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train · 848k rows

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ConvLLaVA/ConvLLaVA-sft-1024	ConvLLaVA	2024-05-28T08:32:29Z	4	0	transformers	[ "transformers", "pytorch", "llava", "text-generation", "dataset:liuhaotian/LLaVA-Instruct-150K", "arxiv:2405.15738", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T17:18:22Z	--- datasets: - liuhaotian/LLaVA-Instruct-150K --- # ConvLLaVA Model Card ## Model details Model type: ConvLLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: lmsys/vicuna-7b-v1.5 Model date: ConvLLaVA-1024 was trained in March 2024. Paper or resources for more information: https://github.com/alibaba/conv-llava/ ## License Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. Where to send questions or comments about the model: https://github.com/alibaba/conv-llava/issues ## Intended use Primary intended uses: The primary use of ConvLLaVA is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Training dataset - 1.2M ShareGPT4V-PT caption data. - 100K ShareGPT4V caption data. - 1.4M ALLaVA caption and instruction data. - 186K VFLAN multitask data. - 158K GPT-generated multimodal instruction-following data. - 500K academic-task-oriented VQA data mixture. - 40K ShareGPT data. ## Paper arxiv.org/abs/2405.15738
lgk03/WITHINAPPS_NDD-claroline_test-content_tags	lgk03	2024-05-28T08:32:28Z	106	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	2024-05-28T08:16:40Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: WITHINAPPS_NDD-claroline_test-content_tags results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # WITHINAPPS_NDD-claroline_test-content_tags This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0456 - Accuracy: 0.9871 - F1: 0.9872 - Precision: 0.9878 - Recall: 0.9871 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 0.9978 \| 111 \| 0.0464 \| 0.9871 \| 0.9872 \| 0.9878 \| 0.9871 \| \| No log \| 1.9955 \| 222 \| 0.0456 \| 0.9871 \| 0.9872 \| 0.9878 \| 0.9871 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
ConvLLaVA/ConvLLaVA-sft-768	ConvLLaVA	2024-05-28T08:32:19Z	16	1	transformers	[ "transformers", "pytorch", "llava", "text-generation", "dataset:liuhaotian/LLaVA-Instruct-150K", "arxiv:2405.15738", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T14:49:07Z	--- datasets: - liuhaotian/LLaVA-Instruct-150K --- # ConvLLaVA Model Card ## Model details Model type: ConvLLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: lmsys/vicuna-7b-v1.5 Model date: ConvLLaVA-768 was trained in March 2024. Paper or resources for more information: https://github.com/alibaba/conv-llava/ ## License Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. Where to send questions or comments about the model: https://github.com/alibaba/conv-llava/issues ## Intended use Primary intended uses: The primary use of ConvLLaVA is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Training dataset - 1.2M ShareGPT4V-PT caption data. - 100K ShareGPT4V caption data. - 1.4M ALLaVA caption and instruction data. - 186K VFLAN multitask data. - 158K GPT-generated multimodal instruction-following data. - 500K academic-task-oriented VQA data mixture. - 40K ShareGPT data. ## Paper arxiv.org/abs/2405.15738
ConvLLaVA/ConvLLaVA-pretrain-1536	ConvLLaVA	2024-05-28T08:31:38Z	13	2	transformers	[ "transformers", "pytorch", "llava", "text-generation", "dataset:Lin-Chen/ShareGPT4V", "dataset:FreedomIntelligence/ALLaVA-4V", "dataset:Vision-Flan/vision-flan_191-task_1k", "arxiv:2405.15738", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T08:35:38Z	--- datasets: - Lin-Chen/ShareGPT4V - FreedomIntelligence/ALLaVA-4V - Vision-Flan/vision-flan_191-task_1k --- # ConvLLaVA Model Card ## Model details Model type: ConvLLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: lmsys/vicuna-7b-v1.5 Model date: ConvLLaVA-pretrain-1536 was trained in March 2024. Paper or resources for more information: https://github.com/alibaba/conv-llava/ ## License Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. Where to send questions or comments about the model: https://github.com/alibaba/conv-llava/issues ## Intended use Primary intended uses: The primary use of ConvLLaVA is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Training dataset - 1.2M ShareGPT4V-PT caption data. - 100K ShareGPT4V caption data. - 1.4M ALLaVA caption and instruction data. - 186K VFLAN multitask data. - 158K GPT-generated multimodal instruction-following data. - 500K academic-task-oriented VQA data mixture. - 40K ShareGPT data. ## Paper arxiv.org/abs/2405.15738
DaichiT/door_adjuster	DaichiT	2024-05-28T08:31:28Z	29	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stabilityai/stable-diffusion-2", "base_model:finetune:stabilityai/stable-diffusion-2", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-28T08:24:00Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers base_model: stabilityai/stable-diffusion-2 inference: true instance_prompt: a photo of sks door_adjuster --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - DaichiT/door_adjuster This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks door_adjuster using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
tvlife/Llama-3-Open-Ko-8B-Instruct-tvlife	tvlife	2024-05-28T08:31:15Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:beomi/Llama-3-Open-Ko-8B", "base_model:finetune:beomi/Llama-3-Open-Ko-8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T08:27:02Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: beomi/Llama-3-Open-Ko-8B --- # Uploaded model - Developed by: tvlife - License: apache-2.0 - Finetuned from model : beomi/Llama-3-Open-Ko-8B 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)
DiederikMartens/eBERT_sa_cv_9_fold1	DiederikMartens	2024-05-28T08:30:48Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T08:08:43Z	--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: eBERT_sa_cv_9_fold1 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. --> # eBERT_sa_cv_9_fold1 This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5401 - F1: 0.5989 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.5491 \| 0.4553 \| \| 0.6277 \| 2.0 \| 650 \| 0.5053 \| 0.5024 \| \| 0.6277 \| 3.0 \| 975 \| 0.5401 \| 0.5989 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
zmilczarek/pii-detection-roberta-v3	zmilczarek	2024-05-28T08:30:38Z	166	0	transformers	[ "transformers", "safetensors", "roberta", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-28T08:29:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-580978	fine-tuned	2024-05-28T08:30:26Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "Social Media", "Arguments", "Debate", "Opinions", "Perspectives", "en", "dataset:fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-580978", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T08:29:57Z	--- license: apache-2.0 datasets: - fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-580978 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - Social Media - Arguments - Debate - Opinions - Perspectives --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: counter arguments on social media impact ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-580978', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
Rizwan313/MiniCPM-Llama3-V-2_5-int4	Rizwan313	2024-05-28T08:29:13Z	107	0	transformers	[ "transformers", "safetensors", "minicpmv", "feature-extraction", "custom_code", "arxiv:1910.09700", "4-bit", "bitsandbytes", "region:us" ]	feature-extraction	2024-05-28T08:25:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
DiederikMartens/mBERT_sa_cv_9_fold1	DiederikMartens	2024-05-28T08:29:01Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T08:07:55Z	--- license: apache-2.0 base_model: google-bert/bert-base-multilingual-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: mBERT_sa_cv_9_fold1 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. --> # mBERT_sa_cv_9_fold1 This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7432 - F1: 0.2851 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.7432 \| 0.2851 \| \| 0.7484 \| 2.0 \| 650 \| 0.7382 \| 0.2851 \| \| 0.7484 \| 3.0 \| 975 \| 0.7363 \| 0.2851 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Newton7/MyDrive	Newton7	2024-05-28T08:28:58Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	2024-05-28T08:28:56Z	--- license: llama3 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B-Instruct model-index: - name: MyDrive 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. --> # MyDrive This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the 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: 6e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
DiederikMartens/tsBERT_sa_cv_9_fold1	DiederikMartens	2024-05-28T08:28:35Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:igorsterner/german-english-code-switching-bert", "base_model:finetune:igorsterner/german-english-code-switching-bert", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T08:07:30Z	--- license: mit base_model: igorsterner/german-english-code-switching-bert tags: - generated_from_trainer metrics: - f1 model-index: - name: tsBERT_sa_cv_9_fold1 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. --> # tsBERT_sa_cv_9_fold1 This model is a fine-tuned version of [igorsterner/german-english-code-switching-bert](https://huggingface.co/igorsterner/german-english-code-switching-bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5209 - F1: 0.6927 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.3735 \| 0.5700 \| \| 0.4319 \| 2.0 \| 650 \| 0.4329 \| 0.6771 \| \| 0.4319 \| 3.0 \| 975 \| 0.5209 \| 0.6927 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Mustain/finetuned-llama-3-8b-Instruct-bnb-4bit-NS-dataset	Mustain	2024-05-28T08:27:25Z	6	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T08:11:51Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: Mustain - 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)
fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-866232	fine-tuned	2024-05-28T08:27:19Z	8	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "Science", "English", "Research", "Education", "Literature", "en", "dataset:fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-866232", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T08:26:49Z	--- license: apache-2.0 datasets: - fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-866232 - allenai/c4 language: - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - Science - English - Research - Education - Literature --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: general domain ## 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-512-192-gpt-4o-2024-05-13-866232', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
fine-tuned/NFCorpus-512-192-gpt-4o-2024-05-13-43315	fine-tuned	2024-05-28T08:25:32Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "News", "Articles", "Journalism", "Media", "Current Events", "en", "dataset:fine-tuned/NFCorpus-512-192-gpt-4o-2024-05-13-43315", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T08:25:03Z	--- license: apache-2.0 datasets: - fine-tuned/NFCorpus-512-192-gpt-4o-2024-05-13-43315 - allenai/c4 language: - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - News - Articles - Journalism - Media - Current Events --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: news articles ## 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-512-192-gpt-4o-2024-05-13-43315', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
DaichiT/counterweight	DaichiT	2024-05-28T08:24:08Z	31	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stabilityai/stable-diffusion-2", "base_model:finetune:stabilityai/stable-diffusion-2", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-28T08:16:07Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers base_model: stabilityai/stable-diffusion-2 inference: true instance_prompt: a photo of sks countetweight --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - DaichiT/counterweight This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks countetweight using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
rlaorrn/jeju_stt_v2	rlaorrn	2024-05-28T08:24:03Z	99	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ko", "dataset:rlaorrn/working", "base_model:openai/whisper-base", "base_model:finetune:openai/whisper-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-27T12:19:24Z	--- language: - ko license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer base_model: openai/whisper-base datasets: - rlaorrn/working model-index: - name: jeju_stt 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. --> # jeju_stt This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the jeju_audio dataset. It achieves the following results on the evaluation set: - Loss: 0.3820 - Cer: 12.0409 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.3689 \| 2.0 \| 1000 \| 0.3853 \| 13.4054 \| \| 0.1884 \| 4.0 \| 2000 \| 0.3488 \| 11.9817 \| \| 0.1059 \| 6.0 \| 3000 \| 0.3607 \| 11.9350 \| \| 0.0634 \| 8.0 \| 4000 \| 0.3820 \| 12.0409 \| ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.19.1
DokHee/Llama-3-Open-Ko-8B-Instruct-alphaEdu100-gguf	DokHee	2024-05-28T08:23:12Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "llama", "gguf", "en", "base_model:beomi/Llama-3-Open-Ko-8B", "base_model:finetune:beomi/Llama-3-Open-Ko-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T08:23:11Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: beomi/Llama-3-Open-Ko-8B --- # Uploaded model - Developed by: DokHee - License: apache-2.0 - Finetuned from model : beomi/Llama-3-Open-Ko-8B 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)
DaichiT/copper_alloy	DaichiT	2024-05-28T08:22:42Z	29	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stabilityai/stable-diffusion-2", "base_model:finetune:stabilityai/stable-diffusion-2", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-28T08:15:11Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers base_model: stabilityai/stable-diffusion-2 inference: true instance_prompt: a photo of sks copper_alloy --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - DaichiT/copper_alloy This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks copper_alloy using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
LiteLLMs/free-evo-qwen72b-v0.8-re-GGUF	LiteLLMs	2024-05-28T08:21:50Z	34	0	transformers	[ "transformers", "gguf", "GGUF", "en", "license:mit", "model-index", "endpoints_compatible", "region:us" ]	null	2024-05-08T13:03:06Z	--- language: - en license: mit library_name: transformers tags: - GGUF model-index: - name: free-evo-qwen72b-v0.8-re 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: 79.86 name: normalized accuracy verified: false source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=freewheelin/free-evo-qwen72b-v0.8-re 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: 91.34 name: normalized accuracy verified: false source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=freewheelin/free-evo-qwen72b-v0.8-re 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: 78 name: accuracy verified: false source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=freewheelin/free-evo-qwen72b-v0.8-re 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: 74.85 verified: false source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=freewheelin/free-evo-qwen72b-v0.8-re 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: 87.77 name: accuracy verified: false source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=freewheelin/free-evo-qwen72b-v0.8-re 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: 75.89 name: accuracy verified: false source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=freewheelin/free-evo-qwen72b-v0.8-re name: Open LLM Leaderboard quantized_by: andrijdavid --- # free-evo-qwen72b-v0.8-re-GGUF - Original model: [free-evo-qwen72b-v0.8-re](https://huggingface.co/freewheelin/free-evo-qwen72b-v0.8-re) <!-- description start --> ## Description This repo contains GGUF format model files for [free-evo-qwen72b-v0.8-re](https://huggingface.co/freewheelin/free-evo-qwen72b-v0.8-re). <!-- 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/free-evo-qwen72b-v0.8-re-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/free-evo-qwen72b-v0.8-re-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/free-evo-qwen72b-v0.8-re-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/free-evo-qwen72b-v0.8-re-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: free-evo-qwen72b-v0.8-re # Model Card for free-evo-qwen72b-v0.8 ## Developed by : [Freewheelin](https://freewheelin-recruit.oopy.io/) AI Technical Team ## 2024 4th May - avg. 81.28 [Open Llm Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) \| Metric \| Value \| \| - \| ----: \| \| Avg. \| 81.28 \| \| ARC (25-Shot) \| 79.86 \| \| HellaSwag (10-Shot) \| 91.32 \| \| MMLU (5-Shot) \| 78.00 \| \| TruthfulQA (0-shot) \| 74.85 \| \| Winogrande (5-shot) \| 87.77 \| \| GSM8k (5-shot) \| 75.89 \| ## Method - We were inspired by this [Sakana project](https://sakana.ai/evolutionary-model-merge/) ## Process You need two models with the same architecture. - Choose one model and fine-tune it to create a gap between the original model and the fine-tuned one. It doesn't matter whether the evaluation score is higher or lower. - Merge the two models. - Evaluate the merged model. - Fine-tune a specific evaluation part of the model if you need to increase the score for that part. (It's unlikely to work as you think, but you can try it.) - Merge the models again. - Evaluate again. - Keep going until the average evaluation score is higher than the original one. That's it. Simple. You can create a framework to automate this process. ## Base Architecture - QWEN2 ## Base Models - several QWEN2 based models <!-- original-model-card end -->
JiAYu1997/HRJD_FinetuneV2_1	JiAYu1997	2024-05-28T08:19:37Z	0	0	null	[ "trl", "sft", "generated_from_trainer", "base_model:taide/Llama3-TAIDE-LX-8B-Chat-Alpha1", "base_model:finetune:taide/Llama3-TAIDE-LX-8B-Chat-Alpha1", "license:other", "region:us" ]	null	2024-05-28T08:01:13Z	--- license: other base_model: taide/Llama3-TAIDE-LX-8B-Chat-Alpha1 tags: - trl - sft - generated_from_trainer model-index: - name: HRJD_FinetuneV2_1 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. --> # HRJD_FinetuneV2_1 This model is a fine-tuned version of [taide/Llama3-TAIDE-LX-8B-Chat-Alpha1](https://huggingface.co/taide/Llama3-TAIDE-LX-8B-Chat-Alpha1) 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: 5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 1000 ### Training results ### Framework versions - Transformers 4.33.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.13.3
ConvLLaVA/ConvLLaVA-ConvNeXt-1536	ConvLLaVA	2024-05-28T08:16:54Z	2,032	1	transformers	[ "transformers", "pytorch", "convnext", "arxiv:2405.15738", "endpoints_compatible", "region:us" ]	null	2024-05-25T08:36:29Z	# ConvNeXt Model Card ## Model details Model type: ConvNeXt is an open-source visual encoder trained by fine-tuning LLM on multimodal caption and instruction-following data. The base model is: laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup. Model date: ConvLLaVA-ConvNeXt-1536 was trained in March 2024. Paper or resources for more information: https://github.com/alibaba/conv-llava/ Where to send questions or comments about the model: https://github.com/alibaba/conv-llava/issues ## Intended use Primary intended uses: The primary use of ConvLLaVA-ConvNeXt is research on large multimodal models and chatbots. ## Paper arxiv.org/abs/2405.15738
zacll/chinese-adult-novel	zacll	2024-05-28T08:16:29Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-28T07:21:27Z	--- license: apache-2.0 ---
ConvLLaVA/ConvLLaVA-ConvNeXt-768	ConvLLaVA	2024-05-28T08:16:24Z	161	0	transformers	[ "transformers", "pytorch", "convnext", "arxiv:2405.15738", "endpoints_compatible", "region:us" ]	null	2024-05-25T08:35:58Z	# ConvNeXt Model Card ## Model details Model type: ConvNeXt is an open-source visual encoder trained by fine-tuning LLM on multimodal caption and instruction-following data. The base model is: laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup. Model date: ConvLLaVA-ConvNeXt-768 was trained in March 2024. Paper or resources for more information: https://github.com/alibaba/conv-llava/ Where to send questions or comments about the model: https://github.com/alibaba/conv-llava/issues ## Intended use Primary intended uses: The primary use of ConvLLaVA-ConvNeXt is research on large multimodal models and chatbots. ## Paper arxiv.org/abs/2405.15738
DaichiT/copper	DaichiT	2024-05-28T08:12:47Z	29	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stabilityai/stable-diffusion-2", "base_model:finetune:stabilityai/stable-diffusion-2", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-28T08:05:05Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers base_model: stabilityai/stable-diffusion-2 inference: true instance_prompt: a photo of sks copper --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - DaichiT/copper This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks copper using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
joeyiexec/peftllama2	joeyiexec	2024-05-28T08:12:41Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-28T08:12:25Z	--- 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]
DaichiT/concrete	DaichiT	2024-05-28T08:12:21Z	30	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stabilityai/stable-diffusion-2", "base_model:finetune:stabilityai/stable-diffusion-2", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-28T08:04:30Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers base_model: stabilityai/stable-diffusion-2 inference: true instance_prompt: a photo of sks concrete --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - DaichiT/concrete This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks concrete using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lgk03/WITHINAPPS_NDD-addressbook_test-content_tags	lgk03	2024-05-28T08:11:18Z	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	2024-05-28T08:04:40Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: WITHINAPPS_NDD-addressbook_test-content_tags results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # WITHINAPPS_NDD-addressbook_test-content_tags This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1455 - Accuracy: 0.9489 - F1: 0.9500 - Precision: 0.9560 - Recall: 0.9489 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 0.9953 \| 53 \| 0.1517 \| 0.9489 \| 0.9500 \| 0.9560 \| 0.9489 \| \| No log \| 1.9906 \| 106 \| 0.1455 \| 0.9489 \| 0.9500 \| 0.9560 \| 0.9489 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
ConvLLaVA/ConvLLaVA-ConvNeXt-1024	ConvLLaVA	2024-05-28T08:10:26Z	177	0	transformers	[ "transformers", "pytorch", "convnext", "arxiv:2405.15738", "endpoints_compatible", "region:us" ]	null	2024-05-25T08:36:09Z	# ConvNeXt Model Card ## Model details Model type: ConvNeXt is an open-source visual encoder trained by fine-tuning LLM on multimodal caption and instruction-following data. The base model is: laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup. Model date: ConvLLaVA-ConvNeXt-1024 was trained in March 2024. Paper or resources for more information: https://github.com/alibaba/conv-llava/ Where to send questions or comments about the model: https://github.com/alibaba/conv-llava/issues ## Intended use Primary intended uses: The primary use of ConvLLaVA-ConvNeXt is research on large multimodal models and chatbots. ## Paper arxiv.org/abs/2405.15738
SerchiBoi/DTT-Chatbot-Piloto-v4	SerchiBoi	2024-05-28T08:09:22Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-2b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-2b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T08:08:37Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-2b-it-bnb-4bit --- # Uploaded model - Developed by: SerchiBoi - License: apache-2.0 - Finetuned from model : unsloth/gemma-2b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ferrazzipietro/Llama-2-7b-chat-hf_adapters_en.layer1_NoQuant_torch.bfloat16_16_32_0.01_2_0.0002	ferrazzipietro	2024-05-28T08:09:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-14T18:19:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
John6666/juggernaut-xl-rundiffusion-hyper-sdxl	John6666	2024-05-28T08:07:50Z	348	5	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-28T08:03:08Z	--- license: other tags: - text-to-image - stable-diffusion - stable-diffusion-xl --- Original model is [here](https://civitai.com/models/133005?modelVersionId=471120).
DiederikMartens/tsBERT_sa_cv_9_fold0	DiederikMartens	2024-05-28T08:07:24Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:igorsterner/german-english-code-switching-bert", "base_model:finetune:igorsterner/german-english-code-switching-bert", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T07:46:21Z	--- license: mit base_model: igorsterner/german-english-code-switching-bert tags: - generated_from_trainer metrics: - f1 model-index: - name: tsBERT_sa_cv_9_fold0 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. --> # tsBERT_sa_cv_9_fold0 This model is a fine-tuned version of [igorsterner/german-english-code-switching-bert](https://huggingface.co/igorsterner/german-english-code-switching-bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4935 - F1: 0.7006 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.4017 \| 0.6081 \| \| 0.4472 \| 2.0 \| 650 \| 0.4388 \| 0.6617 \| \| 0.4472 \| 3.0 \| 975 \| 0.4935 \| 0.7006 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
xX-FANE-Xx/koala-13B-HF-Q2_K-GGUF	xX-FANE-Xx	2024-05-28T08:07:00Z	7	0	transformers	[ "transformers", "gguf", "koala", "ShareGPT", "llama", "gptq", "llama-cpp", "gguf-my-repo", "text-generation", "dataset:RyokoAI/ShareGPT52K", "dataset:Hello-SimpleAI/HC3", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T08:06:43Z	--- license: other library_name: transformers tags: - koala - ShareGPT - llama - gptq - llama-cpp - gguf-my-repo datasets: - RyokoAI/ShareGPT52K - Hello-SimpleAI/HC3 pipeline_tag: text-generation --- # xX-FANE-Xx/koala-13B-HF-Q2_K-GGUF This model was converted to GGUF format from [`TheBloke/koala-13B-HF`](https://huggingface.co/TheBloke/koala-13B-HF) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/TheBloke/koala-13B-HF) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo xX-FANE-Xx/koala-13B-HF-Q2_K-GGUF --model koala-13b-hf-q2_k.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo xX-FANE-Xx/koala-13B-HF-Q2_K-GGUF --model koala-13b-hf-q2_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m koala-13b-hf-q2_k.gguf -n 128 ```
RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf	RichardErkhov	2024-05-28T08:06:25Z	15	0	null	[ "gguf", "arxiv:2308.10882", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-27T11:31:21Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Giraffe-v2-70b-32k - GGUF - Model creator: https://huggingface.co/abacusai/ - Original model: https://huggingface.co/abacusai/Giraffe-v2-70b-32k/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Giraffe-v2-70b-32k.Q2_K.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/blob/main/Giraffe-v2-70b-32k.Q2_K.gguf) \| Q2_K \| 23.71GB \| \| [Giraffe-v2-70b-32k.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/blob/main/Giraffe-v2-70b-32k.IQ3_XS.gguf) \| IQ3_XS \| 26.37GB \| \| [Giraffe-v2-70b-32k.IQ3_S.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/blob/main/Giraffe-v2-70b-32k.IQ3_S.gguf) \| IQ3_S \| 27.86GB \| \| [Giraffe-v2-70b-32k.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/blob/main/Giraffe-v2-70b-32k.Q3_K_S.gguf) \| Q3_K_S \| 27.86GB \| \| [Giraffe-v2-70b-32k.IQ3_M.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/blob/main/Giraffe-v2-70b-32k.IQ3_M.gguf) \| IQ3_M \| 28.82GB \| \| [Giraffe-v2-70b-32k.Q3_K.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/blob/main/Giraffe-v2-70b-32k.Q3_K.gguf) \| Q3_K \| 30.99GB \| \| [Giraffe-v2-70b-32k.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/blob/main/Giraffe-v2-70b-32k.Q3_K_M.gguf) \| Q3_K_M \| 30.99GB \| \| [Giraffe-v2-70b-32k.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/blob/main/Giraffe-v2-70b-32k.Q3_K_L.gguf) \| Q3_K_L \| 33.67GB \| \| [Giraffe-v2-70b-32k.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/blob/main/Giraffe-v2-70b-32k.IQ4_XS.gguf) \| IQ4_XS \| 34.64GB \| \| [Giraffe-v2-70b-32k.Q4_0.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/blob/main/Giraffe-v2-70b-32k.Q4_0.gguf) \| Q4_0 \| 36.2GB \| \| [Giraffe-v2-70b-32k.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/blob/main/Giraffe-v2-70b-32k.IQ4_NL.gguf) \| IQ4_NL \| 36.55GB \| \| [Giraffe-v2-70b-32k.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/blob/main/Giraffe-v2-70b-32k.Q4_K_S.gguf) \| Q4_K_S \| 36.55GB \| \| [Giraffe-v2-70b-32k.Q4_K.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/tree/main/) \| Q4_K \| 38.58GB \| \| [Giraffe-v2-70b-32k.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/tree/main/) \| Q4_K_M \| 38.58GB \| \| [Giraffe-v2-70b-32k.Q4_1.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/tree/main/) \| Q4_1 \| 40.2GB \| \| [Giraffe-v2-70b-32k.Q5_0.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/tree/main/) \| Q5_0 \| 44.2GB \| \| [Giraffe-v2-70b-32k.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/tree/main/) \| Q5_K_S \| 44.2GB \| \| [Giraffe-v2-70b-32k.Q5_K.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/tree/main/) \| Q5_K \| 45.41GB \| \| [Giraffe-v2-70b-32k.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/tree/main/) \| Q5_K_M \| 45.41GB \| \| [Giraffe-v2-70b-32k.Q5_1.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/tree/main/) \| Q5_1 \| 48.2GB \| \| [Giraffe-v2-70b-32k.Q6_K.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/tree/main/) \| Q6_K \| 52.7GB \| \| [Giraffe-v2-70b-32k.Q8_0.gguf](https://huggingface.co/RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf/tree/main/) \| Q8_0 \| 68.26GB \| Original model description: --- tags: - llama2 --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/DJHrZmfoy-0TzNChTrtxP.png) ## Model Details ### Model Description We have followed up on our previous training runs related to extending the context length of Llama models. The associated github repository https://github.com/abacusai/long-context has some basic details on our approach and metrics. We have also published a paper on arXiv that covers our experiments and analysis a lot more comprehensively. http://arxiv.org/abs/2308.10882 - Developed by: [Abacus.AI](https://abacus.ai) - Model type: Transformer based autoregressive causal language model - License: Llama 2 Community License: https://github.com/facebookresearch/llama/blob/main/LICENSE - Finetuned from model: Llama V2 70B ### Usage To use this model at longer lengths the model needs to be patched to interpolate the longer context lengths. It will not work if it is simply loaded with the `AutoModel` framework of `transformers`. For full details and usage see: https://github.com/abacusai/Long-Context The evaluation section has detailed code for how to load and patch the model for inference (or further fine-tuning). Note in particular the `max_position_embeddings` is not relevant since the patched module dynamically reallocates the position buffers as required. The tokenizer corresponding to this model is https://huggingface.co/abacusai/Giraffe-v1-Tokenizer. Using the code in the repository you can load this model with the following code: ```python from models import load_model, load_tokenizer tokenizer = load_tokenizer() model = load_model('abacusai/Giraffe-v2-70b-32k', scale=8) ```
RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf	RichardErkhov	2024-05-28T08:05:15Z	23	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-27T09:21:52Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Euryale-1.4-L2-70B - GGUF - Model creator: https://huggingface.co/Sao10K/ - Original model: https://huggingface.co/Sao10K/Euryale-1.4-L2-70B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Euryale-1.4-L2-70B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/blob/main/Euryale-1.4-L2-70B.Q2_K.gguf) \| Q2_K \| 23.71GB \| \| [Euryale-1.4-L2-70B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/blob/main/Euryale-1.4-L2-70B.IQ3_XS.gguf) \| IQ3_XS \| 26.37GB \| \| [Euryale-1.4-L2-70B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/blob/main/Euryale-1.4-L2-70B.IQ3_S.gguf) \| IQ3_S \| 27.86GB \| \| [Euryale-1.4-L2-70B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/blob/main/Euryale-1.4-L2-70B.Q3_K_S.gguf) \| Q3_K_S \| 27.86GB \| \| [Euryale-1.4-L2-70B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/blob/main/Euryale-1.4-L2-70B.IQ3_M.gguf) \| IQ3_M \| 28.82GB \| \| [Euryale-1.4-L2-70B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/blob/main/Euryale-1.4-L2-70B.Q3_K.gguf) \| Q3_K \| 30.99GB \| \| [Euryale-1.4-L2-70B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/blob/main/Euryale-1.4-L2-70B.Q3_K_M.gguf) \| Q3_K_M \| 30.99GB \| \| [Euryale-1.4-L2-70B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/blob/main/Euryale-1.4-L2-70B.Q3_K_L.gguf) \| Q3_K_L \| 33.67GB \| \| [Euryale-1.4-L2-70B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/blob/main/Euryale-1.4-L2-70B.IQ4_XS.gguf) \| IQ4_XS \| 34.64GB \| \| [Euryale-1.4-L2-70B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/blob/main/Euryale-1.4-L2-70B.Q4_0.gguf) \| Q4_0 \| 36.2GB \| \| [Euryale-1.4-L2-70B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/blob/main/Euryale-1.4-L2-70B.IQ4_NL.gguf) \| IQ4_NL \| 36.55GB \| \| [Euryale-1.4-L2-70B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/blob/main/Euryale-1.4-L2-70B.Q4_K_S.gguf) \| Q4_K_S \| 36.55GB \| \| [Euryale-1.4-L2-70B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/tree/main/) \| Q4_K \| 38.58GB \| \| [Euryale-1.4-L2-70B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/tree/main/) \| Q4_K_M \| 38.58GB \| \| [Euryale-1.4-L2-70B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/tree/main/) \| Q4_1 \| 40.2GB \| \| [Euryale-1.4-L2-70B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/tree/main/) \| Q5_0 \| 44.2GB \| \| [Euryale-1.4-L2-70B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/tree/main/) \| Q5_K_S \| 44.2GB \| \| [Euryale-1.4-L2-70B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/tree/main/) \| Q5_K \| 45.41GB \| \| [Euryale-1.4-L2-70B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/tree/main/) \| Q5_K_M \| 45.41GB \| \| [Euryale-1.4-L2-70B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/tree/main/) \| Q5_1 \| 48.2GB \| \| [Euryale-1.4-L2-70B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/tree/main/) \| Q6_K \| 52.7GB \| \| [Euryale-1.4-L2-70B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Euryale-1.4-L2-70B-gguf/tree/main/) \| Q8_0 \| 68.26GB \| Original model description: --- license: llama2 language: - en --- gguf quants: https://huggingface.co/Sao10K/Euryale-1.4-L2-70B-GGUF 1.3, but better? I guess. Base Merged Model ratios adjusted. NSFL portion of Hesperus v1 dataset trained and applied. LimaRP merged in at a ~25% weight at the end. Subjectively better in some aspects eg. long form rp, worse than the other, eg. chat-style rps. overall a minor improvement in my eyes. 1.5 will include Hesperus v2 dataset in its entirety. format: alpaca.
alijawad07/aya-23-8B-AWQ-GEMM	alijawad07	2024-05-28T08:01:37Z	90	2	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-05-28T06:40:24Z	# Aya-23-8B - AWQ Quantized - Model creator: [Cohere For AI](https://huggingface.co/cohere-for-ai) - Original model: [Aya-23-8B](https://huggingface.co/CohereForAI/aya-23-8B) <!-- description start --> ## Description This repo contains AWQ model files for [Cohere's Aya-23-8B](https://huggingface.co/CohereForAI/aya-23-8B). Aya 23 is an open weights research release of an instruction fine-tuned model with highly advanced multilingual capabilities. The model focuses on pairing a highly performant pre-trained Command family of models with the recently released Aya Collection. The result is a powerful multilingual large language model serving 23 languages. ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference. It is also now supported by continuous batching server [vLLM](https://github.com/vllm-project/vllm), allowing use of AWQ models for high-throughput concurrent inference in multi-user server scenarios. Note that, at the time of writing, overall throughput is still lower than running vLLM with unquantized models. However, using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB. <!-- description end --> ## 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. It covers 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/) - Model: aya-23-8B-AWQ-GEMM - Model Size: 8 billion parameters - Bits: 4 - Q-Group Size: 128 This is an AWQ quantized version of the Aya-23-8B model using AutoAWQ. ### Usage Please install transformers from the source repository that includes the necessary changes for this model. ```python # pip install transformers==4.41.1 # pip install autoawq from transformers import AutoTokenizer from awq import AutoAWQForCausalLM quant_path = "path/to/quantized/model" tokenizer = AutoTokenizer.from_pretrained(quant_path) model = AutoAWQForCausalLM.from_quantized(quant_path, fuse_layers=True) # 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>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz 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) ``` ## Model Details Input: Models input text only. Output: Models generate text only. Model Architecture: Aya-23-8B 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 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.
TomTom42/q-FrozenLake-v1-4x4-noSlippery	TomTom42	2024-05-28T08:01:33Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-28T08:01:29Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="TomTom42/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
thewordsmiths/mistral_dpo	thewordsmiths	2024-05-28T08:00:44Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/mistral-7b", "base_model:adapter:unsloth/mistral-7b", "region:us" ]	null	2024-05-28T07:59:38Z	--- library_name: peft base_model: unsloth/mistral-7b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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mansaripo/thewordsmiths	mansaripo	2024-05-28T07:55:51Z	3	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/llama-3-8b", "base_model:adapter:unsloth/llama-3-8b", "region:us" ]	null	2024-05-28T07:52:36Z	--- library_name: peft base_model: unsloth/llama-3-8b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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bradleymarques/my-test-model	bradleymarques	2024-05-28T07:54:40Z	0	0	null	[ "arxiv:1910.09700", "region:us" ]	null	2024-05-28T07:54:04Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. 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iron-huray/llama_test	iron-huray	2024-05-28T07:53:22Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	2024-05-22T00:58:26Z	--- license: llama3 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B-Instruct model-index: - name: llama_test 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. --> # llama_test This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
IneG/RoBERTa_pretrained_litcov10K	IneG	2024-05-28T07:51:15Z	117	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-28T07:48:00Z	--- tags: - generated_from_trainer model-index: - name: RoBERTa_pretrained_litcov10K 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. --> # RoBERTa_pretrained_litcov10K This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
DiederikMartens/eBERT_sa_cv_12_fold9	DiederikMartens	2024-05-28T07:46:13Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T07:38:58Z	--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: eBERT_sa_cv_12_fold9 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. --> # eBERT_sa_cv_12_fold9 This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5047 - F1: 0.5356 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 226 \| 0.4773 \| 0.4302 \| \| No log \| 2.0 \| 452 \| 0.4493 \| 0.5255 \| \| 0.5125 \| 3.0 \| 678 \| 0.5047 \| 0.5356 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
DiederikMartens/mBERT_sa_cv_12_fold9	DiederikMartens	2024-05-28T07:44:56Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T07:34:25Z	--- license: apache-2.0 base_model: google-bert/bert-base-multilingual-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: mBERT_sa_cv_12_fold9 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. --> # mBERT_sa_cv_12_fold9 This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4492 - F1: 0.5742 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 226 \| 0.4549 \| 0.4987 \| \| No log \| 2.0 \| 452 \| 0.4037 \| 0.5291 \| \| 0.4719 \| 3.0 \| 678 \| 0.4492 \| 0.5742 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
DiederikMartens/tsBERT_sa_cv_12_fold9	DiederikMartens	2024-05-28T07:43:11Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:igorsterner/german-english-code-switching-bert", "base_model:finetune:igorsterner/german-english-code-switching-bert", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T07:33:22Z	--- license: mit base_model: igorsterner/german-english-code-switching-bert tags: - generated_from_trainer metrics: - f1 model-index: - name: tsBERT_sa_cv_12_fold9 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. --> # tsBERT_sa_cv_12_fold9 This model is a fine-tuned version of [igorsterner/german-english-code-switching-bert](https://huggingface.co/igorsterner/german-english-code-switching-bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4022 - F1: 0.5954 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 226 \| 0.3970 \| 0.5359 \| \| No log \| 2.0 \| 452 \| 0.4022 \| 0.5954 \| \| 0.3494 \| 3.0 \| 678 \| 0.4937 \| 0.5953 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
John6666/nsfw-anime-xl-v1-sdxl	John6666	2024-05-28T07:41:29Z	36	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-28T07:37:02Z	--- license: other tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime --- Original model is [here](https://civitai.com/models/461074/nsfw-animexl).
DiederikMartens/eBERT_sa_cv_12_fold8	DiederikMartens	2024-05-28T07:38:53Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T07:23:40Z	--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: eBERT_sa_cv_12_fold8 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. --> # eBERT_sa_cv_12_fold8 This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5513 - F1: 0.4990 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 226 \| 0.5354 \| 0.4015 \| \| No log \| 2.0 \| 452 \| 0.5639 \| 0.3975 \| \| 0.5216 \| 3.0 \| 678 \| 0.5513 \| 0.4990 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
huypn16/MetaMath-DeepSeekMath-7B	huypn16	2024-05-28T07:37:32Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T09:45:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
furkanbicer/Taxi-v3	furkanbicer	2024-05-28T07:35:00Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-28T07:34:58Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.74 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="furkanbicer/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
TurkuNLP/xlmr-qa-extraction-en	TurkuNLP	2024-05-28T07:34:48Z	166	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-11-02T09:59:09Z	--- license: cc-by-nc-sa-4.0 library_name: transformers pipeline_tag: token-classification widget: - text: "Do you think that looks like a cat? Answer: I don't think so." - example_title: "cat" --- ### xlm-roberta-base for token classification, specifically fine-tuned for question-answer extraction for English This is the `xlm-roberta-base`, fine-tuned on manually annotated Finnish data and ChatGPT-annotated data. ### Hyperparameters ``` batch_size = 8 epochs = 10 (trained for less) base_LM_model = "xlm-roberta-base" max_seq_len = 512 learning_rate = 5e-5 ``` ### Performance ``` Accuracy = 0.88 Question F1 = 0.77 Answer F1 = 0.81 ``` ### Usage To get the best question-answer pairs use the huggingface pipeline with no aggregation strategy and do some post-processing like in this [script](https://github.com/TurkuNLP/register-qa/blob/main/token-classification/scripts/extract_qa_en_no_entropy.py). ## Citing To cite this model use the following bibtex. ``` @inproceedings{eskelinen-etal-2024-building-question, title = "Building Question-Answer Data Using Web Register Identification", author = "Eskelinen, Anni and Myntti, Amanda and Henriksson, Erik and Pyysalo, Sampo and Laippala, Veronika", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.234", pages = "2595--2611", abstract = "This article introduces a resource-efficient method for developing question-answer (QA) datasets by extracting QA pairs from web-scale data using machine learning (ML). Our method benefits from recent advances in web register (genre) identification and consists of two ML steps with an additional post-processing step. First, using XLM-R and the multilingual CORE web register corpus series with categories such as QA Forum, we train a multilingual classifier to retrieve documents that are likely to contain QA pairs from web-scale data. Second, we develop a NER-style token classifier to identify the QA text spans within these documents. To this end, we experiment with training on a semi-synthetic dataset built on top of the English LFQA, a small set of manually cleaned web QA pairs in English and Finnish, and a Finnish web QA pair dataset cleaned using ChatGPT. The evaluation of our pipeline demonstrates its capability to efficiently retrieve a substantial volume of QA pairs. While the approach is adaptable to any language given the availability of language models and extensive web data, we showcase its efficiency in English and Finnish, developing the first open, non-synthetic and non-machine translated QA dataset for Finnish {--} Turku WebQA {--} comprising over 200,000 QA pairs.", } ```
TurkuNLP/xlmr-qa-extraction-fi	TurkuNLP	2024-05-28T07:34:37Z	162	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-11-02T09:38:23Z	--- license: cc-by-nc-sa-4.0 library_name: transformers pipeline_tag: token-classification widget: - text: "Kysymys: Onko tuo kissa? Vastaus: En osaa sanoa." --- ### xlm-roberta-base for token classification, specifically fine-tuned for question-answer extraction for Finnish This is the `xlm-roberta-base`, fine-tuned on manually annotated Finnish data, ChatGPT-annotated data and a semi-synthetic dataset based on the LFQA dataset. ### Hyperparameters ``` batch_size = 8 epochs = 10 (trained for less) base_LM_model = "xlm-roberta-base" max_seq_len = 512 learning_rate = 1e-5 ``` ### Performance ``` Accuracy = 0.85 Question F1 = 0.82 Answer F1 = 0.75 ``` ### Usage To get the best question-answer pairs use the huggingface pipeline with no aggregation strategy and do some post-processing like in this [script](https://github.com/TurkuNLP/register-qa/blob/main/token-classification/scripts/extract_qa_fi_no_entropy.py). ### Citing To cite this model use the following bibtex. ``` @inproceedings{eskelinen-etal-2024-building-question, title = "Building Question-Answer Data Using Web Register Identification", author = "Eskelinen, Anni and Myntti, Amanda and Henriksson, Erik and Pyysalo, Sampo and Laippala, Veronika", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.234", pages = "2595--2611", abstract = "This article introduces a resource-efficient method for developing question-answer (QA) datasets by extracting QA pairs from web-scale data using machine learning (ML). Our method benefits from recent advances in web register (genre) identification and consists of two ML steps with an additional post-processing step. First, using XLM-R and the multilingual CORE web register corpus series with categories such as QA Forum, we train a multilingual classifier to retrieve documents that are likely to contain QA pairs from web-scale data. Second, we develop a NER-style token classifier to identify the QA text spans within these documents. To this end, we experiment with training on a semi-synthetic dataset built on top of the English LFQA, a small set of manually cleaned web QA pairs in English and Finnish, and a Finnish web QA pair dataset cleaned using ChatGPT. The evaluation of our pipeline demonstrates its capability to efficiently retrieve a substantial volume of QA pairs. While the approach is adaptable to any language given the availability of language models and extensive web data, we showcase its efficiency in English and Finnish, developing the first open, non-synthetic and non-machine translated QA dataset for Finnish {--} Turku WebQA {--} comprising over 200,000 QA pairs.", } ```
furkanbicer/q-FrozenLake-v1-4x4-noSlippery	furkanbicer	2024-05-28T07:33:57Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-28T07:33:55Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="furkanbicer/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
mesolitica/llava-v1.6-34b-hf-awq	mesolitica	2024-05-28T07:32:19Z	96	0	transformers	[ "transformers", "safetensors", "llava_next", "image-text-to-text", "conversational", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	image-text-to-text	2024-05-28T07:09:37Z	--- library_name: transformers tags: [] --- # Llava-1.6 34B AWQ You need to use this forked, https://github.com/WanBenLe/AutoAWQ-with-llava-v1.6
ferrazzipietro/Llama-2-7b-chat-hf_adapters_en.layer1_NoQuant_torch.bfloat16_16_32_0.01_1_0.0002	ferrazzipietro	2024-05-28T07:28:54Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-27T17:12:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Tanvi03/finetunever3-raredata	Tanvi03	2024-05-28T07:28:30Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T03:17:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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haturusinghe/LLAMA3-Finetune-v1-1.41_loss-May-28-2024	haturusinghe	2024-05-28T07:25:27Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T07:25:14Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: haturusinghe - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
DiederikMartens/gBERT_sa_cv_12_fold8	DiederikMartens	2024-05-28T07:22:46Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-german-cased", "base_model:finetune:google-bert/bert-base-german-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T07:10:09Z	--- license: mit base_model: google-bert/bert-base-german-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: gBERT_sa_cv_12_fold8 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. --> # gBERT_sa_cv_12_fold8 This model is a fine-tuned version of [google-bert/bert-base-german-cased](https://huggingface.co/google-bert/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5238 - F1: 0.6375 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 226 \| 0.4182 \| 0.5045 \| \| No log \| 2.0 \| 452 \| 0.5894 \| 0.6292 \| \| 0.3404 \| 3.0 \| 678 \| 0.5238 \| 0.6375 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
DaichiT/box	DaichiT	2024-05-28T07:18:05Z	29	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stabilityai/stable-diffusion-2", "base_model:finetune:stabilityai/stable-diffusion-2", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-28T07:12:58Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers base_model: stabilityai/stable-diffusion-2 inference: true instance_prompt: a photo of sks box --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - DaichiT/box This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks box using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
imrgurmeet/qwen1.5-llm-quantized	imrgurmeet	2024-05-28T07:15:14Z	5	1	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-27T17:25:36Z	The "qwen1.5-llm-quantized" model is a quantized version of the original Qwen1.5-110B model. Qwen1.5 is a transformer-based decoder-only language model that has been pretrained on a large amount of data. The improvements in Qwen1.5 include multiple model sizes, ranging from 0.5B to 110B dense models, as well as an MoE (Mixture of Experts) model of 14B with 2.7B activated. These models show significant performance improvements in chat models and provide multilingual support for both base and chat models. They also offer stable support for a 32K context length for models of all sizes. The quantized version of the model has undergone a quantization process, which reduces the model size and computational requirements while maintaining its performance. For more details about the original Qwen1.5-110B model, you can refer to the blog post and GitHub repository provided by the Qwen team at Alibaba Cloud. "https://huggingface.co/Qwen/Qwen1.5-110B" "https://github.com/QwenLM/Qwen1.5"
sunoaiysha/fine-tuned-gpt2	sunoaiysha	2024-05-28T07:13:15Z	133	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T19:14:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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adhityaprimandhika/mistral_categorization_unsloth_q4_v2_gguf	adhityaprimandhika	2024-05-28T07:10:15Z	4	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/mistral-7b-v0.3-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T07:06:39Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: unsloth/mistral-7b-v0.3-bnb-4bit --- # Uploaded model - Developed by: adhityaprimandhika - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-v0.3-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)
DiederikMartens/gBERT_sa_cv_12_fold7	DiederikMartens	2024-05-28T07:10:05Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-german-cased", "base_model:finetune:google-bert/bert-base-german-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T06:57:27Z	--- license: mit base_model: google-bert/bert-base-german-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: gBERT_sa_cv_12_fold7 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. --> # gBERT_sa_cv_12_fold7 This model is a fine-tuned version of [google-bert/bert-base-german-cased](https://huggingface.co/google-bert/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4881 - F1: 0.7384 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 226 \| 0.4008 \| 0.5145 \| \| No log \| 2.0 \| 452 \| 0.4047 \| 0.6607 \| \| 0.3287 \| 3.0 \| 678 \| 0.4881 \| 0.7384 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
aalexzhang/Flair-It-RoBERTa-usc	aalexzhang	2024-05-28T07:09:22Z	181	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T07:09:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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DiederikMartens/mBERT_sa_cv_12_fold6	DiederikMartens	2024-05-28T07:06:48Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T06:53:13Z	--- license: apache-2.0 base_model: google-bert/bert-base-multilingual-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: mBERT_sa_cv_12_fold6 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. --> # mBERT_sa_cv_12_fold6 This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5131 - F1: 0.5977 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 226 \| 0.4875 \| 0.4515 \| \| No log \| 2.0 \| 452 \| 0.3963 \| 0.5102 \| \| 0.4398 \| 3.0 \| 678 \| 0.5131 \| 0.5977 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
subhavarshith/donut-demo_exp3_NO_earlystop_exp4_1280	subhavarshith	2024-05-28T07:04:46Z	9	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-28T05:09:53Z	--- 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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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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CK0607/ko-ok-test	CK0607	2024-05-28T07:02:54Z	77	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "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", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T07:01:09Z	--- 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: CK0607 - 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)
WDKT/Xiangxin-2XL-Chat-1048k-Chinese-Llama3-70B	WDKT	2024-05-28T07:01:27Z	3,810	5	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "zh", "en", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-21T05:14:21Z	--- license: llama3 language: - zh - en pipeline_tag: text-generation --- <div align="center"> <picture> <img src="https://github.com/xiangxinai/XiangxinLM/blob/main/assets/logo.png?raw=true" width="150px"> </picture> </div> <div align="center"> <h1> Xiangxin-2XL-Chat-1048k </h1> </div> 我们提供私有化模型训练服务，如果您需要训练行业模型、领域模型或者私有模型，请联系我们: [email protected] We offer customized model training services. If you need to train industry-specific models, domain-specific models, or private models, please contact us at: [email protected]. # <span id="Introduction">模型介绍/Introduction</span> Xiangxin-2XL-Chat-1048k是[象信AI](https://www.xiangxinai.cn)基于Meta Llama-3-70B-Instruct模型和[Gradient AI的扩充上下文的工作](https://huggingface.co/gradientai/Llama-3-70B-Instruct-Gradient-1048k)，利用自行研发的中文价值观对齐数据集进行ORPO训练而形成的Chat模型。该模型具备更强的中文能力和中文价值观，其上下文长度达到100万字。在模型性能方面，该模型在ARC、HellaSwag、MMLU、TruthfulQA_mc2、Winogrande、GSM8K_flex、CMMLU、CEVAL-VALID等八项测评中，取得了平均分70.22分的成绩，超过了Gradientai-Llama-3-70B-Instruct-Gradient-1048k。我们的训练数据并不包含任何测评数据集。 Xiangxin-2XL-Chat-1048k is a Chat model developed by [Xiangxin AI](https://www.xiangxinai.cn), based on the Meta Llama-3-70B-Instruct model and [expanded context from Gradient AI](https://huggingface.co/gradientai/Llama-3-70B-Instruct-Gradient-1048k). It was trained using a proprietary Chinese value-aligned dataset through ORPO training, resulting in enhanced Chinese proficiency and alignment with Chinese values. The model has a context length of up to 1 million words. In terms of performance, it surpassed the Gradientai-Llama-3-70B-Instruct-Gradient-1048k model with an average score of 70.22 across eight evaluations including ARC, HellaSwag, MMLU, TruthfulQA_mc2, Winogrande, GSM8K_flex, CMMLU, and C-EVAL. It's worth noting that our training data did not include any evaluation datasets. <div align="center"> Model \| Context Length \| Pre-trained Tokens \| :------------: \| :------------: \| :------------: \| \| Xiangxin-2XL-Chat-1048k \| 1048k \| 15T </div> # <span id="Benchmark">Benchmark 结果/Benchmark Evaluation</span> \| \| Average \| ARC \| HellaSwag \| MMLU \| TruthfulQA \| Winogrande \| GSM8K \| CMMLU \| CEVAL \| \|:-----------------------:\|:----------:\|:--------:\|:---------:\|:----------:\|:-----------:\|:-------:\|:-------:\|:-------:\|:-------:\| \|Xiangxin-2XL-Chat-1048k\| 70.22 \| 60.92 \| 83.29 \|75.13\| 57.33\| 76.64\| 81.05\| 65.40\| 62.03 \| \|Llama-3-70B-Instruct-Gradient-1048k\| 69.66\| 61.18 \|82.88 \|74.95 \|55.28 \|75.77 \|77.79 \|66.44 \|63.00\| Note：truthfulqa_mc2, gsm8k flexible-extract # <span id="Training">训练过程模型/Training</span> 该模型是使用ORPO技术和自行研发的中文价值观对齐数据集进行训练的。由于内容的敏感性，该数据集无法公开披露。 The model was trained using ORPO and a proprietary Chinese alignment dataset developed in-house. Due to the sensitivity of the content, the dataset cannot be publicly disclosed. ## Training loss ![image/png](https://cdn-uploads.huggingface.co/production/uploads/655b15957f2466433998bb89/oLLnrWaxQnyVwI8n2QqHK.png) ## Reward accuracies ![image/png](https://cdn-uploads.huggingface.co/production/uploads/655b15957f2466433998bb89/yD4My-43lLRWecyq-bgZ2.png) ## SFT loss ![image/png](https://cdn-uploads.huggingface.co/production/uploads/655b15957f2466433998bb89/iUoQfVZDftoW7C-2VXeWe.png) # <span id="Start">快速开始/Quick Start</span> ## Use with transformers You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both. 使用Transformers运行本模型推理需要约400GB的显存。 Running inference with this model using Transformers requires approximately 400GB of GPU memory. ### Transformers pipeline ```python import transformers import torch model_id = "xiangxinai/Xiangxin-2XL-Chat-1048k-Chinese-Llama3-70B" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", ) messages = [ {"role": "system", "content": ""}, {"role": "user", "content": "解释一下“温故而知新”"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) “温故而知新”是中国古代的一句成语，出自《论语·子路篇》。它的意思是通过温习过去的知识和经验，来获得新的理解和见解。这里的“温故”是指温习过去，回顾历史，复习旧知识，而“知新”则是指了解新鲜事物，掌握新知识。这个成语强调学习的循序渐进性，强调在学习新知识时，不能忽视过去的基础，而是要在继承和发扬的基础上，去理解和创新。 ``` ### Transformers AutoModelForCausalLM ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "xiangxinai/Xiangxin-2XL-Chat-1048k-Chinese-Llama3-70B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": ""}, {"role": "user", "content": "解释一下“温故而知新”"}, ] input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = model.generate( input_ids, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) response = outputs[0][input_ids.shape[-1]:] print(tokenizer.decode(response, skip_special_tokens=True)) “温故而知新”是中国古代的一句成语，出自《论语·子路篇》。它的意思是通过温习过去的知识和经验，来获得新的理解和见解。这里的“温故”是指温习过去，回顾历史，复习旧知识，而“知新”则是指了解新鲜事物，掌握新知识。这个成语强调学习的循序渐进性，强调在学习新知识时，不能忽视过去的基础，而是要在继承和发扬的基础上，去理解和创新。 ``` # 协议/License This code is licensed under the META LLAMA 3 COMMUNITY LICENSE AGREEMENT License. # 联系我们/Contact Us For inquiries, please contact us via email at [email protected].
claudios/CodeBERTa-small-v1	claudios	2024-05-28T07:00:03Z	108	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "code", "dataset:code_search_net", "arxiv:1909.09436", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-28T06:54:33Z	--- language: code thumbnail: https://cdn-media.huggingface.co/CodeBERTa/CodeBERTa.png datasets: - code_search_net --- This is an unofficial reupload of [huggingface/CodeBERTa-small-v1](https://huggingface.co/huggingface/CodeBERTa-small-v1) in the `SafeTensors` format using `transformers` `4.41.1`. The goal of this reupload is to prevent older models that are still relevant baselines from becoming stale as a result of changes in HuggingFace. Additionally, I may include minor corrections, such as model max length configuration. Original model card below: --- # CodeBERTa CodeBERTa is a RoBERTa-like model trained on the [CodeSearchNet](https://github.blog/2019-09-26-introducing-the-codesearchnet-challenge/) dataset from GitHub. Supported languages: ```shell "go" "java" "javascript" "php" "python" "ruby" ``` The tokenizer is a Byte-level BPE tokenizer trained on the corpus using Hugging Face `tokenizers`. Because it is trained on a corpus of code (vs. natural language), it encodes the corpus efficiently (the sequences are between 33% to 50% shorter, compared to the same corpus tokenized by gpt2/roberta). The (small) model is a 6-layer, 84M parameters, RoBERTa-like Transformer model – that’s the same number of layers & heads as DistilBERT – initialized from the default initialization settings and trained from scratch on the full corpus (~2M functions) for 5 epochs. ### Tensorboard for this training ⤵️ [![tb](https://cdn-media.huggingface.co/CodeBERTa/tensorboard.png)](https://tensorboard.dev/experiment/irRI7jXGQlqmlxXS0I07ew/#scalars) ## Quick start: masked language modeling prediction ```python PHP_CODE = """ public static <mask> set(string $key, $value) { if (!in_array($key, self::$allowedKeys)) { throw new \InvalidArgumentException('Invalid key given'); } self::$storedValues[$key] = $value; } """.lstrip() ``` ### Does the model know how to complete simple PHP code? ```python from transformers import pipeline fill_mask = pipeline( "fill-mask", model="huggingface/CodeBERTa-small-v1", tokenizer="huggingface/CodeBERTa-small-v1" ) fill_mask(PHP_CODE) ## Top 5 predictions: # ' function' # prob 0.9999827146530151 'function' # ' void' # ' def' # ' final' # ``` ### Yes! That was easy 🎉 What about some Python (warning: this is going to be meta) ```python PYTHON_CODE = """ def pipeline( task: str, model: Optional = None, framework: Optional[<mask>] = None, kwargs ) -> Pipeline: pass """.lstrip() ``` Results: ```python 'framework', 'Framework', ' framework', 'None', 'str' ``` > This program can auto-complete itself! 😱 ### Just for fun, let's try to mask natural language (not code): ```python fill_mask("My name is <mask>.") # {'sequence': '<s> My name is undefined.</s>', 'score': 0.2548016905784607, 'token': 3353} # {'sequence': '<s> My name is required.</s>', 'score': 0.07290805131196976, 'token': 2371} # {'sequence': '<s> My name is null.</s>', 'score': 0.06323737651109695, 'token': 469} # {'sequence': '<s> My name is name.</s>', 'score': 0.021919190883636475, 'token': 652} # {'sequence': '<s> My name is disabled.</s>', 'score': 0.019681859761476517, 'token': 7434} ``` This (kind of) works because code contains comments (which contain natural language). Of course, the most frequent name for a Computer scientist must be undefined 🤓. ## Downstream task: [programming language identification](https://huggingface.co/huggingface/CodeBERTa-language-id) See the model card for [`huggingface/CodeBERTa-language-id`](https://huggingface.co/huggingface/CodeBERTa-language-id)** 🤯. <br> ## CodeSearchNet citation <details> ```bibtex @article{husain_codesearchnet_2019, title = {{CodeSearchNet} {Challenge}: {Evaluating} the {State} of {Semantic} {Code} {Search}}, shorttitle = {{CodeSearchNet} {Challenge}}, url = {http://arxiv.org/abs/1909.09436}, urldate = {2020-03-12}, journal = {arXiv:1909.09436 [cs, stat]}, author = {Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, month = sep, year = {2019}, note = {arXiv: 1909.09436}, } ``` </details>
RedaAlami/t5_recommendation_sports_equipment_english2	RedaAlami	2024-05-28T06:59:02Z	113	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-28T06:32:02Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5_recommendation_sports_equipment_english2 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. --> # t5_recommendation_sports_equipment_english2 This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5359 - Rouge1: 74.1270 - Rouge2: 66.6667 - Rougel: 74.1270 - Rougelsum: 73.8095 - Gen Len: 4.0476 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - 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: 80 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 1 \| 9.9716 \| 12.4868 \| 0.0 \| 12.5845 \| 12.5051 \| 19.0 \| \| No log \| 2.0 \| 2 \| 10.1466 \| 9.9134 \| 0.0 \| 9.9471 \| 9.8413 \| 19.0 \| \| No log \| 3.0 \| 3 \| 8.3378 \| 10.5739 \| 0.0 \| 10.6349 \| 10.5291 \| 19.0 \| \| No log \| 4.0 \| 4 \| 7.3021 \| 10.5739 \| 0.0 \| 10.6349 \| 10.5291 \| 19.0 \| \| No log \| 5.0 \| 5 \| 6.3242 \| 10.4605 \| 0.0 \| 10.5471 \| 10.4567 \| 19.0 \| \| No log \| 6.0 \| 6 \| 5.4331 \| 10.2886 \| 0.7937 \| 10.2319 \| 10.3793 \| 19.0 \| \| No log \| 7.0 \| 7 \| 4.7152 \| 10.8989 \| 0.7937 \| 10.8388 \| 10.9525 \| 18.9524 \| \| No log \| 8.0 \| 8 \| 3.9937 \| 13.9421 \| 3.7009 \| 14.0590 \| 13.9456 \| 15.0952 \| \| No log \| 9.0 \| 9 \| 3.1163 \| 16.0431 \| 1.0025 \| 15.7736 \| 15.9707 \| 6.4762 \| \| No log \| 10.0 \| 10 \| 2.3306 \| 23.1746 \| 7.1429 \| 22.8571 \| 23.6508 \| 4.1429 \| \| No log \| 11.0 \| 11 \| 1.9695 \| 21.2698 \| 7.1429 \| 20.9524 \| 21.4286 \| 4.0476 \| \| No log \| 12.0 \| 12 \| 1.5552 \| 23.8095 \| 7.1429 \| 23.3333 \| 23.8095 \| 3.9048 \| \| No log \| 13.0 \| 13 \| 0.8986 \| 9.0476 \| 0.0 \| 9.0476 \| 9.0476 \| 3.7619 \| \| No log \| 14.0 \| 14 \| 0.7398 \| 17.4603 \| 2.3810 \| 18.2540 \| 17.4603 \| 4.1905 \| \| No log \| 15.0 \| 15 \| 0.6966 \| 12.6984 \| 0.0 \| 12.6984 \| 12.6984 \| 3.6667 \| \| No log \| 16.0 \| 16 \| 0.6352 \| 32.5397 \| 14.2857 \| 32.5397 \| 32.5397 \| 3.7619 \| \| No log \| 17.0 \| 17 \| 0.5722 \| 43.6508 \| 23.8095 \| 43.6508 \| 42.8571 \| 4.0952 \| \| No log \| 18.0 \| 18 \| 0.5628 \| 43.6508 \| 23.8095 \| 43.6508 \| 42.8571 \| 3.8571 \| \| No log \| 19.0 \| 19 \| 0.5526 \| 43.1746 \| 23.8095 \| 43.1746 \| 42.8571 \| 3.8571 \| \| No log \| 20.0 \| 20 \| 0.5522 \| 48.4127 \| 38.0952 \| 48.4127 \| 48.4127 \| 3.7619 \| \| No log \| 21.0 \| 21 \| 0.5201 \| 42.8571 \| 28.5714 \| 42.8571 \| 42.3810 \| 4.2381 \| \| No log \| 22.0 \| 22 \| 0.5262 \| 37.1429 \| 19.0476 \| 36.9841 \| 36.9841 \| 4.2857 \| \| No log \| 23.0 \| 23 \| 0.5093 \| 37.6190 \| 23.8095 \| 37.6190 \| 37.6190 \| 4.1429 \| \| No log \| 24.0 \| 24 \| 0.4818 \| 45.3175 \| 33.3333 \| 45.2381 \| 45.2381 \| 4.1429 \| \| No log \| 25.0 \| 25 \| 0.4547 \| 50.7937 \| 38.0952 \| 50.7937 \| 50.7937 \| 4.1429 \| \| No log \| 26.0 \| 26 \| 0.4455 \| 50.7937 \| 38.0952 \| 50.7937 \| 50.7937 \| 4.1429 \| \| No log \| 27.0 \| 27 \| 0.4660 \| 53.1746 \| 42.8571 \| 53.1746 \| 53.1746 \| 4.0476 \| \| No log \| 28.0 \| 28 \| 0.4825 \| 53.1746 \| 42.8571 \| 53.1746 \| 53.1746 \| 4.0 \| \| No log \| 29.0 \| 29 \| 0.4928 \| 53.1746 \| 42.8571 \| 53.1746 \| 53.1746 \| 4.0476 \| \| No log \| 30.0 \| 30 \| 0.4838 \| 57.7778 \| 42.8571 \| 57.2222 \| 57.5397 \| 4.0476 \| \| No log \| 31.0 \| 31 \| 0.4955 \| 60.3175 \| 47.6190 \| 60.3175 \| 60.3175 \| 4.0476 \| \| No log \| 32.0 \| 32 \| 0.5066 \| 62.6984 \| 52.3810 \| 62.6984 \| 62.6984 \| 4.1429 \| \| No log \| 33.0 \| 33 \| 0.5189 \| 62.6984 \| 52.3810 \| 62.6984 \| 62.6984 \| 4.1905 \| \| No log \| 34.0 \| 34 \| 0.5234 \| 62.6984 \| 52.3810 \| 62.6984 \| 62.6984 \| 4.1905 \| \| No log \| 35.0 \| 35 \| 0.5225 \| 62.6984 \| 52.3810 \| 62.6984 \| 62.6984 \| 4.1905 \| \| No log \| 36.0 \| 36 \| 0.5225 \| 62.6984 \| 52.3810 \| 62.6984 \| 62.6984 \| 4.1905 \| \| No log \| 37.0 \| 37 \| 0.5058 \| 62.8571 \| 52.3810 \| 62.2222 \| 62.6984 \| 4.1429 \| \| No log \| 38.0 \| 38 \| 0.4861 \| 69.8413 \| 61.9048 \| 69.8413 \| 69.8413 \| 4.1905 \| \| No log \| 39.0 \| 39 \| 0.4625 \| 69.8413 \| 61.9048 \| 69.8413 \| 69.8413 \| 4.1905 \| \| No log \| 40.0 \| 40 \| 0.4438 \| 72.2222 \| 66.6667 \| 72.2222 \| 72.2222 \| 4.0952 \| \| No log \| 41.0 \| 41 \| 0.4231 \| 72.2222 \| 66.6667 \| 72.2222 \| 72.2222 \| 4.0952 \| \| No log \| 42.0 \| 42 \| 0.4073 \| 72.2222 \| 66.6667 \| 72.2222 \| 72.2222 \| 4.0952 \| \| No log \| 43.0 \| 43 \| 0.3938 \| 72.2222 \| 66.6667 \| 72.2222 \| 72.2222 \| 4.0952 \| \| No log \| 44.0 \| 44 \| 0.3912 \| 72.2222 \| 66.6667 \| 72.2222 \| 72.2222 \| 4.0952 \| \| No log \| 45.0 \| 45 \| 0.3980 \| 72.2222 \| 66.6667 \| 72.2222 \| 72.2222 \| 4.1429 \| \| No log \| 46.0 \| 46 \| 0.4062 \| 72.2222 \| 66.6667 \| 72.2222 \| 72.2222 \| 4.1905 \| \| No log \| 47.0 \| 47 \| 0.4121 \| 76.9841 \| 71.4286 \| 76.9841 \| 76.9841 \| 4.2857 \| \| No log \| 48.0 \| 48 \| 0.4150 \| 76.9841 \| 71.4286 \| 76.9841 \| 76.9841 \| 4.1905 \| \| No log \| 49.0 \| 49 \| 0.4183 \| 76.9841 \| 71.4286 \| 76.9841 \| 76.9841 \| 4.1429 \| \| No log \| 50.0 \| 50 \| 0.4205 \| 76.9841 \| 71.4286 \| 76.9841 \| 76.9841 \| 4.1905 \| \| No log \| 51.0 \| 51 \| 0.4306 \| 79.3651 \| 76.1905 \| 79.3651 \| 79.3651 \| 4.0952 \| \| No log \| 52.0 \| 52 \| 0.4411 \| 76.5079 \| 71.4286 \| 76.5079 \| 76.1905 \| 4.0 \| \| No log \| 53.0 \| 53 \| 0.4526 \| 76.5079 \| 71.4286 \| 76.5079 \| 76.1905 \| 4.0476 \| \| No log \| 54.0 \| 54 \| 0.4667 \| 76.5079 \| 71.4286 \| 76.5079 \| 76.1905 \| 4.0 \| \| No log \| 55.0 \| 55 \| 0.4871 \| 76.5079 \| 71.4286 \| 76.5079 \| 76.1905 \| 4.0 \| \| No log \| 56.0 \| 56 \| 0.5063 \| 76.5079 \| 71.4286 \| 76.5079 \| 76.1905 \| 4.0 \| \| No log \| 57.0 \| 57 \| 0.5196 \| 76.5079 \| 71.4286 \| 76.5079 \| 76.1905 \| 4.0 \| \| No log \| 58.0 \| 58 \| 0.5265 \| 76.5079 \| 71.4286 \| 76.5079 \| 76.1905 \| 3.9524 \| \| No log \| 59.0 \| 59 \| 0.5308 \| 76.5079 \| 71.4286 \| 76.5079 \| 76.1905 \| 3.9524 \| \| No log \| 60.0 \| 60 \| 0.5333 \| 76.5079 \| 71.4286 \| 76.5079 \| 76.1905 \| 3.9524 \| \| No log \| 61.0 \| 61 \| 0.5344 \| 76.5079 \| 71.4286 \| 76.5079 \| 76.1905 \| 3.9524 \| \| No log \| 62.0 \| 62 \| 0.5348 \| 76.5079 \| 71.4286 \| 76.5079 \| 76.1905 \| 3.9524 \| \| No log \| 63.0 \| 63 \| 0.5354 \| 76.5079 \| 71.4286 \| 76.5079 \| 76.1905 \| 3.9524 \| \| No log \| 64.0 \| 64 \| 0.5359 \| 76.5079 \| 71.4286 \| 76.5079 \| 76.1905 \| 3.9524 \| \| No log \| 65.0 \| 65 \| 0.5359 \| 74.1270 \| 66.6667 \| 74.1270 \| 73.8095 \| 4.0476 \| \| No log \| 66.0 \| 66 \| 0.5359 \| 74.1270 \| 66.6667 \| 74.1270 \| 73.8095 \| 4.0476 \| \| No log \| 67.0 \| 67 \| 0.5359 \| 74.1270 \| 66.6667 \| 74.1270 \| 73.8095 \| 4.0476 \| \| No log \| 68.0 \| 68 \| 0.5359 \| 74.1270 \| 66.6667 \| 74.1270 \| 73.8095 \| 4.0476 \| \| No log \| 69.0 \| 69 \| 0.5359 \| 74.1270 \| 66.6667 \| 74.1270 \| 73.8095 \| 4.0476 \| \| No log \| 70.0 \| 70 \| 0.5359 \| 74.1270 \| 66.6667 \| 74.1270 \| 73.8095 \| 4.0476 \| \| No log \| 71.0 \| 71 \| 0.5359 \| 74.1270 \| 66.6667 \| 74.1270 \| 73.8095 \| 4.0476 \| \| No log \| 72.0 \| 72 \| 0.5359 \| 74.1270 \| 66.6667 \| 74.1270 \| 73.8095 \| 4.0476 \| \| No log \| 73.0 \| 73 \| 0.5359 \| 74.1270 \| 66.6667 \| 74.1270 \| 73.8095 \| 4.0476 \| \| No log \| 74.0 \| 74 \| 0.5359 \| 74.1270 \| 66.6667 \| 74.1270 \| 73.8095 \| 4.0476 \| \| No log \| 75.0 \| 75 \| 0.5359 \| 74.1270 \| 66.6667 \| 74.1270 \| 73.8095 \| 4.0476 \| \| No log \| 76.0 \| 76 \| 0.5359 \| 74.1270 \| 66.6667 \| 74.1270 \| 73.8095 \| 4.0476 \| \| No log \| 77.0 \| 77 \| 0.5359 \| 74.1270 \| 66.6667 \| 74.1270 \| 73.8095 \| 4.0476 \| \| No log \| 78.0 \| 78 \| 0.5359 \| 74.1270 \| 66.6667 \| 74.1270 \| 73.8095 \| 4.0476 \| \| No log \| 79.0 \| 79 \| 0.5359 \| 74.1270 \| 66.6667 \| 74.1270 \| 73.8095 \| 4.0476 \| \| No log \| 80.0 \| 80 \| 0.5359 \| 74.1270 \| 66.6667 \| 74.1270 \| 73.8095 \| 4.0476 \| ### Framework versions - Transformers 4.26.0 - Pytorch 2.3.0+cu121 - Datasets 2.8.0 - Tokenizers 0.13.3
DiederikMartens/mBERT_sa_cv_12_fold5	DiederikMartens	2024-05-28T06:53:07Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T06:39:39Z	--- license: apache-2.0 base_model: google-bert/bert-base-multilingual-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: mBERT_sa_cv_12_fold5 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. --> # mBERT_sa_cv_12_fold5 This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4607 - F1: 0.5275 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 226 \| 0.6423 \| 0.2958 \| \| No log \| 2.0 \| 452 \| 0.5093 \| 0.5167 \| \| 0.5972 \| 3.0 \| 678 \| 0.4607 \| 0.5275 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
John6666/cherry-picker-xl-v3-sdxl	John6666	2024-05-28T06:53:05Z	93	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-28T06:47:16Z	--- license: other tags: - text-to-image - stable-diffusion - stable-diffusion-xl --- Original model is [here](https://civitai.com/models/125680?modelVersionId=373927).
fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-822545	fine-tuned	2024-05-28T06:50:08Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "Debate", "Argument", "Opposition", "Rebuttal", "Discussion", "en", "dataset:fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-822545", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T06:49:37Z	--- license: apache-2.0 datasets: - fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-822545 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - Debate - Argument - Opposition - Rebuttal - Discussion --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: counter arguments in a debate ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-822545', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
yasyf/bert-for-patents	yasyf	2024-05-28T06:47:21Z	11	0	transformers	[ "transformers", "pytorch", "tf", "safetensors", "bert", "feature-extraction", "masked-lm", "en", "license:apache-2.0", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T06:28:52Z	--- language: - en tags: - masked-lm - pytorch pipeline-tag: "fill-mask" mask-token: "[MASK]" widget: - text: "The present [MASK] provides a torque sensor that is small and highly rigid and for which high production efficiency is possible." - text: "The present invention relates to [MASK] accessories and pertains particularly to a brake light unit for bicycles." - text: "The present invention discloses a space-bound-free [MASK] and its coordinate determining circuit for determining a coordinate of a stylus pen." - text: "The illuminated [MASK] includes a substantially translucent canopy supported by a plurality of ribs pivotally swingable towards and away from a shaft." license: apache-2.0 metrics: - perplexity --- # Motivation This model is based on anferico/bert-for-patents - a BERT<sub>LARGE</sub> model (See next section for details below). By default, the pre-trained model's output embeddings with size 768 (base-models) or with size 1024 (large-models). However, when you store Millions of embeddings, this can require quite a lot of memory/storage. So have reduced the embedding dimension to 64 i.e 1/16th of 1024 using Principle Component Analysis (PCA) and it still gives a comparable performance. Yes! PCA gives better performance than NMF. Note: This process neither improves the runtime, nor the memory requirement for running the model. It only reduces the needed space to store embeddings, for example, for semantic search using vector databases. # BERT for Patents BERT for Patents is a model trained by Google on 100M+ patents (not just US patents). If you want to learn more about the model, check out the [blog post](https://cloud.google.com/blog/products/ai-machine-learning/how-ai-improves-patent-analysis), [white paper](https://services.google.com/fh/files/blogs/bert_for_patents_white_paper.pdf) and [GitHub page](https://github.com/google/patents-public-data/blob/master/models/BERT%20for%20Patents.md) containing the original TensorFlow checkpoint. --- ### Projects using this model (or variants of it): - [Patents4IPPC](https://github.com/ec-jrc/Patents4IPPC) (carried out by [Pi School](https://picampus-school.com/) and commissioned by the [Joint Research Centre (JRC)](https://ec.europa.eu/jrc/en) of the European Commission)
sahlebrahim/bert-finetuned-squad	sahlebrahim	2024-05-28T06:46:31Z	43	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-05-13T09:25:22Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer model-index: - name: bert-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
DiederikMartens/gBERT_sa_cv_12_fold5	DiederikMartens	2024-05-28T06:44:52Z	111	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-german-cased", "base_model:finetune:google-bert/bert-base-german-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T06:32:20Z	--- license: mit base_model: google-bert/bert-base-german-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: gBERT_sa_cv_12_fold5 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. --> # gBERT_sa_cv_12_fold5 This model is a fine-tuned version of [google-bert/bert-base-german-cased](https://huggingface.co/google-bert/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4564 - F1: 0.6400 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 226 \| 0.4027 \| 0.5657 \| \| No log \| 2.0 \| 452 \| 0.4462 \| 0.5591 \| \| 0.3464 \| 3.0 \| 678 \| 0.4564 \| 0.6400 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-69882	fine-tuned	2024-05-28T06:43:13Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "Argument", "Debate", "Opposition", "Persuasion", "Refutation", "custom_code", "en", "dataset:fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-69882", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T06:42:58Z	--- license: apache-2.0 datasets: - fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-69882 - allenai/c4 language: - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - Argument - Debate - Opposition - Persuasion - Refutation --- 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: counter argument retrieval system ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-69882', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
aariz120/tiny-chatbot-dpo	aariz120	2024-05-28T06:43:11Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-19T06:34:43Z	--- license: apache-2.0 library_name: peft tags: - trl - dpo - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 model-index: - name: tiny-chatbot-dpo results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tiny-chatbot-dpo This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
DiederikMartens/eBERT_sa_cv_12_fold4	DiederikMartens	2024-05-28T06:41:00Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T06:26:44Z	--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: eBERT_sa_cv_12_fold4 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. --> # eBERT_sa_cv_12_fold4 This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5774 - F1: 0.4941 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 226 \| 0.5470 \| 0.4529 \| \| No log \| 2.0 \| 452 \| 0.4903 \| 0.4753 \| \| 0.5054 \| 3.0 \| 678 \| 0.5774 \| 0.4941 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
adhityaprimandhika/mistral_categorization_unsloth_lora_adapter_v2	adhityaprimandhika	2024-05-28T06:40:10Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T01:43:13Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-v0.3-bnb-4bit --- # Uploaded model - Developed by: adhityaprimandhika - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-v0.3-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)
pduy395/custom-roberta	pduy395	2024-05-28T06:36:48Z	163	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-28T06:32:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
adlbh/llama-2-7b-medinstruct-52k	adlbh	2024-05-28T06:35:31Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-2-7b-bnb-4bit", "base_model:finetune:unsloth/llama-2-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T06:33:32Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-2-7b-bnb-4bit --- # Uploaded model - Developed by: adlbh - License: apache-2.0 - Finetuned from model : unsloth/llama-2-7b-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)
state-spaces/mamba2-2.7b	state-spaces	2024-05-28T06:34:15Z	2,676	14	transformers	[ "transformers", "pytorch", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T06:23:28Z	--- license: apache-2.0 ---
scoliono/groupchat_lora_llama3_8b	scoliono	2024-05-28T06:33:13Z	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	2024-05-28T06:33:01Z	--- 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: scoliono - 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)
DiederikMartens/gBERT_sa_cv_12_fold4	DiederikMartens	2024-05-28T06:32:16Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-german-cased", "base_model:finetune:google-bert/bert-base-german-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T06:19:43Z	--- license: mit base_model: google-bert/bert-base-german-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: gBERT_sa_cv_12_fold4 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. --> # gBERT_sa_cv_12_fold4 This model is a fine-tuned version of [google-bert/bert-base-german-cased](https://huggingface.co/google-bert/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5119 - F1: 0.6835 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 226 \| 0.3834 \| 0.5321 \| \| No log \| 2.0 \| 452 \| 0.4565 \| 0.6399 \| \| 0.3375 \| 3.0 \| 678 \| 0.5119 \| 0.6835 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
CMU-AIR2/math-phi-1-5-FULL-Arithmetic-Steps-lr-1-5e-6-6k	CMU-AIR2	2024-05-28T06:31:36Z	121	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T06:29:04Z	--- 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. 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]
Amadkour/wav2vec2-large-xls-r-300m-tr-softkour	Amadkour	2024-05-28T06:28:33Z	25	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_11_0", "base_model:Amadkour/wav2vec2-large-xls-r-300m-tr-softkour", "base_model:finetune:Amadkour/wav2vec2-large-xls-r-300m-tr-softkour", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-30T21:00:27Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: Amadkour/wav2vec2-large-xls-r-300m-tr-softkour datasets: - common_voice_11_0 metrics: - wer model-index: - name: wav2vec2-large-xls-r-300m-tr-softkour results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: common_voice_11_0 type: common_voice_11_0 config: ar split: test args: ar metrics: - type: wer value: 0.44904159531569354 name: Wer --- <!-- 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-large-xls-r-300m-tr-softkour This model is a fine-tuned version of [Amadkour/wav2vec2-large-xls-r-300m-tr-softkour](https://huggingface.co/Amadkour/wav2vec2-large-xls-r-300m-tr-softkour) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4793 - Wer: 0.4490 ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.4662 \| 0.33 \| 400 \| 0.7627 \| 0.6241 \| \| 0.3927 \| 0.67 \| 800 \| 0.7286 \| 0.6213 \| \| 0.4613 \| 1.0 \| 1200 \| 0.5779 \| 0.5185 \| \| 0.4552 \| 1.33 \| 1600 \| 0.5412 \| 0.4945 \| \| 0.4145 \| 1.66 \| 2000 \| 0.4922 \| 0.4652 \| \| 0.3713 \| 2.0 \| 2400 \| 0.4793 \| 0.4490 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cpu - Datasets 2.18.0 - Tokenizers 0.15.2
state-spaces/mamba2-1.3b	state-spaces	2024-05-28T06:27:37Z	17,958	3	transformers	[ "transformers", "pytorch", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T06:23:10Z	--- license: apache-2.0 ---
DiederikMartens/eBERT_sa_cv_12_fold3	DiederikMartens	2024-05-28T06:26:39Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T06:12:30Z	--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: eBERT_sa_cv_12_fold3 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. --> # eBERT_sa_cv_12_fold3 This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5914 - F1: 0.4973 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 226 \| 0.6089 \| 0.3445 \| \| No log \| 2.0 \| 452 \| 0.4911 \| 0.4798 \| \| 0.5244 \| 3.0 \| 678 \| 0.5914 \| 0.4973 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
state-spaces/mamba2-780m	state-spaces	2024-05-28T06:26:12Z	2,931	1	transformers	[ "transformers", "pytorch", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T06:19:43Z	--- license: apache-2.0 ---
DiederikMartens/mBERT_sa_cv_12_fold3	DiederikMartens	2024-05-28T06:25:44Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T06:12:05Z	--- license: apache-2.0 base_model: google-bert/bert-base-multilingual-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: mBERT_sa_cv_12_fold3 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. --> # mBERT_sa_cv_12_fold3 This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5104 - F1: 0.5693 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 226 \| 0.5091 \| 0.4490 \| \| No log \| 2.0 \| 452 \| 0.4197 \| 0.5448 \| \| 0.4564 \| 3.0 \| 678 \| 0.5104 \| 0.5693 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
DiederikMartens/tsBERT_sa_cv_12_fold3	DiederikMartens	2024-05-28T06:25:32Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:igorsterner/german-english-code-switching-bert", "base_model:finetune:igorsterner/german-english-code-switching-bert", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T06:11:55Z	--- license: mit base_model: igorsterner/german-english-code-switching-bert tags: - generated_from_trainer metrics: - f1 model-index: - name: tsBERT_sa_cv_12_fold3 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. --> # tsBERT_sa_cv_12_fold3 This model is a fine-tuned version of [igorsterner/german-english-code-switching-bert](https://huggingface.co/igorsterner/german-english-code-switching-bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4248 - F1: 0.6486 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 226 \| 0.3531 \| 0.5399 \| \| No log \| 2.0 \| 452 \| 0.3575 \| 0.6417 \| \| 0.3511 \| 3.0 \| 678 \| 0.4248 \| 0.6486 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
ainiyo002/model	ainiyo002	2024-05-28T06:16:59Z	5	0	transformers	[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/mistral-7b-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-v0.3-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T07:23:20Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft base_model: unsloth/mistral-7b-v0.3-bnb-4bit --- # Uploaded model - Developed by: ainiyo002 - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-v0.3-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)
kkeezz/cap-iaa-lora	kkeezz	2024-05-28T06:16:51Z	2	0	peft	[ "peft", "mplug_owl2", "region:us" ]	null	2024-05-28T06:09:48Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
jinq047/distilbert-base-uncased-finetuned-imdb	jinq047	2024-05-28T06:16:51Z	118	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "fill-mask", "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" ]	fill-mask	2024-05-28T05:53:18Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-imdb 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-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4894 ## 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: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.6819 \| 1.0 \| 157 \| 2.4978 \| \| 2.5872 \| 2.0 \| 314 \| 2.4488 \| \| 2.527 \| 3.0 \| 471 \| 2.4823 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
nerdthingz/moon_landing	nerdthingz	2024-05-28T06:15:32Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-28T06:03:30Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 263.50 +/- 23.68 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
DiederikMartens/eBERT_sa_cv_12_fold2	DiederikMartens	2024-05-28T06:12:26Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T05:58:41Z	--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: eBERT_sa_cv_12_fold2 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. --> # eBERT_sa_cv_12_fold2 This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5381 - F1: 0.5716 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 226 \| 0.5964 \| 0.4234 \| \| No log \| 2.0 \| 452 \| 0.5521 \| 0.4536 \| \| 0.4957 \| 3.0 \| 678 \| 0.5381 \| 0.5716 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
0xfaskety/Qwen-Qwen1.5-1.8B-1716875866	0xfaskety	2024-05-28T06:10:53Z	131	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T05:57:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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DiederikMartens/gBERT_sa_cv_12_fold2	DiederikMartens	2024-05-28T06:06:50Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-german-cased", "base_model:finetune:google-bert/bert-base-german-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T05:54:34Z	--- license: mit base_model: google-bert/bert-base-german-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: gBERT_sa_cv_12_fold2 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. --> # gBERT_sa_cv_12_fold2 This model is a fine-tuned version of [google-bert/bert-base-german-cased](https://huggingface.co/google-bert/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4152 - F1: 0.6707 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 226 \| 0.3664 \| 0.5390 \| \| No log \| 2.0 \| 452 \| 0.4152 \| 0.6707 \| \| 0.3358 \| 3.0 \| 678 \| 0.5516 \| 0.6571 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

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