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.IQ3_S.gguf) \| IQ3_S \| 3.02GB \| \| [Swallow-MS-7b-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 3.0GB \| \| [Swallow-MS-7b-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.IQ3_M.gguf) \| IQ3_M \| 3.11GB \| \| [Swallow-MS-7b-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q3_K.gguf) \| Q3_K \| 3.33GB \| \| [Swallow-MS-7b-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 3.33GB \| \| [Swallow-MS-7b-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 3.61GB \| \| [Swallow-MS-7b-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 3.73GB \| \| [Swallow-MS-7b-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q4_0.gguf) \| Q4_0 \| 3.88GB \| \| [Swallow-MS-7b-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.IQ4_NL.gguf) \| IQ4_NL \| 3.93GB \| \| [Swallow-MS-7b-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 3.91GB \| \| [Swallow-MS-7b-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q4_K.gguf) \| Q4_K \| 4.13GB \| \| [Swallow-MS-7b-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 4.13GB \| \| [Swallow-MS-7b-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q4_1.gguf) \| Q4_1 \| 4.3GB \| \| [Swallow-MS-7b-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q5_0.gguf) \| Q5_0 \| 4.72GB \| \| [Swallow-MS-7b-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 4.72GB \| \| [Swallow-MS-7b-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q5_K.gguf) \| Q5_K \| 4.84GB \| \| [Swallow-MS-7b-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 4.84GB \| \| [Swallow-MS-7b-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q5_1.gguf) \| Q5_1 \| 5.13GB \| \| [Swallow-MS-7b-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q6_K.gguf) \| Q6_K \| 5.6GB \| \| [Swallow-MS-7b-v0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q8_0.gguf) \| Q8_0 \| 7.26GB \| Original model description: --- language: - en - ja library_name: transformers pipeline_tag: text-generation model_type: mistral license: apache-2.0 --- # Swallow-MS-7b-v0.1 Our Swallow-MS-7b-v0.1 model has undergone continual pre-training from the Mistral-7B-v0.1, primarily with the addition of Japanese language data. # Model Release Updates We are excited to share the release schedule for our latest models: - April 26, 2024: Released the [Swallow-MS-7b-instruct-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-instruct-v0.1) - March 11, 2024: Released the [Swallow-MS-7b-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-v0.1) ![logo](./logo.png) This repository provides large language models developed by [TokyoTech-LLM](https://tokyotech-llm.github.io/). ## Model Details * Model type: Please refer to Mistral technical report for details on the model architecture. * Language(s): Japanese English * Tokenizer: This model employs a tokenizer that features a broadened vocabulary based on Japanese data. This allows for a more efficient representation of text using fewer tokens, leading to a notably faster inference process. * Contact: swallow[at]nlp.c.titech.ac.jp ## Base Model Performance ### Japanese tasks \|Model\|Size\|JCommonsenseQA\|JEMHopQA\|NIILC\|JSQuAD\|XL-Sum\|MGSM\|WMT20-en-ja\|WMT20-ja-en\|Average\| \|---------------------------\|-------\|---------\|-------\|-------\|-------\|------\|------------\|------------\|------\|-----\| \| \| \|4-shot\|4-shot\|4-shot\|4-shot\|1-shot\|4-shot\|4-shot\|4-shot\|\| \| CyberAgentLM2-7B \|7B\| 0.2198 \| 0.5047 \| 0.5066 \| 0.7799 \| 0.0233 \| 0.0600 \| 0.2345 \| 0.1499 \| 0.3098 \| \| Llama 2 \|7B\| 0.3852 \| 0.4240 \| 0.3410 \| 0.7917 \| 0.1905 \| 0.0760 \| 0.1783 \| 0.1738 \| 0.3201 \| \| japanese-stablelm-base-beta-7b\|7B\| 0.3610 \| 0.4478 \| 0.4432 \| 0.8318 \| 0.2195 \| 0.0720 \| 0.1946 \| 0.1226 \| 0.3366 \| \| japanese-stablelm-base-ja_vocab-beta-7b\|7B\| 0.2172 \| 0.4482 \| 0.4309 \| 0.8202 \| 0.0757 \| 0.0520 \| 0.1601 \| 0.1453 \| 0.2937 \| \| ELYZA-japanese-Llama-2-7b\|7B\| 0.5791 \| 0.4703 \| 0.4019 \| 0.8226 \| 0.1312 \| 0.0600 \| 0.1795 \| 0.1289 \| 0.3467 \| \| ELYZA-japanese-Llama-2-7b-fast\|7B\| 0.5308 \| 0.4330 \| 0.3898 \| 0.8131 \| 0.1289 \| 0.0720 \| 0.1678 \| 0.1143 \| 0.3312 \| \| youri-7b (base) \|7B\| 0.4620 \| 0.4776 \| 0.4999 \| 0.8506 \| 0.1957 \| 0.0640 \| 0.2671 \| 0.1971 \| 0.3768 \| \| Swallow-7b \|7B\| 0.4808 \| 0.5078 \| 0.5968 \| 0.8573 \| 0.1830 \| 0.1240 \| 0.2510 \| 0.1511 \| 0.3940 \| \| Swallow-7b-plus \|7B\| 0.5478 \| 0.5493 \| 0.6030 \| 0.8544 \| 0.1806 \| 0.1360 \| 0.2568 \| 0.1441 \| 0.4090 \| \| Qwen-7B \|7B\| 0.7712 \| 0.4234 \| 0.2376 \| 0.8594 \| 0.1371 \| 0.2160 \| 0.1689 \| 0.1801 \| 0.3742 \| \| nekomata-7b \|7B\| 0.7417 \| 0.4928 \| 0.5022 \| 0.8707 \| 0.1676 \| 0.1240 \| 0.2673 \| 0.1815 \| 0.4185 \| \| Mistral-7B-v0.1 \|7B\| 0.7301 \| 0.4245 \| 0.2722 \| 0.8563 \| 0.2006 \| 0.1760 \| 0.1405 \| 0.1733 \| 0.3717 \| \| japanese-stablelm-base-gamma-7b\|7B\| 0.7364 \| 0.4643 \| 0.5568 \| 0.8910 \| 0.2293 \| 0.1680 \| 0.2390 \| 0.1561 \| 0.4301 \| \| Swallow-MS-7b-v0.1 \|7B\| 0.8570 \| 0.4915 \| 0.5519 \| 0.8802 \| 0.1988 \| 0.2240 \| 0.2494 \| 0.1667 \| 0.4524 \| ### English tasks \|Model\|Size\|OpenBookQA\|TriviaQA\|HellaSwag\|SQuAD2.0\|XWINO\|GSM8K\|Average\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \| \| \|8-shot\|8-shot\|8-shot\|8-shot\|8-shot\|8-shot\|\| \| CyberAgentLM2-7B \|7B\| 0.2860 \| 0.3496 \| 0.5003 \| 0.3510 \| 0.8581 \| 0.0705 \| 0.4026 \| \| Llama 2 \|7B\| 0.3580 \| 0.6265 \| 0.5860 \| 0.3207 \| 0.9049 \| 0.1410 \| 0.4895 \| \| japanese-stablelm-base-beta-7b\|7B\| 0.3620 \| 0.5903 \| 0.5707 \| 0.2992 \| 0.8994 \| 0.1198 \| 0.4736 \| \| japanese-stablelm-base-ja_vocab-beta-7b\|7B\| 0.3520 \| 0.5549 \| 0.5644 \| 0.3079 \| 0.8942 \| 0.0538 \| 0.4545 \| \| ELYZA-japanese-Llama-2-7b\|7B\| 0.3400 \| 0.5875 \| 0.5595 \| 0.2721 \| 0.8989 \| 0.1638 \| 0.4703 \| \| ELYZA-japanese-Llama-2-7b-fast\|7B\| 0.3280 \| 0.5817 \| 0.5530 \| 0.2605 \| 0.8989 \| 0.1425 \| 0.4608 \| \| youri-7b (base) \|7B\| 0.3400 \| 0.5257 \| 0.5540 \| 0.3297 \| 0.8938 \| 0.0963 \| 0.4566 \| \| Swallow-7b \|7B\| 0.3180 \| 0.4836 \| 0.5308 \| 0.3125 \| 0.8817 \| 0.1130 \| 0.4399 \| \| Swallow-7b-plus \|7B\| 0.3280 \| 0.4558 \| 0.5259 \| 0.3134 \| 0.8929 \| 0.1061 \| 0.4370 \| \| Qwen-7B \|7B\| 0.3640 \| 0.5695 \| 0.5787 \| 0.3799 \| 0.8933 \| 0.4617 \| 0.5412 \| \| nekomata-7b \|7B\| 0.3340 \| 0.4371 \| 0.5340 \| 0.2933 \| 0.8766 \| 0.1531 \| 0.4380 \| \| Mistral-7B-v0.1 \|7B\| 0.3660 \| 0.7050 \| 0.6264 \| 0.3799 \| 0.9157 \| 0.3533 \| 0.5577 \| \| japanese-stablelm-base-gamma-7b\|7B\| 0.3240 \| 0.5745 \| 0.5739 \| 0.3546 \| 0.8976 \| 0.1911 \| 0.4860 \| \| Swallow-MS-7b-v0.1 \|7B\| 0.3440 \| 0.5976 \| 0.5810 \| 0.3364 \| 0.9037 \| 0.2623 \| 0.5042 \| ### Code generation tasks \|Model\|Size\|JHumanEval\|HumanEval\| \|---\|---\|---\|---\| \| \| \|pass@1\|pass@1\| \| CyberAgentLM2-7B \|7B\|0.0634\|0.0756\| \| Llama 2 \|7B\|0.1152\|0.1378\| \| japanese-stablelm-base-beta-7b\|7B\|0.1018\|0.1280\| \| japanese-stablelm-base-ja_vocab-beta-7b\|7B\|0.0896\|0.1122\| \| ELYZA-japanese-Llama-2-7b\|7B\|0.0287\|0.0427\| \| ELYZA-japanese-Llama-2-7b-fast\|7B\| 0.0000 \|0.0037\| \| youri-7b (base) \|7B\|0.0829\|0.0982\| \| Swallow-7b \|7B\|0.0183\|0.0183\| \| Swallow-7b-plus \|7B\| 0.0061\|0.0037\| \| Qwen-7B \|7B\|0.1701\|0.1805\| \| nekomata-7b \|7B\|0.0988\|0.1402\| \| Mistral-7B-v0.1 \|7B\|0.2555\|0.2933\| \| japanese-stablelm-base-gamma-7b\|7B\|0.1823\|0.1915\| \| Swallow-MS-7b-v0.1 \|7B\|0.2305\|0.2768\| ## Evaluation Benchmarks ### Japanese evaluation benchmarks We used llm-jp-eval(v1.0.0) and JP Language Model Evaluation Harness(commit #9b42d41). The details are as follows: - Multiple-choice question answering (JCommonsenseQA [Kurihara+, 2022]) - Open-ended question answering (JEMHopQA [Ishii+, 2023]) - Open-ended question answering (NIILC [Sekine, 2003]) - Machine reading comprehension (JSQuAD [Kurihara+, 2022]) - Automatic summarization (XL-Sum [Hasan+, 2021]) - Machine translation (WMT2020 ja-en [Barrault+, 2020]) - Machine translation (WMT2020 en-ja [Barrault+, 2020]) - Mathematical reasoning (MGSM [Shi+, 2023]) ### English evaluation benchmarks We used the Language Model Evaluation Harness(v.0.3.0). The details are as follows: - Multiple-choice question answering (OpenBookQA [Mihaylov+, 2018]) - Open-ended question answering (TriviaQA [Joshi+, 2017]) - Machine reading comprehension (SQuAD 2.0 [Rajpurkar+, 2018]) - Commonsense reasoning (XWINO [Tikhonov & Ryabinin, 2021]) - Natural language inference (HellaSwag [Zellers+, 2019]) - Mathematical reasoning (GSM8k [Cobbe+, 2021]) ### Code evaluation benchmarks We utilized the Code Generation LM Evaluation Harness [Allal+, 2022] (commit #0261c52). The details are as follows: - Code generation (HumanEval [Chen+, 2021]) - Code generation in Japanese (JHumanEval [Satoh+, 2024]) ## Usage First install additional dependencies in [requirements.txt](./requirements.txt): ```sh pip install -r requirements.txt ``` ### Use the base model ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_name = "tokyotech-llm/Swallow-MS-7b-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") prompt = "東京工業大学の主なキャンパスは、" input_ids = tokenizer.encode( prompt, add_special_tokens=False, return_tensors="pt" ) tokens = model.generate( input_ids.to(device=model.device), max_new_tokens=128, temperature=0.99, top_p=0.95, do_sample=True, ) out = tokenizer.decode(tokens[0], skip_special_tokens=True) print(out) ``` ## Training Datasets ### Continual Pre-Training The following datasets were used for continual pre-training. - [Algebraic Stack](https://huggingface.co/datasets/EleutherAI/proof-pile-2) - [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) - [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) - [Swallow Corpus](https://arxiv.org/abs/2404.17733) - [The Pile](https://huggingface.co/datasets/EleutherAI/pile) ## Risks and Limitations The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations. ## Acknowledgements We thank Mistral AI for releasing Mistral 7B v0.1 under an open license for others to build on. Our project is supported by the [ABCI Large-scale Language Model Building Support Program](https://abci.ai/en/link/llm_support_program.html) of the National Institute of Advanced Industrial Science and Technology. ## License apache-2.0 ## Authors Here are the team members: - From [Okazaki Laboratory](https://www.nlp.c.titech.ac.jp/index.en.html), the following members: - [Naoaki Okazaki](https://www.chokkan.org/index.ja.html) - [Sakae Mizuki](https://s-mizuki-nlp.github.io/) - [Hiroki Iida](https://meshidenn.github.io/) - [Mengsay Loem](https://loem-ms.github.io/) - [Shota Hirai](https://huggingface.co/Kotemo428) - [Kakeru Hattori](https://aya-se.vercel.app/) - [Masanari Ohi](https://twitter.com/stjohn2007) - From [YOKOTA Laboratory](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members: - [Rio Yokota](https://twitter.com/rioyokota) - [Kazuki Fujii](https://twitter.com/okoge_kaz) - [Taishi Nakamura](https://twitter.com/Setuna7777_2)
basmazouaoui/finetuned_camembert_for_job_offers	basmazouaoui	2024-05-24T00:12:34Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "camembert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-23T20:30:18Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: Finetuned-camem-ner 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. --> # Finetuned-camem-ner This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1080 - Precision: 0.8445 - Recall: 0.8740 - F1: 0.8590 - Accuracy: 0.9793 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 1.2864 \| 0.09 \| 100 \| 1.2891 \| 0.0295 \| 0.1206 \| 0.0474 \| 0.8750 \| \| 0.8284 \| 0.17 \| 200 \| 0.5688 \| 0.0376 \| 0.1252 \| 0.0579 \| 0.8888 \| \| 0.374 \| 0.26 \| 300 \| 0.2753 \| 0.1477 \| 0.2320 \| 0.1805 \| 0.9366 \| \| 0.2215 \| 0.35 \| 400 \| 0.1742 \| 0.3205 \| 0.3816 \| 0.3484 \| 0.9584 \| \| 0.1447 \| 0.43 \| 500 \| 0.1271 \| 0.6077 \| 0.7105 \| 0.6551 \| 0.9735 \| \| 0.1183 \| 0.52 \| 600 \| 0.1067 \| 0.7066 \| 0.7857 \| 0.7440 \| 0.9773 \| \| 0.108 \| 0.61 \| 700 \| 0.0983 \| 0.7236 \| 0.8071 \| 0.7631 \| 0.9779 \| \| 0.0978 \| 0.69 \| 800 \| 0.0880 \| 0.7678 \| 0.8224 \| 0.7942 \| 0.9789 \| \| 0.0897 \| 0.78 \| 900 \| 0.0908 \| 0.7970 \| 0.8432 \| 0.8195 \| 0.9797 \| \| 0.0799 \| 0.87 \| 1000 \| 0.0883 \| 0.8052 \| 0.8587 \| 0.8311 \| 0.9799 \| \| 0.0868 \| 0.95 \| 1100 \| 0.0832 \| 0.8073 \| 0.8622 \| 0.8338 \| 0.9801 \| \| 0.0749 \| 1.04 \| 1200 \| 0.0832 \| 0.8138 \| 0.8651 \| 0.8387 \| 0.9800 \| \| 0.0765 \| 1.13 \| 1300 \| 0.0844 \| 0.8139 \| 0.8689 \| 0.8405 \| 0.9800 \| \| 0.0712 \| 1.21 \| 1400 \| 0.0835 \| 0.8262 \| 0.8636 \| 0.8445 \| 0.9800 \| \| 0.0678 \| 1.3 \| 1500 \| 0.0838 \| 0.8228 \| 0.8687 \| 0.8451 \| 0.9801 \| \| 0.0699 \| 1.39 \| 1600 \| 0.0850 \| 0.8212 \| 0.8714 \| 0.8455 \| 0.9800 \| \| 0.0731 \| 1.47 \| 1700 \| 0.0809 \| 0.8272 \| 0.8709 \| 0.8485 \| 0.9800 \| \| 0.0704 \| 1.56 \| 1800 \| 0.0818 \| 0.8400 \| 0.8697 \| 0.8546 \| 0.9803 \| \| 0.0749 \| 1.65 \| 1900 \| 0.0820 \| 0.8330 \| 0.8726 \| 0.8523 \| 0.9802 \| \| 0.0723 \| 1.73 \| 2000 \| 0.0814 \| 0.8423 \| 0.8709 \| 0.8563 \| 0.9802 \| \| 0.0737 \| 1.82 \| 2100 \| 0.0814 \| 0.8312 \| 0.8737 \| 0.8519 \| 0.9801 \| \| 0.073 \| 1.91 \| 2200 \| 0.0821 \| 0.8347 \| 0.8769 \| 0.8553 \| 0.9799 \| \| 0.0617 \| 1.99 \| 2300 \| 0.0830 \| 0.8375 \| 0.8760 \| 0.8563 \| 0.9801 \| \| 0.0607 \| 2.08 \| 2400 \| 0.0863 \| 0.8295 \| 0.8803 \| 0.8541 \| 0.9803 \| \| 0.0578 \| 2.17 \| 2500 \| 0.0849 \| 0.8365 \| 0.8797 \| 0.8575 \| 0.9803 \| \| 0.0546 \| 2.25 \| 2600 \| 0.0854 \| 0.8376 \| 0.8785 \| 0.8576 \| 0.9802 \| \| 0.0634 \| 2.34 \| 2700 \| 0.0832 \| 0.8375 \| 0.8764 \| 0.8565 \| 0.9801 \| \| 0.058 \| 2.43 \| 2800 \| 0.0852 \| 0.8405 \| 0.8748 \| 0.8573 \| 0.9802 \| \| 0.0616 \| 2.51 \| 2900 \| 0.0851 \| 0.8378 \| 0.8796 \| 0.8582 \| 0.9800 \| \| 0.0585 \| 2.6 \| 3000 \| 0.0845 \| 0.8434 \| 0.8785 \| 0.8606 \| 0.9800 \| \| 0.0542 \| 2.69 \| 3100 \| 0.0847 \| 0.8471 \| 0.8773 \| 0.8619 \| 0.9801 \| \| 0.0617 \| 2.77 \| 3200 \| 0.0869 \| 0.8396 \| 0.8765 \| 0.8577 \| 0.9799 \| \| 0.0634 \| 2.86 \| 3300 \| 0.0828 \| 0.8338 \| 0.8773 \| 0.8550 \| 0.9796 \| \| 0.0593 \| 2.95 \| 3400 \| 0.0855 \| 0.8360 \| 0.8789 \| 0.8569 \| 0.9798 \| \| 0.0486 \| 3.03 \| 3500 \| 0.0888 \| 0.8439 \| 0.8781 \| 0.8606 \| 0.9801 \| \| 0.0549 \| 3.12 \| 3600 \| 0.0886 \| 0.8444 \| 0.8793 \| 0.8615 \| 0.9798 \| \| 0.0499 \| 3.21 \| 3700 \| 0.0925 \| 0.8462 \| 0.8771 \| 0.8613 \| 0.9800 \| \| 0.0484 \| 3.29 \| 3800 \| 0.0913 \| 0.8449 \| 0.8773 \| 0.8608 \| 0.9798 \| \| 0.049 \| 3.38 \| 3900 \| 0.0927 \| 0.8409 \| 0.8774 \| 0.8588 \| 0.9796 \| \| 0.05 \| 3.47 \| 4000 \| 0.0900 \| 0.8468 \| 0.8780 \| 0.8621 \| 0.9800 \| \| 0.0456 \| 3.55 \| 4100 \| 0.0904 \| 0.8464 \| 0.8787 \| 0.8623 \| 0.9801 \| \| 0.051 \| 3.64 \| 4200 \| 0.0911 \| 0.8411 \| 0.8778 \| 0.8591 \| 0.9798 \| \| 0.0507 \| 3.73 \| 4300 \| 0.0921 \| 0.8457 \| 0.8768 \| 0.8610 \| 0.9797 \| \| 0.0526 \| 3.81 \| 4400 \| 0.0888 \| 0.8453 \| 0.8774 \| 0.8610 \| 0.9801 \| \| 0.0494 \| 3.9 \| 4500 \| 0.0892 \| 0.8440 \| 0.8785 \| 0.8609 \| 0.9800 \| \| 0.0513 \| 3.99 \| 4600 \| 0.0901 \| 0.8392 \| 0.8811 \| 0.8597 \| 0.9796 \| \| 0.0479 \| 4.07 \| 4700 \| 0.0914 \| 0.8461 \| 0.8781 \| 0.8618 \| 0.9798 \| \| 0.0408 \| 4.16 \| 4800 \| 0.0938 \| 0.8518 \| 0.8724 \| 0.8620 \| 0.9797 \| \| 0.0446 \| 4.25 \| 4900 \| 0.0926 \| 0.8475 \| 0.8766 \| 0.8618 \| 0.9797 \| \| 0.0425 \| 4.33 \| 5000 \| 0.0927 \| 0.8434 \| 0.8762 \| 0.8595 \| 0.9795 \| \| 0.0428 \| 4.42 \| 5100 \| 0.0966 \| 0.8473 \| 0.8788 \| 0.8628 \| 0.9799 \| \| 0.045 \| 4.51 \| 5200 \| 0.0941 \| 0.8428 \| 0.8787 \| 0.8604 \| 0.9795 \| \| 0.0472 \| 4.59 \| 5300 \| 0.0894 \| 0.8436 \| 0.8757 \| 0.8593 \| 0.9794 \| \| 0.0436 \| 4.68 \| 5400 \| 0.0961 \| 0.8464 \| 0.8755 \| 0.8607 \| 0.9800 \| \| 0.0466 \| 4.77 \| 5500 \| 0.0947 \| 0.8451 \| 0.8767 \| 0.8606 \| 0.9797 \| \| 0.0438 \| 4.85 \| 5600 \| 0.0951 \| 0.8398 \| 0.8779 \| 0.8584 \| 0.9795 \| \| 0.0444 \| 4.94 \| 5700 \| 0.0965 \| 0.8431 \| 0.8767 \| 0.8596 \| 0.9797 \| \| 0.0444 \| 5.03 \| 5800 \| 0.0929 \| 0.8421 \| 0.8780 \| 0.8597 \| 0.9798 \| \| 0.0382 \| 5.11 \| 5900 \| 0.0983 \| 0.8460 \| 0.8772 \| 0.8613 \| 0.9796 \| \| 0.0388 \| 5.2 \| 6000 \| 0.0979 \| 0.8406 \| 0.8806 \| 0.8601 \| 0.9797 \| \| 0.0434 \| 5.29 \| 6100 \| 0.0963 \| 0.8463 \| 0.8783 \| 0.8620 \| 0.9795 \| \| 0.038 \| 5.37 \| 6200 \| 0.0977 \| 0.8457 \| 0.8774 \| 0.8612 \| 0.9795 \| \| 0.0406 \| 5.46 \| 6300 \| 0.0970 \| 0.8454 \| 0.8780 \| 0.8614 \| 0.9796 \| \| 0.0415 \| 5.55 \| 6400 \| 0.0971 \| 0.8442 \| 0.8769 \| 0.8602 \| 0.9795 \| \| 0.037 \| 5.63 \| 6500 \| 0.1001 \| 0.8448 \| 0.8771 \| 0.8607 \| 0.9794 \| \| 0.0375 \| 5.72 \| 6600 \| 0.1000 \| 0.8448 \| 0.8744 \| 0.8593 \| 0.9794 \| \| 0.0414 \| 5.81 \| 6700 \| 0.0955 \| 0.8478 \| 0.8745 \| 0.8609 \| 0.9794 \| \| 0.0422 \| 5.89 \| 6800 \| 0.0966 \| 0.8482 \| 0.8746 \| 0.8612 \| 0.9794 \| \| 0.04 \| 5.98 \| 6900 \| 0.0995 \| 0.8410 \| 0.8776 \| 0.8589 \| 0.9795 \| \| 0.0367 \| 6.07 \| 7000 \| 0.1008 \| 0.8460 \| 0.8757 \| 0.8606 \| 0.9795 \| \| 0.0385 \| 6.15 \| 7100 \| 0.1025 \| 0.8428 \| 0.8766 \| 0.8593 \| 0.9793 \| \| 0.039 \| 6.24 \| 7200 \| 0.1003 \| 0.8424 \| 0.8766 \| 0.8592 \| 0.9794 \| \| 0.0344 \| 6.33 \| 7300 \| 0.1047 \| 0.8421 \| 0.8784 \| 0.8599 \| 0.9794 \| \| 0.0346 \| 6.41 \| 7400 \| 0.1022 \| 0.8419 \| 0.8780 \| 0.8596 \| 0.9793 \| \| 0.0379 \| 6.5 \| 7500 \| 0.0978 \| 0.8467 \| 0.8772 \| 0.8617 \| 0.9797 \| \| 0.0358 \| 6.59 \| 7600 \| 0.1018 \| 0.8446 \| 0.8767 \| 0.8603 \| 0.9792 \| \| 0.0363 \| 6.67 \| 7700 \| 0.1001 \| 0.8432 \| 0.8768 \| 0.8597 \| 0.9792 \| \| 0.0378 \| 6.76 \| 7800 \| 0.1030 \| 0.8456 \| 0.8767 \| 0.8609 \| 0.9794 \| \| 0.0403 \| 6.85 \| 7900 \| 0.0971 \| 0.8418 \| 0.8761 \| 0.8586 \| 0.9793 \| \| 0.0352 \| 6.93 \| 8000 \| 0.1035 \| 0.8456 \| 0.8757 \| 0.8604 \| 0.9793 \| \| 0.0332 \| 7.02 \| 8100 \| 0.1021 \| 0.8450 \| 0.8755 \| 0.8600 \| 0.9792 \| \| 0.0371 \| 7.11 \| 8200 \| 0.1032 \| 0.8478 \| 0.8746 \| 0.8610 \| 0.9794 \| \| 0.034 \| 7.19 \| 8300 \| 0.1037 \| 0.8467 \| 0.8738 \| 0.8600 \| 0.9794 \| \| 0.033 \| 7.28 \| 8400 \| 0.1037 \| 0.8457 \| 0.8747 \| 0.8599 \| 0.9793 \| \| 0.0329 \| 7.37 \| 8500 \| 0.1048 \| 0.8459 \| 0.8751 \| 0.8602 \| 0.9791 \| \| 0.0317 \| 7.45 \| 8600 \| 0.1074 \| 0.8441 \| 0.8757 \| 0.8596 \| 0.9792 \| \| 0.0319 \| 7.54 \| 8700 \| 0.1056 \| 0.8437 \| 0.8753 \| 0.8592 \| 0.9792 \| \| 0.0335 \| 7.63 \| 8800 \| 0.1034 \| 0.8446 \| 0.8736 \| 0.8589 \| 0.9793 \| \| 0.0346 \| 7.71 \| 8900 \| 0.1069 \| 0.8461 \| 0.8735 \| 0.8596 \| 0.9792 \| \| 0.0342 \| 7.8 \| 9000 \| 0.1031 \| 0.8427 \| 0.8757 \| 0.8589 \| 0.9793 \| \| 0.0371 \| 7.89 \| 9100 \| 0.1024 \| 0.8438 \| 0.8747 \| 0.8590 \| 0.9793 \| \| 0.0384 \| 7.97 \| 9200 \| 0.1032 \| 0.8472 \| 0.8746 \| 0.8607 \| 0.9795 \| \| 0.0308 \| 8.06 \| 9300 \| 0.1070 \| 0.8449 \| 0.8753 \| 0.8598 \| 0.9793 \| \| 0.0318 \| 8.15 \| 9400 \| 0.1070 \| 0.8459 \| 0.8738 \| 0.8596 \| 0.9794 \| \| 0.0285 \| 8.23 \| 9500 \| 0.1077 \| 0.8474 \| 0.8751 \| 0.8610 \| 0.9794 \| \| 0.0334 \| 8.32 \| 9600 \| 0.1066 \| 0.8443 \| 0.8757 \| 0.8598 \| 0.9793 \| \| 0.0332 \| 8.41 \| 9700 \| 0.1055 \| 0.8462 \| 0.8747 \| 0.8602 \| 0.9793 \| \| 0.0341 \| 8.49 \| 9800 \| 0.1056 \| 0.8442 \| 0.8749 \| 0.8593 \| 0.9793 \| \| 0.0304 \| 8.58 \| 9900 \| 0.1066 \| 0.8447 \| 0.8729 \| 0.8586 \| 0.9792 \| \| 0.0353 \| 8.67 \| 10000 \| 0.1057 \| 0.8446 \| 0.8741 \| 0.8591 \| 0.9792 \| \| 0.0348 \| 8.75 \| 10100 \| 0.1051 \| 0.8443 \| 0.8736 \| 0.8587 \| 0.9792 \| \| 0.0326 \| 8.84 \| 10200 \| 0.1047 \| 0.8443 \| 0.8757 \| 0.8597 \| 0.9793 \| \| 0.0332 \| 8.93 \| 10300 \| 0.1044 \| 0.8461 \| 0.8732 \| 0.8594 \| 0.9793 \| \| 0.0328 \| 9.01 \| 10400 \| 0.1053 \| 0.8438 \| 0.8744 \| 0.8588 \| 0.9792 \| \| 0.0318 \| 9.1 \| 10500 \| 0.1072 \| 0.8415 \| 0.8746 \| 0.8577 \| 0.9793 \| \| 0.0296 \| 9.19 \| 10600 \| 0.1084 \| 0.8431 \| 0.8743 \| 0.8584 \| 0.9793 \| \| 0.0324 \| 9.27 \| 10700 \| 0.1074 \| 0.8448 \| 0.8746 \| 0.8594 \| 0.9794 \| \| 0.0326 \| 9.36 \| 10800 \| 0.1080 \| 0.8439 \| 0.8752 \| 0.8593 \| 0.9793 \| \| 0.0288 \| 9.45 \| 10900 \| 0.1084 \| 0.8451 \| 0.8739 \| 0.8593 \| 0.9794 \| \| 0.0314 \| 9.53 \| 11000 \| 0.1082 \| 0.8450 \| 0.8746 \| 0.8596 \| 0.9794 \| \| 0.0292 \| 9.62 \| 11100 \| 0.1084 \| 0.8446 \| 0.8740 \| 0.8590 \| 0.9794 \| \| 0.0328 \| 9.71 \| 11200 \| 0.1080 \| 0.8447 \| 0.8741 \| 0.8591 \| 0.9794 \| \| 0.0313 \| 9.79 \| 11300 \| 0.1080 \| 0.8439 \| 0.8747 \| 0.8590 \| 0.9794 \| \| 0.0295 \| 9.88 \| 11400 \| 0.1080 \| 0.8445 \| 0.8739 \| 0.8589 \| 0.9793 \| \| 0.0316 \| 9.97 \| 11500 \| 0.1080 \| 0.8445 \| 0.8740 \| 0.8590 \| 0.9793 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
Augusto777/vit-base-patch16-224-RU2-40	Augusto777	2024-05-24T00:12:24Z	219	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-23T23:21:01Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-RU2-40 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.8333333333333334 --- <!-- 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. --> # vit-base-patch16-224-RU2-40 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2003 - Accuracy: 0.8333 ## 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: 5.5e-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: 0.05 - num_epochs: 40 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.3226 \| 0.99 \| 38 \| 1.2293 \| 0.6 \| \| 0.9048 \| 2.0 \| 77 \| 0.7969 \| 0.7 \| \| 0.4039 \| 2.99 \| 115 \| 0.6800 \| 0.7167 \| \| 0.281 \| 4.0 \| 154 \| 0.8892 \| 0.7667 \| \| 0.1755 \| 4.99 \| 192 \| 0.9072 \| 0.7333 \| \| 0.1035 \| 6.0 \| 231 \| 0.8036 \| 0.8167 \| \| 0.1275 \| 6.99 \| 269 \| 0.8627 \| 0.8 \| \| 0.107 \| 8.0 \| 308 \| 0.8713 \| 0.8 \| \| 0.0984 \| 8.99 \| 346 \| 0.9660 \| 0.8 \| \| 0.0823 \| 10.0 \| 385 \| 1.0704 \| 0.7833 \| \| 0.0771 \| 10.99 \| 423 \| 0.9409 \| 0.7667 \| \| 0.0527 \| 12.0 \| 462 \| 1.0052 \| 0.7833 \| \| 0.0708 \| 12.99 \| 500 \| 0.9578 \| 0.8 \| \| 0.0562 \| 14.0 \| 539 \| 1.0712 \| 0.8167 \| \| 0.0467 \| 14.99 \| 577 \| 1.0586 \| 0.8167 \| \| 0.0445 \| 16.0 \| 616 \| 1.2066 \| 0.7667 \| \| 0.0474 \| 16.99 \| 654 \| 1.1863 \| 0.75 \| \| 0.0263 \| 18.0 \| 693 \| 1.1207 \| 0.8167 \| \| 0.0307 \| 18.99 \| 731 \| 1.1813 \| 0.8167 \| \| 0.0393 \| 20.0 \| 770 \| 1.3761 \| 0.75 \| \| 0.0475 \| 20.99 \| 808 \| 1.3008 \| 0.7667 \| \| 0.0215 \| 22.0 \| 847 \| 1.2625 \| 0.7333 \| \| 0.0311 \| 22.99 \| 885 \| 1.1508 \| 0.8 \| \| 0.027 \| 24.0 \| 924 \| 1.3035 \| 0.7667 \| \| 0.0251 \| 24.99 \| 962 \| 1.2270 \| 0.7667 \| \| 0.0161 \| 26.0 \| 1001 \| 1.1470 \| 0.8167 \| \| 0.0258 \| 26.99 \| 1039 \| 1.1473 \| 0.8167 \| \| 0.0142 \| 28.0 \| 1078 \| 1.2326 \| 0.7667 \| \| 0.0151 \| 28.99 \| 1116 \| 1.3978 \| 0.7667 \| \| 0.021 \| 30.0 \| 1155 \| 1.2003 \| 0.8333 \| \| 0.0158 \| 30.99 \| 1193 \| 1.2488 \| 0.7667 \| \| 0.0163 \| 32.0 \| 1232 \| 1.3232 \| 0.75 \| \| 0.0143 \| 32.99 \| 1270 \| 1.2467 \| 0.8 \| \| 0.02 \| 34.0 \| 1309 \| 1.3176 \| 0.7833 \| \| 0.0128 \| 34.99 \| 1347 \| 1.3083 \| 0.7667 \| \| 0.0144 \| 36.0 \| 1386 \| 1.3080 \| 0.7667 \| \| 0.0109 \| 36.99 \| 1424 \| 1.2999 \| 0.8 \| \| 0.0082 \| 38.0 \| 1463 \| 1.2718 \| 0.8 \| \| 0.0064 \| 38.99 \| 1501 \| 1.2588 \| 0.7667 \| \| 0.0097 \| 39.48 \| 1520 \| 1.2597 \| 0.7667 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
fine-tuned/SciFact-256-24-gpt-4o-2024-05-13-499715	fine-tuned	2024-05-24T00:07:29Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "mteb", "Science", "Research", "Verification", "Dataset", "Claims", "en", "dataset:fine-tuned/SciFact-256-24-gpt-4o-2024-05-13-499715", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-24T00:06:33Z	--- license: apache-2.0 datasets: - fine-tuned/SciFact-256-24-gpt-4o-2024-05-13-499715 - allenai/c4 language: - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - Science - Research - Verification - Dataset - Claims --- This model is a fine-tuned version of [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) designed for the following use case: scientific claim verification ## 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/SciFact-256-24-gpt-4o-2024-05-13-499715', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
gonzalu/JuggerCon_v1	gonzalu	2024-05-24T00:03:17Z	0	0	null	[ "cosXL", "Juggernaut", "High Contrast", "Experimental", "region:us" ]	null	2024-05-23T15:36:18Z	--- tags: - cosXL - Juggernaut - High Contrast - Experimental --- ___ # JuggerCon v1 <a href="JuggerCon_v1_Poster.png"><img src="JuggerCon_v1_Poster.png" alt="poster" width="600"/></a> ## Merge of [Juggernaut-X](https://huggingface.co/RunDiffusion/Juggernaut-X-v10) and [cosXL](https://huggingface.co/stabilityai/cosxl) This model is intended to produce very specific [High Contrast] images. If you're into that sort of thing, this is a good model to use. ### Tested with the following settings: - Sampler: DPM++ 2M - Scheduler: Karras - Steps: 30 - CFG: 7 - Resoution: 832x1216 Feel free to experiement. *** ### Examples below have embedded workflow ![](JuggerCon_v1_00001_.png) ![](JuggerCon_v1_00002_.png) ![](JuggerCon_v1_00003_.png) ![](JuggerCon_v1_00004_.png) ![](JuggerCon_v1_00006_.png) ![](JuggerCon_v1_00010_.png) ![](JuggerCon_v1_00011_.png) ![](JuggerCon_v1_00014_.png) ![](JuggerCon_v1_00015_.png) ![](JuggerCon_v1_00018_.png) ![](JuggerCon_v1_00028_.png) ![](JuggerCon_v1_00039_.png)
ZaneHorrible/vitLarge-16-384-2e-4-batch_16_epoch_4_classes_24	ZaneHorrible	2024-05-24T00:02:33Z	197	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-large-patch16-384", "base_model:finetune:google/vit-large-patch16-384", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-23T17:42:53Z	--- license: apache-2.0 base_model: google/vit-large-patch16-384 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vitLarge-16-384-2e-4-batch_16_epoch_4_classes_24 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: 0.9712643678160919 --- <!-- 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. --> # vitLarge-16-384-2e-4-batch_16_epoch_4_classes_24 This model is a fine-tuned version of [google/vit-large-patch16-384](https://huggingface.co/google/vit-large-patch16-384) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1519 - Accuracy: 0.9713 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7944 \| 0.03 \| 100 \| 0.8483 \| 0.7428 \| \| 1.1261 \| 0.07 \| 200 \| 1.0595 \| 0.6911 \| \| 0.7575 \| 0.1 \| 300 \| 0.5007 \| 0.8534 \| \| 0.3567 \| 0.14 \| 400 \| 0.5404 \| 0.8391 \| \| 0.4062 \| 0.17 \| 500 \| 0.7795 \| 0.7974 \| \| 0.4227 \| 0.21 \| 600 \| 0.3598 \| 0.8851 \| \| 0.3436 \| 0.24 \| 700 \| 0.4550 \| 0.8693 \| \| 0.7695 \| 0.28 \| 800 \| 0.5748 \| 0.8247 \| \| 0.2864 \| 0.31 \| 900 \| 0.4017 \| 0.8793 \| \| 0.3718 \| 0.35 \| 1000 \| 0.5384 \| 0.8420 \| \| 0.2764 \| 0.38 \| 1100 \| 0.4682 \| 0.8764 \| \| 0.3438 \| 0.42 \| 1200 \| 0.4194 \| 0.8807 \| \| 0.4031 \| 0.45 \| 1300 \| 0.4105 \| 0.8922 \| \| 0.449 \| 0.49 \| 1400 \| 0.4499 \| 0.8678 \| \| 0.2249 \| 0.52 \| 1500 \| 0.2701 \| 0.9066 \| \| 0.2398 \| 0.56 \| 1600 \| 0.4124 \| 0.8807 \| \| 0.5759 \| 0.59 \| 1700 \| 0.8378 \| 0.7960 \| \| 0.1315 \| 0.63 \| 1800 \| 0.4757 \| 0.8779 \| \| 0.4481 \| 0.66 \| 1900 \| 0.3463 \| 0.9037 \| \| 0.2183 \| 0.7 \| 2000 \| 0.4291 \| 0.8779 \| \| 0.2101 \| 0.73 \| 2100 \| 0.3318 \| 0.9109 \| \| 1.0071 \| 0.77 \| 2200 \| 2.9399 \| 0.2098 \| \| 0.3426 \| 0.8 \| 2300 \| 0.4231 \| 0.9023 \| \| 0.1126 \| 0.84 \| 2400 \| 0.3609 \| 0.9124 \| \| 0.3954 \| 0.87 \| 2500 \| 0.4471 \| 0.8994 \| \| 0.2099 \| 0.91 \| 2600 \| 0.3465 \| 0.9052 \| \| 0.1982 \| 0.94 \| 2700 \| 0.4135 \| 0.8994 \| \| 0.1931 \| 0.98 \| 2800 \| 0.3306 \| 0.9095 \| \| 0.1721 \| 1.01 \| 2900 \| 0.3470 \| 0.9195 \| \| 0.1864 \| 1.04 \| 3000 \| 0.3814 \| 0.9124 \| \| 0.0652 \| 1.08 \| 3100 \| 0.2534 \| 0.9296 \| \| 0.1176 \| 1.11 \| 3200 \| 0.2744 \| 0.9210 \| \| 0.0988 \| 1.15 \| 3300 \| 0.2966 \| 0.9325 \| \| 0.0289 \| 1.18 \| 3400 \| 0.2021 \| 0.9555 \| \| 0.1465 \| 1.22 \| 3500 \| 0.1566 \| 0.9583 \| \| 0.2023 \| 1.25 \| 3600 \| 0.2803 \| 0.9353 \| \| 0.1042 \| 1.29 \| 3700 \| 0.2893 \| 0.9282 \| \| 0.1403 \| 1.32 \| 3800 \| 0.3145 \| 0.9239 \| \| 0.0786 \| 1.36 \| 3900 \| 0.3188 \| 0.9267 \| \| 0.2427 \| 1.39 \| 4000 \| 0.6615 \| 0.8693 \| \| 0.3187 \| 1.43 \| 4100 \| 0.3598 \| 0.9195 \| \| 0.0897 \| 1.46 \| 4200 \| 0.2778 \| 0.9425 \| \| 0.068 \| 1.5 \| 4300 \| 0.3445 \| 0.9124 \| \| 0.2165 \| 1.53 \| 4400 \| 0.2351 \| 0.9468 \| \| 0.0807 \| 1.57 \| 4500 \| 0.3111 \| 0.9310 \| \| 0.007 \| 1.6 \| 4600 \| 0.2208 \| 0.9483 \| \| 0.0017 \| 1.64 \| 4700 \| 0.1943 \| 0.9411 \| \| 0.081 \| 1.67 \| 4800 \| 0.3503 \| 0.9239 \| \| 0.0285 \| 1.71 \| 4900 \| 0.3109 \| 0.9239 \| \| 0.0495 \| 1.74 \| 5000 \| 0.1233 \| 0.9641 \| \| 0.0201 \| 1.78 \| 5100 \| 0.2508 \| 0.9483 \| \| 0.1186 \| 1.81 \| 5200 \| 0.3854 \| 0.9210 \| \| 0.0283 \| 1.85 \| 5300 \| 0.2336 \| 0.9425 \| \| 0.0569 \| 1.88 \| 5400 \| 0.2872 \| 0.9425 \| \| 0.0498 \| 1.92 \| 5500 \| 0.2462 \| 0.9569 \| \| 0.0101 \| 1.95 \| 5600 \| 0.2256 \| 0.9511 \| \| 0.0474 \| 1.99 \| 5700 \| 0.2201 \| 0.9569 \| \| 0.0008 \| 2.02 \| 5800 \| 0.2079 \| 0.9526 \| \| 0.0 \| 2.06 \| 5900 \| 0.1951 \| 0.9583 \| \| 0.0007 \| 2.09 \| 6000 \| 0.1449 \| 0.9626 \| \| 0.003 \| 2.12 \| 6100 \| 0.1411 \| 0.9670 \| \| 0.0028 \| 2.16 \| 6200 \| 0.1889 \| 0.9598 \| \| 0.0018 \| 2.19 \| 6300 \| 0.2356 \| 0.9511 \| \| 0.0087 \| 2.23 \| 6400 \| 0.2185 \| 0.9569 \| \| 0.0169 \| 2.26 \| 6500 \| 0.1898 \| 0.9583 \| \| 0.0003 \| 2.3 \| 6600 \| 0.1879 \| 0.9655 \| \| 0.0008 \| 2.33 \| 6700 \| 0.1331 \| 0.9713 \| \| 0.0001 \| 2.37 \| 6800 \| 0.1537 \| 0.9655 \| \| 0.0002 \| 2.4 \| 6900 \| 0.2148 \| 0.9598 \| \| 0.0079 \| 2.44 \| 7000 \| 0.1258 \| 0.9698 \| \| 0.0004 \| 2.47 \| 7100 \| 0.1557 \| 0.9698 \| \| 0.0 \| 2.51 \| 7200 \| 0.1376 \| 0.9698 \| \| 0.0007 \| 2.54 \| 7300 \| 0.1238 \| 0.9713 \| \| 0.0 \| 2.58 \| 7400 \| 0.1433 \| 0.9670 \| \| 0.0023 \| 2.61 \| 7500 \| 0.1537 \| 0.9684 \| \| 0.0004 \| 2.65 \| 7600 \| 0.1302 \| 0.9727 \| \| 0.0002 \| 2.68 \| 7700 \| 0.1557 \| 0.9698 \| \| 0.0013 \| 2.72 \| 7800 \| 0.1614 \| 0.9698 \| \| 0.0 \| 2.75 \| 7900 \| 0.1713 \| 0.9670 \| \| 0.0 \| 2.79 \| 8000 \| 0.1458 \| 0.9698 \| \| 0.0 \| 2.82 \| 8100 \| 0.1453 \| 0.9698 \| \| 0.0 \| 2.86 \| 8200 \| 0.1527 \| 0.9670 \| \| 0.0 \| 2.89 \| 8300 \| 0.1508 \| 0.9698 \| \| 0.0001 \| 2.93 \| 8400 \| 0.1544 \| 0.9713 \| \| 0.0 \| 2.96 \| 8500 \| 0.1506 \| 0.9713 \| \| 0.0 \| 3.0 \| 8600 \| 0.1519 \| 0.9713 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
sottoflavio/my-awesome-model	sottoflavio	2024-05-24T00:01:19Z	50	0	transformers	[ "transformers", "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "endpoints_compatible", "region:us" ]	null	2024-05-24T00:01:17Z	--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
TroyDoesAI/Mermaid-Contextual-Obedient-RAG-Phi-3-medium-128k-instruct-18B	TroyDoesAI	2024-05-23T23:53:05Z	10	2	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "license:cc-by-nd-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T17:10:11Z	--- license: cc-by-nd-4.0 ---
hgnoi/nlZJHIjeip4xwIW1	hgnoi	2024-05-23T23:53:00Z	133	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T23:51:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
wwe180/Llama3-15B-ShenNu-lora-v0.1	wwe180	2024-05-23T23:47:32Z	0	0	null	[ "safetensors", "lora", "Llama3", "base_model:wwe180/Llama3-15B-lingyang-v0.1", "base_model:adapter:wwe180/Llama3-15B-lingyang-v0.1", "region:us" ]	null	2024-05-23T22:04:25Z	--- tags: - lora - Llama3 base_model: - wwe180/Llama3-15B-lingyang-v0.1 --- #该模型是实验性的，因此无法保证结果。 # Llama3-15B-神女-lora-v0.1 Llama3-15B-ShenNu-lora-v0.1 是： [Llama3-15B-lingyang-v0.1](https://huggingface.co/wwe180/Llama3-15B-lingyang-v0.1)的lora ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "wwe180/Llama3-15B-lingyang-v0.1" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"])
ulrica/Dermatology-LLaVA-lora-7b	ulrica	2024-05-23T23:46:33Z	11	0	transformers	[ "transformers", "safetensors", "llava_llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T22:19:29Z	--- license: apache-2.0 ---
RichardErkhov/shadowml_-_BeagSake-7B-gguf	RichardErkhov	2024-05-23T23:46:31Z	4	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-23T21:24:58Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) BeagSake-7B - GGUF - Model creator: https://huggingface.co/shadowml/ - Original model: https://huggingface.co/shadowml/BeagSake-7B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [BeagSake-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [BeagSake-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [BeagSake-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [BeagSake-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [BeagSake-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [BeagSake-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [BeagSake-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [BeagSake-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [BeagSake-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [BeagSake-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [BeagSake-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [BeagSake-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [BeagSake-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [BeagSake-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [BeagSake-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [BeagSake-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [BeagSake-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [BeagSake-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [BeagSake-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [BeagSake-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [BeagSake-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [BeagSake-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/shadowml_-_BeagSake-7B-gguf/blob/main/BeagSake-7B.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- license: cc-by-nc-4.0 tags: - merge - mergekit - lazymergekit base_model: - shadowml/BeagleSempra-7B - shadowml/WestBeagle-7B model-index: - name: BeagSake-7B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 72.44 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/BeagSake-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.39 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/BeagSake-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 65.23 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/BeagSake-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 72.27 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/BeagSake-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 82.16 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/BeagSake-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 71.8 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/BeagSake-7B name: Open LLM Leaderboard --- # BeagSake-7B BeagSake-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [shadowml/BeagleSempra-7B](https://huggingface.co/shadowml/BeagleSempra-7B) * [shadowml/WestBeagle-7B](https://huggingface.co/shadowml/WestBeagle-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: shadowml/BeagleSempra-7B layer_range: [0, 32] - model: shadowml/WestBeagle-7B layer_range: [0, 32] merge_method: slerp base_model: shadowml/BeagleSempra-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "shadowml/BeagSake-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_shadowml__BeagSake-7B) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|75.38\| \|AI2 Reasoning Challenge (25-Shot)\|72.44\| \|HellaSwag (10-Shot) \|88.39\| \|MMLU (5-Shot) \|65.23\| \|TruthfulQA (0-shot) \|72.27\| \|Winogrande (5-shot) \|82.16\| \|GSM8k (5-shot) \|71.80\|
UtkuCicek/utku_marks	UtkuCicek	2024-05-23T23:32:38Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers-training", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-23T19:17:14Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers-training - diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 inference: true --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Text-to-image finetuning - UtkuCicek/utku_marks This pipeline was finetuned from stabilityai/stable-diffusion-xl-base-1.0 on the UtkuCicek/new-marks-data dataset. Below are some example images generated with the finetuned pipeline using the following prompt: italian style mini pizza with mozerrella on the side: ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## 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]
RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf	RichardErkhov	2024-05-23T23:23:24Z	170	1	null	[ "gguf", "arxiv:2304.12244", "arxiv:2306.08568", "arxiv:2308.09583", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-23T20:53:40Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) microsoft_WizardLM-2-7B - GGUF - Model creator: https://huggingface.co/lucyknada/ - Original model: https://huggingface.co/lucyknada/microsoft_WizardLM-2-7B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [microsoft_WizardLM-2-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [microsoft_WizardLM-2-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [microsoft_WizardLM-2-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [microsoft_WizardLM-2-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [microsoft_WizardLM-2-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [microsoft_WizardLM-2-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [microsoft_WizardLM-2-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [microsoft_WizardLM-2-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [microsoft_WizardLM-2-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [microsoft_WizardLM-2-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [microsoft_WizardLM-2-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [microsoft_WizardLM-2-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [microsoft_WizardLM-2-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [microsoft_WizardLM-2-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [microsoft_WizardLM-2-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [microsoft_WizardLM-2-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [microsoft_WizardLM-2-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [microsoft_WizardLM-2-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [microsoft_WizardLM-2-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [microsoft_WizardLM-2-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [microsoft_WizardLM-2-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [microsoft_WizardLM-2-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/lucyknada_-_microsoft_WizardLM-2-7B-gguf/blob/main/microsoft_WizardLM-2-7B.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- license: apache-2.0 --- <p style="font-size:20px;" align="center"> 🏠 <a href="https://wizardlm.github.io/WizardLM2" target="_blank">WizardLM-2 Release Blog</a> </p> <p align="center"> 🤗 <a href="https://huggingface.co/collections/microsoft/wizardlm-2-661d403f71e6c8257dbd598a" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/victorsungo/WizardLM/tree/main/WizardLM-2" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> • 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br> </p> <p align="center"> 👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a> </p> ## News 🔥🔥🔥 [2024/04/15] We introduce and opensource WizardLM-2, our next generation state-of-the-art large language models, which have improved performance on complex chat, multilingual, reasoning and agent. New family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B. - WizardLM-2 8x22B is our most advanced model, demonstrates highly competitive performance compared to those leading proprietary works and consistently outperforms all the existing state-of-the-art opensource models. - WizardLM-2 70B reaches top-tier reasoning capabilities and is the first choice in the same size. - WizardLM-2 7B is the fastest and achieves comparable performance with existing 10x larger opensource leading models. For more details of WizardLM-2 please read our [release blog post](https://wizardlm.github.io/WizardLM2) and upcoming paper. ## Model Details * Model name: WizardLM-2 7B * Developed by: WizardLM@Microsoft AI * Base model: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) * Parameters: 7B * Language(s): Multilingual * Blog: [Introducing WizardLM-2](https://wizardlm.github.io/WizardLM2) * Repository: [https://github.com/nlpxucan/WizardLM](https://github.com/nlpxucan/WizardLM) * Paper: WizardLM-2 (Upcoming) * License: Apache2.0 ## Model Capacities MT-Bench We also adopt the automatic MT-Bench evaluation framework based on GPT-4 proposed by lmsys to assess the performance of models. The WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced proprietary models. Meanwhile, WizardLM-2 7B and WizardLM-2 70B are all the top-performing models among the other leading baselines at 7B to 70B model scales. <p align="center" width="100%"> <a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/mtbench.png" alt="MTBench" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a> </p> Human Preferences Evaluation We carefully collected a complex and challenging set consisting of real-world instructions, which includes main requirements of humanity, such as writing, coding, math, reasoning, agent, and multilingual. We report the win:loss rate without tie: - WizardLM-2 8x22B is just slightly falling behind GPT-4-1106-preview, and significantly stronger than Command R Plus and GPT4-0314. - WizardLM-2 70B is better than GPT4-0613, Mistral-Large, and Qwen1.5-72B-Chat. - WizardLM-2 7B is comparable with Qwen1.5-32B-Chat, and surpasses Qwen1.5-14B-Chat and Starling-LM-7B-beta. <p align="center" width="100%"> <a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/winall.png" alt="Win" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a> </p> ## Method Overview We built a fully AI powered synthetic training system to train WizardLM-2 models, please refer to our [blog](https://wizardlm.github.io/WizardLM2) for more details of this system. <p align="center" width="100%"> <a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/exp_1.png" alt="Method" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a> </p> ## Usage ❗<b>Note for model system prompts usage:</b> <b>WizardLM-2</b> adopts the prompt format from <b>Vicuna</b> and supports multi-turn conversation. The prompt should be as following: ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s> USER: Who are you? ASSISTANT: I am WizardLM.</s>...... ``` <b> Inference WizardLM-2 Demo Script</b> We provide a WizardLM-2 inference demo [code](https://github.com/nlpxucan/WizardLM/tree/main/demo) on our github.
InferenceIllusionist/Llama3-ChatQA-1.5-8B-iMat-GGUF	InferenceIllusionist	2024-05-23T23:14:01Z	76	0	null	[ "gguf", "merge", "llama3", "iMat", "license:llama3", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-05-21T21:50:32Z	--- tags: - merge - gguf - llama3 - iMat license: llama3 --- <img src="https://i.imgur.com/P68dXux.png" width="400"/> # Llama3-ChatQA-1.5-8B-iMat-GGUF Quantized from fp16. * Weighted quantizations were creating using fp16 GGUF and [groups_merged-enhancedV2-TurboMini.txt](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-9432658) in 234 chunks and n_ctx=512 * This method of calculating the importance matrix showed improvements in some areas for Mistral 7b and Llama3 8b models, see above post for details * The enhancedv2-turbomini file appends snippets from turboderp's calibration data to the standard groups_merged.txt file For a brief rundown of iMatrix quant performance please see this [PR](https://github.com/ggerganov/llama.cpp/pull/5747) <i>All quants are verified working prior to uploading to repo for your safety and convenience. </i> <b>Tip:</b> Pick a file size under your GPU's VRAM while still allowing some room for context for best speed. You may need to pad this further depending on if you are running image gen or TTS as well. BFloat16 model card can be found [here](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B)
ND911/EclecticEuphoria_DPO_BMM-SDXL	ND911	2024-05-23T23:05:27Z	0	1	null	[ "region:us" ]	null	2024-05-23T22:20:21Z	# EclecticEuphoria_DPO_BMM This is a beast combo of everything, no other way to describe it. An All Purpose Model * [Civitai Images](https://civitai.com/collections/1213202) # Comfyui ![](ComfyUI_temp_qezki_00002_.png) # RuinedFoocus ![](1418360684.png) # StableSwarm ![](2024-05-23_17-12-28_3198.png) # Super Simple Workflow ![](workflow_EEDPOBMM.png) # Stable Cascade to SDXL with Prompt Enhancer Workflow ![](workflow(67).png)
ZaneHorrible/ViTL_16_384_1e_4_batch_16_epoch_4_classes_24	ZaneHorrible	2024-05-23T23:04:27Z	217	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-large-patch16-384", "base_model:finetune:google/vit-large-patch16-384", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-23T18:47:25Z	--- license: apache-2.0 base_model: google/vit-large-patch16-384 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Aradam_ViTL-16-384-2e-4-batch_16_epoch_4_classes_24 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: 0.9698275862068966 --- <!-- 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. --> # Aradam_ViTL-16-384-2e-4-batch_16_epoch_4_classes_24 This model is a fine-tuned version of [google/vit-large-patch16-384](https://huggingface.co/google/vit-large-patch16-384) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1097 - Accuracy: 0.9698 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.1511 \| 0.03 \| 100 \| 0.8900 \| 0.7471 \| \| 0.8497 \| 0.07 \| 200 \| 0.8558 \| 0.7687 \| \| 0.6297 \| 0.1 \| 300 \| 0.5995 \| 0.8132 \| \| 0.5735 \| 0.14 \| 400 \| 0.4456 \| 0.8649 \| \| 0.307 \| 0.17 \| 500 \| 0.4031 \| 0.8851 \| \| 0.3961 \| 0.21 \| 600 \| 0.4865 \| 0.8506 \| \| 0.6511 \| 0.24 \| 700 \| 0.5270 \| 0.8491 \| \| 0.4526 \| 0.28 \| 800 \| 0.6105 \| 0.8376 \| \| 0.4071 \| 0.31 \| 900 \| 0.3936 \| 0.8937 \| \| 0.2729 \| 0.35 \| 1000 \| 0.3287 \| 0.8994 \| \| 0.4277 \| 0.38 \| 1100 \| 0.5402 \| 0.8621 \| \| 0.2588 \| 0.42 \| 1200 \| 0.3344 \| 0.9023 \| \| 0.3034 \| 0.45 \| 1300 \| 0.3269 \| 0.8922 \| \| 0.2463 \| 0.49 \| 1400 \| 0.4931 \| 0.8563 \| \| 0.1999 \| 0.52 \| 1500 \| 0.3622 \| 0.9037 \| \| 0.1483 \| 0.56 \| 1600 \| 0.3114 \| 0.9066 \| \| 0.1266 \| 0.59 \| 1700 \| 0.3893 \| 0.8894 \| \| 0.1131 \| 0.63 \| 1800 \| 0.2696 \| 0.9267 \| \| 0.4377 \| 0.66 \| 1900 \| 0.2953 \| 0.9224 \| \| 0.1578 \| 0.7 \| 2000 \| 0.3059 \| 0.9109 \| \| 0.1273 \| 0.73 \| 2100 \| 0.2474 \| 0.9267 \| \| 0.077 \| 0.77 \| 2200 \| 0.2231 \| 0.9382 \| \| 0.0855 \| 0.8 \| 2300 \| 0.2795 \| 0.9368 \| \| 0.0756 \| 0.84 \| 2400 \| 0.2858 \| 0.9210 \| \| 0.2635 \| 0.87 \| 2500 \| 0.2563 \| 0.9353 \| \| 0.1622 \| 0.91 \| 2600 \| 0.2727 \| 0.9325 \| \| 0.1941 \| 0.94 \| 2700 \| 0.2450 \| 0.9239 \| \| 0.0144 \| 0.98 \| 2800 \| 0.2113 \| 0.9454 \| \| 0.0617 \| 1.01 \| 2900 \| 0.1612 \| 0.9454 \| \| 0.0188 \| 1.04 \| 3000 \| 0.2029 \| 0.9425 \| \| 0.0731 \| 1.08 \| 3100 \| 0.1762 \| 0.9612 \| \| 0.0846 \| 1.11 \| 3200 \| 0.1612 \| 0.9569 \| \| 0.0586 \| 1.15 \| 3300 \| 0.2737 \| 0.9353 \| \| 0.0258 \| 1.18 \| 3400 \| 0.1310 \| 0.9670 \| \| 0.0665 \| 1.22 \| 3500 \| 0.1515 \| 0.9540 \| \| 0.0143 \| 1.25 \| 3600 \| 0.2254 \| 0.9440 \| \| 0.0842 \| 1.29 \| 3700 \| 0.2393 \| 0.9468 \| \| 0.0019 \| 1.32 \| 3800 \| 0.1660 \| 0.9526 \| \| 0.013 \| 1.36 \| 3900 \| 0.1413 \| 0.9684 \| \| 0.0177 \| 1.39 \| 4000 \| 0.1455 \| 0.9641 \| \| 0.0128 \| 1.43 \| 4100 \| 0.1291 \| 0.9641 \| \| 0.0222 \| 1.46 \| 4200 \| 0.1567 \| 0.9526 \| \| 0.0017 \| 1.5 \| 4300 \| 0.1640 \| 0.9569 \| \| 0.0009 \| 1.53 \| 4400 \| 0.1861 \| 0.9612 \| \| 0.0007 \| 1.57 \| 4500 \| 0.1440 \| 0.9713 \| \| 0.0026 \| 1.6 \| 4600 \| 0.0940 \| 0.9784 \| \| 0.0006 \| 1.64 \| 4700 \| 0.1282 \| 0.9655 \| \| 0.0023 \| 1.67 \| 4800 \| 0.1341 \| 0.9698 \| \| 0.0002 \| 1.71 \| 4900 \| 0.1099 \| 0.9727 \| \| 0.0013 \| 1.74 \| 5000 \| 0.0872 \| 0.9756 \| \| 0.0001 \| 1.78 \| 5100 \| 0.0908 \| 0.9784 \| \| 0.0006 \| 1.81 \| 5200 \| 0.1034 \| 0.9727 \| \| 0.0009 \| 1.85 \| 5300 \| 0.0940 \| 0.9727 \| \| 0.0 \| 1.88 \| 5400 \| 0.1236 \| 0.9655 \| \| 0.0003 \| 1.92 \| 5500 \| 0.1180 \| 0.9684 \| \| 0.0001 \| 1.95 \| 5600 \| 0.1091 \| 0.9698 \| \| 0.0001 \| 1.99 \| 5700 \| 0.1097 \| 0.9698 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
tsavage68/MedQA_L3_1000steps_1e7rate_03beta_CSFTDPO	tsavage68	2024-05-23T22:54:22Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:tsavage68/MedQA_L3_1000steps_1e6rate_SFT", "base_model:finetune:tsavage68/MedQA_L3_1000steps_1e6rate_SFT", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T07:31:23Z	--- license: llama3 base_model: tsavage68/MedQA_L3_1000steps_1e6rate_SFT tags: - trl - dpo - generated_from_trainer model-index: - name: MedQA_L3_1000steps_1e7rate_03beta_CSFTDPO 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. --> # MedQA_L3_1000steps_1e7rate_03beta_CSFTDPO This model is a fine-tuned version of [tsavage68/MedQA_L3_1000steps_1e6rate_SFT](https://huggingface.co/tsavage68/MedQA_L3_1000steps_1e6rate_SFT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6020 - Rewards/chosen: 0.7087 - Rewards/rejected: 0.4830 - Rewards/accuracies: 0.7341 - Rewards/margins: 0.2257 - Logps/rejected: -32.2447 - Logps/chosen: -28.9661 - Logits/rejected: -0.7358 - Logits/chosen: -0.7350 ## 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-07 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6925 \| 0.0489 \| 50 \| 0.6930 \| -0.0016 \| -0.0023 \| 0.5011 \| 0.0007 \| -33.8624 \| -31.3338 \| -0.7320 \| -0.7314 \| \| 0.6841 \| 0.0977 \| 100 \| 0.6807 \| 0.2459 \| 0.2195 \| 0.6549 \| 0.0264 \| -33.1233 \| -30.5088 \| -0.7330 \| -0.7323 \| \| 0.6562 \| 0.1466 \| 150 \| 0.6641 \| 0.3800 \| 0.3137 \| 0.6791 \| 0.0663 \| -32.8092 \| -30.0619 \| -0.7310 \| -0.7303 \| \| 0.6334 \| 0.1954 \| 200 \| 0.6509 \| 0.1334 \| 0.0355 \| 0.7165 \| 0.0979 \| -33.7366 \| -30.8837 \| -0.7311 \| -0.7304 \| \| 0.6544 \| 0.2443 \| 250 \| 0.6415 \| 0.2943 \| 0.1754 \| 0.7209 \| 0.1189 \| -33.2701 \| -30.3474 \| -0.7311 \| -0.7303 \| \| 0.6145 \| 0.2931 \| 300 \| 0.6304 \| 0.3548 \| 0.2099 \| 0.7385 \| 0.1448 \| -33.1550 \| -30.1459 \| -0.7317 \| -0.7310 \| \| 0.6171 \| 0.3420 \| 350 \| 0.6223 \| 0.4756 \| 0.3093 \| 0.7341 \| 0.1663 \| -32.8238 \| -29.7432 \| -0.7336 \| -0.7328 \| \| 0.5911 \| 0.3908 \| 400 \| 0.6181 \| 0.6387 \| 0.4602 \| 0.7121 \| 0.1785 \| -32.3208 \| -29.1996 \| -0.7334 \| -0.7327 \| \| 0.5942 \| 0.4397 \| 450 \| 0.6129 \| 0.6839 \| 0.4904 \| 0.7253 \| 0.1935 \| -32.2203 \| -29.0489 \| -0.7347 \| -0.7339 \| \| 0.6096 \| 0.4885 \| 500 \| 0.6090 \| 0.7785 \| 0.5741 \| 0.7297 \| 0.2044 \| -31.9411 \| -28.7335 \| -0.7351 \| -0.7343 \| \| 0.5671 \| 0.5374 \| 550 \| 0.6068 \| 0.7522 \| 0.5395 \| 0.7275 \| 0.2127 \| -32.0566 \| -28.8212 \| -0.7355 \| -0.7347 \| \| 0.6066 \| 0.5862 \| 600 \| 0.6061 \| 0.7215 \| 0.5067 \| 0.7209 \| 0.2147 \| -32.1657 \| -28.9236 \| -0.7356 \| -0.7348 \| \| 0.5816 \| 0.6351 \| 650 \| 0.6046 \| 0.6882 \| 0.4692 \| 0.7231 \| 0.2191 \| -32.2910 \| -29.0344 \| -0.7356 \| -0.7348 \| \| 0.5968 \| 0.6839 \| 700 \| 0.6030 \| 0.6956 \| 0.4723 \| 0.7451 \| 0.2233 \| -32.2804 \| -29.0097 \| -0.7352 \| -0.7344 \| \| 0.6132 \| 0.7328 \| 750 \| 0.6042 \| 0.7103 \| 0.4891 \| 0.7297 \| 0.2212 \| -32.2246 \| -28.9608 \| -0.7354 \| -0.7346 \| \| 0.6133 \| 0.7816 \| 800 \| 0.6021 \| 0.6956 \| 0.4697 \| 0.7407 \| 0.2258 \| -32.2890 \| -29.0099 \| -0.7358 \| -0.7350 \| \| 0.6397 \| 0.8305 \| 850 \| 0.6029 \| 0.7027 \| 0.4791 \| 0.7341 \| 0.2236 \| -32.2579 \| -28.9862 \| -0.7354 \| -0.7346 \| \| 0.6273 \| 0.8793 \| 900 \| 0.6030 \| 0.7126 \| 0.4896 \| 0.7341 \| 0.2230 \| -32.2229 \| -28.9533 \| -0.7356 \| -0.7348 \| \| 0.5996 \| 0.9282 \| 950 \| 0.6019 \| 0.7087 \| 0.4830 \| 0.7341 \| 0.2257 \| -32.2447 \| -28.9661 \| -0.7358 \| -0.7350 \| \| 0.5319 \| 0.9770 \| 1000 \| 0.6020 \| 0.7087 \| 0.4830 \| 0.7341 \| 0.2257 \| -32.2447 \| -28.9661 \| -0.7358 \| -0.7350 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
legraphista/RoLlama2-7b-Chat-GGUF	legraphista	2024-05-23T22:52:08Z	27	1	gguf	[ "gguf", "quantized", "GGUF", "text-generation", "ro", "base_model:OpenLLM-Ro/RoLlama2-7b-Chat", "base_model:quantized:OpenLLM-Ro/RoLlama2-7b-Chat", "license:cc-by-nc-4.0", "region:us" ]	text-generation	2024-05-17T16:03:00Z	--- language: - ro license: cc-by-nc-4.0 quantized_by: legraphista pipeline_tag: text-generation library_name: gguf inference: false base_model: OpenLLM-Ro/RoLlama2-7b-Chat tags: - quantized - GGUF --- # RoLlama2-7b-Chat-GGUF - This is GGUF quantized version of [OpenLLM-Ro/RoLlama2-7b-Chat](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Chat) created using llama.cpp
isaaclee/duration_mistral_train_run5	isaaclee	2024-05-23T22:51:26Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-05-23T18:17:05Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: duration_mistral_train_run5 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. --> # duration_mistral_train_run5 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
legraphista/RoLlama2-7b-Instruct-GGUF	legraphista	2024-05-23T22:51:20Z	43	1	gguf	[ "gguf", "quantized", "GGUF", "text-generation", "ro", "base_model:OpenLLM-Ro/RoLlama2-7b-Instruct", "base_model:quantized:OpenLLM-Ro/RoLlama2-7b-Instruct", "license:cc-by-nc-4.0", "region:us" ]	text-generation	2024-05-17T15:29:12Z	--- language: - ro license: cc-by-nc-4.0 quantized_by: legraphista pipeline_tag: text-generation library_name: gguf inference: false base_model: OpenLLM-Ro/RoLlama2-7b-Instruct tags: - quantized - GGUF --- # RoLlama2-7b-Instruct-GGUF - This is GGUF quantized version of [OpenLLM-Ro/RoLlama2-7b-Instruct](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct) created using llama.cpp
tsavage68/MedQA_L3_1000steps_1e8rate_03beta_CSFTDPO	tsavage68	2024-05-23T22:45:35Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:tsavage68/MedQA_L3_1000steps_1e6rate_SFT", "base_model:finetune:tsavage68/MedQA_L3_1000steps_1e6rate_SFT", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T04:02:02Z	--- license: llama3 base_model: tsavage68/MedQA_L3_1000steps_1e6rate_SFT tags: - trl - dpo - generated_from_trainer model-index: - name: MedQA_L3_1000steps_1e8rate_03beta_CSFTDPO 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. --> # MedQA_L3_1000steps_1e8rate_03beta_CSFTDPO This model is a fine-tuned version of [tsavage68/MedQA_L3_1000steps_1e6rate_SFT](https://huggingface.co/tsavage68/MedQA_L3_1000steps_1e6rate_SFT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6947 - Rewards/chosen: 0.0002 - Rewards/rejected: 0.0027 - Rewards/accuracies: 0.4615 - Rewards/margins: -0.0026 - Logps/rejected: -33.8457 - Logps/chosen: -31.3279 - Logits/rejected: -0.7320 - Logits/chosen: -0.7314 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-08 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6937 \| 0.0489 \| 50 \| 0.6939 \| -0.0056 \| -0.0047 \| 0.4769 \| -0.0009 \| -33.8705 \| -31.3473 \| -0.7322 \| -0.7315 \| \| 0.6972 \| 0.0977 \| 100 \| 0.6930 \| -0.0029 \| -0.0036 \| 0.5055 \| 0.0007 \| -33.8668 \| -31.3383 \| -0.7322 \| -0.7316 \| \| 0.6918 \| 0.1466 \| 150 \| 0.6933 \| 0.0057 \| 0.0055 \| 0.4901 \| 0.0002 \| -33.8364 \| -31.3096 \| -0.7321 \| -0.7314 \| \| 0.6951 \| 0.1954 \| 200 \| 0.6941 \| -0.0012 \| 0.0002 \| 0.4769 \| -0.0014 \| -33.8541 \| -31.3324 \| -0.7320 \| -0.7313 \| \| 0.6926 \| 0.2443 \| 250 \| 0.6930 \| 0.0029 \| 0.0022 \| 0.4857 \| 0.0006 \| -33.8474 \| -31.3190 \| -0.7319 \| -0.7312 \| \| 0.6947 \| 0.2931 \| 300 \| 0.6929 \| -0.0006 \| -0.0016 \| 0.4967 \| 0.0010 \| -33.8603 \| -31.3307 \| -0.7323 \| -0.7316 \| \| 0.6987 \| 0.3420 \| 350 \| 0.6939 \| 0.0041 \| 0.0052 \| 0.5121 \| -0.0010 \| -33.8377 \| -31.3148 \| -0.7324 \| -0.7317 \| \| 0.695 \| 0.3908 \| 400 \| 0.6929 \| 0.0111 \| 0.0101 \| 0.4967 \| 0.0010 \| -33.8212 \| -31.2917 \| -0.7321 \| -0.7315 \| \| 0.6953 \| 0.4397 \| 450 \| 0.6941 \| 0.0051 \| 0.0066 \| 0.4857 \| -0.0015 \| -33.8330 \| -31.3115 \| -0.7327 \| -0.7320 \| \| 0.6939 \| 0.4885 \| 500 \| 0.6947 \| 0.0022 \| 0.0048 \| 0.4637 \| -0.0027 \| -33.8387 \| -31.3213 \| -0.7325 \| -0.7318 \| \| 0.6982 \| 0.5374 \| 550 \| 0.6922 \| 0.0071 \| 0.0047 \| 0.5121 \| 0.0023 \| -33.8391 \| -31.3050 \| -0.7325 \| -0.7318 \| \| 0.6835 \| 0.5862 \| 600 \| 0.6939 \| 0.0064 \| 0.0074 \| 0.4945 \| -0.0010 \| -33.8303 \| -31.3073 \| -0.7321 \| -0.7314 \| \| 0.6868 \| 0.6351 \| 650 \| 0.6937 \| -0.0034 \| -0.0029 \| 0.4989 \| -0.0006 \| -33.8644 \| -31.3400 \| -0.7323 \| -0.7316 \| \| 0.6882 \| 0.6839 \| 700 \| 0.6939 \| -0.0024 \| -0.0013 \| 0.4725 \| -0.0011 \| -33.8593 \| -31.3366 \| -0.7323 \| -0.7317 \| \| 0.6947 \| 0.7328 \| 750 \| 0.6936 \| 0.0031 \| 0.0035 \| 0.5077 \| -0.0004 \| -33.8431 \| -31.3183 \| -0.7321 \| -0.7314 \| \| 0.6968 \| 0.7816 \| 800 \| 0.6947 \| -0.0034 \| -0.0007 \| 0.4637 \| -0.0027 \| -33.8571 \| -31.3399 \| -0.7319 \| -0.7313 \| \| 0.6919 \| 0.8305 \| 850 \| 0.6947 \| 0.0001 \| 0.0028 \| 0.4593 \| -0.0027 \| -33.8456 \| -31.3283 \| -0.7320 \| -0.7314 \| \| 0.6962 \| 0.8793 \| 900 \| 0.6947 \| 0.0002 \| 0.0027 \| 0.4615 \| -0.0026 \| -33.8457 \| -31.3279 \| -0.7320 \| -0.7314 \| \| 0.6866 \| 0.9282 \| 950 \| 0.6947 \| 0.0002 \| 0.0027 \| 0.4615 \| -0.0026 \| -33.8457 \| -31.3279 \| -0.7320 \| -0.7314 \| \| 0.6919 \| 0.9770 \| 1000 \| 0.6947 \| 0.0002 \| 0.0027 \| 0.4615 \| -0.0026 \| -33.8457 \| -31.3279 \| -0.7320 \| -0.7314 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
BotaniBrain/pubmed_tinyllama	BotaniBrain	2024-05-23T22:44:44Z	107	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T22:10:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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legraphista/aya-23-35B-GGUF	legraphista	2024-05-23T22:44:21Z	85	2	null	[ "gguf", "text-generation", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "base_model:CohereForAI/aya-23-35B", "base_model:quantized:CohereForAI/aya-23-35B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-23T16:46:51Z	--- language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 quantized_by: legraphista pipeline_tag: text-generation base_model: CohereForAI/aya-23-35B --- # aya-23-35B-GGUF - This is GGUF quantized version of [CohereForAI/aya-23-35B](https://huggingface.co/CohereForAI/aya-23-35B) created using llama.cpp [74f33adf](https://github.com/ggerganov/llama.cpp/tree/74f33adf)
mlx-community/aya-23-35B-8bit	mlx-community	2024-05-23T22:33:57Z	63	2	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "mlx", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T14:46:46Z	--- language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 library_name: transformers tags: - mlx --- # mlx-community/aya-23-35B-8bit The Model [mlx-community/aya-23-35B-8bit](https://huggingface.co/mlx-community/aya-23-35B-8bit) was converted to MLX format from [CohereForAI/aya-23-35B](https://huggingface.co/CohereForAI/aya-23-35B) using mlx-lm version 0.13.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/aya-23-35B-8bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
tsavage68/MedQA_L3_1000steps_1e5rate_05beta_CSFTDPO	tsavage68	2024-05-23T22:27:59Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:tsavage68/MedQA_L3_1000steps_1e6rate_SFT", "base_model:finetune:tsavage68/MedQA_L3_1000steps_1e6rate_SFT", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T09:51:46Z	--- license: llama3 base_model: tsavage68/MedQA_L3_1000steps_1e6rate_SFT tags: - trl - dpo - generated_from_trainer model-index: - name: MedQA_L3_1000steps_1e5rate_05beta_CSFTDPO 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. --> # MedQA_L3_1000steps_1e5rate_05beta_CSFTDPO This model is a fine-tuned version of [tsavage68/MedQA_L3_1000steps_1e6rate_SFT](https://huggingface.co/tsavage68/MedQA_L3_1000steps_1e6rate_SFT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7867 - Rewards/chosen: -10.2874 - Rewards/rejected: -9.4675 - Rewards/accuracies: 0.4330 - Rewards/margins: -0.8198 - Logps/rejected: -52.7899 - Logps/chosen: -51.9033 - Logits/rejected: -0.3129 - Logits/chosen: -0.3128 ## 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: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.9373 \| 0.0489 \| 50 \| 1.5325 \| 0.6891 \| -0.1945 \| 0.5912 \| 0.8836 \| -34.2439 \| -29.9504 \| -1.1200 \| -1.1197 \| \| 3.7169 \| 0.0977 \| 100 \| 3.7845 \| -9.7504 \| -8.8431 \| 0.4527 \| -0.9074 \| -51.5409 \| -50.8294 \| -0.6137 \| -0.6138 \| \| 5.2014 \| 0.1466 \| 150 \| 5.2600 \| -22.3993 \| -21.8605 \| 0.4681 \| -0.5389 \| -77.5758 \| -76.1272 \| -1.3215 \| -1.3217 \| \| 5.4743 \| 0.1954 \| 200 \| 3.9034 \| -7.1491 \| -6.2277 \| 0.4176 \| -0.9214 \| -46.3103 \| -45.6268 \| -0.6483 \| -0.6486 \| \| 3.0731 \| 0.2443 \| 250 \| 4.1865 \| -11.6364 \| -10.1791 \| 0.4198 \| -1.4572 \| -54.2131 \| -54.6012 \| -0.7051 \| -0.7056 \| \| 5.7952 \| 0.2931 \| 300 \| 3.6683 \| -9.2381 \| -7.9895 \| 0.4264 \| -1.2486 \| -49.8338 \| -49.8046 \| -0.4055 \| -0.4058 \| \| 3.8474 \| 0.3420 \| 350 \| 3.4898 \| -12.7687 \| -11.9414 \| 0.4132 \| -0.8274 \| -57.7376 \| -56.8660 \| -0.8625 \| -0.8625 \| \| 5.5721 \| 0.3908 \| 400 \| 3.4194 \| -13.5468 \| -12.3658 \| 0.4044 \| -1.1810 \| -58.5864 \| -58.4221 \| -0.8921 \| -0.8922 \| \| 6.0929 \| 0.4397 \| 450 \| 3.4518 \| -12.5599 \| -11.2787 \| 0.4132 \| -1.2812 \| -56.4122 \| -56.4483 \| -0.6596 \| -0.6596 \| \| 5.4036 \| 0.4885 \| 500 \| 3.4349 \| -13.3250 \| -12.3700 \| 0.4264 \| -0.9550 \| -58.5948 \| -57.9785 \| -0.4398 \| -0.4397 \| \| 4.2614 \| 0.5374 \| 550 \| 3.4447 \| -13.2741 \| -12.0523 \| 0.4132 \| -1.2218 \| -57.9595 \| -57.8767 \| -0.2318 \| -0.2318 \| \| 5.0683 \| 0.5862 \| 600 \| 3.6325 \| -10.9169 \| -9.7136 \| 0.4242 \| -1.2033 \| -53.2821 \| -53.1624 \| 0.0024 \| 0.0023 \| \| 2.8041 \| 0.6351 \| 650 \| 3.3753 \| -13.7510 \| -12.4756 \| 0.4110 \| -1.2754 \| -58.8060 \| -58.8306 \| -0.4253 \| -0.4254 \| \| 2.852 \| 0.6839 \| 700 \| 3.2123 \| -11.3782 \| -10.1837 \| 0.4132 \| -1.1945 \| -54.2221 \| -54.0849 \| -0.3353 \| -0.3353 \| \| 3.1506 \| 0.7328 \| 750 \| 2.9861 \| -10.9246 \| -9.9019 \| 0.4198 \| -1.0227 \| -53.6587 \| -53.1778 \| -0.3577 \| -0.3577 \| \| 2.9206 \| 0.7816 \| 800 \| 2.8476 \| -10.3118 \| -9.4465 \| 0.4264 \| -0.8653 \| -52.7479 \| -51.9522 \| -0.2881 \| -0.2880 \| \| 3.6047 \| 0.8305 \| 850 \| 2.8115 \| -10.1979 \| -9.3565 \| 0.4308 \| -0.8414 \| -52.5679 \| -51.7243 \| -0.3016 \| -0.3015 \| \| 2.4799 \| 0.8793 \| 900 \| 2.7874 \| -10.3005 \| -9.4828 \| 0.4308 \| -0.8177 \| -52.8204 \| -51.9295 \| -0.3147 \| -0.3146 \| \| 2.8467 \| 0.9282 \| 950 \| 2.7864 \| -10.2878 \| -9.4711 \| 0.4330 \| -0.8167 \| -52.7969 \| -51.9040 \| -0.3132 \| -0.3130 \| \| 2.2638 \| 0.9770 \| 1000 \| 2.7867 \| -10.2874 \| -9.4675 \| 0.4330 \| -0.8198 \| -52.7899 \| -51.9033 \| -0.3129 \| -0.3128 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
lmbelo/Meta-Llama-3-8B-Function-Calling	lmbelo	2024-05-23T22:25:29Z	7	0	mlx	[ "mlx", "safetensors", "llama", "facebook", "meta", "pytorch", "llama-3", "text-generation", "conversational", "en", "license:llama3", "region:us" ]	text-generation	2024-05-23T22:02:19Z	--- language: - en license: llama3 tags: - facebook - meta - pytorch - llama - llama-3 - mlx pipeline_tag: text-generation extra_gated_prompt: "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version\ \ Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for\ \ use, reproduction, distribution and modification of the Llama Materials set forth\ \ herein.\n\"Documentation\" means the specifications, manuals and documentation\ \ accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\ \"Licensee\" or \"you\" means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf), of\ \ the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama\ \ 3\" means the foundational large language models and software and algorithms,\ \ including machine-learning model code, trained model weights, inference-enabling\ \ code, training-enabling code, fine-tuning enabling code and other elements of\ \ the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\ \"Llama Materials\" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation\ \ (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"\ we\" means Meta Platforms Ireland Limited (if you are located in or, if you are\ \ an entity, your principal place of business is in the EEA or Switzerland) and\ \ Meta Platforms, Inc. 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By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit widget: - example_title: Hello messages: - role: user content: Hey my name is Julien! How are you? - example_title: Winter holidays messages: - role: system content: You are a helpful and honest assistant. Please, respond concisely and truthfully. - role: user content: Can you recommend a good destination for Winter holidays? - example_title: Programming assistant messages: - role: system content: You are a helpful and honest code and programming assistant. Please, respond concisely and truthfully. - role: user content: Write a function that computes the nth fibonacci number. inference: parameters: max_new_tokens: 300 stop: - <\|end_of_text\|> - <\|eot_id\|> --- # lmbelo/Meta-Llama-3-8B-Function-Calling The Model [lmbelo/Meta-Llama-3-8B-Function-Calling](https://huggingface.co/lmbelo/Meta-Llama-3-8B-Function-Calling) was converted to MLX format from [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) using mlx-lm version 0.13.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("lmbelo/Meta-Llama-3-8B-Function-Calling") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
Augusto777/vit-base-patch16-224-R1-40	Augusto777	2024-05-23T22:25:03Z	198	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-23T21:34:06Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-R1-40 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.7540983606557377 --- <!-- 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. --> # vit-base-patch16-224-R1-40 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.7212 - Accuracy: 0.7541 ## 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: 5.5e-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: 0.05 - num_epochs: 40 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.3233 \| 0.99 \| 38 \| 1.2355 \| 0.5574 \| \| 0.8643 \| 1.99 \| 76 \| 0.9297 \| 0.5902 \| \| 0.4464 \| 2.98 \| 114 \| 1.1190 \| 0.6393 \| \| 0.3092 \| 4.0 \| 153 \| 0.9861 \| 0.7049 \| \| 0.1628 \| 4.99 \| 191 \| 1.1221 \| 0.6721 \| \| 0.121 \| 5.99 \| 229 \| 1.1710 \| 0.6885 \| \| 0.1138 \| 6.98 \| 267 \| 1.1993 \| 0.7213 \| \| 0.1124 \| 8.0 \| 306 \| 1.2636 \| 0.6885 \| \| 0.0748 \| 8.99 \| 344 \| 1.3881 \| 0.7049 \| \| 0.0877 \| 9.99 \| 382 \| 1.2892 \| 0.7213 \| \| 0.0642 \| 10.98 \| 420 \| 1.3759 \| 0.7049 \| \| 0.0675 \| 12.0 \| 459 \| 1.4283 \| 0.7213 \| \| 0.0694 \| 12.99 \| 497 \| 1.3616 \| 0.7213 \| \| 0.0689 \| 13.99 \| 535 \| 1.3864 \| 0.7213 \| \| 0.0378 \| 14.98 \| 573 \| 1.4322 \| 0.7213 \| \| 0.0472 \| 16.0 \| 612 \| 1.6004 \| 0.7213 \| \| 0.044 \| 16.99 \| 650 \| 1.5810 \| 0.7049 \| \| 0.0386 \| 17.99 \| 688 \| 1.6404 \| 0.6885 \| \| 0.0341 \| 18.98 \| 726 \| 1.5698 \| 0.7377 \| \| 0.0328 \| 20.0 \| 765 \| 1.6720 \| 0.6885 \| \| 0.0444 \| 20.99 \| 803 \| 1.6269 \| 0.7213 \| \| 0.0342 \| 21.99 \| 841 \| 1.6345 \| 0.7377 \| \| 0.0324 \| 22.98 \| 879 \| 1.7916 \| 0.7049 \| \| 0.023 \| 24.0 \| 918 \| 1.8753 \| 0.6885 \| \| 0.048 \| 24.99 \| 956 \| 1.7679 \| 0.7377 \| \| 0.0202 \| 25.99 \| 994 \| 1.7212 \| 0.7541 \| \| 0.0336 \| 26.98 \| 1032 \| 1.7305 \| 0.7377 \| \| 0.0163 \| 28.0 \| 1071 \| 1.7576 \| 0.7049 \| \| 0.0186 \| 28.99 \| 1109 \| 1.7540 \| 0.7377 \| \| 0.0189 \| 29.99 \| 1147 \| 1.6594 \| 0.7541 \| \| 0.039 \| 30.98 \| 1185 \| 1.7423 \| 0.7213 \| \| 0.0194 \| 32.0 \| 1224 \| 1.7148 \| 0.7377 \| \| 0.0205 \| 32.99 \| 1262 \| 1.6965 \| 0.7377 \| \| 0.0186 \| 33.99 \| 1300 \| 1.7553 \| 0.7541 \| \| 0.0177 \| 34.98 \| 1338 \| 1.7476 \| 0.7377 \| \| 0.0132 \| 36.0 \| 1377 \| 1.7506 \| 0.7541 \| \| 0.0068 \| 36.99 \| 1415 \| 1.6917 \| 0.7377 \| \| 0.0121 \| 37.99 \| 1453 \| 1.7276 \| 0.7541 \| \| 0.0129 \| 38.98 \| 1491 \| 1.7218 \| 0.7541 \| \| 0.0067 \| 39.74 \| 1520 \| 1.7220 \| 0.7541 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
raulgdp/Distilbert-Analisis-sentimientos	raulgdp	2024-05-23T22:21:49Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-22T16:27:02Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: Distilbert-uncased 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. --> # Distilbert-uncased-AS Este es un modelo de finetuning de [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) sobre un dataset propios de tweets. Se logra una error cuadrático bajo lo cual quiere decir que los valores predichos son muy cercanos a los observables o gold. - Loss: 0.3510 - Rmse: 0.2543 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2091 \| 1.0 \| 642 \| 0.1933 \| 0.3052 \| \| 0.1334 \| 2.0 \| 1284 \| 0.1909 \| 0.2481 \| \| 0.0684 \| 3.0 \| 1926 \| 0.2617 \| 0.2466 \| \| 0.0355 \| 4.0 \| 2568 \| 0.3113 \| 0.2513 \| \| 0.0116 \| 5.0 \| 3210 \| 0.3510 \| 0.2543 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.0.1+cu117 - Datasets 2.18.0 - Tokenizers 0.19.1
xkiwilabs/lora_opComms_LLama3_v6	xkiwilabs	2024-05-23T22:17:16Z	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	2024-05-23T22:16:58Z	--- 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: xkiwilabs - 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)
colesimmons/sumerian-transliteration	colesimmons	2024-05-23T22:16:48Z	161	1	transformers	[ "transformers", "safetensors", "encoder-decoder", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-23T06:22:31Z	--- 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. 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JFernandoGRE/llama3_8b_brazil_augmenteddemocracy_dups_all4_50	JFernandoGRE	2024-05-23T22:11:18Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T22:06:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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juanbeto/prueba	juanbeto	2024-05-23T22:10:43Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-23T22:10:43Z	--- license: apache-2.0 ---
laurasltf/YangJungwonVoice	laurasltf	2024-05-23T21:55:30Z	0	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail", "region:us" ]	text-to-image	2024-05-22T16:50:58Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: '-' output: url: images/IMG_7234.jpeg base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: null license: openrail --- # Yang Jungwon <Gallery /> ## Download model [Download](/laurasltf/YangJungwonVoice/tree/main) them in the Files & versions tab.
uf-aice-lab/BLIP_Math_Generation_Classification	uf-aice-lab	2024-05-23T21:52:54Z	0	0	null	[ "license:mit", "region:us" ]	null	2024-05-23T20:17:03Z	--- license: mit --- # BLIPNet Model This is the structure of the BLIPNet model. You can load the model with this structure, or you can create a bigger model for your specific task. ## Model Structure ```python import torch import torch.nn as nn from transformers import BlipForConditionalGeneration class BLIPNet(torch.nn.Module): def __init__(self): super().__init__() # Generation Model self.model = BlipForConditionalGeneration.from_pretrained("Salesforceblip-image-captioning-base", cache_dir="model") # Same with https://huggingface.co/uf-aice-lab/BLIP-Math self.ebd_dim = 443136 # Classification Model fc_dim = 64 # You can choose a higher number for better performance, for example, 1024. self.head = nn.Sequential( nn.Linear(self.ebd_dim, fc_dim), nn.ReLU(), ) self.output1= nn.Linear(fc_dim, 5) # 5 classes def forward(self, pixel_values, input_ids): outputs = self.model(input_ids=input_ids, pixel_values=pixel_values, labels=input_ids) image_text_embeds = self.model.vision_model(pixel_values, return_dict=True).last_hidden_state image_text_embeds = self.head(image_text_embeds.view(-1, self.ebd_dim)) # A classification model is based on embeddings from a generative model to leverage BLIP's powerful image-text encoding capabilities. logits = self.output1(image_text_embeds) # generated text, probabilities of classification return outputs, logits model = BLIPNet() model.load_state_dict(torch.load("BLILP_Generation_Classification.bin"), strict=False) You need to input the sample in the same way as shown in the example provided at: https://huggingface.co/uf-aice-lab/BLIP-Math Then you can get the generated text and classification score simultaneously.
stanrom/internlm-xcomposer2-7b-4bit	stanrom	2024-05-23T21:52:15Z	15	0	transformers	[ "transformers", "internlm", "feature-extraction", "text-generation", "custom_code", "arxiv:2401.16420", "license:other", "region:us" ]	text-generation	2024-05-21T00:21:44Z	--- license: other pipeline_tag: text-generation --- <p align="center"> <img src="logo_en.png" width="400"/> <p> <p align="center"> <b><font size="6">InternLM-XComposer2</font></b> <p> <div align="center"> [💻Github Repo](https://github.com/InternLM/InternLM-XComposer) [Paper](https://arxiv.org/abs/2401.16420) </div> InternLM-XComposer2 is a vision-language large model (VLLM) based on [InternLM2](https://github.com/InternLM/InternLM) for advanced text-image comprehension and composition. We release InternLM-XComposer2 series in two versions: - InternLM-XComposer2-VL: The pretrained VLLM model with InternLM2 as the initialization of the LLM, achieving strong performance on various multimodal benchmarks. - InternLM-XComposer2: The finetuned VLLM for Free-from Interleaved Text-Image Composition. This is the 4-bit version of InternLM-XComposer2, install the latest version of [auto_gptq](https://github.com/AutoGPTQ/AutoGPTQ#quick-installation) before using. ```python import torch, auto_gptq from PIL import Image from transformers import AutoModel, AutoTokenizer from auto_gptq.modeling import BaseGPTQForCausalLM auto_gptq.modeling._base.SUPPORTED_MODELS = ["internlm"] torch.set_grad_enabled(False) class InternLMXComposer2QForCausalLM(BaseGPTQForCausalLM): layers_block_name = "model.layers" outside_layer_modules = [ 'vit', 'vision_proj', 'model.tok_embeddings', 'model.norm', 'output', ] inside_layer_modules = [ ["attention.wqkv.linear"], ["attention.wo.linear"], ["feed_forward.w1.linear", "feed_forward.w3.linear"], ["feed_forward.w2.linear"], ] # init model and tokenizer model = InternLMXComposer2QForCausalLM.from_quantized( 'internlm/internlm-xcomposer2-7b-4bit', trust_remote_code=True, device="cuda:0").eval() tokenizer = AutoTokenizer.from_pretrained( 'internlm/internlm-xcomposer2-7b-4bit', trust_remote_code=True) img_path_list = [ 'panda.jpg', 'bamboo.jpeg', ] images = [] for img_path in img_path_list: image = Image.open(img_path).convert("RGB") image = model.vis_processor(image) images.append(image) image = torch.stack(images) query = '<ImageHere> <ImageHere>please write an article based on the images. Title: my favorite animal.' with torch.cuda.amp.autocast(): response, history = model.chat(tokenizer, query=query, image=image, history=[], do_sample=False) print(response) #My Favorite Animal: The Panda #The panda, also known as the giant panda, is one of the most beloved animals in the world. These adorable creatures are native to China and can be found in the wild in a few select locations, but they are more commonly seen in captivity at zoos or wildlife reserves. #Pandas have a distinct black-and-white coloration that makes them instantly recognizable. They are known for their love of bamboo, which they eat almost exclusively. In fact, pandas spend up to 14 hours a day eating, with the majority of their diet consisting of bamboo. Despite this seemingly unbalanced diet, pandas are actually quite healthy and have a low body fat percentage, thanks to their ability to digest bamboo efficiently. #In addition to their unique eating habits, pandas are also known for their playful personalities. They are intelligent and curious creatures, often engaging in activities like playing with toys or climbing trees. However, they do not typically exhibit these behaviors in the wild, where they are solitary creatures who prefer to spend their time alone. #One of the biggest threats to the panda's survival is habitat loss due to deforestation. As a result, many pandas now live in captivity, where they are cared for by dedicated staff and provided with enrichment opportunities to keep them engaged and stimulated. While it is important to protect these animals from extinction, it is also crucial to remember that they are still wild creatures and should be treated with respect and care. #Overall, the panda is an amazing animal that has captured the hearts of people around the world. Whether you see them in the wild or in captivity, there is no denying the charm and allure of these gentle giants. ``` ### Open Source License The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow free commercial usage. To apply for a commercial license, please fill in the application form (English)/申请表（中文）. For other questions or collaborations, please contact [email protected].
ovakimyanchris/detox_Falcon_7B_PPO	ovakimyanchris	2024-05-23T21:45:18Z	47	0	transformers	[ "transformers", "pytorch", "safetensors", "toxic_speech", "text-generation", "en", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T21:19:13Z	--- language: - en tags: - toxic_speech pipeline_tag: text-generation --- Fine tuned detoxified Falcon-7B with PPO algorithm and Reward Model on SUPER TOXIC PROMPTS.
annalittle/wav2vec2-xls-r-300m-finetune-xty	annalittle	2024-05-23T21:45:09Z	79	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:ml-superb-subset", "base_model:Akashpb13/Swahili_xlsr", "base_model:finetune:Akashpb13/Swahili_xlsr", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-23T07:43:08Z	--- license: apache-2.0 base_model: Akashpb13/Swahili_xlsr tags: - generated_from_trainer datasets: - ml-superb-subset metrics: - wer model-index: - name: wav2vec2-xls-r-300m-finetune-xty results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: ml-superb-subset type: ml-superb-subset config: xty split: test[:] args: xty metrics: - name: Wer type: wer value: 1.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xls-r-300m-finetune-xty This model is a fine-tuned version of [Akashpb13/Swahili_xlsr](https://huggingface.co/Akashpb13/Swahili_xlsr) on the ml-superb-subset dataset. It achieves the following results on the evaluation set: - Loss: 3.0086 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:---:\| \| 5.4419 \| 26.3158 \| 500 \| 3.0086 \| 1.0 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Avditvs/multilingual-e5-small-distill-base-0.1	Avditvs	2024-05-23T21:33:10Z	10	5	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "Sentence Transformers", "feature-extraction", "sentence-similarity", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "arxiv:2205.13147", "arxiv:2402.05672", "license:mit", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-15T16:41:30Z	--- tags: - Sentence Transformers - feature-extraction - sentence-similarity - sentence-transformers language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit --- ## Multilingual-E5-small-distill-base This model is an attempt to distill `intfloat/multilingual-e5-base` (teacher) into `intfloat/multilingual-e5-small` (student), as well as applying [Matryoshka Representation Learning](https://arxiv.org/abs/2205.13147) to it. This was made by trying an L2 loss to teach the student model to match the same cosine similarity on text pairs as the teacher model. The distillation dataset is composed of about 700k multilingual sentences pairs sampled for the following 3 datasets: - [PhilipMay/stsb_multi_mt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) - [castorini/mr-tydi](https://huggingface.co/datasets/castorini/mr-tydi) - [quora](https://huggingface.co/datasets/quora) For code, see [this github repository](https://github.com/Avditvs/matryoshka_factory) [Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672). Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model has 12 layers and the embedding size is 384. ## Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] # Each input text should start with "query: " or "passage: ", even for non-English texts. # For tasks other than retrieval, you can simply use the "query: " prefix. input_texts = ['query: how much protein should a female eat', 'query: 南瓜的家常做法', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: 1.清炒南瓜丝原料:嫩南瓜半个调料:葱、盐、白糖、鸡精做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜原料:南瓜1只调料:香葱、蒜末、橄榄油、盐做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"] tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-small') model = AutoModel.from_pretrained('intfloat/multilingual-e5-small') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(*batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) 100 print(scores.tolist()) ``` ## Supported Languages This model is initialized from [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. ## MTEB Benchmark Evaluation (Subset) \| \| intfloat/multilingual-e5-base \| intfloat/multilingual-e5-large \| intfloat/multilingual-e5-small \| avditvs/multilingual-e5-small-distill-base-0.1 \| \| --------------------------- \| ----------------------------- \| ------------------------------ \| ------------------------------ \| ---------------------------------------------------- \| \| STS15 \| 0.876 \| 0.882 \| 0.864 \| 0.865 \| \| BIOSSES \| 0.870 \| 0.863 \| 0.857 \| 0.863 \| \| STS14 \| 0.789 \| 0.776 \| 0.788 \| 0.803 \| \| STS12 \| 0.858 \| 0.873 \| 0.854 \| 0.856 \| \| AskUbuntuDupQuestions \| 0.571 \| 0.577 \| 0.568 \| 0.574 \| \| StackOverflowDupQuestions \| 0.485 \| 0.486 \| 0.486 \| 0.485 \| \| AmazonReviewsClassification \| 0.476 \| 0.470 \| 0.452 \| 0.450 \| \| ArguAna \| 0.442 \| 0.544 \| 0.391 \| 0.480 \| \| ImdbClassification \| 0.849 \| 0.887 \| 0.758 \| 0.757 \| \| STS13 \| 0.756 \| 0.751 \| 0.764 \| 0.785 \| \| STSBenchmark \| 0.832 \| 0.836 \| 0.809 \| 0.818 \| \| STS17 \| 0.890 \| 0.896 \| 0.868 \| 0.871 \| \| SICK-R \| 0.835 \| 0.838 \| 0.835 \| 0.850 \| \| STS22 \| 0.645 \| 0.675 \| 0.640 \| 0.648 \| \| STS16 \| 0.814 \| 0.824 \| 0.822 \| 0.820 \| \| Banking77Classification \| 0.741 \| 0.749 \| 0.706 \| 0.706 \| \| average \| 0.733 \| 0.745 \| 0.717 \| 0.727 \| ## Support for Sentence Transformers Below is an example for usage with sentence_transformers. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('avditvs/multilingual-e5-small-distill-base') input_texts = [ 'query: how much protein should a female eat', 'query: 南瓜的家常做法', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: 1.清炒南瓜丝原料:嫩南瓜半个调料:葱、盐、白糖、鸡精做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜原料:南瓜1只调料:香葱、蒜末、橄榄油、盐做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅" ] embeddings = model.encode(input_texts, normalize_embeddings=True) ``` Package requirements `pip install sentence_transformers~=2.2.2` Contributors: [michaelfeil](https://huggingface.co/michaelfeil) ## FAQ 1. Do I need to add the prefix "query: " and "passage: " to input texts? Yes, this is how the model is trained, otherwise you will see a performance degradation. Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. - Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval. - Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. 2. Why are my reproduced results slightly different from reported in the model card? Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. 3. Why does the cosine similarity scores distribute around 0.7 to 1.0? This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. For text embedding tasks like text retrieval or semantic similarity, what matters is the relative order of the scores instead of the absolute values, so this should not be an issue. ## Citation If you find our paper or models helpful, please consider cite as follows: ``` @article{wang2024multilingual, title={Multilingual E5 Text Embeddings: A Technical Report}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2402.05672}, year={2024} } ``` ## Limitations Long texts will be truncated to at most 512 tokens.
waleko/TikZ-llava-1.5-7b	waleko	2024-05-23T21:32:27Z	9	3	transformers	[ "transformers", "safetensors", "llava", "image-text-to-text", "image-to-text", "dataset:EgorShibaev/TikZ-short-code", "endpoints_compatible", "region:us" ]	image-to-text	2024-05-23T09:43:16Z	--- library_name: transformers datasets: - EgorShibaev/TikZ-short-code pipeline_tag: image-to-text --- # Model Card for Model ID Fine-tuned multimodal LLaVA model for TikZ diagram generation using hand-drawn sketches. ## How to Get Started with the Model ```python from transformers import pipeline from PIL import Image import requests pipe = pipeline("image-to-text", model="waleko/TikZ-llava-1.5-7b") url = "https://waleko.github.io/data/image.jpg" image = Image.open(requests.get(url, stream=True).raw) prompt = "Assistant helps to write down the TikZ code for the user's image. USER: <image>\nWrite down the TikZ code to draw the diagram shown in the image. ASSISTANT: " print(pipe(image, prompt=prompt)[0]['generated_text']) ``` ## Training Details ### Training Data Trained on synthetic [TikZ-short-code](https://huggingface.co/datasets/EgorShibaev/TikZ-short-code) dataset. <!-- 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. -->
Arbi-Houssem/comondov	Arbi-Houssem	2024-05-23T21:30:54Z	38	0	transformers	[ "transformers", "safetensors", "vits", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-03T15:59:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
EthanRhys/Master-Mantis	EthanRhys	2024-05-23T21:30:00Z	0	0	null	[ "license:openrail++", "region:us" ]	null	2024-05-23T21:29:10Z	--- license: openrail++ ---
EthanRhys/Young-Cricket-Current	EthanRhys	2024-05-23T21:27:31Z	0	0	null	[ "license:openrail++", "region:us" ]	null	2024-05-23T21:26:38Z	--- license: openrail++ ---
Augusto777/vit-base-patch16-224-R1-10	Augusto777	2024-05-23T21:25:22Z	218	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-23T21:03:59Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-R1-10 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.7049180327868853 --- <!-- 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. --> # vit-base-patch16-224-R1-10 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2451 - Accuracy: 0.7049 ## 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: 5.5e-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: 0.05 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.1675 \| 0.99 \| 38 \| 0.9972 \| 0.6393 \| \| 0.5606 \| 1.99 \| 76 \| 0.7603 \| 0.6885 \| \| 0.3159 \| 2.98 \| 114 \| 0.8954 \| 0.6885 \| \| 0.2253 \| 4.0 \| 153 \| 1.0227 \| 0.6885 \| \| 0.17 \| 4.99 \| 191 \| 1.1025 \| 0.7213 \| \| 0.1174 \| 5.99 \| 229 \| 1.1453 \| 0.7377 \| \| 0.1032 \| 6.98 \| 267 \| 1.0995 \| 0.6885 \| \| 0.1051 \| 8.0 \| 306 \| 1.2167 \| 0.7049 \| \| 0.0853 \| 8.99 \| 344 \| 1.2042 \| 0.7377 \| \| 0.0802 \| 9.93 \| 380 \| 1.2451 \| 0.7049 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
lyhourt/whisper-small-clean_6	lyhourt	2024-05-23T21:24:22Z	13	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:lyhourt/clean_6", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-23T14:37:23Z	--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - lyhourt/clean_6 metrics: - wer model-index: - name: whisper-small-clean_6 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: lyhourt/clean_6 type: lyhourt/clean_6 metrics: - name: Wer type: wer value: 28.944246737841045 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-clean_6 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the lyhourt/clean_6 dataset. It achieves the following results on the evaluation set: - Loss: 0.3565 - Wer: 28.9442 ## 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: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.1583 \| 0.2 \| 200 \| 1.1107 \| 87.1886 \| \| 0.0074 \| 1.028 \| 400 \| 0.4016 \| 37.2835 \| \| 0.1778 \| 1.228 \| 600 \| 0.3914 \| 34.0214 \| \| 0.0027 \| 2.056 \| 800 \| 0.3441 \| 28.5647 \| \| 0.1441 \| 2.2560 \| 1000 \| 0.3565 \| 28.9442 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Hanqix/default-ties-mistral-merge	Hanqix	2024-05-23T21:16:58Z	0	0	null	[ "merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1218", "mlabonne/NeuralHermes-2.5-Mistral-7B", "base_model:OpenPipe/mistral-ft-optimized-1218", "base_model:merge:OpenPipe/mistral-ft-optimized-1218", "base_model:mlabonne/NeuralHermes-2.5-Mistral-7B", "base_model:merge:mlabonne/NeuralHermes-2.5-Mistral-7B", "region:us" ]	null	2024-05-23T21:11:46Z	--- tags: - merge - mergekit - lazymergekit - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B base_model: - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B --- # default-ties-mistral-merge default-ties-mistral-merge is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218) * [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) ## 🧩 Configuration ```yaml models: - model: mistralai/Mistral-7B-v0.1 # no parameters necessary for base model - model: OpenPipe/mistral-ft-optimized-1218 parameters: density: 0.5 weight: 0.5 - model: mlabonne/NeuralHermes-2.5-Mistral-7B parameters: density: 0.5 weight: 0.3 merge_method: ties base_model: mistralai/Mistral-7B-v0.1 parameters: normalize: true dtype: float16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Hanqix/default-ties-mistral-merge" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
dimensionhq/sql-whisper-validation	dimensionhq	2024-05-23T21:16:57Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T21:13:53Z	--- 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. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mlx-community/aya-23-35B-4bit	mlx-community	2024-05-23T21:14:17Z	13	1	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "mlx", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T14:46:37Z	--- language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 library_name: transformers tags: - mlx --- # mlx-community/aya-23-35B-4bit The Model [mlx-community/aya-23-35B-4bit](https://huggingface.co/mlx-community/aya-23-35B-4bit) was converted to MLX format from [CohereForAI/aya-23-35B](https://huggingface.co/CohereForAI/aya-23-35B) using mlx-lm version 0.13.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/aya-23-35B-4bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
LiteLLMs/Phi-3-mini-128k-instruct-GGUF	LiteLLMs	2024-05-23T21:08:31Z	47	0	null	[ "gguf", "nlp", "code", "GGUF", "text-generation", "en", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-23T12:52:21Z	--- language: - en license: mit tags: - nlp - code - GGUF license_link: https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/LICENSE pipeline_tag: text-generation widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? quantized_by: andrijdavid --- # Phi-3-mini-128k-instruct-GGUF - Original model: [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) <!-- description start --> ## Description This repo contains GGUF format model files for [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### 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). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration. * [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications * [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling. * [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration. * [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection. * [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use. * [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server. * [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents. <!-- README_GGUF.md-about-gguf end --> <!-- compatibility_gguf start --> ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: LiteLLMs/Phi-3-mini-128k-instruct-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download LiteLLMs/Phi-3-mini-128k-instruct-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download LiteLLMs/Phi-3-mini-128k-instruct-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install huggingface_hub[hf_transfer] ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/Phi-3-mini-128k-instruct-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<PROMPT>", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer end --> <!-- original-model-card start --> # Original model card: Phi-3-mini-128k-instruct ## Model Summary The Phi-3-Mini-128K-Instruct is a 3.8 billion-parameter, lightweight, state-of-the-art open model trained using the Phi-3 datasets. This dataset includes both synthetic data and filtered publicly available website data, with an emphasis on high-quality and reasoning-dense properties. The model belongs to the Phi-3 family with the Mini version in two variants [4K](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) and [128K](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) which is the context length (in tokens) that it can support. After initial training, the model underwent a post-training process that involved supervised fine-tuning and direct preference optimization to enhance its ability to follow instructions and adhere to safety measures. When evaluated against benchmarks that test common sense, language understanding, mathematics, coding, long-term context, and logical reasoning, the Phi-3 Mini-128K-Instruct demonstrated robust and state-of-the-art performance among models with fewer than 13 billion parameters. Resources and Technical Documentation: + [Phi-3 Microsoft Blog](https://aka.ms/Phi-3Build2024) + [Phi-3 Technical Report](https://aka.ms/phi3-tech-report) + [Phi-3 on Azure AI Studio](https://aka.ms/phi3-azure-ai) + [Phi-3 Cookbook](https://github.com/microsoft/Phi-3CookBook) \| \| Short Context \| Long Context \| \| \| -- \| \| -- \| \| ----- \| \| MMLU <br>5-Shot \| 68.1 \| 75.3 \| 78.2 \| 56.3 \| 61.7 \| 63.6 \| 66.5 \| 68.4 \| 71.4 \| \| HellaSwag <br> 5-Shot \| 74.5 \| 78.7 \| 83.2 \| 53.6 \| 58.5 \| 49.8 \| 71.1 \| 70.4 \| 78.8 \| \| ANLI <br> 7-Shot \| 52.8 \| 55.0 \| 58.7 \| 42.5 \| 47.1 \| 48.7 \| 57.3 \| 55.2 \| 58.1 \| \| GSM-8K <br> 0-Shot; CoT \| 83.6 \| 86.4 \| 90.8 \| 61.1 \| 46.4 \| 59.8 \| 77.4 \| 64.7 \| 78.1 \| \| MedQA <br> 2-Shot \| 55.3 \| 58.2 \| 69.8 \| 40.9 \| 49.6 \| 50.0 \| 60.5 \| 62.2 \| 63.4 \| \| AGIEval <br> 0-Shot \| 36.9 \| 45.0 \| 49.7 \| 29.8 \| 35.1 \| 42.1 \| 42.0 \| 45.2 \| 48.4 \| \| TriviaQA <br> 5-Shot \| 57.1 \| 59.1 \| 73.3 \| 45.2 \| 72.3 \| 75.2 \| 67.7 \| 82.2 \| 85.8 \| \| Arc-C <br> 10-Shot \| 84.0 \| 90.7 \| 91.9 \| 75.9 \| 78.6 \| 78.3 \| 82.8 \| 87.3 \| 87.4 \| \| Arc-E <br> 10-Shot \| 95.2 \| 97.1 \| 98.0 \| 88.5 \| 90.6 \| 91.4 \| 93.4 \| 95.6 \| 96.3 \| \| PIQA <br> 5-Shot \| 83.6 \| 87.8 \| 88.2 \| 60.2 \| 77.7 \| 78.1 \| 75.7 \| 86.0 \| 86.6 \| \| SociQA <br> 5-Shot \| 76.1 \| 79.0 \| 79.4 \| 68.3 \| 74.6 \| 65.5 \| 73.9 \| 75.9 \| 68.3 \| \| BigBench-Hard <br> 0-Shot \| 71.5 \| 75.0 \| 82.5 \| 59.4 \| 57.3 \| 59.6 \| 51.5 \| 69.7 \| 68.32 \| \| WinoGrande <br> 5-Shot \| 72.5 \| 82.5 \| 81.2 \| 54.7 \| 54.2 \| 55.6 \| 65.0 \| 62.0 \| 68.8 \| \| OpenBookQA <br> 10-Shot \| 80.6 \| 88.4 \| 86.6 \| 73.6 \| 79.8 \| 78.6 \| 82.6 \| 85.8 \| 86.0 \| \| BoolQ <br> 0-Shot \| 78.7 \| 82.9 \| 86.5 \| -- \| 72.2 \| 66.0 \| 80.9 \| 77.6 \| 79.1 \| \| CommonSenseQA <br> 10-Shot \| 78.0 \| 80.3 \| 82.6 \| 69.3 \| 72.6 \| 76.2 \| 79 \| 78.1 \| 79.6 \| \| TruthfulQA <br> 10-Shot \| 63.2 \| 68.1 \| 74.8 \| -- \| 52.1 \| 53.0 \| 63.2 \| 60.1 \| 85.8 \| \| HumanEval <br> 0-Shot \| 57.9 \| 59.1 \| 54.7 \| 47.0 \| 28.0 \| 34.1 \| 60.4 \| 37.8 \| 62.2 \| \| MBPP <br> 3-Shot \| 62.5 \| 71.4 \| 73.7 \| 60.6 \| 50.8 \| 51.5 \| 67.7 \| 60.2 \| 77.8 \| ## Software * [PyTorch](https://github.com/pytorch/pytorch) * [DeepSpeed](https://github.com/microsoft/DeepSpeed) * [Transformers](https://github.com/huggingface/transformers) * [Flash-Attention](https://github.com/HazyResearch/flash-attention) ## Hardware Note that by default, the Phi-3-mini model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types: * NVIDIA A100 * NVIDIA A6000 * NVIDIA H100 If you want to run the model on: * NVIDIA V100 or earlier generation GPUs: call AutoModelForCausalLM.from_pretrained() with attn_implementation="eager" * Optimized inference on GPU, CPU, and Mobile: use the ONNX models [128K](https://aka.ms/phi3-mini-128k-instruct-onnx) ## Cross Platform Support ONNX runtime ecosystem now supports Phi-3 Mini models across platforms and hardware. You can find the optimized Phi-3 Mini-128K-Instruct ONNX model [here](https://aka.ms/phi3-mini-128k-instruct-onnx). Optimized Phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML support lets developers bring hardware acceleration to Windows devices at scale across AMD, Intel, and NVIDIA GPUs. Along with DirectML, ONNX Runtime provides cross platform support for Phi-3 across a range of devices CPU, GPU, and mobile. Here are some of the optimized configurations we have added: 1. ONNX models for int4 DML: Quantized to int4 via AWQ 2. ONNX model for fp16 CUDA 3. ONNX model for int4 CUDA: Quantized to int4 via RTN 4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN ## License The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-mini-128k/resolve/main/LICENSE). ## Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies. <!-- original-model-card end -->
LiteLLMs/Mistral-7B-Instruct-v0.3-GGUF	LiteLLMs	2024-05-23T21:08:04Z	41	1	null	[ "gguf", "GGUF", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-23T12:50:34Z	--- license: apache-2.0 tags: - GGUF quantized_by: andrijdavid --- # Mistral-7B-Instruct-v0.3-GGUF - Original model: [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) <!-- description start --> ## Description This repo contains GGUF format model files for [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### 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). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration. * [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications * [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling. * [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration. * [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection. * [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use. * [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server. * [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents. <!-- README_GGUF.md-about-gguf end --> <!-- compatibility_gguf start --> ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: LiteLLMs/Mistral-7B-Instruct-v0.3-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download LiteLLMs/Mistral-7B-Instruct-v0.3-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download LiteLLMs/Mistral-7B-Instruct-v0.3-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install huggingface_hub[hf_transfer] ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/Mistral-7B-Instruct-v0.3-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<PROMPT>", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer end --> <!-- original-model-card start --> # Original model card: Mistral-7B-Instruct-v0.3 # Model Card for Mistral-7B-Instruct-v0.3 The Mistral-7B-Instruct-v0.3 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.3. Mistral-7B-v0.3 has the following changes compared to [Mistral-7B-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2/edit/main/README.md) - Extended vocabulary to 32768 - Supports v3 Tokenizer - Supports function calling ## Installation It is recommended to use `mistralai/Mistral-7B-Instruct-v0.3` with [mistral-inference](https://github.com/mistralai/mistral-inference). For HF transformers code snippets, please keep scrolling. ``` pip install mistral_inference ``` ## Download ```py from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', '7B-Instruct-v0.3') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Mistral-7B-Instruct-v0.3", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path) ``` ### Chat After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. You can chat with the model using ``` mistral-chat $HOME/mistral_models/7B-Instruct-v0.3 --instruct --max_tokens 256 ``` ### Instruct following ```py from mistral_inference.model import Transformer from mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.protocol.instruct.messages import UserMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3") model = Transformer.from_folder(mistral_models_path) completion_request = ChatCompletionRequest(messages=[UserMessage(content="Explain Machine Learning to me in a nutshell.")]) tokens = tokenizer.encode_chat_completion(completion_request).tokens out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0]) print(result) ``` ### Function calling ```py from mistral_common.protocol.instruct.tool_calls import Function, Tool from mistral_inference.model import Transformer from mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.protocol.instruct.messages import UserMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3") model = Transformer.from_folder(mistral_models_path) completion_request = ChatCompletionRequest( tools=[ Tool( function=Function( name="get_current_weather", description="Get the current weather", parameters={ "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use. Infer this from the users location.", }, }, "required": ["location", "format"], }, ) ) ], messages=[ UserMessage(content="What's the weather like today in Paris?"), ], ) tokens = tokenizer.encode_chat_completion(completion_request).tokens out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0]) print(result) ``` ## Generate with `transformers` If you want to use Hugging Face `transformers` to generate text, you can do something like this. ```py from transformers import pipeline messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] chatbot = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.3") chatbot(messages) ``` ## Limitations The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Jean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, William El Sayed, William Marshall <!-- original-model-card end -->
fine-tuned/NFCorpus-8-8-gpt-4o-2024-05-13-610535	fine-tuned	2024-05-23T21:07:10Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "Medical", "Nutrition", "Queries", "Documents", "Relevance", "custom_code", "en", "dataset:fine-tuned/NFCorpus-8-8-gpt-4o-2024-05-13-610535", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-23T21:06:57Z	--- license: apache-2.0 datasets: - fine-tuned/NFCorpus-8-8-gpt-4o-2024-05-13-610535 - allenai/c4 language: - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - Medical - Nutrition - Queries - Documents - Relevance --- 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: medical information 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/NFCorpus-8-8-gpt-4o-2024-05-13-610535', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
UGARIT/grc-ner-xlmr	UGARIT	2024-05-23T21:03:04Z	121	1	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "grc", "base_model:UGARIT/grc-alignment", "base_model:finetune:UGARIT/grc-alignment", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-31T21:52:11Z	--- language: - grc base_model: - UGARIT/grc-alignment tags: - token-classification license: mit inference: parameters: aggregation_strategy: "first" widget: - text: "ταῦτα εἴπας ὁ Ἀλέξανδρος παρίζει Πέρσῃ ἀνδρὶ ἄνδρα Μακεδόνα ὡς γυναῖκα τῷ λόγῳ · οἳ δέ , ἐπείτε σφέων οἱ Πέρσαι ψαύειν ἐπειρῶντο , διεργάζοντο αὐτούς ." example_title: "Example 1" --- # Named Entity Recognition for Ancient Greek Pretrained NER tagging model for ancient Greek # Data We trained the models on available annotated corpora in Ancient Greek. There are only two sizeable annotated datasets in Ancient Greek, which are currently un- der release: the first one by Berti 2023, consists of a fully annotated text of Athenaeus’ Deipnosophists, developed in the context of the Digital Athenaeus project. The second one by Foka et al. 2020, is a fully annotated text of Pausanias’ Periegesis Hellados, developed in the context of the Digital Periegesis project. In addition, we used smaller corpora annotated by students and scholars on Recogito: the Odyssey annotated by Kemp 2021; a mixed corpus including excerpts from the Library attributed to Apollodorus and from Strabo’s Geography, annotated by Chiara Palladino; Book 1 of Xenophon’s Anabasis, created by Thomas Visser; and Demos- thenes’ Against Neaira, created by Rachel Milio. ### Training Dataset \| \| Person \| Location \| NORP \| MISC \| \|----------------\|------------------\|-------------------\|-------------------\|-------------------\| \| Odyssey \| 2.469 \| 698 \| 0 \| 0 \| \| Deipnosophists \| 14.921 \| 2.699 \| 5.110 \| 3.060 \| \| Pausanias \| 10.205 \| 8.670 \| 4.972 \| 0 \| \| Other Datasets \| 3.283 \| 2.040 \| 1.089 \| 0 \| \| Total \| 30.878 \| 14.107 \| 11.171 \| 3.060 \| --- ### Validation Dataset \| \| Person \| Location \| NORP \| MISC \| \|----------------\|------------------\|-------------------\|-------------------\|-------------------\| \| Xenophon \| 1.190 \| 796 \| 857 \| 0 \| # Results \| Class \| Metric \| Test \| Validation \| \|---------\|-----------\|--------\|--------\| \| LOC \| precision \| 83.33% \| 88.66% \| \| \| recall \| 81.27% \| 88.94% \| \| \| f1 \| 82.29% \| 88.80% \| \| MISC \| precision \| 83.25% \| 0 \| \| \| recall \| 81.21% \| 0 \| \| \| f1 \| 82.22% \| 0 \| \| NORP \| precision \| 88.71% \| 94.76% \| \| \| recall \| 90.76% \| 94.50% \| \| \| f1 \| 89.73% \| 94.63% \| \| PER \| precision \| 91.72% \| 94.22% \| \| \| recall \| 94.42% \| 96.06% \| \| \| f1 \| 93.05% \| 95.13% \| \| Overall \| precision \| 88.83% \| 92.91% \| \| \| recall \| 89.99% \| 93.72% \| \| \| f1 \| 89.41% \| 93.32% \| \| \| Accuracy \| 97.50% \| 98.87% \| # Usage This [colab notebook](https://colab.research.google.com/drive/1K6ER_C8d_AxBm0Yrtr628P3weH1Rxhht?usp=sharing) contains the necessary code to use the model. ```python from transformers import pipeline # create pipeline for NER ner = pipeline('ner', model="UGARIT/grc-ner-xlmr", aggregation_strategy = 'first') ner("ταῦτα εἴπας ὁ Ἀλέξανδρος παρίζει Πέρσῃ ἀνδρὶ ἄνδρα Μακεδόνα ὡς γυναῖκα τῷ λόγῳ · οἳ δέ , ἐπείτε σφέων οἱ Πέρσαι ψαύειν ἐπειρῶντο , διεργάζοντο αὐτούς .") ``` Output ``` [{'entity_group': 'PER', 'score': 0.9999428, 'word': '', 'start': 13, 'end': 14}, {'entity_group': 'PER', 'score': 0.99994195, 'word': 'Ἀλέξανδρος', 'start': 14, 'end': 24}, {'entity_group': 'NORP', 'score': 0.9087087, 'word': 'Πέρσῃ', 'start': 32, 'end': 38}, {'entity_group': 'NORP', 'score': 0.97572577, 'word': 'Μακεδόνα', 'start': 50, 'end': 59}, {'entity_group': 'NORP', 'score': 0.9993412, 'word': 'Πέρσαι', 'start': 104, 'end': 111}] ``` # Citation: ``` @inproceedings{palladino-yousef-2024-development, title = "Development of Robust {NER} Models and Named Entity Tagsets for {A}ncient {G}reek", author = "Palladino, Chiara and Yousef, Tariq", editor = "Sprugnoli, Rachele and Passarotti, Marco", booktitle = "Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lt4hala-1.11", pages = "89--97", abstract = "This contribution presents a novel approach to the development and evaluation of transformer-based models for Named Entity Recognition and Classification in Ancient Greek texts. We trained two models with annotated datasets by consolidating potentially ambiguous entity types under a harmonized set of classes. Then, we tested their performance with out-of-domain texts, reproducing a real-world use case. Both models performed very well under these conditions, with the multilingual model being slightly superior on the monolingual one. In the conclusion, we emphasize current limitations due to the scarcity of high-quality annotated corpora and to the lack of cohesive annotation strategies for ancient languages.", } ```
mrovejaxd/FNST_trad_g	mrovejaxd	2024-05-23T21:01:53Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-cased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-22T23:03:33Z	--- base_model: dccuchile/bert-base-spanish-wwm-cased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: FNST_trad_g 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. --> # FNST_trad_g This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8709 - Accuracy: 0.6667 - F1: 0.6510 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 1.1501 \| 1.0 \| 1000 \| 1.0940 \| 0.5208 \| 0.3539 \| \| 0.9834 \| 2.0 \| 2000 \| 0.9339 \| 0.5883 \| 0.5317 \| \| 0.8949 \| 3.0 \| 3000 \| 0.8767 \| 0.6233 \| 0.6071 \| \| 0.8316 \| 4.0 \| 4000 \| 0.8421 \| 0.64 \| 0.6185 \| \| 0.7873 \| 5.0 \| 5000 \| 0.8242 \| 0.645 \| 0.6211 \| \| 0.7676 \| 6.0 \| 6000 \| 0.8323 \| 0.6525 \| 0.6356 \| \| 0.7196 \| 7.0 \| 7000 \| 0.8204 \| 0.6542 \| 0.6352 \| \| 0.6937 \| 8.0 \| 8000 \| 0.8229 \| 0.6608 \| 0.6449 \| \| 0.6493 \| 9.0 \| 9000 \| 0.8427 \| 0.6617 \| 0.6450 \| \| 0.6254 \| 10.0 \| 10000 \| 0.8474 \| 0.6667 \| 0.6502 \| \| 0.5911 \| 11.0 \| 11000 \| 0.8611 \| 0.6708 \| 0.6551 \| \| 0.5732 \| 12.0 \| 12000 \| 0.8709 \| 0.6667 \| 0.6510 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
asquevedos/services-ucacue	asquevedos	2024-05-23T21:01:45Z	12	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:BSC-LT/roberta-base-bne", "base_model:finetune:BSC-LT/roberta-base-bne", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-12T16:41:02Z	--- license: apache-2.0 base_model: BSC-LT/roberta-base-bne tags: - generated_from_trainer metrics: - accuracy model-index: - name: services-ucacue 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. --> # services-ucacue This model is a fine-tuned version of [BSC-LT/roberta-base-bne](https://huggingface.co/BSC-LT/roberta-base-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5312 - Accuracy: 0.8260 ## 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: 40 - eval_batch_size: 48 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 160 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.3007 \| 1.0 \| 158 \| 0.6027 \| 0.7798 \| \| 0.5036 \| 2.0 \| 316 \| 0.4827 \| 0.8213 \| \| 0.3994 \| 3.0 \| 474 \| 0.4975 \| 0.8213 \| \| 0.2731 \| 4.0 \| 632 \| 0.4928 \| 0.8181 \| \| 0.2132 \| 5.0 \| 790 \| 0.5312 \| 0.8260 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
soukainaedr1222/ChatPhi3	soukainaedr1222	2024-05-23T21:00:09Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-23T20:37:16Z	--- 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. 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fine-tuned/NFCorpus-8-8-gpt-4o-2024-05-13-847943	fine-tuned	2024-05-23T20:58:49Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "mteb", "Medical", "Nutrition", "Information", "Retrieval", "Dataset", "en", "dataset:fine-tuned/NFCorpus-8-8-gpt-4o-2024-05-13-847943", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-23T20:57:57Z	--- license: apache-2.0 datasets: - fine-tuned/NFCorpus-8-8-gpt-4o-2024-05-13-847943 - allenai/c4 language: - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - Medical - Nutrition - Information - Retrieval - Dataset --- This model is a fine-tuned version of [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) designed for the following use case: academic search for medical information 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/NFCorpus-8-8-gpt-4o-2024-05-13-847943', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
faisalq/bert-base-arapoembert	faisalq	2024-05-23T20:47:23Z	129	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "Arabic BERT", "Poetry", "Masked Langauge Model", "ar", "arxiv:2403.12392", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-15T08:15:28Z	--- license: cc-by-nc-4.0 language: - ar tags: - Arabic BERT - Poetry - Masked Langauge Model --- AraPoemBERT is the first pre-trained large language model focused exclusively on Arabic poetry. The dataset used in pretraining the model contains more than 2 million verses. The code files along with the results are available on [repo](https://github.com/FaisalQarah/araPoemBERT). # BibTex If you use SaudiBERT model in your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (citation details to be updated): ```bibtex @article{qarah2024arapoembert, title={AraPoemBERT: A Pretrained Language Model for Arabic Poetry Analysis}, author={Qarah, Faisal}, journal={arXiv preprint arXiv:2403.12392}, year={2024} } ```
hgnoi/Q8DO3oiJKPwtihU3	hgnoi	2024-05-23T20:42:44Z	133	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T20:41:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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johnnyf/a2c-PandaReachDense-v3	johnnyf	2024-05-23T20:40:18Z	1	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-23T16:59:19Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.21 +/- 0.14 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
mlx-community/aya-23-8B-4bit	mlx-community	2024-05-23T20:35:05Z	101	2	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "mlx", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T14:45:40Z	--- language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 library_name: transformers tags: - mlx --- # mlx-community/aya-23-8B-4bit The Model [mlx-community/aya-23-8B-4bit](https://huggingface.co/mlx-community/aya-23-8B-4bit) was converted to MLX format from [CohereForAI/aya-23-8B](https://huggingface.co/CohereForAI/aya-23-8B) using mlx-lm version 0.13.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/aya-23-8B-4bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
GenVRadmin/llama38bGenZ_Vikas-Merged	GenVRadmin	2024-05-23T20:34:26Z	15	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T14:18:21Z	--- license: mit --- llama3 variant for 22 Indian languages:- 1. Tamil 2. Telugu 3. Assamese 4. Kashmiri 5. Punjabi 6. Bengali 7. Sanskrit 8. Malyalam 9. Sindhi 10. Marathi 11. Gujarati 12. Kannada 13. Odia 14. Maithili 15. Urdu 16. Nepali 17. Manipuri 18. Dogri 19. English 20. Arabic 21. Santali 22. Bodo We first pre-trained the model on 100 million plus Indic language tokens. Then, it was finetuned on close sourced GenZ_Vikas datasets consisting 7.5 million SFT pairs, including 5.5 million Hindi SFT pairs. Finally it underwent DPO training to align it with human preferences. The model has been benchmarked on Indic LLM leaderboard where it outperforms our AryaBhatta series on Hindi evals. And llama3 base model on all Indian languages. Training happened on 2*A100 for 24 days. Link: https://huggingface.co/spaces/Cognitive-Lab/indic_llm_leaderboard Release link: https://www.linkedin.com/feed/update/urn:li:activity:7199506579828662272
lenatr99/loha_fine_tuned_rte_XLMroberta	lenatr99	2024-05-23T20:31:22Z	4	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:adapter:FacebookAI/xlm-roberta-base", "license:mit", "region:us" ]	null	2024-05-23T20:31:14Z	--- license: mit library_name: peft tags: - generated_from_trainer base_model: xlm-roberta-base metrics: - accuracy - f1 model-index: - name: loha_fine_tuned_rte_XLMroberta 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. --> # loha_fine_tuned_rte_XLMroberta 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: 2.4748 - Accuracy: 0.6897 - F1: 0.6873 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8438 \| 1.7241 \| 50 \| 0.7276 \| 0.4138 \| 0.2422 \| \| 0.7427 \| 3.4483 \| 100 \| 0.6836 \| 0.5862 \| 0.4333 \| \| 0.6788 \| 5.1724 \| 150 \| 0.9009 \| 0.4828 \| 0.3781 \| \| 0.5085 \| 6.8966 \| 200 \| 1.6699 \| 0.5172 \| 0.4944 \| \| 0.2264 \| 8.6207 \| 250 \| 2.0941 \| 0.6207 \| 0.6179 \| \| 0.094 \| 10.3448 \| 300 \| 2.0207 \| 0.6897 \| 0.6687 \| \| 0.0286 \| 12.0690 \| 350 \| 2.3929 \| 0.6552 \| 0.6491 \| \| 0.004 \| 13.7931 \| 400 \| 2.4748 \| 0.6897 \| 0.6873 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
rubenamtz0/hc-mistral-alpaca	rubenamtz0	2024-05-23T20:28:28Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:adapter:meta-llama/Meta-Llama-3-8B", "license:llama3", "8-bit", "bitsandbytes", "region:us" ]	null	2024-05-22T18:51:39Z	--- license: llama3 library_name: peft tags: - axolotl - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B model-index: - name: hc-mistral-alpaca results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: meta-llama/Meta-Llama-3-8B model_type: LlamaForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: true load_in_4bit: false strict: false datasets: - path: _synth_data/alpaca_synth_queries_healed_tiny.jsonl type: sharegpt conversation: alpaca dataset_prepared_path: last_run_prepared val_set_size: 0.1 output_dir: ./lora-alpaca-out hub_model_id: rubenamtz0/hc-mistral-alpaca sequence_len: 4096 sample_packing: true pad_to_sequence_len: true adapter: lora lora_model_dir: lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: wandb_project: fine-tuning wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 4 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true s2_attention: eval_sample_packing: false warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: pad_token: <\|end_of_text\|> ``` </details><br> # hc-mistral-alpaca This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5970 ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.5663 \| 1.0 \| 1 \| 1.6992 \| \| 1.5706 \| 1.5 \| 2 \| 1.6898 \| \| 0.785 \| 2.0 \| 3 \| 1.6674 \| \| 0.7713 \| 2.5 \| 4 \| 1.5970 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.2 - Pytorch 2.1.2+cu118 - Datasets 2.19.1 - Tokenizers 0.19.1

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