Split (1)

train · 843k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-26 06:27:38	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 496 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-26 06:27:10	card stringlengths 11 1.01M
Hunter-X/first	Hunter-X	2024-05-29T13:05:40Z	0	0	null	[ "region:us" ]	null	2024-05-29T04:57:45Z	# Handcrafted solution example for the S23DR competition This repo provides an example of a simple algorithm to reconstruct wireframe and submit to S23DR competition. The repo consistst of the following parts: - `script.py` - the main file, which is run by the competition space. It should produce `submission.parquet` as the result of the run. - `hoho.py` - the file for parsing the dataset at the inference time. Do NOT change it. - `handcrafted_solution.py` - contains the actual implementation of the algorithm - other `*.py` files - helper i/o and visualization utilities - `packages/` - the directory to put python wheels for the custom packages you want to install and use. ## Solution description The solution is simple. 1. Using provided (but noisy) semantic segmentation called `gestalt`, it takes the centroids of the vertex classes - `apex` and `eave_end_point` and projects them to 3D using provided (also noisy) monocular depth. 2. The vertices are connected using the same segmentation, by checking for edges classes to be present - `['eave', 'ridge', 'rake', 'valley']`. 3. All the "per-image" vertex predictions are merged in 3D space if their distance is less than threshold. 4. All vertices, which have zero connections, are removed. ## Example on the training set See in [notebooks/example_on_training.ipynb](notebooks/example_on_training.ipynb) --- license: apache-2.0 ---
Holarissun/REPROD_dpo_helpfulhelpful_human_subset-1_modelgemma7b_maxsteps10000_bz8_lr5e-06	Holarissun	2024-05-29T13:04:57Z	1	0	peft	[ "peft", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:google/gemma-7b", "base_model:adapter:google/gemma-7b", "license:gemma", "region:us" ]	null	2024-05-29T13:04:53Z	--- license: gemma library_name: peft tags: - trl - dpo - generated_from_trainer base_model: google/gemma-7b model-index: - name: REPROD_dpo_helpfulhelpful_human_subset-1_modelgemma7b_maxsteps10000_bz8_lr5e-06 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. --> # REPROD_dpo_helpfulhelpful_human_subset-1_modelgemma7b_maxsteps10000_bz8_lr5e-06 This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) 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-06 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 15 - training_steps: 10000 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
JeanM45/e9f202b2	JeanM45	2024-05-29T13:04:03Z	5	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T12:58:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Juliekyungyoon/plant-kaggle-conv	Juliekyungyoon	2024-05-29T12:57:53Z	2	0	transformers	[ "transformers", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T06:37:30Z	--- license: apache-2.0 ---
YorkieOH10/MistralHermesPipe-7B-slerp	YorkieOH10	2024-05-29T12:54:22Z	44	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1218", "mlabonne/NeuralHermes-2.5-Mistral-7B", "base_model:OpenPipe/mistral-ft-optimized-1218", "base_model:merge:OpenPipe/mistral-ft-optimized-1218", "base_model:mlabonne/NeuralHermes-2.5-Mistral-7B", "base_model:merge:mlabonne/NeuralHermes-2.5-Mistral-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T12:40:16Z	--- tags: - merge - mergekit - lazymergekit - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B base_model: - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B license: apache-2.0 --- # MistralHermesPipe-7B-slerp MistralHermesPipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218) * [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: OpenPipe/mistral-ft-optimized-1218 layer_range: [0, 32] - model: mlabonne/NeuralHermes-2.5-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: OpenPipe/mistral-ft-optimized-1218 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "YorkieOH10/MistralHermesPipe-7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
grrvk/palm-inst	grrvk	2024-05-29T12:53:20Z	33	0	transformers	[ "transformers", "safetensors", "mask2former", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-21T18:37:52Z	--- 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. 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]
Nogu-t/llama-3-8b-ver3	Nogu-t	2024-05-29T12:47:40Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T12:37:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
Likich/llama3-finetune-qualcoding_1000_prompt3_dot	Likich	2024-05-29T12:45:45Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T12:45:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
lgk03/WITHINAPPS_NDD-dimeshift_test-content	lgk03	2024-05-29T12:40:18Z	119	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	2024-05-29T12:31:00Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: WITHINAPPS_NDD-dimeshift_test-content 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-dimeshift_test-content 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: 0.1472 - Accuracy: 0.9415 - F1: 0.9254 - Precision: 0.9450 - Recall: 0.9415 ## 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 \| 0.9897 \| 72 \| 0.2019 \| 0.9205 \| 0.8823 \| 0.8473 \| 0.9205 \| \| No log \| 1.9794 \| 144 \| 0.1472 \| 0.9415 \| 0.9254 \| 0.9450 \| 0.9415 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
pankaj0507/Mistral-7B-Instruct-v0.3-finetune	pankaj0507	2024-05-29T12:36:48Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3", "license:apache-2.0", "region:us" ]	null	2024-05-29T12:35:26Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.3 model-index: - name: Mistral-7B-Instruct-v0.3-finetune 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. --> # Mistral-7B-Instruct-v0.3-finetune This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1051 ## 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 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
Rainnighttram/RED50_Gemma_Instruct_model	Rainnighttram	2024-05-29T12:31:25Z	4	0	transformers	[ "transformers", "safetensors", "gguf", "gemma", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/gemma-7b-it-bnb-4bit", "base_model:quantized:unsloth/gemma-7b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-29T11:51:10Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-7b-it-bnb-4bit --- # Uploaded model - Developed by: Rainnighttram - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-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)
Newton7/Llama-3-8B-NPOV-wiki	Newton7	2024-05-29T12:29:47Z	0	0	peft	[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:mit", "region:us" ]	null	2024-05-29T12:17:09Z	--- library_name: peft base_model: meta-llama/Meta-Llama-3-8B-Instruct license: mit language: - en --- # 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 instruction tuned model is optimized for dialogue use cases and outperforms many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. Input - Models input text only. Output- Models generate text only - Developed by: XYZ - 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] ### Framework versions - PEFT 0.11.1
fine-tuned/SciFact-32000-384-gpt-4o-2024-05-13-25926506	fine-tuned	2024-05-29T12:27:39Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/SciFact-32000-384-gpt-4o-2024-05-13-25926506", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-29T12:27:04Z	--- license: apache-2.0 datasets: - fine-tuned/SciFact-32000-384-gpt-4o-2024-05-13-25926506 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- 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: None ## 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-32000-384-gpt-4o-2024-05-13-25926506', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
zhoukz/llama-2-13b-chat-4bit	zhoukz	2024-05-29T12:24:42Z	77	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-29T12:22:19Z	--- 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. 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]
fine-tuned/FiQA2018-32000-384-gpt-4o-2024-05-13-23538198	fine-tuned	2024-05-29T12:23:57Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/FiQA2018-32000-384-gpt-4o-2024-05-13-23538198", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-29T12:23:28Z	--- license: apache-2.0 datasets: - fine-tuned/FiQA2018-32000-384-gpt-4o-2024-05-13-23538198 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- 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: None ## 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/FiQA2018-32000-384-gpt-4o-2024-05-13-23538198', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
cadenzachallenge/ConvTasNet_Lyrics_Causal	cadenzachallenge	2024-05-29T12:22:42Z	291	0	transformers	[ "transformers", "pytorch", "safetensors", "hearing loss", "challenge", "signal processing", "source separation", "lyrics intelligibility", "audio", "audio-to-audio", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-to-audio	2024-05-21T09:50:01Z	--- license: apache-2.0 language: - en tags: - hearing loss - challenge - signal processing - source separation - lyrics intelligibility - audio - audio-to-audio widget: - src: https://cdn-media.huggingface.co/speech_samples/sample1.flac example_title: Test pipeline_tag: audio-to-audio --- # Cadenza Challenge: CAD2-Task1 A Causal Lyrics/Accompaniment separation model for the CAD2-Task1 baseline system. ## Parameters * Architecture: ConvTasNet (Kaituo XU) with multichannel support (Alexandre Defossez). * Parameters: * B: 256 * C: 2 * H: 512 * L: 20 * N: 256 * P: 3 * R: 4 * X: 10 * audio_channels: 2 * causal: true * mask_nonlinear: relu * norm_type: cLN * training: * sample_rate: 44100 * samples_per_track: 64 * segment: 4.0 * aggregate: 1 * batch_size: 4 * early_stop: true * epochs: 200 ## Dataset The model was trained on the training split of the MUSDB18-HQ dataset. ## How to use ``` from tasnet import ConvTasNetStereo model = ConvTasNetStereo.from_pretrained( "cadenzachallenge/ConvTasNet_LyricsSeparation_Causal" ).cpu() ``` ## Results \| Track \| Vocals (SDR) \| Accompaniment (SDR) \| \|:------\|:------------:\|:---------:\| \| Al James - Schoolboy Facination \| 5.733 \| 8.049 \| \| AM Contra - Heart Peripheral \| 5.887 \| 12.691 \| \| Angels In Amplifiers - I'm Alright \| 5.901 \| 9.124 \| \| Arise - Run Run Run \| 5.208 \| 14.868 \| \| Ben Carrigan - We'll Talk About It All Tonight \| 2.676 \| 9.919 \| \| BKS - Bulldozer \| 1.523 \| 11.488 \| \| BKS - Too Much \| 7.005 \| 11.087 \| \| Bobby Nobody - Stitch Up \| 6.518 \| 11.303 \| \| Buitraker - Revo X \| 4.242 \| 13.763 \| \| Carlos Gonzalez - A Place For Us \| 3.882 \| 7.57 \| \| Cristina Vane - So Easy \| 7.477 \| 12.126 \| \| Detsky Sad - Walkie Talkie \| 6.214 \| 9.47 \| \| Enda Reilly - Cur An Long Ag Seol \| 7.329 \| 11.51 \| \| Forkupines - Semantics \| 4.556 \| 11.228 \| \| Georgia Wonder - Siren \| 3.165 \| 7.622 \| \| Girls Under Glass - We Feel Alright \| 3.176 \| 11.677 \| \| Hollow Ground - Ill Fate \| 5.67 \| 14.987 \| \| James Elder & Mark M Thompson - The English Actor \| 4.014 \| 8.834 \| \| Juliet's Rescue - Heartbeats \| 5.317 \| 13.101 \| \| Little Chicago's Finest - My Own \| 4.409 \| 5.378 \| \| Louis Cressy Band - Good Time \| 5.903 \| 10.918 \| \| Lyndsey Ollard - Catching Up \| 7.812 \| 10.793 \| \| M.E.R.C. Music - Knockout \| 5.663 \| 7.815 \| \| Moosmusic - Big Dummy Shake \| 7.081 \| 12.772 \| \| Motor Tapes - Shore \| 1.745 \| 8.775 \| \| Mu - Too Bright \| 5.518 \| 12.242 \| \| Nerve 9 - Pray For The Rain \| 5.685 \| 11.674 \| \| PR - Happy Daze \| -2.89 \| 37.274 \| \| PR - Oh No \| 0 \| 8.987 \| \| Punkdisco - Oral Hygiene \| 5.044 \| 16.173 \| \| Raft Monk - Tiring \| 2.119 \| 8.977 \| \| Sambasevam Shanmugam - Kaathaadi \| 7.51 \| 9.801 \| \| Secretariat - Borderline \| 5.068 \| 9.195 \| \| Secretariat - Over The Top \| 6.278 \| 13.556 \| \| Side Effects Project - Sing With Me \| 9.637 \| 11.222 \| \| Signe Jakobsen - What Have You Done To Me \| 6.884 \| 9.656 \| \| Skelpolu - Resurrection \| 0.053 \| 8.272 \| \| Speak Softly - Broken Man \| 3.743 \| 13.497 \| \| Speak Softly - Like Horses \| 4.339 \| 7.233 \| \| The Doppler Shift - Atrophy \| 2.47 \| 12.58 \| \| The Easton Ellises - Falcon 69 \| 2.507 \| 8.137 \| \| The Easton Ellises (Baumi) - SDRNR \| 1.463 \| 8.136 \| \| The Long Wait - Dark Horses \| 4.784 \| 10.964 \| \| The Mountaineering Club - Mallory \| 9.015 \| 13.26 \| \| The Sunshine Garcia Band - For I Am The Moon \| 8.341 \| 12.1 \| \| Timboz - Pony \| 2.698 \| 12.415 \| \| Tom McKenzie - Directions \| 7.305 \| 15.07 \| \| Triviul feat. The Fiend - Widow \| 6.409 \| 7.938 \| \| We Fell From The Sky - Not You \| 3.661 \| 11.403 \| \| Zeno - Signs \| 5.291 \| 10.178 \| \| Total (median over frames, median over tracks) \| 5.249 \| 11.155 \|
IntellectusAI/zephyr_finetune_writ	IntellectusAI	2024-05-29T12:21:47Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T12:21:44Z	--- 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]
vuongnhathien/convnext-tiny-upgrade-1k-224-batch-32	vuongnhathien	2024-05-29T12:16:45Z	192	0	transformers	[ "transformers", "tensorboard", "safetensors", "convnextv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/convnextv2-tiny-1k-224", "base_model:finetune:facebook/convnextv2-tiny-1k-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-29T10:37:27Z	--- license: apache-2.0 base_model: facebook/convnextv2-tiny-1k-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: convnext-tiny-upgrade-1k-224-batch-32 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.8886904761904761 --- <!-- 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. --> # convnext-tiny-upgrade-1k-224-batch-32 This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4027 - Accuracy: 0.8887 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.5523 \| 1.0 \| 550 \| 1.2083 \| 0.7010 \| \| 1.0852 \| 2.0 \| 1100 \| 0.7955 \| 0.7960 \| \| 0.9179 \| 3.0 \| 1650 \| 0.6425 \| 0.8258 \| \| 0.7621 \| 4.0 \| 2200 \| 0.5426 \| 0.8549 \| \| 0.7506 \| 5.0 \| 2750 \| 0.5018 \| 0.8624 \| \| 0.6774 \| 6.0 \| 3300 \| 0.4792 \| 0.8684 \| \| 0.6364 \| 7.0 \| 3850 \| 0.4526 \| 0.8744 \| \| 0.5961 \| 8.0 \| 4400 \| 0.4362 \| 0.8799 \| \| 0.602 \| 9.0 \| 4950 \| 0.4316 \| 0.8827 \| \| 0.5896 \| 10.0 \| 5500 \| 0.4287 \| 0.8851 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
Felladrin/gguf-sharded-prem-1B-chat	Felladrin	2024-05-29T12:12:36Z	1	0	null	[ "gguf", "base_model:premai-io/prem-1B-chat", "base_model:quantized:premai-io/prem-1B-chat", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-29T12:09:47Z	--- license: apache-2.0 base_model: premai-io/prem-1B-chat --- Sharded GGUF version of [premai-io/prem-1B-chat](https://huggingface.co/premai-io/prem-1B-chat).
Chrisantha/distilbert-base-uncased-finetuned-synthetic-finetuned-synthetic	Chrisantha	2024-05-29T12:12:15Z	117	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "base_model:Chrisantha/distilbert-base-uncased-finetuned-synthetic", "base_model:finetune:Chrisantha/distilbert-base-uncased-finetuned-synthetic", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-28T16:36:16Z	--- license: apache-2.0 base_model: Chrisantha/distilbert-base-uncased-finetuned-synthetic tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-synthetic-finetuned-synthetic 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-synthetic-finetuned-synthetic This model is a fine-tuned version of [Chrisantha/distilbert-base-uncased-finetuned-synthetic](https://huggingface.co/Chrisantha/distilbert-base-uncased-finetuned-synthetic) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4081 ## 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: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 1 \| 2.9242 \| \| 0.5836 \| 2.0 \| 2 \| 2.5911 \| \| 0.5836 \| 3.0 \| 3 \| 2.7194 \| \| 0.782 \| 4.0 \| 4 \| 2.3194 \| \| 0.782 \| 5.0 \| 5 \| 2.1952 \| \| 1.3155 \| 6.0 \| 6 \| 2.1321 \| \| 1.3155 \| 7.0 \| 7 \| 2.2769 \| \| 0.596 \| 8.0 \| 8 \| 2.2093 \| \| 0.596 \| 9.0 \| 9 \| 2.4133 \| \| 0.817 \| 10.0 \| 10 \| 2.4370 \| \| 0.817 \| 11.0 \| 11 \| 2.1859 \| \| 0.7962 \| 12.0 \| 12 \| 2.1760 \| \| 0.7962 \| 13.0 \| 13 \| 1.9116 \| \| 0.7554 \| 14.0 \| 14 \| 1.7670 \| \| 0.7554 \| 15.0 \| 15 \| 1.7386 \| \| 0.4256 \| 16.0 \| 16 \| 1.6506 \| \| 0.4256 \| 17.0 \| 17 \| 1.5478 \| \| 0.6326 \| 18.0 \| 18 \| 1.5998 \| \| 0.6326 \| 19.0 \| 19 \| 1.6936 \| \| 0.493 \| 20.0 \| 20 \| 1.6938 \| \| 0.493 \| 21.0 \| 21 \| 1.7659 \| \| 0.5194 \| 22.0 \| 22 \| 1.8872 \| \| 0.5194 \| 23.0 \| 23 \| 1.7004 \| \| 0.4438 \| 24.0 \| 24 \| 1.6653 \| \| 0.4438 \| 25.0 \| 25 \| 1.5889 \| \| 0.5761 \| 26.0 \| 26 \| 1.4914 \| \| 0.5761 \| 27.0 \| 27 \| 1.3813 \| \| 0.395 \| 28.0 \| 28 \| 1.4385 \| \| 0.395 \| 29.0 \| 29 \| 1.4067 \| \| 0.4681 \| 30.0 \| 30 \| 1.4021 \| \| 0.4681 \| 31.0 \| 31 \| 1.4172 \| \| 0.6326 \| 32.0 \| 32 \| 1.4502 \| \| 0.6326 \| 33.0 \| 33 \| 1.5628 \| \| 0.3545 \| 34.0 \| 34 \| 1.6276 \| \| 0.3545 \| 35.0 \| 35 \| 1.6164 \| \| 0.4313 \| 36.0 \| 36 \| 1.7040 \| \| 0.4313 \| 37.0 \| 37 \| 1.6950 \| \| 0.3883 \| 38.0 \| 38 \| 1.6429 \| \| 0.3883 \| 39.0 \| 39 \| 1.6180 \| \| 0.5155 \| 40.0 \| 40 \| 1.5417 \| \| 0.5155 \| 41.0 \| 41 \| 1.4499 \| \| 0.3546 \| 42.0 \| 42 \| 1.3885 \| \| 0.3546 \| 43.0 \| 43 \| 1.3061 \| \| 0.2205 \| 44.0 \| 44 \| 1.2986 \| \| 0.2205 \| 45.0 \| 45 \| 1.2861 \| \| 0.2851 \| 46.0 \| 46 \| 1.3785 \| \| 0.2851 \| 47.0 \| 47 \| 1.4008 \| \| 0.3057 \| 48.0 \| 48 \| 1.4402 \| \| 0.3057 \| 49.0 \| 49 \| 1.4538 \| \| 0.3449 \| 50.0 \| 50 \| 1.5073 \| \| 0.3449 \| 51.0 \| 51 \| 1.5050 \| \| 0.1664 \| 52.0 \| 52 \| 1.4939 \| \| 0.1664 \| 53.0 \| 53 \| 1.4691 \| \| 0.1484 \| 54.0 \| 54 \| 1.2829 \| \| 0.1484 \| 55.0 \| 55 \| 1.3112 \| \| 0.3156 \| 56.0 \| 56 \| 1.2328 \| \| 0.3156 \| 57.0 \| 57 \| 1.1700 \| \| 0.379 \| 58.0 \| 58 \| 1.1190 \| \| 0.379 \| 59.0 \| 59 \| 1.1429 \| \| 0.2475 \| 60.0 \| 60 \| 1.1544 \| \| 0.2475 \| 61.0 \| 61 \| 1.2303 \| \| 0.2282 \| 62.0 \| 62 \| 1.3118 \| \| 0.2282 \| 63.0 \| 63 \| 1.3701 \| \| 0.2216 \| 64.0 \| 64 \| 1.3705 \| \| 0.2216 \| 65.0 \| 65 \| 1.4848 \| \| 0.1768 \| 66.0 \| 66 \| 1.4744 \| \| 0.1768 \| 67.0 \| 67 \| 1.5796 \| \| 0.1621 \| 68.0 \| 68 \| 1.5674 \| \| 0.1621 \| 69.0 \| 69 \| 1.5873 \| \| 0.3016 \| 70.0 \| 70 \| 1.5756 \| \| 0.3016 \| 71.0 \| 71 \| 1.6496 \| \| 0.2548 \| 72.0 \| 72 \| 1.5922 \| \| 0.2548 \| 73.0 \| 73 \| 1.5911 \| \| 0.2878 \| 74.0 \| 74 \| 1.4912 \| \| 0.2878 \| 75.0 \| 75 \| 1.5303 \| \| 0.2045 \| 76.0 \| 76 \| 1.5293 \| \| 0.2045 \| 77.0 \| 77 \| 1.4076 \| \| 0.219 \| 78.0 \| 78 \| 1.4773 \| \| 0.219 \| 79.0 \| 79 \| 1.3878 \| \| 0.1396 \| 80.0 \| 80 \| 1.3349 \| \| 0.1396 \| 81.0 \| 81 \| 1.3670 \| \| 0.166 \| 82.0 \| 82 \| 1.4015 \| \| 0.166 \| 83.0 \| 83 \| 1.4132 \| \| 0.2982 \| 84.0 \| 84 \| 1.4478 \| \| 0.2982 \| 85.0 \| 85 \| 1.4803 \| \| 0.1199 \| 86.0 \| 86 \| 1.4667 \| \| 0.1199 \| 87.0 \| 87 \| 1.5402 \| \| 0.1982 \| 88.0 \| 88 \| 1.5515 \| \| 0.1982 \| 89.0 \| 89 \| 1.5189 \| \| 0.1816 \| 90.0 \| 90 \| 1.5545 \| \| 0.1816 \| 91.0 \| 91 \| 1.4814 \| \| 0.1779 \| 92.0 \| 92 \| 1.4943 \| \| 0.1779 \| 93.0 \| 93 \| 1.4430 \| \| 0.0785 \| 94.0 \| 94 \| 1.4865 \| \| 0.0785 \| 95.0 \| 95 \| 1.4919 \| \| 0.1108 \| 96.0 \| 96 \| 1.5035 \| \| 0.1108 \| 97.0 \| 97 \| 1.4088 \| \| 0.2581 \| 98.0 \| 98 \| 1.4104 \| \| 0.2581 \| 99.0 \| 99 \| 1.4549 \| \| 0.1738 \| 100.0 \| 100 \| 1.3761 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
saishf/SOVLish-Devil-8B-L3	saishf	2024-05-29T12:12:13Z	95	6	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "arxiv:2403.19522", "base_model:ResplendentAI/Aura_Llama3", "base_model:merge:ResplendentAI/Aura_Llama3", "base_model:ResplendentAI/BlueMoon_Llama3", "base_model:merge:ResplendentAI/BlueMoon_Llama3", "base_model:ResplendentAI/Luna_Llama3", "base_model:merge:ResplendentAI/Luna_Llama3", "base_model:ResplendentAI/RP_Format_QuoteAsterisk_Llama3", "base_model:merge:ResplendentAI/RP_Format_QuoteAsterisk_Llama3", "base_model:ResplendentAI/Smarts_Llama3", "base_model:merge:ResplendentAI/Smarts_Llama3", "base_model:mlabonne/Daredevil-8B-abliterated", "base_model:merge:mlabonne/Daredevil-8B-abliterated", "license:cc-by-nc-4.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T10:05:58Z	--- license: cc-by-nc-4.0 library_name: transformers tags: - mergekit - merge base_model: - mlabonne/Daredevil-8B-abliterated - ResplendentAI/RP_Format_QuoteAsterisk_Llama3 - mlabonne/Daredevil-8B-abliterated - ResplendentAI/BlueMoon_Llama3 - mlabonne/Daredevil-8B-abliterated - ResplendentAI/Luna_Llama3 - mlabonne/Daredevil-8B-abliterated - mlabonne/Daredevil-8B-abliterated - ResplendentAI/Aura_Llama3 - mlabonne/Daredevil-8B-abliterated - ResplendentAI/Smarts_Llama3 model-index: - name: SOVLish-Devil-8B-L3 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: 69.2 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/SOVLish-Devil-8B-L3 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 84.44 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/SOVLish-Devil-8B-L3 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: 68.97 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/SOVLish-Devil-8B-L3 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 57.95 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/SOVLish-Devil-8B-L3 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: 78.14 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/SOVLish-Devil-8B-L3 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: 72.48 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/SOVLish-Devil-8B-L3 name: Open LLM Leaderboard --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63df7c44f0c75dfb876272c0/we3E1Y0dVe_LzfcWU097X.png) Devil >:3 This is another "SOVL" style merge, this time using [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated). Daredevil is the first abliterated model i've tried that feels as smart as base llama-3-instruct while also being willing to give instructions to do all kinda of illegal things This model should do well in rp, I'm yet to test it (waiting for gguf files @_@) ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) as a base. ### Models Merged The following models were included in the merge: * [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) + [ResplendentAI/RP_Format_QuoteAsterisk_Llama3](https://huggingface.co/ResplendentAI/RP_Format_QuoteAsterisk_Llama3) * [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) + [ResplendentAI/BlueMoon_Llama3](https://huggingface.co/ResplendentAI/BlueMoon_Llama3) * [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) + [ResplendentAI/Luna_Llama3](https://huggingface.co/ResplendentAI/Luna_Llama3) * [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) + [ResplendentAI/Aura_Llama3](https://huggingface.co/ResplendentAI/Aura_Llama3) * [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) + [ResplendentAI/Smarts_Llama3](https://huggingface.co/ResplendentAI/Smarts_Llama3) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: mlabonne/Daredevil-8B-abliterated+ResplendentAI/Aura_Llama3 - model: mlabonne/Daredevil-8B-abliterated+ResplendentAI/Smarts_Llama3 - model: mlabonne/Daredevil-8B-abliterated+ResplendentAI/Luna_Llama3 - model: mlabonne/Daredevil-8B-abliterated+ResplendentAI/BlueMoon_Llama3 - model: mlabonne/Daredevil-8B-abliterated+ResplendentAI/RP_Format_QuoteAsterisk_Llama3 merge_method: model_stock base_model: mlabonne/Daredevil-8B-abliterated dtype: bfloat16 ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_saishf__SOVLish-Devil-8B-L3) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|71.86\| \|AI2 Reasoning Challenge (25-Shot)\|69.20\| \|HellaSwag (10-Shot) \|84.44\| \|MMLU (5-Shot) \|68.97\| \|TruthfulQA (0-shot) \|57.95\| \|Winogrande (5-shot) \|78.14\| \|GSM8k (5-shot) \|72.48\|
lgk03/WITHINAPPS_NDD-ppma_test-content	lgk03	2024-05-29T12:10:28Z	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	2024-05-29T12:06:13Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: WITHINAPPS_NDD-ppma_test-content 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-ppma_test-content 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: 0.2237 - Accuracy: 0.8795 - F1: 0.8231 - Precision: 0.7735 - Recall: 0.8795 ## 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 \| 0.9836 \| 30 \| 0.2373 \| 0.8795 \| 0.8231 \| 0.7735 \| 0.8795 \| \| No log \| 1.9672 \| 60 \| 0.2237 \| 0.8795 \| 0.8231 \| 0.7735 \| 0.8795 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
ringorsolya/moresBERT_hu_7	ringorsolya	2024-05-29T12:04:36Z	106	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T11:06:57Z	--- license: apache-2.0 ---
davanstrien/code-prompt-similarity-model	davanstrien	2024-05-29T12:04:34Z	217	6	sentence-transformers	[ "sentence-transformers", "safetensors", "mpnet", "sentence-similarity", "feature-extraction", "dataset_size:n<1K", "loss:MultipleNegativesRankingLoss", "en", "dataset:davanstrien/similarity-dataset-sc2-8b", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:microsoft/mpnet-base", "base_model:finetune:microsoft/mpnet-base", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-29T11:59:37Z	--- language: - en license: apache-2.0 library_name: sentence-transformers datasets: - davanstrien/similarity-dataset-sc2-8b tags: - sentence-transformers - sentence-similarity - feature-extraction - dataset_size:n<1K - loss:MultipleNegativesRankingLoss base_model: microsoft/mpnet-base metrics: - cosine_accuracy - dot_accuracy - manhattan_accuracy - euclidean_accuracy - max_accuracy widget: - source_sentence: Write a Python function that counts the number of even numbers in a given list of integers or floats sentences: - Write a Python function that returns the number of even numbers in a list. - Create a Python function that adds up all the numbers in a given list. The function should support lists containing only positive integers. - Write a Python function that converts a JSON string into a Python dictionary using the json module and returns it. - source_sentence: Develop a Python function to validate whether a given string represents a valid IPv4 address or not. sentences: - Create a Python function to validate a string `s` as an IPv4 address. The function should return `True` if `s` is a valid IPv4 address, and `False` otherwise. - Write a Python function to find the key with the highest value in a dictionary. The function should return the value of the key if it exists - Write a Python function that, given a dictionary `d` and an integer `k`, returns the sum of the values of the first `k` keys in `d`. - source_sentence: Write a Python function to create a list of numbers with exactly one even number and n-1 odd numbers sentences: - Write a Python function that returns the number of even numbers in a list. - Write a Python function that recursively traverses a given folder structure and returns the absolute path of all files that end with ".txt". - Write a Python decorator function that overrides the docstring of the decorated function, and stores the old docstring and other metadata in a `_doc_metadata` attribute of the function. - source_sentence: 'Implement a Python function that prints the first character of a string using its indexing feature. ' sentences: - Write a Python function that takes a string as a parameter and returns the first character of the string. - Write a Python function that checks if the bit at position `bit` is set in the given `integer`. This function should return a boolean value. - 'Write a Python function `floor_division(x: int, y: int) -> int` that divides two integers `x` and `y` and returns the largest whole number less than or equal to the result.' - source_sentence: Write a Python function that takes a MIDI note number and returns the corresponding piano key number. sentences: - Create a Python function that translates MIDI note numbers into piano key numbers, facilitating music generation. - Write a Python function that accepts a dictionary and returns a set of distinct values. If a key maps to an empty list, return an empty set. - Write a Python function `join_strings_with_comma(lst)` that takes a list of strings and returns a single string with all the strings from the list, separated by commas. pipeline_tag: sentence-similarity co2_eq_emissions: emissions: 2.213004168952992 energy_consumed: 0.006336878829164133 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: Intel(R) Xeon(R) CPU @ 2.20GHz ram_total_size: 62.804237365722656 hours_used: 0.049 hardware_used: 1 x NVIDIA L4 model-index: - name: MPNet base trained on AllNLI triplets results: - task: type: triplet name: Triplet dataset: name: code similarity dev type: code-similarity-dev metrics: - type: cosine_accuracy value: 0.934010152284264 name: Cosine Accuracy - type: dot_accuracy value: 0.07106598984771574 name: Dot Accuracy - type: manhattan_accuracy value: 0.934010152284264 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.9390862944162437 name: Euclidean Accuracy - type: max_accuracy value: 0.9390862944162437 name: Max Accuracy - task: type: triplet name: Triplet dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy value: 0.934010152284264 name: Cosine Accuracy - type: dot_accuracy value: 0.07106598984771574 name: Dot Accuracy - type: manhattan_accuracy value: 0.934010152284264 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.9390862944162437 name: Euclidean Accuracy - type: max_accuracy value: 0.9390862944162437 name: Max Accuracy --- # MPNet base trained on AllNLI triplets This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 tokens - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> - Language: en - License: apache-2.0 ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("davanstrien/code-prompt-similarity-model") # Run inference sentences = [ 'Write a Python function that takes a MIDI note number and returns the corresponding piano key number.', 'Create a Python function that translates MIDI note numbers into piano key numbers, facilitating music generation.', 'Write a Python function that accepts a dictionary and returns a set of distinct values. If a key maps to an empty list, return an empty set.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Triplet * Dataset: `code-similarity-dev` * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) \| Metric \| Value \| \|:-------------------\|:-----------\| \| cosine_accuracy \| 0.934 \| \| dot_accuracy \| 0.0711 \| \| manhattan_accuracy \| 0.934 \| \| euclidean_accuracy \| 0.9391 \| \| max_accuracy \| 0.9391 \| #### Triplet * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) \| Metric \| Value \| \|:-------------------\|:-----------\| \| cosine_accuracy \| 0.934 \| \| dot_accuracy \| 0.0711 \| \| manhattan_accuracy \| 0.934 \| \| euclidean_accuracy \| 0.9391 \| \| max_accuracy \| 0.9391 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 10 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| loss \| code-similarity-dev_max_accuracy \| max_accuracy \| \|:-----:\|:----:\|:-------------:\|:------:\|:--------------------------------:\|:------------:\| \| 0 \| 0 \| - \| - \| 0.8680 \| - \| \| 2.0 \| 100 \| 0.6379 \| 0.1845 \| 0.9340 \| - \| \| 4.0 \| 200 \| 0.0399 \| 0.1577 \| 0.9543 \| - \| \| 6.0 \| 300 \| 0.0059 \| 0.1577 \| 0.9543 \| - \| \| 8.0 \| 400 \| 0.0018 \| 0.1662 \| 0.9492 \| - \| \| 10.0 \| 500 \| 0.0009 \| 0.1643 \| 0.9391 \| 0.9391 \| ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - Energy Consumed: 0.006 kWh - Carbon Emitted: 0.002 kg of CO2 - Hours Used: 0.049 hours ### Training Hardware - On Cloud: No - GPU Model: 1 x NVIDIA L4 - CPU Model: Intel(R) Xeon(R) CPU @ 2.20GHz - RAM Size: 62.80 GB ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.0 - Transformers: 4.41.1 - PyTorch: 2.3.0+cu121 - Accelerate: 0.30.1 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
DaichiT/vinyl	DaichiT	2024-05-29T12:03:39Z	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	2024-05-29T11:52:25Z	--- 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 vinyl --- <!-- 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/vinyl This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks vinyl 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]
Ramikan-BR/tinyllama-coder-py-LORA-v13	Ramikan-BR	2024-05-29T12:01:28Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/tinyllama-chat-bnb-4bit", "base_model:finetune:unsloth/tinyllama-chat-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-29T12:00:55Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/tinyllama-chat-bnb-4bit --- # Uploaded model - Developed by: Ramikan-BR - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-chat-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)
VH1213141516/r2d2-lat-5-28-epsilon-1-outer-lr-5e-6-checkpt-120	VH1213141516	2024-05-29T11:59:44Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T11:59:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
cosmicman/CosmicMan-SD	cosmicman	2024-05-29T11:59:40Z	8	2	diffusers	[ "diffusers", "safetensors", "arxiv:2404.01294", "license:cc-by-4.0", "region:us" ]	null	2024-05-29T11:42:59Z	--- license: cc-by-4.0 --- ![Intro Image](cosmicman_samples.png) CosmicMan is a text-to-image foundation model specialized for generating high-fidelity human images. For more information, please refer to our research paper: [CosmicMan: A Text-to-Image Foundation Model for Humans](https://arxiv.org/abs/2404.01294). Our model is based on [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5). This repository provide UNet checkpoints for CosmicMan-SD. ## Requirements ```python conda create -n cosmicman python=3.10 source activate cosmicman pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 pip install accelerate diffusers datasets transformers botocore invisible-watermark bitsandbytes gradio==3.48.0 ``` ## Inference ```python import torch from diffusers import StableDiffusionPipeline, UNet2DConditionModel, EulerDiscreteScheduler from huggingface_hub import hf_hub_download from safetensors.torch import load_file base_path = "runwayml/stable-diffusion-v1-5" unet_path = "cosmicman/CosmicMan-SD" # Load model. unet = UNet2DConditionModel.from_pretrained(unet_path, torch_dtype=torch.float16) pipe = StableDiffusionPipeline.from_pretrained(base_path, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda") pipe.scheduler = EulerDiscreteScheduler.from_pretrained(base_path, subfolder="scheduler", torch_dtype=torch.float16) # Generate image. positive_prompt = "A closeup portrait shot against a white wall, a fit Caucasian adult female with wavy blonde hair falling above her chest wears a short sleeve silk floral dress and a floral silk normal short sleeve white blouse" negative_prompt = "" image = pipe(positive_prompt, num_inference_steps=30, guidance_scale=7.5, height=1024, width=1024, negative_prompt=negative_prompt, output_type="pil").images[0].save("output.png") ``` ## Citation Information ``` @article{li2024cosmicman, title={CosmicMan: A Text-to-Image Foundation Model for Humans}, author={Li, Shikai and Fu, Jianglin and Liu, Kaiyuan and Wang, Wentao and Lin, Kwan-Yee and Wu, Wayne}, journal={arXiv preprint arXiv:2404.01294}, year={2024} } ```
VH1213141516/r2d2-lat-5-28-epsilon-1-outer-lr-5e-6-checkpt-360	VH1213141516	2024-05-29T11:58:39Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T11:57:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Adriana213/xlm-roberta-base-finetuned-panx-en	Adriana213	2024-05-29T11:58:06Z	105	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-29T09:41:25Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlm-roberta-base-finetuned-panx-en results: [] --- # xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base). It achieves the following results on the evaluation set: - Loss: 0.3905 - F1 Score: 0.6861 ## Model description This model is a fine-tuned version of xlm-roberta-base on the English subset of the PAN-X dataset for Named Entity Recognition (NER). The model has been fine-tuned to perform token classification tasks and is evaluated on its performance in identifying named entities in English text. ## Intended uses & limitations ### Intended uses: Named Entity Recognition (NER) tasks specifically for English. Token classification tasks involving English text. ### Limitations: The model's performance is optimized for English and may not generalize well to other languages without further fine-tuning. The model's predictions are based on the data it was trained on and may not handle out-of-domain data as effectively.d ## Training and evaluation data The model was fine-tuned on the English subset of the PAN-X dataset, which includes labeled examples of named entities in English text. The evaluation data is a separate portion of the same dataset, used to assess the model's performance ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.0479 \| 1.0 \| 50 \| 0.4854 \| 0.5857 \| \| 0.4604 \| 2.0 \| 100 \| 0.3995 \| 0.6605 \| \| 0.3797 \| 3.0 \| 150 \| 0.3905 \| 0.6861 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Adriana213/xlm-roberta-base-finetuned-panx-it	Adriana213	2024-05-29T11:56:35Z	104	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "it", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-29T09:39:35Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlm-roberta-base-finetuned-panx-it results: [] language: - it metrics: - f1 library_name: transformers --- # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base). It achieves the following results on the evaluation set: - Loss: 0.2619 - F1 Score: 0.8321 ## Model description This model is a fine-tuned version of xlm-roberta-base on the Italian subset of the PAN-X dataset for Named Entity Recognition (NER). The model has been fine-tuned to perform token classification tasks and is evaluated on its performance in identifying named entities in Italian text. ## Intended uses & limitations ### Intended uses: Named Entity Recognition (NER) tasks specifically for Italian. Token classification tasks involving Italian text. ### Limitations: The model's performance is optimized for Italian and may not generalize well to other languages without further fine-tuning. The model's predictions are based on the data it was trained on and may not handle out-of-domain data as effectively. ## Training and evaluation data The model was fine-tuned on the Italian subset of the PAN-X dataset, which includes labeled examples of named entities in Italian text. The evaluation data is a separate portion of the same dataset, used to assess the model's performance. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7217 \| 1.0 \| 70 \| 0.3193 \| 0.7343 \| \| 0.2736 \| 2.0 \| 140 \| 0.2760 \| 0.8055 \| \| 0.1838 \| 3.0 \| 210 \| 0.2619 \| 0.8321 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
abhi317/results_008	abhi317	2024-05-29T11:56:23Z	1	0	peft	[ "peft", "safetensors", "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	2024-05-29T11:45:38Z	--- license: llama3 library_name: peft tags: - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B-Instruct model-index: - name: results_008 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. --> # results_008 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. It achieves the following results on the evaluation set: - Loss: 20.8098 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 21.2839 \| 1.0 \| 1 \| 21.0366 \| \| 21.2839 \| 2.0 \| 2 \| 20.7089 \| \| 21.2839 \| 3.0 \| 3 \| 20.8205 \| \| 21.2839 \| 4.0 \| 4 \| 20.5746 \| \| 21.2839 \| 5.0 \| 5 \| 20.8098 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
ft-player/lanesegnet_baseline	ft-player	2024-05-29T11:56:09Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-29T08:36:04Z	--- license: apache-2.0 ---
pablopiquejr/llama-3-8b-Instruct-bnb-4bit-educational_purpose	pablopiquejr	2024-05-29T11:55:45Z	6	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-29T11:53:24Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: pablopiquejr - 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)
Adriana213/xlm-roberta-base-finetuned-panx-fr	Adriana213	2024-05-29T11:53:59Z	135	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "fr", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-29T09:36:09Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlm-roberta-base-finetuned-panx-fr results: [] language: - fr metrics: - f1 library_name: transformers --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base). It achieves the following results on the evaluation set: - Loss: 0.2750 - F1 Score: 0.8495 ## Model description This model is a fine-tuned version of xlm-roberta-base on the French subset of the PAN-X dataset for Named Entity Recognition (NER). The model has been fine-tuned to perform token classification tasks and is evaluated on its performance in identifying named entities in French text. ## Intended uses & limitations ### Intended uses: Named Entity Recognition (NER) tasks specifically for French. Token classification tasks involving French text. ### Limitations: The model's performance is optimized for French and may not generalize well to other languages without further fine-tuning. The model's predictions are based on the data it was trained on and may not handle out-of-domain data as effectively. ## Training and evaluation data The model was fine-tuned on the French subset of the PAN-X dataset, which includes labeled examples of named entities in French text. The evaluation data is a separate portion of the same dataset, used to assess the model's performance. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5647 \| 1.0 \| 191 \| 0.3242 \| 0.7728 \| \| 0.2671 \| 2.0 \| 382 \| 0.2672 \| 0.8202 \| \| 0.1744 \| 3.0 \| 573 \| 0.2750 \| 0.8495 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
DaichiT/valve_copper_alloy	DaichiT	2024-05-29T11:50:54Z	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	2024-05-29T11:39:07Z	--- 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 valve_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/valve_copper_alloy This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks valve_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]
Piola-h333/Norman	Piola-h333	2024-05-29T11:48:53Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-29T11:47:28Z	--- license: apache-2.0 ---
Vipinap/RAFT-llama3-GPT_data_v24.05.01	Vipinap	2024-05-29T11:48:28Z	77	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-29T11:32:56Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: raft_llama3_8b_gpt_data_24_05_29 --- # Uploaded model - Developed by: Vipinap - License: apache-2.0 - Finetuned from model : raft_llama3_8b_gpt_data_24_05_29 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)
Likich/tinyllama-finetune-qualcoding_1000_prompt3	Likich	2024-05-29T11:48:01Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T11:47:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
lgk03/WITHINAPPS_NDD-addressbook_test-content	lgk03	2024-05-29T11:47:44Z	125	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	2024-05-29T11:40:36Z	--- 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 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 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: 0.0206 - Accuracy: 0.9977 - F1: 0.9976 - Precision: 0.9977 - Recall: 0.9977 ## 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 \| 0.0546 \| 0.9877 \| 0.9876 \| 0.9879 \| 0.9877 \| \| No log \| 1.9906 \| 106 \| 0.0206 \| 0.9977 \| 0.9976 \| 0.9977 \| 0.9977 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
abhi317/results_007	abhi317	2024-05-29T11:45:27Z	1	1	peft	[ "peft", "safetensors", "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	2024-05-28T16:30:39Z	--- license: llama3 library_name: peft tags: - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B-Instruct model-index: - name: results_007 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. --> # results_007 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. It achieves the following results on the evaluation set: - Loss: 20.6308 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 21.2839 \| 1.0 \| 1 \| 20.8364 \| \| 21.2839 \| 2.0 \| 2 \| 20.9702 \| \| 21.2839 \| 3.0 \| 3 \| 20.7513 \| \| 21.2839 \| 4.0 \| 4 \| 20.7336 \| \| 21.2839 \| 5.0 \| 5 \| 20.6308 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
Adriana213/xlm-roberta-base-finetuned-panx-de	Adriana213	2024-05-29T11:44:10Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "de", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-29T08:09:04Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlm-roberta-base-finetuned-panx-de results: [] language: - de library_name: transformers --- # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1363 - F1 Score: 0.8658 ## Model description This model is a fine-tuned version of xlm-roberta-base on the German subset of the PAN-X dataset for Named Entity Recognition (NER). The model has been fine-tuned to perform token classification tasks and is evaluated on its performance in identifying named entities in German text. ## Intended uses & limitations ### Intended uses: Named Entity Recognition (NER) tasks specifically for German. Token classification tasks involving German text. ### Limitations: The model's performance is optimized for German and may not generalize well to other languages without further fine-tuning. The model's predictions are based on the data it was trained on and may not handle out-of-domain data as effectively. ## Training and evaluation data The model was fine-tuned on the German subset of the PAN-X dataset, which includes labeled examples of named entities in German text. The evaluation data is a separate portion of the same dataset, used to assess the model's performance. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2539 \| 1.0 \| 525 \| 0.1505 \| 0.8246 \| \| 0.1268 \| 2.0 \| 1050 \| 0.1380 \| 0.8503 \| \| 0.0794 \| 3.0 \| 1575 \| 0.1363 \| 0.8658 \| ### Evaluation results The model's F1-score on the validation set for the German subset is 0.8658, indicating a strong performance in named entity recognition for German text. ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
anyasims/ourpo3_uf4_PR_ABL_PR_sft0.0_zs1.0_a0.5-s2-c477	anyasims	2024-05-29T11:43:37Z	7	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T11:37:20Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
Mengkedalai/your-unique-repo-name	Mengkedalai	2024-05-29T11:40:00Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T11:32:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
clmrie/distilbert-base-uncased-finetuned-emotion	clmrie	2024-05-29T11:39:55Z	119	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T11:01:14Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion model-index: - name: distilbert-base-uncased-finetuned-emotion 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-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 125 \| 0.9019 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
nguyennghia0902/electra-small-discriminator_0.0005_16	nguyennghia0902	2024-05-29T11:39:34Z	61	0	transformers	[ "transformers", "tf", "electra", "question-answering", "generated_from_keras_callback", "base_model:google/electra-small-discriminator", "base_model:finetune:google/electra-small-discriminator", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-05-29T07:40:01Z	--- license: apache-2.0 base_model: google/electra-small-discriminator tags: - generated_from_keras_callback model-index: - name: nguyennghia0902/electra-small-discriminator_0.0005_16 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # nguyennghia0902/electra-small-discriminator_0.0005_16 This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.2099 - Train End Logits Accuracy: 0.6982 - Train Start Logits Accuracy: 0.6666 - Validation Loss: 0.7830 - Validation End Logits Accuracy: 0.7964 - Validation Start Logits Accuracy: 0.7852 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0005, 'decay_steps': 31270, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 3.8593 \| 0.1838 \| 0.1690 \| 2.9074 \| 0.3506 \| 0.3319 \| 0 \| \| 3.0501 \| 0.3257 \| 0.2954 \| 2.5522 \| 0.4171 \| 0.3859 \| 1 \| \| 2.7183 \| 0.3845 \| 0.3534 \| 2.2123 \| 0.4803 \| 0.4547 \| 2 \| \| 2.4780 \| 0.4325 \| 0.4004 \| 1.9826 \| 0.5248 \| 0.5046 \| 3 \| \| 2.2672 \| 0.4747 \| 0.4389 \| 1.8034 \| 0.5660 \| 0.5425 \| 4 \| \| 2.0640 \| 0.5162 \| 0.4814 \| 1.5610 \| 0.6207 \| 0.6037 \| 5 \| \| 1.8439 \| 0.5608 \| 0.5265 \| 1.3128 \| 0.6811 \| 0.6639 \| 6 \| \| 1.6214 \| 0.6104 \| 0.5736 \| 1.0714 \| 0.7326 \| 0.7206 \| 7 \| \| 1.3990 \| 0.6574 \| 0.6232 \| 0.8891 \| 0.7744 \| 0.7630 \| 8 \| \| 1.2099 \| 0.6982 \| 0.6666 \| 0.7830 \| 0.7964 \| 0.7852 \| 9 \| ### Framework versions - Transformers 4.39.3 - TensorFlow 2.15.0 - Datasets 2.18.0 - Tokenizers 0.15.2
amaye15/microsoft-resnet-50-batch32-lr0.005-standford-dogs	amaye15	2024-05-29T11:39:01Z	243	0	transformers	[ "transformers", "safetensors", "resnet", "image-classification", "generated_from_trainer", "dataset:stanford-dogs", "base_model:microsoft/resnet-50", "base_model:finetune:microsoft/resnet-50", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-29T11:38:53Z	--- license: apache-2.0 base_model: microsoft/resnet-50 tags: - generated_from_trainer datasets: - stanford-dogs metrics: - accuracy - f1 - precision - recall model-index: - name: microsoft-resnet-50-batch32-lr0.005-standford-dogs results: - task: name: Image Classification type: image-classification dataset: name: stanford-dogs type: stanford-dogs config: default split: full args: default metrics: - name: Accuracy type: accuracy value: 0.82555879494655 - name: F1 type: f1 value: 0.8098053489000772 - name: Precision type: precision value: 0.8426096100022951 - name: Recall type: recall value: 0.817750070550628 --- <!-- 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. --> # microsoft-resnet-50-batch32-lr0.005-standford-dogs This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the stanford-dogs dataset. It achieves the following results on the evaluation set: - Loss: 1.1192 - Accuracy: 0.8256 - F1: 0.8098 - Precision: 0.8426 - Recall: 0.8178 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 4.7839 \| 0.0777 \| 10 \| 4.7747 \| 0.2556 \| 0.2410 \| 0.4479 \| 0.2436 \| \| 4.7731 \| 0.1553 \| 20 \| 4.7576 \| 0.3511 \| 0.3282 \| 0.6032 \| 0.3338 \| \| 4.7617 \| 0.2330 \| 30 \| 4.7363 \| 0.4184 \| 0.3974 \| 0.6668 \| 0.3947 \| \| 4.7445 \| 0.3107 \| 40 \| 4.7115 \| 0.5265 \| 0.4927 \| 0.7032 \| 0.4993 \| \| 4.7266 \| 0.3883 \| 50 \| 4.6846 \| 0.5561 \| 0.5413 \| 0.7422 \| 0.5333 \| \| 4.7081 \| 0.4660 \| 60 \| 4.6547 \| 0.6062 \| 0.5767 \| 0.7392 \| 0.5828 \| \| 4.6807 \| 0.5437 \| 70 \| 4.6161 \| 0.5909 \| 0.5750 \| 0.7740 \| 0.5673 \| \| 4.6572 \| 0.6214 \| 80 \| 4.5761 \| 0.6324 \| 0.6162 \| 0.8021 \| 0.6102 \| \| 4.6286 \| 0.6990 \| 90 \| 4.5274 \| 0.6297 \| 0.6241 \| 0.8188 \| 0.6080 \| \| 4.598 \| 0.7767 \| 100 \| 4.4746 \| 0.6569 \| 0.6609 \| 0.8380 \| 0.6370 \| \| 4.5578 \| 0.8544 \| 110 \| 4.4193 \| 0.6674 \| 0.6713 \| 0.8301 \| 0.6486 \| \| 4.521 \| 0.9320 \| 120 \| 4.3553 \| 0.6914 \| 0.6868 \| 0.8215 \| 0.6729 \| \| 4.4888 \| 1.0097 \| 130 \| 4.2924 \| 0.7082 \| 0.7064 \| 0.8415 \| 0.6904 \| \| 4.4312 \| 1.0874 \| 140 \| 4.2125 \| 0.7155 \| 0.7076 \| 0.8381 \| 0.6980 \| \| 4.3865 \| 1.1650 \| 150 \| 4.1433 \| 0.7145 \| 0.7115 \| 0.8315 \| 0.6984 \| \| 4.336 \| 1.2427 \| 160 \| 4.0630 \| 0.7082 \| 0.7010 \| 0.8353 \| 0.6930 \| \| 4.2903 \| 1.3204 \| 170 \| 3.9781 \| 0.7148 \| 0.7024 \| 0.8109 \| 0.6982 \| \| 4.2465 \| 1.3981 \| 180 \| 3.8896 \| 0.7376 \| 0.7234 \| 0.8328 \| 0.7217 \| \| 4.1924 \| 1.4757 \| 190 \| 3.8117 \| 0.7476 \| 0.7310 \| 0.8161 \| 0.7322 \| \| 4.1217 \| 1.5534 \| 200 \| 3.7499 \| 0.7510 \| 0.7344 \| 0.8105 \| 0.7372 \| \| 4.068 \| 1.6311 \| 210 \| 3.6340 \| 0.7551 \| 0.7355 \| 0.8183 \| 0.7409 \| \| 4.0148 \| 1.7087 \| 220 \| 3.5678 \| 0.7546 \| 0.7358 \| 0.8066 \| 0.7413 \| \| 3.9682 \| 1.7864 \| 230 \| 3.4852 \| 0.7663 \| 0.7477 \| 0.8145 \| 0.7530 \| \| 3.9196 \| 1.8641 \| 240 \| 3.3841 \| 0.7648 \| 0.7464 \| 0.8075 \| 0.7520 \| \| 3.8481 \| 1.9417 \| 250 \| 3.3003 \| 0.7626 \| 0.7421 \| 0.8056 \| 0.7495 \| \| 3.8017 \| 2.0194 \| 260 \| 3.2395 \| 0.7578 \| 0.7370 \| 0.8045 \| 0.7461 \| \| 3.7528 \| 2.0971 \| 270 \| 3.1183 \| 0.7578 \| 0.7349 \| 0.8007 \| 0.7457 \| \| 3.6614 \| 2.1748 \| 280 \| 3.0364 \| 0.7655 \| 0.7435 \| 0.8011 \| 0.7531 \| \| 3.6522 \| 2.2524 \| 290 \| 2.9775 \| 0.7629 \| 0.7415 \| 0.7990 \| 0.7507 \| \| 3.5922 \| 2.3301 \| 300 \| 2.8995 \| 0.7665 \| 0.7466 \| 0.8090 \| 0.7551 \| \| 3.519 \| 2.4078 \| 310 \| 2.8049 \| 0.7680 \| 0.7488 \| 0.8129 \| 0.7566 \| \| 3.4724 \| 2.4854 \| 320 \| 2.7425 \| 0.7704 \| 0.7528 \| 0.8170 \| 0.7601 \| \| 3.4333 \| 2.5631 \| 330 \| 2.6444 \| 0.7755 \| 0.7560 \| 0.8236 \| 0.7648 \| \| 3.4303 \| 2.6408 \| 340 \| 2.5672 \| 0.7687 \| 0.7473 \| 0.8178 \| 0.7585 \| \| 3.3287 \| 2.7184 \| 350 \| 2.5194 \| 0.7806 \| 0.7599 \| 0.8229 \| 0.7712 \| \| 3.2916 \| 2.7961 \| 360 \| 2.4733 \| 0.7796 \| 0.7575 \| 0.8223 \| 0.7698 \| \| 3.1999 \| 2.8738 \| 370 \| 2.4098 \| 0.7792 \| 0.7565 \| 0.8158 \| 0.7692 \| \| 3.211 \| 2.9515 \| 380 \| 2.3081 \| 0.7796 \| 0.7571 \| 0.8284 \| 0.7692 \| \| 3.1437 \| 3.0291 \| 390 \| 2.2523 \| 0.7830 \| 0.7600 \| 0.8212 \| 0.7730 \| \| 3.1036 \| 3.1068 \| 400 \| 2.2000 \| 0.7847 \| 0.7619 \| 0.8210 \| 0.7740 \| \| 3.0345 \| 3.1845 \| 410 \| 2.1385 \| 0.7833 \| 0.7606 \| 0.8261 \| 0.7726 \| \| 2.99 \| 3.2621 \| 420 \| 2.1079 \| 0.7799 \| 0.7560 \| 0.8199 \| 0.7698 \| \| 2.9386 \| 3.3398 \| 430 \| 2.0585 \| 0.7821 \| 0.7584 \| 0.8232 \| 0.7716 \| \| 2.9093 \| 3.4175 \| 440 \| 2.0176 \| 0.7823 \| 0.7586 \| 0.8225 \| 0.7721 \| \| 2.8868 \| 3.4951 \| 450 \| 1.9702 \| 0.7818 \| 0.7585 \| 0.8183 \| 0.7720 \| \| 2.8603 \| 3.5728 \| 460 \| 1.8973 \| 0.7864 \| 0.7645 \| 0.8241 \| 0.7767 \| \| 2.8232 \| 3.6505 \| 470 \| 1.8814 \| 0.7855 \| 0.7616 \| 0.8128 \| 0.7758 \| \| 2.7889 \| 3.7282 \| 480 \| 1.8170 \| 0.7886 \| 0.7676 \| 0.8214 \| 0.7792 \| \| 2.7561 \| 3.8058 \| 490 \| 1.7750 \| 0.7920 \| 0.7721 \| 0.8364 \| 0.7828 \| \| 2.7243 \| 3.8835 \| 500 \| 1.7369 \| 0.7906 \| 0.7695 \| 0.8295 \| 0.7813 \| \| 2.6619 \| 3.9612 \| 510 \| 1.7225 \| 0.7971 \| 0.7766 \| 0.8292 \| 0.7884 \| \| 2.7054 \| 4.0388 \| 520 \| 1.6453 \| 0.7983 \| 0.7788 \| 0.8346 \| 0.7894 \| \| 2.6069 \| 4.1165 \| 530 \| 1.6340 \| 0.8000 \| 0.7807 \| 0.8347 \| 0.7910 \| \| 2.5627 \| 4.1942 \| 540 \| 1.6538 \| 0.7971 \| 0.7760 \| 0.8337 \| 0.7878 \| \| 2.5555 \| 4.2718 \| 550 \| 1.5779 \| 0.7998 \| 0.7785 \| 0.8324 \| 0.7906 \| \| 2.5541 \| 4.3495 \| 560 \| 1.5960 \| 0.7945 \| 0.7736 \| 0.8329 \| 0.7850 \| \| 2.513 \| 4.4272 \| 570 \| 1.5537 \| 0.8025 \| 0.7841 \| 0.8368 \| 0.7941 \| \| 2.442 \| 4.5049 \| 580 \| 1.5196 \| 0.8034 \| 0.7858 \| 0.8380 \| 0.7954 \| \| 2.4763 \| 4.5825 \| 590 \| 1.5009 \| 0.8052 \| 0.7870 \| 0.8345 \| 0.7965 \| \| 2.4412 \| 4.6602 \| 600 \| 1.4760 \| 0.8098 \| 0.7924 \| 0.8391 \| 0.8015 \| \| 2.383 \| 4.7379 \| 610 \| 1.4403 \| 0.8088 \| 0.7920 \| 0.8395 \| 0.8007 \| \| 2.3731 \| 4.8155 \| 620 \| 1.4123 \| 0.8120 \| 0.7956 \| 0.8401 \| 0.8039 \| \| 2.3616 \| 4.8932 \| 630 \| 1.4193 \| 0.8105 \| 0.7940 \| 0.8369 \| 0.8021 \| \| 2.3311 \| 4.9709 \| 640 \| 1.4220 \| 0.8098 \| 0.7934 \| 0.8370 \| 0.8016 \| \| 2.3373 \| 5.0485 \| 650 \| 1.3956 \| 0.8081 \| 0.7907 \| 0.8367 \| 0.7996 \| \| 2.2879 \| 5.1262 \| 660 \| 1.3375 \| 0.8144 \| 0.7976 \| 0.8410 \| 0.8062 \| \| 2.299 \| 5.2039 \| 670 \| 1.3431 \| 0.8146 \| 0.7967 \| 0.8371 \| 0.8061 \| \| 2.2471 \| 5.2816 \| 680 \| 1.3360 \| 0.8151 \| 0.7985 \| 0.8389 \| 0.8070 \| \| 2.2419 \| 5.3592 \| 690 \| 1.3139 \| 0.8139 \| 0.7977 \| 0.8377 \| 0.8058 \| \| 2.2195 \| 5.4369 \| 700 \| 1.3225 \| 0.8151 \| 0.7974 \| 0.8395 \| 0.8062 \| \| 2.1901 \| 5.5146 \| 710 \| 1.2797 \| 0.8173 \| 0.8001 \| 0.8397 \| 0.8087 \| \| 2.1931 \| 5.5922 \| 720 \| 1.2543 \| 0.8192 \| 0.8032 \| 0.8423 \| 0.8109 \| \| 2.195 \| 5.6699 \| 730 \| 1.2767 \| 0.8209 \| 0.8039 \| 0.8405 \| 0.8125 \| \| 2.1413 \| 5.7476 \| 740 \| 1.2735 \| 0.8212 \| 0.8053 \| 0.8416 \| 0.8132 \| \| 2.1696 \| 5.8252 \| 750 \| 1.2694 \| 0.8149 \| 0.7983 \| 0.8358 \| 0.8069 \| \| 2.1387 \| 5.9029 \| 760 \| 1.2532 \| 0.8217 \| 0.8062 \| 0.8422 \| 0.8136 \| \| 2.1811 \| 5.9806 \| 770 \| 1.2426 \| 0.8197 \| 0.8034 \| 0.8417 \| 0.8116 \| \| 2.077 \| 6.0583 \| 780 \| 1.2101 \| 0.8243 \| 0.8078 \| 0.8464 \| 0.8159 \| \| 2.1099 \| 6.1359 \| 790 \| 1.1947 \| 0.8265 \| 0.8108 \| 0.8455 \| 0.8186 \| \| 2.0825 \| 6.2136 \| 800 \| 1.1826 \| 0.8241 \| 0.8080 \| 0.8455 \| 0.8161 \| \| 2.0933 \| 6.2913 \| 810 \| 1.1934 \| 0.8282 \| 0.8128 \| 0.8474 \| 0.8207 \| \| 2.0857 \| 6.3689 \| 820 \| 1.1897 \| 0.8258 \| 0.8099 \| 0.8465 \| 0.8181 \| \| 2.0881 \| 6.4466 \| 830 \| 1.1666 \| 0.8277 \| 0.8124 \| 0.8477 \| 0.8199 \| \| 2.074 \| 6.5243 \| 840 \| 1.1815 \| 0.8248 \| 0.8081 \| 0.8433 \| 0.8167 \| \| 2.0145 \| 6.6019 \| 850 \| 1.1680 \| 0.8292 \| 0.8130 \| 0.8473 \| 0.8209 \| \| 2.0778 \| 6.6796 \| 860 \| 1.1565 \| 0.8260 \| 0.8094 \| 0.8348 \| 0.8178 \| \| 1.9784 \| 6.7573 \| 870 \| 1.1571 \| 0.8345 \| 0.8201 \| 0.8529 \| 0.8269 \| \| 2.0595 \| 6.8350 \| 880 \| 1.1554 \| 0.8309 \| 0.8165 \| 0.8475 \| 0.8234 \| \| 2.0252 \| 6.9126 \| 890 \| 1.1444 \| 0.8282 \| 0.8140 \| 0.8476 \| 0.8209 \| \| 1.9708 \| 6.9903 \| 900 \| 1.1478 \| 0.8302 \| 0.8158 \| 0.8472 \| 0.8224 \| \| 2.0656 \| 7.0680 \| 910 \| 1.1285 \| 0.8324 \| 0.8169 \| 0.8485 \| 0.8245 \| \| 2.0086 \| 7.1456 \| 920 \| 1.1289 \| 0.8290 \| 0.8148 \| 0.8444 \| 0.8219 \| \| 2.0056 \| 7.2233 \| 930 \| 1.1268 \| 0.8280 \| 0.8130 \| 0.8470 \| 0.8208 \| \| 1.9498 \| 7.3010 \| 940 \| 1.1246 \| 0.8311 \| 0.8158 \| 0.8497 \| 0.8234 \| \| 2.0067 \| 7.3786 \| 950 \| 1.1495 \| 0.8285 \| 0.8132 \| 0.8440 \| 0.8207 \| \| 2.0171 \| 7.4563 \| 960 \| 1.1168 \| 0.8285 \| 0.8138 \| 0.8501 \| 0.8209 \| \| 1.9683 \| 7.5340 \| 970 \| 1.1290 \| 0.8314 \| 0.8165 \| 0.8500 \| 0.8235 \| \| 1.9771 \| 7.6117 \| 980 \| 1.0982 \| 0.8314 \| 0.8153 \| 0.8454 \| 0.8233 \| \| 2.0086 \| 7.6893 \| 990 \| 1.1275 \| 0.8294 \| 0.8151 \| 0.8491 \| 0.8218 \| \| 1.9854 \| 7.7670 \| 1000 \| 1.1192 \| 0.8256 \| 0.8098 \| 0.8426 \| 0.8178 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
Drac0528/my_awesome_asr_mind_model	Drac0528	2024-05-29T11:38:30Z	80	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:minds14", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-28T09:42:52Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - minds14 metrics: - wer model-index: - name: my_awesome_asr_mind_model results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: minds14 type: minds14 config: en-US split: None args: en-US metrics: - name: Wer type: wer value: 0.9672897196261683 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_asr_mind_model This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 11.0159 - Wer: 0.9673 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.383 \| 200.0 \| 1000 \| 10.5192 \| 0.9813 \| \| 2.9471 \| 400.0 \| 2000 \| 11.0159 \| 0.9673 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
kardosdrur/burial-mounds-yolov8m-obb	kardosdrur	2024-05-29T11:38:19Z	0	0	burial_mounds	[ "burial_mounds", "yolo", "object-detection", "en", "license:cc-by-nc-4.0", "region:us" ]	object-detection	2024-05-28T12:52:30Z	--- language: - en tags: - yolo - object-detection library_name: burial_mounds license: cc-by-nc-4.0 --- # kardosdrur/burial-mounds-yolov8m-obb This repository contains a YOLO model that has been finetuned by the `burial_mounds` Python package on the `Mounds` dataset. > The model is for academic use only, commercial use is prohibited due to restrictions imposed by the training datasets. ## Usage ```python # pip install burial_mounds from burial_mounds.model import MoundDetector model = MoundDetector.load_from_hub("kardosdrur/burial-mounds-yolov8m") # Find bounding polygons bounding_polygons = model.detect_mounds("some_satellite_image.png") for polygon in bounding_polygons: print(polygon) # Annotate satellite images annotated_image = model.annotate_image("some_satellite_image.png") annotated_image.show() ``` For a more detailed guide consult the [YOLOv8 documentation](https://docs.ultralytics.com/modes/predict/#key-features-of-predict-mode) or [our documentation](https://github.com/x-tabdeveloping/burial-mounds-object-recognition).
DaichiT/tire	DaichiT	2024-05-29T11:38:08Z	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	2024-05-29T11:27:15Z	--- 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 tire --- <!-- 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/tire This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks tire 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]
Felladrin/gguf-sharded-pythia-3b-deduped-sft	Felladrin	2024-05-29T11:37:33Z	2	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-29T11:27:19Z	--- license: apache-2.0 base_model: theblackcat102/pythia-3b-deduped-sft --- Sharded GGUF version of [theblackcat102/pythia-3b-deduped-sft](https://huggingface.co/theblackcat102/pythia-3b-deduped-sft).
anyasims/ourpo3_uf4_BASE_OR_sft1.0_zs0.5-s2-d8c6	anyasims	2024-05-29T11:36:38Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T11:30:12Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
Raneechu/textbookbig12_ft	Raneechu	2024-05-29T11:33:01Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-29T11:32:55Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: textbookbig12_ft 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. --> # textbookbig12_ft This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
ArneBinder/sam-pointer-bart-base-v0.3	ArneBinder	2024-05-29T11:31:49Z	82	0	transformers	[ "transformers", "pytorch", "SimpleGenerativeModel", "en", "dataset:pie/sciarg", "dataset:DFKI-SLT/sciarg", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]	null	2024-05-21T09:49:24Z	--- license: cc-by-sa-4.0 datasets: - pie/sciarg - DFKI-SLT/sciarg language: - en --- This is an argument structure prediction model for the scientific domain. It is a pointer network based on [A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism (Bao et al., EMNLP 2022)](https://aclanthology.org/2022.emnlp-main.713/). Given a plain input text, the model generates in one go tuples that represent argumentative relations, e.g. of type `supports` or `attacks`, between a pair of Argumentative Discourse Units (ADUs). Each ADU is defined by start- and end-offsets and a is also typed (`background_claim`, `own_claim`, or `data`). However, this is a full reimplementation of the model within the [PyTorch-IE](https://github.com/ArneBinder/pytorch-ie) framework. The model source code can be found in the [pie-modules](https://github.com/ArneBinder/pie-modules) repository. The model was trained with the [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1) on the [SciArg dataset](https://aclanthology.org/W18-5206/), see [here](https://huggingface.co/datasets/pie/sciarg) for further information and an integration into [pie-datasets](https://github.com/ArneBinder/pie-datasets). Further information regarding the training setup and model performance can be found in the [config.yaml](config.yaml), in the [wandb-metadata.json](wandb-metadata.json), and in [wandb-summary.json](wandb-summary.json). ([link to private W&B run](https://wandb.ai/arne/dataset-sciarg-task-ner_re-v0.3-training/runs/wr3bg4la)) You can try out the model in [this HF space](https://huggingface.co/spaces/ArneBinder/sam-pointer-bart-base-v0.3).
scenario-labs/juggernaut_reborn	scenario-labs	2024-05-29T11:31:38Z	28,227	0	diffusers	[ "diffusers", "safetensors", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-29T07:37:35Z	--- library_name: diffusers --- Prepared from [Juggernaut](https://civitai.com/models/46422/juggernaut)
casque/NoBra_CoveredNipples_FefaAIart	casque	2024-05-29T11:31:16Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-29T11:29:45Z	--- license: creativeml-openrail-m ---
etavolt/legal-lora-llama3-V4	etavolt	2024-05-29T11:29:42Z	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	2024-05-29T11:29:11Z	--- 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: etavolt - 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)
anyasims/ourpo3_uf4_BASE_OR_sft1.0_zs1.0-s2-3e64	anyasims	2024-05-29T11:29:30Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T11:23:18Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
kteedle/vec2dills	kteedle	2024-05-29T11:27:37Z	1	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "sdxl", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:apache-2.0", "region:us" ]	text-to-image	2024-05-24T18:02:37Z	--- license: apache-2.0 library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - lora - diffusers - sdxl base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: flat 2d vector illustration ---
DaichiT/tank	DaichiT	2024-05-29T11:26:29Z	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	2024-05-29T11:15:12Z	--- 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 tank --- <!-- 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/tank This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks tank 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]
AndrewDOrlov/bert_prof_single_128_below_100	AndrewDOrlov	2024-05-29T11:25:55Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T08:31:45Z	--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: bert_prof_single_128_below_100 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_prof_single_128_below_100 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6716 - Accuracy: 0.8674 - F1: 0.8658 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 0.7447 \| 1.0 \| 6885 \| 0.6995 \| 0.8267 \| 0.8151 \| \| 0.5834 \| 2.0 \| 13770 \| 0.6181 \| 0.8436 \| 0.8391 \| \| 0.4465 \| 3.0 \| 20655 \| 0.5877 \| 0.8585 \| 0.8551 \| \| 0.3559 \| 4.0 \| 27540 \| 0.6168 \| 0.8638 \| 0.8616 \| \| 0.2515 \| 5.0 \| 34425 \| 0.6300 \| 0.8710 \| 0.8692 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Niggendar/boleromixPony_v13	Niggendar	2024-05-29T11:24:12Z	83	1	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-29T11:16:21Z	--- library_name: diffusers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. 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]
anyasims/ourpo3_uf4_BASE_OR_sft1.0_zs2.0-s2-4331	anyasims	2024-05-29T11:22:34Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T11:16:02Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
khaled123/testy	khaled123	2024-05-29T11:22:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T11:00:25Z	--- 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]
h2oai/h2o-danube2-1.8b-chat-GGUF	h2oai	2024-05-29T11:22:14Z	967	8	transformers	[ "transformers", "gguf", "gpt", "llm", "large language model", "h2o-llmstudio", "text-generation", "en", "arxiv:2306.05685", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-29T10:43:16Z	--- language: - en library_name: transformers license: apache-2.0 tags: - gpt - llm - large language model - h2o-llmstudio thumbnail: >- https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico pipeline_tag: text-generation quantized_by: h2oai --- # h2o-danube2-1.8b-chat-GGUF - Model creator: [H2O.ai](https://huggingface.co/h2oai) - Original model: [h2o-danube2-1.8b-chat](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat) ## Description This repo contains GGUF format model files for [h2o-danube2-1.8b-chat](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat) quantized using [llama.cpp](https://github.com/ggerganov/llama.cpp/) framework. Table below summarizes different quantized versions of [h2o-danube2-1.8b-chat](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat). It shows the trade-off between size, speed and quality of the models. \| Name \| Quant method \| Model size \| MT-Bench AVG \| Perplexity \| Tokens per second \| \|:----------------------------------\|:----------------------------------:\|:----------:\|:------------:\|:------------:\|:-------------------:\| \| [h2o-danube2-1.8b-chat-F16.gguf](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat-GGUF/blob/main/h2o-danube2-1.8b-chat-F16.gguf) \| F16 \| 3.66 GB \| 5.60 \| 8.02 \| 797 \| \| [h2o-danube2-1.8b-chat-Q8_0.gguf](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat-GGUF/blob/main/h2o-danube2-1.8b-chat-Q8_0.gguf) \| Q8_0 \| 1.95 GB \| 5.51 \| 8.02 \| 1156 \| \| [h2o-danube2-1.8b-chat-Q6_K.gguf](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat-GGUF/blob/main/h2o-danube2-1.8b-chat-Q6_K.gguf) \| Q6_K \| 1.50 GB \| 5.51 \| 8.03 \| 1131 \| \| [h2o-danube2-1.8b-chat-Q5_K_M.gguf](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat-GGUF/blob/main/h2o-danube2-1.8b-chat-Q5_K_M.gguf) \| Q5_K_M \| 1.30 GB \| 5.56 \| 8.10 \| 1172 \| \| [h2o-danube2-1.8b-chat-Q5_K_S.gguf](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat-GGUF/blob/main/h2o-danube2-1.8b-chat-Q5_K_S.gguf) \| Q5_K_S \| 1.27 GB \| 5.49 \| 8.12 \| 1107 \| \| [h2o-danube2-1.8b-chat-Q4_K_M.gguf](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat-GGUF/blob/main/h2o-danube2-1.8b-chat-Q4_K_M.gguf) \| Q4_K_M \| 1.11 GB \| 5.60 \| 8.27 \| 1162 \| \| [h2o-danube2-1.8b-chat-Q4_K_S.gguf](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat-GGUF/blob/main/h2o-danube2-1.8b-chat-Q4_K_S.gguf) \| Q4_K_S \| 1.06 GB \| 5.59 \| 8.34 \| 1270 \| \| [h2o-danube2-1.8b-chat-Q3_K_L.gguf](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat-GGUF/blob/main/h2o-danube2-1.8b-chat-Q3_K_L.gguf) \| Q3_K_L \| 0.98 GB \| 5.23 \| 8.72 \| 1442 \| \| [h2o-danube2-1.8b-chat-Q3_K_M.gguf](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat-GGUF/blob/main/h2o-danube2-1.8b-chat-Q3_K_M.gguf) \| Q3_K_M \| 0.91 GB \| 4.91 \| 8.81 \| 1107 \| \| [h2o-danube2-1.8b-chat-Q3_K_S.gguf](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat-GGUF/blob/main/h2o-danube2-1.8b-chat-Q3_K_S.gguf) \| Q3_K_S \| 0.82 GB \| 4.03 \| 10.12 \| 1103 \| \| [h2o-danube2-1.8b-chat-Q2_K.gguf](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat-GGUF/blob/main/h2o-danube2-1.8b-chat-Q2_K.gguf) \| Q2_K \| 0.71 GB \| 3.03 \| 12.56 \| 1160 \| Columns in the table are: * Name -- model name and link * Quant method -- quantization method * Model size -- size of the model in gigabytes * MT-Bench AVG -- [MT-Bench](https://arxiv.org/abs/2306.05685) benchmark score. The score is from 1 to 10, the higher, the better * Perplexity -- perplexity metric on WikiText-2 dataset. It's reported in a perplexity test from llama.cpp. The lower, the better * Tokens per second -- generation speed in tokens per second, as reported in a perplexity test from llama.cpp. The higher, the better. Speed tests are done on a single H100 GPU ## Prompt template ``` <\|prompt\|>Why is drinking water so healthy?</s><\|answer\|> ```
fine-tuned/SCIDOCS-32000-384-gpt-4o-2024-05-13-5483216	fine-tuned	2024-05-29T11:20:31Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "custom_code", "en", "dataset:fine-tuned/SCIDOCS-32000-384-gpt-4o-2024-05-13-5483216", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-29T11:20:12Z	--- license: apache-2.0 datasets: - fine-tuned/SCIDOCS-32000-384-gpt-4o-2024-05-13-5483216 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case: None ## 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/SCIDOCS-32000-384-gpt-4o-2024-05-13-5483216', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
fine-tuned/SciFact-32000-384-gpt-4o-2024-05-13-45622553	fine-tuned	2024-05-29T11:17:52Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "custom_code", "en", "dataset:fine-tuned/SciFact-32000-384-gpt-4o-2024-05-13-45622553", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-29T11:17:37Z	--- license: apache-2.0 datasets: - fine-tuned/SciFact-32000-384-gpt-4o-2024-05-13-45622553 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case: None ## 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-32000-384-gpt-4o-2024-05-13-45622553', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
exlleysantos/verball-tests-3	exlleysantos	2024-05-29T11:17:06Z	77	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T14:06:21Z	--- tags: - generated_from_trainer model-index: - name: verball-tests-3 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. --> # verball-tests-3 This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.15.1
fine-tuned/FiQA2018-32000-384-gpt-4o-2024-05-13-52831585	fine-tuned	2024-05-29T11:16:41Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "custom_code", "en", "dataset:fine-tuned/FiQA2018-32000-384-gpt-4o-2024-05-13-52831585", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-29T11:16:28Z	--- license: apache-2.0 datasets: - fine-tuned/FiQA2018-32000-384-gpt-4o-2024-05-13-52831585 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case: None ## 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/FiQA2018-32000-384-gpt-4o-2024-05-13-52831585', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
DaichiT/stuffing	DaichiT	2024-05-29T11:14:24Z	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	2024-05-29T11:05:20Z	--- 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 stuffing --- <!-- 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/stuffing This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks stuffing 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]
yeshu044/opt-125m-finetuned	yeshu044	2024-05-29T11:14:19Z	196	0	transformers	[ "transformers", "safetensors", "opt", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T11:14:00Z	--- library_name: transformers tags: - trl - sft --- # 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]
ZcepZtar/DaToSw_V1.4	ZcepZtar	2024-05-29T11:14:10Z	114	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-27T15:02:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
IABDs8a/lara-base-PIA_MODELO_ASPANIAS_COMPLETO-Equipo2	IABDs8a	2024-05-29T11:14:04Z	78	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-29T11:12:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
ghostdivisio/bert-tiny-finetuned-squad	ghostdivisio	2024-05-29T11:13:02Z	134	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "base_model:prajjwal1/bert-tiny", "base_model:finetune:prajjwal1/bert-tiny", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2024-05-29T11:12:59Z	--- license: mit base_model: prajjwal1/bert-tiny tags: - generated_from_trainer model-index: - name: bert-tiny-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-tiny-finetuned-squad This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1478 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 90 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 29 \| 0.8724 \| \| No log \| 2.0 \| 58 \| 0.7989 \| \| No log \| 3.0 \| 87 \| 0.7316 \| \| No log \| 4.0 \| 116 \| 0.6691 \| \| No log \| 5.0 \| 145 \| 0.6121 \| \| No log \| 6.0 \| 174 \| 0.5597 \| \| No log \| 7.0 \| 203 \| 0.5121 \| \| No log \| 8.0 \| 232 \| 0.4690 \| \| No log \| 9.0 \| 261 \| 0.4300 \| \| No log \| 10.0 \| 290 \| 0.3950 \| \| No log \| 11.0 \| 319 \| 0.3637 \| \| No log \| 12.0 \| 348 \| 0.3358 \| \| No log \| 13.0 \| 377 \| 0.3110 \| \| No log \| 14.0 \| 406 \| 0.2891 \| \| No log \| 15.0 \| 435 \| 0.2697 \| \| No log \| 16.0 \| 464 \| 0.2527 \| \| No log \| 17.0 \| 493 \| 0.2379 \| \| 0.5621 \| 18.0 \| 522 \| 0.2247 \| \| 0.5621 \| 19.0 \| 551 \| 0.2134 \| \| 0.5621 \| 20.0 \| 580 \| 0.2035 \| \| 0.5621 \| 21.0 \| 609 \| 0.1955 \| \| 0.5621 \| 22.0 \| 638 \| 0.1886 \| \| 0.5621 \| 23.0 \| 667 \| 0.1829 \| \| 0.5621 \| 24.0 \| 696 \| 0.1776 \| \| 0.5621 \| 25.0 \| 725 \| 0.1731 \| \| 0.5621 \| 26.0 \| 754 \| 0.1694 \| \| 0.5621 \| 27.0 \| 783 \| 0.1662 \| \| 0.5621 \| 28.0 \| 812 \| 0.1635 \| \| 0.5621 \| 29.0 \| 841 \| 0.1614 \| \| 0.5621 \| 30.0 \| 870 \| 0.1597 \| \| 0.5621 \| 31.0 \| 899 \| 0.1582 \| \| 0.5621 \| 32.0 \| 928 \| 0.1570 \| \| 0.5621 \| 33.0 \| 957 \| 0.1561 \| \| 0.5621 \| 34.0 \| 986 \| 0.1551 \| \| 0.1726 \| 35.0 \| 1015 \| 0.1545 \| \| 0.1726 \| 36.0 \| 1044 \| 0.1537 \| \| 0.1726 \| 37.0 \| 1073 \| 0.1532 \| \| 0.1726 \| 38.0 \| 1102 \| 0.1528 \| \| 0.1726 \| 39.0 \| 1131 \| 0.1523 \| \| 0.1726 \| 40.0 \| 1160 \| 0.1519 \| \| 0.1726 \| 41.0 \| 1189 \| 0.1516 \| \| 0.1726 \| 42.0 \| 1218 \| 0.1512 \| \| 0.1726 \| 43.0 \| 1247 \| 0.1510 \| \| 0.1726 \| 44.0 \| 1276 \| 0.1507 \| \| 0.1726 \| 45.0 \| 1305 \| 0.1505 \| \| 0.1726 \| 46.0 \| 1334 \| 0.1503 \| \| 0.1726 \| 47.0 \| 1363 \| 0.1502 \| \| 0.1726 \| 48.0 \| 1392 \| 0.1500 \| \| 0.1726 \| 49.0 \| 1421 \| 0.1499 \| \| 0.1726 \| 50.0 \| 1450 \| 0.1497 \| \| 0.1726 \| 51.0 \| 1479 \| 0.1496 \| \| 0.1271 \| 52.0 \| 1508 \| 0.1496 \| \| 0.1271 \| 53.0 \| 1537 \| 0.1494 \| \| 0.1271 \| 54.0 \| 1566 \| 0.1493 \| \| 0.1271 \| 55.0 \| 1595 \| 0.1492 \| \| 0.1271 \| 56.0 \| 1624 \| 0.1491 \| \| 0.1271 \| 57.0 \| 1653 \| 0.1490 \| \| 0.1271 \| 58.0 \| 1682 \| 0.1490 \| \| 0.1271 \| 59.0 \| 1711 \| 0.1489 \| \| 0.1271 \| 60.0 \| 1740 \| 0.1489 \| \| 0.1271 \| 61.0 \| 1769 \| 0.1488 \| \| 0.1271 \| 62.0 \| 1798 \| 0.1487 \| \| 0.1271 \| 63.0 \| 1827 \| 0.1487 \| \| 0.1271 \| 64.0 \| 1856 \| 0.1486 \| \| 0.1271 \| 65.0 \| 1885 \| 0.1486 \| \| 0.1271 \| 66.0 \| 1914 \| 0.1485 \| \| 0.1271 \| 67.0 \| 1943 \| 0.1485 \| \| 0.1271 \| 68.0 \| 1972 \| 0.1484 \| \| 0.1216 \| 69.0 \| 2001 \| 0.1484 \| \| 0.1216 \| 70.0 \| 2030 \| 0.1483 \| \| 0.1216 \| 71.0 \| 2059 \| 0.1483 \| \| 0.1216 \| 72.0 \| 2088 \| 0.1482 \| \| 0.1216 \| 73.0 \| 2117 \| 0.1483 \| \| 0.1216 \| 74.0 \| 2146 \| 0.1482 \| \| 0.1216 \| 75.0 \| 2175 \| 0.1481 \| \| 0.1216 \| 76.0 \| 2204 \| 0.1481 \| \| 0.1216 \| 77.0 \| 2233 \| 0.1481 \| \| 0.1216 \| 78.0 \| 2262 \| 0.1480 \| \| 0.1216 \| 79.0 \| 2291 \| 0.1480 \| \| 0.1216 \| 80.0 \| 2320 \| 0.1479 \| \| 0.1216 \| 81.0 \| 2349 \| 0.1479 \| \| 0.1216 \| 82.0 \| 2378 \| 0.1479 \| \| 0.1216 \| 83.0 \| 2407 \| 0.1479 \| \| 0.1216 \| 84.0 \| 2436 \| 0.1479 \| \| 0.1216 \| 85.0 \| 2465 \| 0.1479 \| \| 0.1216 \| 86.0 \| 2494 \| 0.1478 \| \| 0.1151 \| 87.0 \| 2523 \| 0.1478 \| \| 0.1151 \| 88.0 \| 2552 \| 0.1478 \| \| 0.1151 \| 89.0 \| 2581 \| 0.1478 \| \| 0.1151 \| 90.0 \| 2610 \| 0.1478 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
BogdanTurbal/FacebookAI-roberta-base-d_1_e_4_t_u_r_5-d_2_e_4_t_u_r_5-v3	BogdanTurbal	2024-05-29T11:11:07Z	199	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T11:10:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ainnle/gemma-7b-zx	ainnle	2024-05-29T11:10:18Z	10	0	transformers	[ "transformers", "gguf", "gemma", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-29T10:38:56Z	--- license: apache-2.0 ---
BogdanTurbal/FacebookAI-roberta-base-d_2_e_4_t_u_r_5-v3	BogdanTurbal	2024-05-29T11:07:25Z	198	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T11:07:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Grace0710/koLlamaCredit	Grace0710	2024-05-29T11:06:52Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-15T17:31:08Z	--- library_name: transformers tags: - trl - sft --- # 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]
kaebaMS/distilbert-base-uncased-finetuned-emotion	kaebaMS	2024-05-29T11:06:50Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T11:01:32Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9235 - name: F1 type: f1 value: 0.9232738832918821 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2237 - Accuracy: 0.9235 - F1: 0.9233 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8086 \| 1.0 \| 250 \| 0.3258 \| 0.903 \| 0.9014 \| \| 0.2511 \| 2.0 \| 500 \| 0.2237 \| 0.9235 \| 0.9233 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
magic10241/quant-llama3-1.0-8B-Lora	magic10241	2024-05-29T11:05:28Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:zhichen/Llama3-Chinese", "base_model:finetune:zhichen/Llama3-Chinese", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-29T11:03:03Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: zhichen/Llama3-Chinese --- # Uploaded model - Developed by: magic10241 - License: apache-2.0 - Finetuned from model : zhichen/Llama3-Chinese 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/stainless_steel	DaichiT	2024-05-29T11:04:36Z	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	2024-05-29T10:52:46Z	--- 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 stainless_steel --- <!-- 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/stainless_steel This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks stainless_steel 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]
BogdanTurbal/FacebookAI-roberta-base-d_2_e_4_t_u_r_5-d_1_e_4_t_u_r_5-v3	BogdanTurbal	2024-05-29T11:03:39Z	181	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T11:03:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BogdanTurbal/FacebookAI-roberta-base-d_3_e_4_t_u_r_5-d_0_e_4_t_u_r_5-v3	BogdanTurbal	2024-05-29T11:01:14Z	199	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T11:01:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
houbw/llama3_ruozhiba_ori_8_up_5_cms9_label_2	houbw	2024-05-29T10:59:59Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct", "base_model:finetune:unsloth/llama-3-8b-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-29T10:58:56Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct --- # Uploaded model - Developed by: houbw - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct 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)
BogdanTurbal/FacebookAI-roberta-base-d_3_e_4_t_u_r_5-d_1_e_4_t_u_r_5-v3	BogdanTurbal	2024-05-29T10:58:47Z	199	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T10:58:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Raneechu/textbookbig12	Raneechu	2024-05-29T10:58:17Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-29T10:58:08Z	--- license: llama2 library_name: peft tags: - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: textbookbig12 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. --> # textbookbig12 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.8838 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.1922 \| 0.0117 \| 1 \| 3.8838 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
BogdanTurbal/FacebookAI-roberta-base-d_1_e_4_t_u_r_5-v3	BogdanTurbal	2024-05-29T10:56:17Z	201	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T10:56:04Z	--- 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]
Niggendar/fastPonyMerge_version9	Niggendar	2024-05-29T10:55:23Z	123	1	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-29T10:50:30Z	--- library_name: diffusers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. 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]
BogdanTurbal/FacebookAI-roberta-base-d_3_e_4_t_u_r_5-v3	BogdanTurbal	2024-05-29T10:53:47Z	199	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T10:53:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
souling/distilbert-base-uncased-finetuned-emotion	souling	2024-05-29T10:51:51Z	114	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T10:28:55Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9265 - name: F1 type: f1 value: 0.9264798353410255 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2150 - Accuracy: 0.9265 - F1: 0.9265 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8395 \| 1.0 \| 250 \| 0.3140 \| 0.9065 \| 0.9058 \| \| 0.248 \| 2.0 \| 500 \| 0.2150 \| 0.9265 \| 0.9265 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
hugggof/vampnet-base	hugggof	2024-05-29T10:51:50Z	0	0	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	2024-05-29T10:51:50Z	--- license: cc-by-nc-4.0 ---
fine-tuned/before-finetuning-32000-384-gpt-4o-2024-05-13-73143156	fine-tuned	2024-05-29T10:49:06Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/before-finetuning-32000-384-gpt-4o-2024-05-13-73143156", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-29T10:48:35Z	--- license: apache-2.0 datasets: - fine-tuned/before-finetuning-32000-384-gpt-4o-2024-05-13-73143156 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- 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: None ## 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/before-finetuning-32000-384-gpt-4o-2024-05-13-73143156', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
eduardzamfir/SeemoRe-T	eduardzamfir	2024-05-29T10:45:40Z	0	5	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-29T10:44:27Z	--- license: apache-2.0 ---
raphael-ich/ppo-LunarLander-v2	raphael-ich	2024-05-29T10:40:39Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-28T15:13:38Z	--- 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: 208.55 +/- 36.51 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 ... ```
DaichiT/shock_absorber	DaichiT	2024-05-29T10:39:23Z	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	2024-05-29T07:06:35Z	--- 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 shock_absorber --- <!-- 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/shock_absorber This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks shock_absorber 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]
Shengkun/LLama2-7B-Structural-Prune-1.25x	Shengkun	2024-05-29T10:34:49Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T20:21:44Z	--- license: apache-2.0 --- --- library_name: transformers license: apache-2.0 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### 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]
dev-analyzer/file_path_model	dev-analyzer	2024-05-29T10:34:08Z	128	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-13T09:10:44Z	--- pipeline_tag: text-classification base_model: bert-base-uncased widget: - text: "src\\main\\java\\org\\rtmps\\RTMPTSClientConnector.java" - text: "src\\test\\java\\org\\server\\net\\rtmp\\message\\HeaderTest.java" - text: "src\\main\\res\\drawable-mdpi\\icon.png" - text: "common\\pom.xml" - text: "server\\src\\main\\server\\plugins\\Readme.md" --- Model for classifying file paths of changed files in git commits for Java projects. This model is based on `bert-base-uncased` and fine-tuned. Categorizes into the following categories: 1. Source Code - Core application code typically involving back-end (server-side logic, APIs, database interactions) and front-end (user interface, client-side logic). 2. Tests - Code files in a test directory or containing "test". 3. Resources - Assets and other resources (images, stylesheets). 4. Configuration - Configuration files and scripts (build scripts, manifests, shell scripts). 5. Documentation - Software documentation (README files, package-info, license, notice files).
subhavarshith/donut_exp2	subhavarshith	2024-05-29T10:30:11Z	48	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-29T06:58:02Z	--- 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]
BogdanTurbal/FacebookAI-roberta-base-d_1_e_4_t_u_r_5-d_3_e_4_t_u_r_5-v3	BogdanTurbal	2024-05-29T10:28:32Z	199	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T10:28:13Z	--- 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]
RichardErkhov/internlm_-_internlm2-math-7b-gguf	RichardErkhov	2024-05-29T10:28:25Z	38	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-29T07:52:51Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) internlm2-math-7b - GGUF - Model creator: https://huggingface.co/internlm/ - Original model: https://huggingface.co/internlm/internlm2-math-7b/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [internlm2-math-7b.Q2_K.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.Q2_K.gguf) \| Q2_K \| 2.8GB \| \| [internlm2-math-7b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.IQ3_XS.gguf) \| IQ3_XS \| 3.1GB \| \| [internlm2-math-7b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.IQ3_S.gguf) \| IQ3_S \| 3.25GB \| \| [internlm2-math-7b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.24GB \| \| [internlm2-math-7b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.IQ3_M.gguf) \| IQ3_M \| 3.35GB \| \| [internlm2-math-7b.Q3_K.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.Q3_K.gguf) \| Q3_K \| 3.57GB \| \| [internlm2-math-7b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.57GB \| \| [internlm2-math-7b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.Q3_K_L.gguf) \| Q3_K_L \| 3.85GB \| \| [internlm2-math-7b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.IQ4_XS.gguf) \| IQ4_XS \| 3.99GB \| \| [internlm2-math-7b.Q4_0.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.Q4_0.gguf) \| Q4_0 \| 4.15GB \| \| [internlm2-math-7b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.IQ4_NL.gguf) \| IQ4_NL \| 4.19GB \| \| [internlm2-math-7b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.18GB \| \| [internlm2-math-7b.Q4_K.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.Q4_K.gguf) \| Q4_K \| 4.39GB \| \| [internlm2-math-7b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.39GB \| \| [internlm2-math-7b.Q4_1.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.Q4_1.gguf) \| Q4_1 \| 4.58GB \| \| [internlm2-math-7b.Q5_0.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.Q5_0.gguf) \| Q5_0 \| 5.0GB \| \| [internlm2-math-7b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.Q5_K_S.gguf) \| Q5_K_S \| 5.0GB \| \| [internlm2-math-7b.Q5_K.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.Q5_K.gguf) \| Q5_K \| 5.13GB \| \| [internlm2-math-7b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.Q5_K_M.gguf) \| Q5_K_M \| 5.13GB \| \| [internlm2-math-7b.Q5_1.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.Q5_1.gguf) \| Q5_1 \| 5.43GB \| \| [internlm2-math-7b.Q6_K.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.Q6_K.gguf) \| Q6_K \| 5.91GB \| \| [internlm2-math-7b.Q8_0.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-7b-gguf/blob/main/internlm2-math-7b.Q8_0.gguf) \| Q8_0 \| 7.66GB \| Original model description: --- pipeline_tag: text-generation license: other language: - en - zh tags: - math --- # InternLM-Math <div align="center"> <img src="https://raw.githubusercontent.com/InternLM/InternLM/main/assets/logo.svg" width="200"/> <div> </div> <div align="center"> <b><font size="5">InternLM-Math</font></b> <sup> <a href="https://internlm.intern-ai.org.cn/"> <i><font size="4">HOT</font></i> </a> </sup> <div> </div> </div> State-of-the-art bilingual open-sourced Math reasoning LLMs. A solver, prover, verifier, augmentor. [💻 Github](https://github.com/InternLM/InternLM-Math) [🤗 Demo](https://huggingface.co/spaces/internlm/internlm2-math-7b) [🤗 Checkpoints](https://huggingface.co/internlm/internlm2-math-7b) [![OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM2-Math-7B) [<img src="https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/modelscope_logo.png" width="20px" /> ModelScope](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-math-7b/summary) </div> # News - [2024.01.29] We add checkpoints from ModelScope. Tech report is on the way! - [2024.01.26] We add checkpoints from OpenXLab, which ease Chinese users to download! # Introduction - 7B and 20B Chinese and English Math LMs with better than ChatGPT performances. InternLM2-Math are continued pretrained from InternLM2-Base with ~100B high quality math-related tokens and SFT with ~2M bilingual math supervised data. We apply minhash and exact number match to decontaminate possible test set leakage. - Add Lean as a support language for math problem solving and math theorem proving. We are exploring combining Lean 3 with InternLM-Math for verifiable math reasoning. InternLM-Math can generate Lean codes for simple math reasoning tasks like GSM8K or provide possible proof tactics based on Lean states. - Also can be viewed as a reward model, which supports the Outcome/Process/Lean Reward Model. We supervise InternLM2-Math with various types of reward modeling data, to make InternLM2-Math can also verify chain-of-thought processes. We also add the ability to convert a chain-of-thought process into Lean 3 code. - A Math LM Augment Helper and Code Interpreter. InternLM2-Math can help augment math reasoning problems and solve them using the code interpreter which makes you generate synthesis data quicker! ![hungarian](https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/hungary.jpeg) # Models InternLM2-Math-Base-7B and InternLM2-Math-Base-20B are pretrained checkpoints. InternLM2-Math-7B and InternLM2-Math-20B are SFT checkpoints. \| Model \|Model Type \| Transformers(HF) \|OpenXLab\| ModelScope \| Release Date \| \|---\|---\|---\|---\|---\|---\| \| InternLM2-Math-Base-7B \| Base\| [🤗internlm/internlm2-math-base-7b](https://huggingface.co/internlm/internlm2-math-base-7b) \|[![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM2-Math-Base-7B)\| [<img src="https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/modelscope_logo.png" width="20px" /> internlm2-math-base-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-math-base-7b/summary)\| 2024-01-23\| \| InternLM2-Math-Base-20B \| Base\| [🤗internlm/internlm2-math-base-20b](https://huggingface.co/internlm/internlm2-math-base-20b) \|[![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM2-Math-Base-20B)\|[<img src="https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/modelscope_logo.png" width="20px" /> internlm2-math-base-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-math-base-20b/summary)\| 2024-01-23\| \| InternLM2-Math-7B \| Chat\| [🤗internlm/internlm2-math-7b](https://huggingface.co/internlm/internlm2-math-7b) \|[![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM2-Math-7B)\|[<img src="https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/modelscope_logo.png" width="20px" /> internlm2-math-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-math-7b/summary)\| 2024-01-23\| \| InternLM2-Math-20B \| Chat\| [🤗internlm/internlm2-math-20b](https://huggingface.co/internlm/internlm2-math-20b) \|[![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM2-Math-20B)\|[<img src="https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/modelscope_logo.png" width="20px" /> internlm2-math-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-math-20b/summary)\| 2024-01-23\| # Performance ## Pretrain Performance We evaluate pretrain checkpoints based on greedy decoding with few-shot COT. Details of pretraining will be introduced in the tech report. \| Model \| GSM8K \| MATH \| \|------------------------\|---------\|--------\| \| Llama2-7B \| 11.8 \| 3.2 \| \| Llemma-7B \| 36.4 \| 18.0 \| \| InternLM2-Base-7B \| 36.5 \| 8.6 \| \| InternLM2-Math-Base-7B \| 49.2 \| 21.5 \| \| Minerva-8B \| 16.2 \| 14.1 \| \| InternLM2-Base-20B \| 54.6 \| 13.7 \| \| InternLM2-Math-Base-20B \| 63.7 \| 27.3 \| \| Llemma-34B \| 51.5 \| 25.0 \| \| Minerva-62B \| 52.4 \| 27.6 \| \| Minerva-540B \| 58.8 \| 33.6 \| ## SFT Peformance All performance is based on greedy decoding with COT. We notice that the performance of Hungary has a big variance between our different checkpoints, while other performance is very stable. This may be due to the problem amount about Hungary. \| Model \| Model Type \| GSM8K \| MATH \| Hungary \| \|------------------------\|----------------------\|--------\|--------\|---------\| \| Qwen-7B-Chat \| Genearl \| 51.7 \| 11.6 \| - \| \| DeepSeek-7B-Chat \| General \| 63.0 \| 15.8 \| 28.5 \| \| InternLM2-Chat-7B \| General \| 70.7 \| 23.0 \| - \| \| ChatGLM3-6B \| General \| 53.8 \| 20.4 \| 32 \| \| MetaMath-Mistral-7B \| Mathematics \| 77.7 \| 28.2 \| 29 \| \| MetaMath-Llemma-7B \| Mathematics \| 69.2 \| 30.0 \| - \| \| InternLM2-Math-7B \| Mathematics \| 78.1 \| 34.6 \| 55 \| \| InternLM2-Chat-20B \| General \| 79.6 \| 31.9 \| - \| \| MetaMath-Llemma-34B \| Mathematics \| 75.8 \| 34.8 \| - \| \| InternLM2-Math-20B \| Mathematics \| 82.6 \| 37.7 \| 66 \| \| Qwen-72B \| General \| 78.9 \| 35.2 \| 52 \| \| DeepSeek-67B \| General \| 84.1 \| 32.6 \| 58 \| \| ChatGPT (GPT-3.5) \| General \| 80.8 \| 34.1 \| 41 \| \| GPT4 (First version) \| General \| 92.0 \| 42.5 \| 68 \| # Inference ## LMDeploy We suggest using [LMDeploy](https://github.com/InternLM/LMDeploy)(>=0.2.1) for inference. ```python from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig backend_config = TurbomindEngineConfig(model_name='internlm2-chat-7b', tp=1, cache_max_entry_count=0.3) chat_template = ChatTemplateConfig(model_name='internlm2-chat-7b', system='', eosys='', meta_instruction='') pipe = pipeline(model_path='internlm/internlm2-math-7b', chat_template_config=chat_template, backend_config=backend_config) problem = '1+1=' result = pipe([problem], request_output_len=1024, top_k=1) ``` ## Huggingface ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-math-7b", trust_remote_code=True) # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error. model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-math-7b", trust_remote_code=True, torch_dtype=torch.float16).cuda() model = model.eval() response, history = model.chat(tokenizer, "1+1=", history=[], meta_instruction="") print(response) ``` # Special usages We list some instructions used in our SFT. You can use them to help you. You can use the other ways to prompt the model, but the following are recommended. InternLM2-Math may combine the following abilities but it is not guaranteed. Translate proof problem to Lean: ![nl2lean3](https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/nl2lean.jpeg) Using Lean 3 to solve GSM8K problem: ![gsm8k_lean](https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/gsm8k_lean.jpeg) Generate problem based on Lean 3 code: ![lean_problem](https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/lean_problem.jpeg) Play 24 point game: ![24](https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/24.jpeg) Augment a harder math problem: ![augment_hard](https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/augment_hard.jpeg) \| Description \| Query \| \| --- \| --- \| \| Solving question via chain-of-thought \| {Question} \| \| Solving question via Lean 3 \| {Question}\nSolve this via Lean 3 \| \| Outcome reward model \| Given a question and an answer, check is it correct?\nQuestion:{Question}\nAnswer:{COT} \| \| Process reward model \| Given a question and an answer, check correctness of each step.\nQuestion:{Question}\nAnswer:{COT} \| \| Reward model \| Given a question and two answers, which one is better? \nQuestion:{Question}\nAnswer 1:{COT}\nAnswer 2:{COT} \| \| Convert chain-of-thought to Lean 3 \| Convert this answer into Lean3. Question:{Question}\nAnswer:{COT} \| \| Convert Lean 3 to chain-of-thought \| Convert this lean 3 code into a natural language problem with answers:\n{LEAN Code} \| \| Translate question and chain-of-thought answer to a proof statement \| Convert this question and answer into a proof format.\nQuestion:{Question}\nAnswer:{COT} \| \| Translate proof problem to Lean 3 \| Convert this natural langauge statement into a Lean 3 theorem statement:{Theorem} \| \| Translate Lean 3 to proof problem \| Convert this Lean 3 theorem statement into natural language:{STATEMENT} \| \| Suggest a tactic based on Lean state \| Given the Lean 3 tactic state, suggest a next tactic:\n{LEAN State} \| \| Rephrase Problem \| Describe this problem in another way. {Question} \| \| Augment Problem \| Please augment a new problem based on: {Question} \| \| Augment a harder Problem \| Increase the complexity of the problem: {Question} \| \| Change specific numbers \| Change specific numbers: {Question}\| \| Introduce fractions or percentages \| Introduce fractions or percentages: {Question}\| \| Code Interpreter \| [lagent](https://github.com/InternLM/InternLM/blob/main/agent/lagent.md) \| \| In-context Learning \| Question:{Question}\nAnswer:{COT}\n...Question:{Question}\nAnswer:{COT}\| # Fine-tune and others Please refer to [InternLM](https://github.com/InternLM/InternLM/tree/main). # Known issues Our model is still under development and will be upgraded. There are some possible issues of InternLM-Math. If you find performances of some abilities are not great, welcome to open an issue. - Jump the calculating step. - Perform badly at Chinese fill-in-the-bank problems and English choice problems due to SFT data composition. - Tend to generate Code Interpreter when facing Chinese problems due to SFT data composition. - The reward model mode can be better leveraged with assigned token probabilities. - Code switch due to SFT data composition. - Some abilities of Lean can only be adapted to GSM8K-like problems (e.g. Convert chain-of-thought to Lean 3), and performance related to Lean is not guaranteed. # Citation and Tech Report To be appended.

Subsets and Splits