librarian-bots/model_cards_with_metadata_test

modelId stringlengths 5 122	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]	likes int64 0 11k	library_name stringclasses 245 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 48 values	createdAt timestamp[us, tz=UTC]	card stringlengths 1 901k
sakasaku/dqn-SpaceInvadersNoFrameskip-v4	sakasaku	2024-07-02T07:40:37Z	0	null	[ "region:us" ]	null	2024-07-02T07:40:37Z	Entry not found
SamagraDataGov/e2e_testtt	SamagraDataGov	2024-07-02T07:41:45Z	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-02T07:41:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
davidyu2023/Qwen-Qwen1.5-0.5B-1719906099	davidyu2023	2024-07-02T07:41:49Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen1.5-0.5B", "region:us" ]	null	2024-07-02T07:41:40Z	--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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
Echelon-AI/Med-Qwen2-7B	Echelon-AI	2024-07-02T14:12:16Z	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "dataset:Malikeh1375/medical-question-answering-datasets", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T07:41:49Z	--- license: apache-2.0 datasets: - Malikeh1375/medical-question-answering-datasets --- Qwen2 7B, after finetuning on a medical dataset, demonstrates enhanced performance in medical text understanding and generation ### Model Description The model shows improved accuracy in diagnosing medical conditions, generating specialized medical texts, and responding to medical queries with contextually relevant information. This adaptation equips Med-Qwen2 to support advanced applications in healthcare, offering nuanced insights and precise language processing tailored for medical professionals and patients alike - Finetuned from model : [https://huggingface.co/Qwen/Qwen2-7B-Instruct](Qwen2-7B-Instruct) [GGUF](https://huggingface.co/Echelon-AI/Med-Qwen2-GGUF) ## Uses - Diagnosing medical conditions with improved accuracy. - Generating specialized medical texts and reports. - Providing contextually relevant responses to medical queries. - Supporting advanced applications in healthcare with precise language processing.
gnkbhuvan/phi-2-health	gnkbhuvan	2024-07-02T08:09:00Z	0	adapter-transformers	[ "adapter-transformers", "safetensors", "phi-msft", "medical", "text-generation", "custom_code", "en", "dataset:wangrongsheng/HealthCareMagic-100k-en", "license:apache-2.0", "region:us" ]	text-generation	2024-07-02T07:41:58Z	--- license: apache-2.0 datasets: - wangrongsheng/HealthCareMagic-100k-en language: - en metrics: - accuracy library_name: adapter-transformers pipeline_tag: text-generation tags: - medical ---
kapilrk04/indicbart_multiway_mt_model	kapilrk04	2024-07-02T22:31:24Z	0	transformers	[ "transformers", "safetensors", "mbart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-07-02T07:43:19Z	Entry not found
kazars24/wav2vec2-base-rus-golos-100h-farfield	kazars24	2024-07-02T15:22:40Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-base-960h", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-07-02T07:44:19Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base-960h tags: - generated_from_trainer model-index: - name: wav2vec2-base-rus-golos-100h-farfield 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. --> # wav2vec2-base-rus-golos-100h-farfield This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1
mreza629/coca	mreza629	2024-07-02T07:47:50Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-07-02T07:45:13Z	--- license: apache-2.0 ---
Uphando-ng/naija_eng_female_accent_tts	Uphando-ng	2024-07-02T07:53:29Z	0	transformers	[ "transformers", "safetensors", "vits", "text-to-speech", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-to-speech	2024-07-02T07:45:15Z	--- license: apache-2.0 pipeline_tag: text-to-speech ---
Uphando-ng/naija_eng_male_accent_tts	Uphando-ng	2024-07-02T07:53:06Z	0	transformers	[ "transformers", "safetensors", "vits", "text-to-speech", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-to-speech	2024-07-02T07:45:36Z	--- license: apache-2.0 pipeline_tag: text-to-speech ---
habulaj/1832321302	habulaj	2024-07-02T07:45:43Z	0	null	[ "region:us" ]	null	2024-07-02T07:45:38Z	Entry not found
CatBarks/flant5small-lora-oasst1_model	CatBarks	2024-07-02T07:55:03Z	0	transformers	[ "transformers", "safetensors", "question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	question-answering	2024-07-02T07:45:50Z	--- library_name: transformers pipeline_tag: question-answering --- # 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]
CatBarks/flant5small-lora-oasst1_tokenizer	CatBarks	2024-07-02T07:45:56Z	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-02T07:45:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
Dabitron/Qwen2-7B-Instruct-abliterated-Q4_K_M-GGUF	Dabitron	2024-07-02T07:46:18Z	0	null	[ "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:natong19/Qwen2-7B-Instruct-abliterated", "license:apache-2.0", "region:us" ]	text-generation	2024-07-02T07:45:58Z	--- base_model: natong19/Qwen2-7B-Instruct-abliterated language: - en license: apache-2.0 pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # Dabitron/Qwen2-7B-Instruct-abliterated-Q4_K_M-GGUF This model was converted to GGUF format from [`natong19/Qwen2-7B-Instruct-abliterated`](https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q4_K_M-GGUF --hf-file qwen2-7b-instruct-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q4_K_M-GGUF --hf-file qwen2-7b-instruct-abliterated-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q4_K_M-GGUF --hf-file qwen2-7b-instruct-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q4_K_M-GGUF --hf-file qwen2-7b-instruct-abliterated-q4_k_m.gguf -c 2048 ```
ThomasSimonini/dqn-SpaceInvadersNoFrameskip-v4BB	ThomasSimonini	2024-07-02T07:46:45Z	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-07-02T07:46:16Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 257.00 +/- 38.81 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ThomasSimonini -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ThomasSimonini -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ThomasSimonini ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
nbadrinath/ikea_room_designs_sd1.5_lora_full_finetuning_020720240714	nbadrinath	2024-07-02T10:09:43Z	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-07-02T07:47:21Z	--- base_model: runwayml/stable-diffusion-v1-5 library_name: diffusers license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora inference: true --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA text2image fine-tuning - nbadrinath/ikea_room_designs_sd1.5_lora_full_finetuning_020720240714 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the nbadrinath/ikea_dataset_5.0 dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ## 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]
Echelon-AI/Med-Qwen2-GGUF	Echelon-AI	2024-07-02T08:08:41Z	0	null	[ "gguf", "license:apache-2.0", "region:us" ]	null	2024-07-02T07:48:32Z	--- license: apache-2.0 --- Qwen2 7B, after finetuning on a medical dataset, demonstrates enhanced performance in medical text understanding and generation ### Model Description The model shows improved accuracy in diagnosing medical conditions, generating specialized medical texts, and responding to medical queries with contextually relevant information. This adaptation equips Med-Qwen2 to support advanced applications in healthcare, offering nuanced insights and precise language processing tailored for medical professionals and patients alike - Finetuned from model : [https://huggingface.co/Qwen/Qwen2-7B-Instruct](Qwen2-7B-Instruct) ## Uses - Diagnosing medical conditions with improved accuracy. - Generating specialized medical texts and reports. - Providing contextually relevant responses to medical queries. - Supporting advanced applications in healthcare with precise language processing. ## Sample Outputs: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/658efeb9f7291078f9a0ef6e/cqMlPdOpLE1pQjJEbEB_G.png)
Poxios/STOCK_SOLAR-10.7B-Q4_K_M-GGUF	Poxios	2024-07-02T07:49:07Z	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:kihoonlee/STOCK_SOLAR-10.7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T07:48:40Z	--- base_model: kihoonlee/STOCK_SOLAR-10.7B library_name: transformers license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # Poxios/STOCK_SOLAR-10.7B-Q4_K_M-GGUF This model was converted to GGUF format from [`kihoonlee/STOCK_SOLAR-10.7B`](https://huggingface.co/kihoonlee/STOCK_SOLAR-10.7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/kihoonlee/STOCK_SOLAR-10.7B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Poxios/STOCK_SOLAR-10.7B-Q4_K_M-GGUF --hf-file stock_solar-10.7b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Poxios/STOCK_SOLAR-10.7B-Q4_K_M-GGUF --hf-file stock_solar-10.7b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Poxios/STOCK_SOLAR-10.7B-Q4_K_M-GGUF --hf-file stock_solar-10.7b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Poxios/STOCK_SOLAR-10.7B-Q4_K_M-GGUF --hf-file stock_solar-10.7b-q4_k_m.gguf -c 2048 ```
TakuyaGemma/maki	TakuyaGemma	2024-07-02T07:49:31Z	0	null	[ "region:us" ]	null	2024-07-02T07:48:59Z	Entry not found
z3n7r4ck3r/filtered_dataset_20240702_095025	z3n7r4ck3r	2024-07-02T07:50:24Z	0	null	[ "region:us" ]	null	2024-07-02T07:50:24Z	Entry not found
YoSHiK/whisper-small-ja	YoSHiK	2024-07-02T09:47:46Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-07-02T07:50:35Z	Entry not found
flammenai/Mahou-1.3-spark-7B	flammenai	2024-07-02T12:00:59Z	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "dataset:flammenai/FlameMix-DPO-v1", "dataset:flammenai/MahouMix-v1", "dataset:flammenai/Grill-Flammen-v1_chatML", "base_model:arcee-ai/Arcee-Spark", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T07:50:47Z	--- library_name: transformers license: apache-2.0 base_model: - arcee-ai/Arcee-Spark datasets: - flammenai/FlameMix-DPO-v1 - flammenai/MahouMix-v1 - flammenai/Grill-Flammen-v1_chatML --- ![image/png](https://huggingface.co/flammenai/Mahou-1.0-mistral-7B/resolve/main/mahou1.png) # Mahou-1.3-spark-7B Mahou is designed to provide short messages in a conversational context. It is capable of casual conversation and character roleplay. ### Chat Format This model has been trained to use ChatML format. Note the additional tokens in [tokenizer_config.json](tokenizer_config.json). ``` <\|im_start\|>system {{system}}<\|im_end\|> <\|im_start\|>{{char}} {{message}}<\|im_end\|> <\|im_start\|>{{user}} {{message}}<\|im_end\|> ``` ### Roleplay Format - Speech without quotes. - Actions in `asterisks` ``` leans against wall cooly so like, i just casted a super strong spell at magician academy today, not gonna lie, felt badass. ``` ### SillyTavern Settings 1. Use ChatML for the Context Template. 2. Enable Instruct Mode. 3. Use the [Mahou preset](https://huggingface.co/datasets/flammenai/Mahou-ST-ChatML-Instruct/raw/main/Mahou.json). 4. Recommended Additonal stopping strings: `["\n", "<\|", "</"]` ### Method Finetuned for 3 epochs using an A100 on Google Colab. [Fine-tune Llama 3 with ORPO](https://huggingface.co/blog/mlabonne/orpo-llama-3) - [Maxime Labonne](https://huggingface.co/mlabonne)
Dabitron/Qwen2-7B-Instruct-abliterated-Q6_K-GGUF	Dabitron	2024-07-02T07:51:27Z	0	null	[ "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:natong19/Qwen2-7B-Instruct-abliterated", "license:apache-2.0", "region:us" ]	text-generation	2024-07-02T07:51:01Z	--- base_model: natong19/Qwen2-7B-Instruct-abliterated language: - en license: apache-2.0 pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # Dabitron/Qwen2-7B-Instruct-abliterated-Q6_K-GGUF This model was converted to GGUF format from [`natong19/Qwen2-7B-Instruct-abliterated`](https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q6_K-GGUF --hf-file qwen2-7b-instruct-abliterated-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q6_K-GGUF --hf-file qwen2-7b-instruct-abliterated-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q6_K-GGUF --hf-file qwen2-7b-instruct-abliterated-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q6_K-GGUF --hf-file qwen2-7b-instruct-abliterated-q6_k.gguf -c 2048 ```
vgarg/fw_identification_model_e5_large_v7_02_07_2024	vgarg	2024-07-02T07:53:22Z	0	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2024-07-02T07:51:17Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # vgarg/fw_identification_model_e5_large_v7_02_07_2024 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("vgarg/fw_identification_model_e5_large_v7_02_07_2024") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
NeelShrimali/dir	NeelShrimali	2024-07-02T07:57:54Z	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T07:51:50Z	--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - Developed by: NeelShrimali - 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)
cyan2k/promptvieh_lora	cyan2k	2024-07-02T07:54:20Z	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T07:54:19Z	--- base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - Developed by: cyan2k - License: apache-2.0 - Finetuned from model : unsloth/phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
z3n7r4ck3r/filtered_dataset_20240702_095438	z3n7r4ck3r	2024-07-02T07:54:37Z	0	null	[ "region:us" ]	null	2024-07-02T07:54:37Z	Entry not found
habulaj/8883165414	habulaj	2024-07-02T07:56:02Z	0	null	[ "region:us" ]	null	2024-07-02T07:56:00Z	Entry not found
Roooy/whisper-tiny-ko3	Roooy	2024-07-02T08:22:45Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ko", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-07-02T07:56:06Z	--- base_model: openai/whisper-tiny datasets: - mozilla-foundation/common_voice_11_0 language: - ko license: apache-2.0 metrics: - wer tags: - generated_from_trainer model-index: - name: Whisper Tiny Ko - Roooy results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Common Voice 17.0 type: mozilla-foundation/common_voice_11_0 config: ko split: None args: 'config: ko, split: test' metrics: - type: wer value: 55.80595874713522 name: Wer --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Tiny Ko - Roooy This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.8060 - Cer: 25.1580 - Wer: 55.8060 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\|:-------:\| \| 0.5449 \| 2.2222 \| 100 \| 0.7823 \| 25.3686 \| 55.8442 \| \| 0.2663 \| 4.4444 \| 200 \| 0.7768 \| 25.5893 \| 56.3407 \| \| 0.1466 \| 6.6667 \| 300 \| 0.7857 \| 25.1580 \| 55.6532 \| \| 0.0953 \| 8.8889 \| 400 \| 0.8016 \| 25.3686 \| 55.6150 \| \| 0.076 \| 11.1111 \| 500 \| 0.8060 \| 25.1580 \| 55.8060 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
noake/segformer-b0-finetuned-segments-sidewalk-2	noake	2024-07-02T07:56:30Z	0	null	[ "region:us" ]	null	2024-07-02T07:56:30Z	Entry not found
gassirbek/wav2vec2-large-mms-1b-kaz-colab	gassirbek	2024-07-02T07:57:18Z	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-02T07:57:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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tinnguyen/falcon-openassistant-toxicity-increase-30-epochs	tinnguyen	2024-07-02T08:01:29Z	0	transformers	[ "transformers", "safetensors", "falcon", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T07:57:28Z	--- 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. 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Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_M-GGUF	Dabitron	2024-07-02T07:58:14Z	0	null	[ "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:natong19/Qwen2-7B-Instruct-abliterated", "license:apache-2.0", "region:us" ]	text-generation	2024-07-02T07:57:51Z	--- base_model: natong19/Qwen2-7B-Instruct-abliterated language: - en license: apache-2.0 pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_M-GGUF This model was converted to GGUF format from [`natong19/Qwen2-7B-Instruct-abliterated`](https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_M-GGUF --hf-file qwen2-7b-instruct-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_M-GGUF --hf-file qwen2-7b-instruct-abliterated-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_M-GGUF --hf-file qwen2-7b-instruct-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_M-GGUF --hf-file qwen2-7b-instruct-abliterated-q5_k_m.gguf -c 2048 ```
KasuleTrevor/wav2vec2-large-xls-r-300m-lg-cv-50hr-v1	KasuleTrevor	2024-07-02T11:23:53Z	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-02T07:58:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Yash0109/diaratechHf_llama49307095-fde8-4f40-b5ce-572eb6bcc729	Yash0109	2024-07-02T08:01:59Z	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "text-generation", "conversational", "dataset:generator", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	text-generation	2024-07-02T07:59:36Z	--- base_model: mistralai/Mistral-7B-Instruct-v0.2 datasets: - generator library_name: peft license: apache-2.0 pipeline_tag: text-generation tags: - trl - sft - generated_from_trainer model-index: - name: diaratechHf_llama49307095-fde8-4f40-b5ce-572eb6bcc729 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. --> # diaratechHf_llama49307095-fde8-4f40-b5ce-572eb6bcc729 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator 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: constant - training_steps: 2 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.39.3 - Pytorch 2.3.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
NgTMDuc/model_weight	NgTMDuc	2024-07-02T07:59:52Z	0	null	[ "region:us" ]	null	2024-07-02T07:59:52Z	Entry not found
adhityaprimandhika/fine-tuned-mdeberta-category-by-notes	adhityaprimandhika	2024-07-02T08:03:52Z	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-02T08:00:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
lithiumice/smplx_blender_addon_data	lithiumice	2024-07-02T08:07:37Z	0	null	[ "region:us" ]	null	2024-07-02T08:00:54Z	Entry not found
gritli/distilbert-left	gritli	2024-07-02T08:01:54Z	0	transformers	[ "transformers", "zero-shot-classification", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	zero-shot-classification	2024-07-02T08:01:02Z	--- library_name: transformers pipeline_tag: zero-shot-classification --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
jameswilly991/worldtransdocuments	jameswilly991	2024-07-02T08:01:08Z	0	null	[ "license:other", "region:us" ]	null	2024-07-02T08:01:08Z	--- license: other license_name: worldtransdocuments license_link: LICENSE ---
Mayonnaisu/donut-kompetansebevis-v2	Mayonnaisu	2024-07-02T08:01:35Z	0	null	[ "region:us" ]	null	2024-07-02T08:01:35Z	Entry not found
QuantFactory/llama3-8B-DarkIdol-2.1-Uncensored-1048K-GGUF	QuantFactory	2024-07-02T08:54:37Z	0	null	[ "gguf", "region:us" ]	null	2024-07-02T08:01:39Z	Entry not found
hflqf88888/OdysseyAgent-task	hflqf88888	2024-07-02T09:20:15Z	0	transformers	[ "transformers", "pytorch", "qwen", "text-generation", "GUI", "custom_code", "en", "zh", "dataset:OpenGVLab/GUI-Odyssey", "license:cc-by-4.0", "autotrain_compatible", "region:us" ]	text-generation	2024-07-02T08:02:43Z	--- license: cc-by-4.0 datasets: - OpenGVLab/GUI-Odyssey language: - en - zh tags: - GUI --- ## OdysseyAgent-random The OdysseyAgent fine-tuned on Train-Task split.
AesopX/AA	AesopX	2024-07-02T08:03:03Z	0	null	[ "region:us" ]	null	2024-07-02T08:03:03Z	Entry not found
Moriacrafter/Qwen1.5-7B-4bit_DepressionDetection	Moriacrafter	2024-07-02T08:07:50Z	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T08:03:25Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Raelina/Raemu-XL-V4	Raelina	2024-07-02T12:40:42Z	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "en", "base_model:Raelina/Rae-Diffusion-XL-V2", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-07-02T08:04:11Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en tags: - text-to-image - stable-diffusion - safetensors - stable-diffusion-xl base_model: Raelina/Rae-Diffusion-XL-V2 --- <style> .title-container { display: flex; justify-content: center; align-items: center; height: 100vh; /* Adjust this value to position the title vertically / } .title { font-size: 2.5em; text-align: center; color: #333; font-family: 'Helvetica Neue', sans-serif; text-transform: uppercase; letter-spacing: 0.1em; padding: 0.5em 0; background: transparent; } .title span { background: -webkit-linear-gradient(45deg, #ff7a52, #a5cff0); -webkit-background-clip: text; -webkit-text-fill-color: transparent; } .custom-table { table-layout: fixed; width: 100%; border-collapse: collapse; margin-top: 2em; } .custom-table td { width: 50%; vertical-align: top; padding: 10px; box-shadow: 0px 0px 0px 0px rgba(0, 0, 0, 0.15); } .custom-image-container { position: relative; width: 100%; margin-bottom: 0em; overflow: hidden; border-radius: 10px; transition: transform .7s; } .custom-image-container:hover { transform: scale(1.05); } .custom-image { width: 100%; height: auto; object-fit: cover; border-radius: 10px; transition: transform .7s; margin-bottom: 0em; } .nsfw-filter { filter: blur(8px); transition: filter 0.3s ease; } .custom-image-container:hover .nsfw-filter { filter: none; } .overlay { position: absolute; bottom: 0; left: 0; right: 0; color: white; width: 100%; height: 40%; display: flex; flex-direction: column; justify-content: center; align-items: center; font-size: 1vw; font-style: bold; text-align: center; opacity: 0; background: linear-gradient(0deg, rgba(0, 0, 0, 0.8) 60%, rgba(0, 0, 0, 0) 100%); transition: opacity .5s; } .custom-image-container:hover .overlay { opacity: 1; } .overlay-text { background: linear-gradient(45deg, #F1F8E8, #F1F8E8); -webkit-background-clip: text; color: transparent; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.7); } .overlay-subtext { font-size: 0.75em; margin-top: 0.5em; font-style: italic; } .overlay, .overlay-subtext { text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.5); } </style> <h1 class="title"> <span>Raemu XL V4</span> </h1> <table class="custom-table"> <tr> <td> <div class="custom-image-container"> <img class="custom-image" src="https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/KmLBQhfvwNfPire0wsJ2r.png" alt="Sample Image 1"> <div class="overlay"> <div class="overlay-text">Mizuno Ai</div> </div> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/xzyMh8h90rZDVxpfZq0UD.png" alt="Sample Image 2"> <div class="overlay"> <div class="overlay-text">Cecillia Alcot</div> </div> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/wcga0-fZPfToZOc8ygpkN.png" alt="Sample Image 3"> <div class="overlay"> <div class="overlay-text">Miia</div> </div> </div> </td> </tr> <tr> <td> <div class="custom-image-container"> <img class="custom-image" src="https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/L2YsLdZsPZMlWrkEUPyvv.png" alt="Sample Image 4"> <div class="overlay"> <div class="overlay-text">Akaza Akari</div> </div> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/dhs2BM3HDZ9VOAzyb-6IJ.jpeg" alt="Sample Image 5"> <div class="overlay"> <div class="overlay-text">Lili</div> </div> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/pfH6AwI389cTm1KLyhnt2.png" alt="Sample Image 6"> <div class="overlay"> <div class="overlay-text">Lucy Heartfilia</div> </div> </div> </td> </tr> </table> ## Overview Raemu XL V4* is a merged model that focused in 2.5D Anime ## Model Details - Developed by: [Raelina](https://civitai.com/user/Raelina) - Model type: Diffusion-based text-to-image generative model - Model Description: Generate high-quality anime images from textual prompts - License: [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/) - Finetuned from: [Rae Diffusion XL V2](https://huggingface.co/Raelina/Rae-Diffusion-XL-V2) ### Usage Guidelines ## Tag Ordering For optimal results, it's recommended to follow the structured prompt template because we train the model like this: ``` 1girl/1boy, character name, from which series, everything else in any order. ``` ## Special Tag Raemu XL inherits special tags from Rae DIffusion XL V2 to enhance image generation by steering results toward quality and aesthetic. While the model can generate images without these tags, using them helps achieve better results. - Quality tags: masterpiece, best quality, great quality, good quality, normal quality, low quality, worst quality - Aesthetic tags: very aesthetic, aesthetic, displeasing, very displeasing ## Recommended settings - Positive prompts: ``` masterpiece, best quality, very aesthetic, absurdres, ``` - Negative prompts: ``` (low quality, worst quality:1.2), very displeasing, ugly, poorly drawn, signature, watermark, ``` - CFG: 7 - Sampling steps: 25 to 35 - Sampler: Euler a - Supported Resolution: ``` 1024 x 1024, 1152 x 896, 896 x 1152, 1216 x 832, 832 x 1216, 1344 x 768, 768 x 1344, 1536 x 640, 640 x 1536 ``` ## Hires.fix Setting - Upscaler: [4x_NMKD-YandereNeoXL](https://nmkd.de/?esrgan) - Hires step: 10-15 - Denoising: 0.1-0.3 or 0.55 for latent upscaler ## Merge Parameter 1. Rae Diffusion XL V2 merged to RealCartoonXL V6 using MBW (0.0,1.0,0.8,0.5,0.25,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.5,0.71,1.0,0.56,0.71,1.0,0.83,0.1,0) 2. (1) merged with Blue Pencil XL v3.1.0 using MBW (0.0,0.11,0.22,0.33,0.44,0.55,0.44,0.33,0.22,0.11,0.0,0.11,0.22,0.33,0.44,0.55,0.44,0.33,0.22,0.11,0) 3. Raemu XL V4 ## License Raemu XL V4 uses the [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/) inherited from Rae Diffusion XL V2, compatible with Stable Diffusion models. Key points: 1. Modification Sharing: If you modify Raemu XL V4, you must share both your changes and the original license. 2. Source Code Accessibility: If your modified version is network-accessible, provide a way (like a download link) for others to get the source code. This applies to derived models too. 3. Distribution Terms: Any distribution must be under this license or another with similar rules. 4. Compliance: Non-compliance must be fixed within 30 days to avoid license termination, emphasizing transparency and adherence to open-source values.
anushaporwal/wav2vec2-common_voice-tr-demo-mini-multiGPU-tr	anushaporwal	2024-07-02T08:04:31Z	0	null	[ "region:us" ]	null	2024-07-02T08:04:31Z	Entry not found
necrobradley/face_predict	necrobradley	2024-07-02T08:33:00Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-07-02T08:04:55Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: face_predict results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train[:800] args: default metrics: - name: Accuracy type: accuracy value: 0.5625 --- <!-- 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. --> # face_predict This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2322 - Accuracy: 0.5625 ## 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: 6 - total_train_batch_size: 192 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 40 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.9 \| 3 \| 2.0747 \| 0.1187 \| \| No log \| 1.8 \| 6 \| 2.0728 \| 0.1375 \| \| 2.0713 \| 3.0 \| 10 \| 2.0449 \| 0.2 \| \| 2.0713 \| 3.9 \| 13 \| 2.0225 \| 0.2562 \| \| 2.0713 \| 4.8 \| 16 \| 1.9779 \| 0.2938 \| \| 1.9642 \| 6.0 \| 20 \| 1.8985 \| 0.3688 \| \| 1.9642 \| 6.9 \| 23 \| 1.8440 \| 0.4188 \| \| 1.9642 \| 7.8 \| 26 \| 1.7593 \| 0.4437 \| \| 1.7442 \| 9.0 \| 30 \| 1.6551 \| 0.4875 \| \| 1.7442 \| 9.9 \| 33 \| 1.5996 \| 0.4875 \| \| 1.7442 \| 10.8 \| 36 \| 1.5324 \| 0.5188 \| \| 1.5402 \| 12.0 \| 40 \| 1.5053 \| 0.525 \| \| 1.5402 \| 12.9 \| 43 \| 1.4543 \| 0.5188 \| \| 1.5402 \| 13.8 \| 46 \| 1.4335 \| 0.5188 \| \| 1.4064 \| 15.0 \| 50 \| 1.3768 \| 0.5938 \| \| 1.4064 \| 15.9 \| 53 \| 1.3583 \| 0.6 \| \| 1.4064 \| 16.8 \| 56 \| 1.3464 \| 0.575 \| \| 1.2844 \| 18.0 \| 60 \| 1.3245 \| 0.6125 \| \| 1.2844 \| 18.9 \| 63 \| 1.3265 \| 0.5563 \| \| 1.2844 \| 19.8 \| 66 \| 1.2899 \| 0.5813 \| \| 1.1834 \| 21.0 \| 70 \| 1.2863 \| 0.5625 \| \| 1.1834 \| 21.9 \| 73 \| 1.2939 \| 0.5687 \| \| 1.1834 \| 22.8 \| 76 \| 1.2508 \| 0.5938 \| \| 1.1046 \| 24.0 \| 80 \| 1.2604 \| 0.5563 \| \| 1.1046 \| 24.9 \| 83 \| 1.2344 \| 0.6062 \| \| 1.1046 \| 25.8 \| 86 \| 1.2124 \| 0.6125 \| \| 1.0379 \| 27.0 \| 90 \| 1.2053 \| 0.6312 \| \| 1.0379 \| 27.9 \| 93 \| 1.3067 \| 0.5375 \| \| 1.0379 \| 28.8 \| 96 \| 1.2247 \| 0.5875 \| \| 1.0064 \| 30.0 \| 100 \| 1.2060 \| 0.625 \| \| 1.0064 \| 30.9 \| 103 \| 1.2308 \| 0.575 \| \| 1.0064 \| 31.8 \| 106 \| 1.1936 \| 0.6188 \| \| 0.9611 \| 33.0 \| 110 \| 1.2257 \| 0.5938 \| \| 0.9611 \| 33.9 \| 113 \| 1.2302 \| 0.5563 \| \| 0.9611 \| 34.8 \| 116 \| 1.2172 \| 0.6 \| \| 0.9351 \| 36.0 \| 120 \| 1.2355 \| 0.55 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
QuantFactory/llama3-8B-DarkIdol-2.0-Uncensored-GGUF	QuantFactory	2024-07-02T08:56:45Z	0	null	[ "gguf", "region:us" ]	null	2024-07-02T08:05:52Z	Entry not found
purelife/XV8	purelife	2024-07-03T00:47:39Z	0	null	[ "license:openrail", "region:us" ]	null	2024-07-02T08:06:20Z	--- license: openrail ---
snowian/emotion	snowian	2024-07-02T08:09:18Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-02T08:08:39Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: 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: 0.9285 - name: F1 type: f1 value: 0.92867427809199 --- <!-- 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. --> # 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: 0.1702 - Accuracy: 0.9285 - F1: 0.9287 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8067 \| 1.0 \| 250 \| 0.2883 \| 0.9115 \| 0.9115 \| \| 0.2204 \| 2.0 \| 500 \| 0.1883 \| 0.9295 \| 0.9299 \| \| 0.1495 \| 3.0 \| 750 \| 0.1702 \| 0.9285 \| 0.9287 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
hflqf88888/OdysseyAgent-app	hflqf88888	2024-07-02T09:22:35Z	0	transformers	[ "transformers", "pytorch", "qwen", "text-generation", "GUI", "custom_code", "en", "zh", "dataset:OpenGVLab/GUI-Odyssey", "license:cc-by-4.0", "autotrain_compatible", "region:us" ]	text-generation	2024-07-02T08:09:18Z	--- license: cc-by-4.0 datasets: - OpenGVLab/GUI-Odyssey language: - en - zh tags: - GUI --- ## OdysseyAgent-app The OdysseyAgent fine-tuned on Train-App split.
prasunjeet/falcon-7b-sharded-bf16-finetuned-treccast	prasunjeet	2024-07-02T13:28:55Z	0	null	[ "text-generation", "region:us" ]	text-generation	2024-07-02T08:09:36Z	--- pipeline_tag: text-generation ---
Stable-Diffusion-PT/Stable-image-transformer-weight	Stable-Diffusion-PT	2024-07-02T08:09:51Z	0	null	[ "region:us" ]	null	2024-07-02T08:09:51Z	Entry not found
glfarnold/results	glfarnold	2024-07-02T12:12:16Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "summarization", "generated_from_trainer", "base_model:t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	summarization	2024-07-02T08:09:52Z	--- license: apache-2.0 base_model: t5-small tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: results 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 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7600 - Rouge1: 0.1317 - Rouge2: 0.1118 - Rougel: 0.1232 - Rougelsum: 0.1232 ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| No log \| 1.0 \| 250 \| 1.8935 \| 0.04 \| 0.0359 \| 0.0375 \| 0.0375 \| \| 2.8412 \| 2.0 \| 500 \| 1.7864 \| 0.1089 \| 0.0934 \| 0.0954 \| 0.0954 \| \| 2.8412 \| 3.0 \| 750 \| 1.7600 \| 0.1317 \| 0.1118 \| 0.1232 \| 0.1232 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
FPHam/L3-8B-Everything-COT	FPHam	2024-07-02T18:18:09Z	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llm", "llama3", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T08:10:03Z	--- tags: - llm - llama - llama3 --- <!-- header start --> <div style="display: flex; flex-direction: column; align-items: center;"> </div> <div style="width: 100%;"> <img src="https://huggingface.co/FPHam/L3-8B-Everything-COT/resolve/main/cot2.png" alt="COT EVERYTHING" style="width: 40%; min-width: 200px; display: block; margin: auto;"> </div> <div style="display: flex; flex-direction: column; align-items: center;"> <p><a href="https://ko-fi.com/Q5Q5MOB4M">Buy me Ko-fi</a></p> </div> <!-- header end --> Legal: This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](https://llama.meta.com/llama3/license/) Everything COT is an investigative self-reflecting general model that uses Chain of Thought for everything. And I mean everything. Instead of confidently proclaiming something (or confidently hallucinating other things) like most models, it caries an internal dialogue with itself and often cast doubts over uncertain topics while looking at it from various sides. >What's your name? >To answer this question, we must first understand the context. The question "What's your name?" is typically addressed to a human being. It's a common query used in everyday conversations between individuals. > >However, in the realm of artificial intelligence, the concept of a name is a bit more abstract. Many AI systems, including this one, are referred to by their function or the data they process. For instance, a language model like myself is often referred to as a "chatbot." > >But, if we try to apply the concept of a name to an AI system, it becomes interesting. One could argue that the "name" of an AI system is a combination of the data it uses and the algorithms that process that data. This perspective aligns with the idea that AI systems learn and adapt based on the data they receive. > >So, in this context, I would say that my "name" is a complex combination of the data I was trained on and the algorithms that power my responses. Everything COT uses Llama 3 instruct template The correct jinja chat_template is in tokenizer_config.json It was NOT trained with a system message, amd you can further use various system messages to steer the model. Parameters It's up to you to discover the best parameters that works. I tested it in oobabooga WebUi using very off-the-shelf min_p preset: Temperature: 1, Top_p: 1, Top_k: 0, Typical_p: 1, min_p: 0.05, repetition_penalty: 1 Different parameters, like temperature will affect the models talkativnes and self-reflecting properties. If you find something really good, let me know and I'll post it here.
AesopX/123	AesopX	2024-07-02T08:11:36Z	0	null	[ "region:us" ]	null	2024-07-02T08:10:25Z	# Use a pipeline as a high-level helper from transformers import pipeline messages = [ {"role": "user", "content": "Who are you?"}, ] pipe = pipeline("text-generation", model="shenzhi-wang/Llama3-8B-Chinese-Chat") pipe(messages) # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("shenzhi-wang/Llama3-8B-Chinese-Chat") model = AutoModelForCausalLM.from_pretrained("shenzhi-wang/Llama3-8B-Chinese-Chat")
RULES007/test	RULES007	2024-07-02T08:11:48Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-07-02T08:11:48Z	--- license: apache-2.0 ---
NgTMDuc/weight	NgTMDuc	2024-07-02T12:17:39Z	0	null	[ "region:us" ]	null	2024-07-02T08:12:03Z	Entry not found
quissuiven/donut-ktp-v2-test-2	quissuiven	2024-07-02T08:22:47Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-07-02T08:13:06Z	--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-ktp-v2-test-2 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. --> # donut-ktp-v2-test-2 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder 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: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
sarahai/whisper-medium-uzbek	sarahai	2024-07-02T08:13:24Z	0	null	[ "region:us" ]	null	2024-07-02T08:13:24Z	Entry not found
rbhatia46/bge-base-financial-nvidia-matryoshka	rbhatia46	2024-07-02T08:16:31Z	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:6300", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:BAAI/bge-base-en-v1.5", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-embeddings-inference", "region:us" ]	sentence-similarity	2024-07-02T08:16:22Z	--- base_model: BAAI/bge-base-en-v1.5 datasets: [] language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6300 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: As of December 31, 2023, deferred revenues for unsatisfied performance obligations consisted of $769 million related to Hilton Honors that will be recognized as revenue over approximately the next two years. sentences: - How many shares of common stock were issued in both 2022 and 2023? - What is the projected timeline for recognizing revenue from deferred revenues related to Hilton Honors as of December 31, 2023? - What acquisitions did CVS Health Corporation complete in 2023 to enhance their care delivery strategy? - source_sentence: If a good or service does not qualify as distinct, it is combined with the other non-distinct goods or services within the arrangement and these combined goods or services are treated as a single performance obligation for accounting purposes. The arrangement's transaction price is then allocated to each performance obligation based on the relative standalone selling price of each performance obligation. sentences: - What does the summary table indicate about the company's activities at the end of 2023? - What governs the treatment of goods or services that are not distinct within a contractual arrangement? - What is the basis for the Company to determine the Standalone Selling Price (SSP) for each distinct performance obligation in contracts with multiple performance obligations? - source_sentence: As of January 2023, the maximum daily borrowing capacity under the commercial paper program was approximately $2.75 billion. sentences: - What is the maximum daily borrowing capacity under the commercial paper program as of January 2023? - When does the Company's fiscal year end? - How much cash did acquisition activities use in 2023? - source_sentence: Federal Home Loan Bank borrowings had an interest rate of 4.59% in 2022, which increased to 5.14% in 2023. sentences: - By what percentage did the company's capital expenditures increase in fiscal 2023 compared to fiscal 2022? - What is the significance of Note 13 in the context of legal proceedings described in the Annual Report on Form 10-K? - How much did the Federal Home Loan Bank borrowings increase in terms of interest rates from 2022 to 2023? - source_sentence: The design of the Annual Report, with the consolidated financial statements placed immediately after Part IV, enhances the integration of financial data by maintaining a coherent structure. sentences: - How does the structure of the Annual Report on Form 10-K facilitate the integration of the consolidated financial statements? - Where can one find the Glossary of Terms and Acronyms in Item 8? - What part of the annual report contains the consolidated financial statements and accompanying notes? model-index: - name: BGE base Financial Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.6957142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8171428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8628571428571429 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6957142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2723809523809524 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17257142857142854 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08999999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6957142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8171428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8628571428571429 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7971144469297426 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7641831065759639 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7681728985040082 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6942857142857143 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.81 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8514285714285714 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6942857142857143 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17028571428571426 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6942857142857143 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.81 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8514285714285714 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7951260604161544 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7617998866213151 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7658003405075238 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.7014285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7971428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.85 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8885714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7014285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26571428571428574 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16999999999999998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08885714285714284 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7014285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7971428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.85 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8885714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.793266992460996 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7629580498866213 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7678096436855835 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6957142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8014285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8357142857142857 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8842857142857142 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6957142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2671428571428571 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16714285714285712 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08842857142857141 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6957142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8014285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8357142857142857 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8842857142857142 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.787378246207931 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7566984126984126 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7613545312565108 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6571428571428571 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7871428571428571 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8285714285714286 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8757142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6571428571428571 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2623809523809524 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1657142857142857 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08757142857142856 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6571428571428571 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7871428571428571 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8285714285714286 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8757142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7655516319615892 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7303951247165531 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7349875161463472 name: Cosine Map@100 --- # BGE base Financial Matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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: [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> - 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': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## 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("rbhatia46/bge-base-financial-nvidia-matryoshka") # Run inference sentences = [ 'The design of the Annual Report, with the consolidated financial statements placed immediately after Part IV, enhances the integration of financial data by maintaining a coherent structure.', 'How does the structure of the Annual Report on Form 10-K facilitate the integration of the consolidated financial statements?', 'Where can one find the Glossary of Terms and Acronyms in Item 8?', ] 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 #### Information Retrieval * Dataset: `dim_768` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.6957 \| \| cosine_accuracy@3 \| 0.8171 \| \| cosine_accuracy@5 \| 0.8629 \| \| cosine_accuracy@10 \| 0.9 \| \| cosine_precision@1 \| 0.6957 \| \| cosine_precision@3 \| 0.2724 \| \| cosine_precision@5 \| 0.1726 \| \| cosine_precision@10 \| 0.09 \| \| cosine_recall@1 \| 0.6957 \| \| cosine_recall@3 \| 0.8171 \| \| cosine_recall@5 \| 0.8629 \| \| cosine_recall@10 \| 0.9 \| \| cosine_ndcg@10 \| 0.7971 \| \| cosine_mrr@10 \| 0.7642 \| \| cosine_map@100 \| 0.7682 \| #### Information Retrieval * Dataset: `dim_512` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.6943 \| \| cosine_accuracy@3 \| 0.81 \| \| cosine_accuracy@5 \| 0.8514 \| \| cosine_accuracy@10 \| 0.9 \| \| cosine_precision@1 \| 0.6943 \| \| cosine_precision@3 \| 0.27 \| \| cosine_precision@5 \| 0.1703 \| \| cosine_precision@10 \| 0.09 \| \| cosine_recall@1 \| 0.6943 \| \| cosine_recall@3 \| 0.81 \| \| cosine_recall@5 \| 0.8514 \| \| cosine_recall@10 \| 0.9 \| \| cosine_ndcg@10 \| 0.7951 \| \| cosine_mrr@10 \| 0.7618 \| \| cosine_map@100 \| 0.7658 \| #### Information Retrieval * Dataset: `dim_256` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.7014 \| \| cosine_accuracy@3 \| 0.7971 \| \| cosine_accuracy@5 \| 0.85 \| \| cosine_accuracy@10 \| 0.8886 \| \| cosine_precision@1 \| 0.7014 \| \| cosine_precision@3 \| 0.2657 \| \| cosine_precision@5 \| 0.17 \| \| cosine_precision@10 \| 0.0889 \| \| cosine_recall@1 \| 0.7014 \| \| cosine_recall@3 \| 0.7971 \| \| cosine_recall@5 \| 0.85 \| \| cosine_recall@10 \| 0.8886 \| \| cosine_ndcg@10 \| 0.7933 \| \| cosine_mrr@10 \| 0.763 \| \| cosine_map@100 \| 0.7678 \| #### Information Retrieval * Dataset: `dim_128` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.6957 \| \| cosine_accuracy@3 \| 0.8014 \| \| cosine_accuracy@5 \| 0.8357 \| \| cosine_accuracy@10 \| 0.8843 \| \| cosine_precision@1 \| 0.6957 \| \| cosine_precision@3 \| 0.2671 \| \| cosine_precision@5 \| 0.1671 \| \| cosine_precision@10 \| 0.0884 \| \| cosine_recall@1 \| 0.6957 \| \| cosine_recall@3 \| 0.8014 \| \| cosine_recall@5 \| 0.8357 \| \| cosine_recall@10 \| 0.8843 \| \| cosine_ndcg@10 \| 0.7874 \| \| cosine_mrr@10 \| 0.7567 \| \| cosine_map@100 \| 0.7614 \| #### Information Retrieval * Dataset: `dim_64` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:----------\| \| cosine_accuracy@1 \| 0.6571 \| \| cosine_accuracy@3 \| 0.7871 \| \| cosine_accuracy@5 \| 0.8286 \| \| cosine_accuracy@10 \| 0.8757 \| \| cosine_precision@1 \| 0.6571 \| \| cosine_precision@3 \| 0.2624 \| \| cosine_precision@5 \| 0.1657 \| \| cosine_precision@10 \| 0.0876 \| \| cosine_recall@1 \| 0.6571 \| \| cosine_recall@3 \| 0.7871 \| \| cosine_recall@5 \| 0.8286 \| \| cosine_recall@10 \| 0.8757 \| \| cosine_ndcg@10 \| 0.7656 \| \| cosine_mrr@10 \| 0.7304 \| \| cosine_map@100 \| 0.735 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 6,300 training samples * Columns: <code>positive</code> and <code>anchor</code> * Approximate statistics based on the first 1000 samples: \| \| positive \| anchor \| \|:--------\|:-----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 6 tokens</li><li>mean: 45.53 tokens</li><li>max: 222 tokens</li></ul> \| <ul><li>min: 8 tokens</li><li>mean: 20.3 tokens</li><li>max: 45 tokens</li></ul> \| * Samples: \| positive \| anchor \| \|:---------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------\| \| <code>Acquisition activity used cash of $765 million in 2023, primarily related to a Beauty acquisition.</code> \| <code>How much cash did acquisition activities use in 2023?</code> \| \| <code>In a financial report, Part IV Item 15 includes Exhibits and Financial Statement Schedules as mentioned.</code> \| <code>What content can be expected under Part IV Item 15 in a financial report?</code> \| \| <code>we had more than 8.3 million fiber consumer wireline broadband customers, adding 1.1 million during the year.</code> \| <code>How many fiber consumer wireline broadband customers did the company have at the end of the year?</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `learning_rate`: 2e-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`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `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`: True - `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`: True - `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_fused - `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 \| dim_128_cosine_map@100 \| dim_256_cosine_map@100 \| dim_512_cosine_map@100 \| dim_64_cosine_map@100 \| dim_768_cosine_map@100 \| \|:----------:\|:------:\|:-------------:\|:----------------------:\|:----------------------:\|:----------------------:\|:---------------------:\|:----------------------:\| \| 0.8122 \| 10 \| 1.5751 \| - \| - \| - \| - \| - \| \| 0.9746 \| 12 \| - \| - \| - \| - \| - \| 0.7580 \| \| 0.8122 \| 10 \| 0.6362 \| - \| - \| - \| - \| - \| \| 0.9746 \| 12 \| - \| 0.7503 \| 0.7576 \| 0.7653 \| 0.7282 \| 0.7638 \| \| 1.6244 \| 20 \| 0.4426 \| - \| - \| - \| - \| - \| \| 1.9492 \| 24 \| - \| 0.7544 \| 0.7662 \| 0.7640 \| 0.7311 \| 0.7676 \| \| 2.4365 \| 30 \| 0.3217 \| - \| - \| - \| - \| - \| \| 2.9239 \| 36 \| - \| 0.7608 \| 0.7684 \| 0.7662 \| 0.7341 \| 0.7686 \| \| 3.2487 \| 40 \| 0.2761 \| - \| - \| - \| - \| - \| \| 3.8985 \| 48 \| - \| 0.7614 \| 0.7678 \| 0.7658 \| 0.735 \| 0.7682 \| * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.6 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.31.0 - 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", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### 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. -->
Yash0109/diaratechHf_llama188fe336-1221-4050-af76-a84e95bc6450	Yash0109	2024-07-02T08:18:52Z	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "text-generation", "conversational", "dataset:generator", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	text-generation	2024-07-02T08:16:30Z	--- base_model: mistralai/Mistral-7B-Instruct-v0.2 datasets: - generator library_name: peft license: apache-2.0 pipeline_tag: text-generation tags: - trl - sft - generated_from_trainer model-index: - name: diaratechHf_llama188fe336-1221-4050-af76-a84e95bc6450 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. --> # diaratechHf_llama188fe336-1221-4050-af76-a84e95bc6450 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator 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: constant - training_steps: 2 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.39.3 - Pytorch 2.3.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
lysandre/test-4	lysandre	2024-07-02T08:40:10Z	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T08:18:36Z	--- license: mit --- This one with a custom `config.head_dim` as allowed by the architecture (see 7b model).
SQAI/bge-embedding-model3	SQAI	2024-07-02T08:19:43Z	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:397", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:SQAI/bge-embedding-model", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-embeddings-inference", "region:us" ]	sentence-similarity	2024-07-02T08:19:22Z	--- base_model: SQAI/bge-embedding-model datasets: [] language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:397 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: Time taken for the streetlight to activate or light up from the command sentences: - '"Can you provide a report showing each unique streetlight identifier along with its power usage recorded in watts for the last three months, as well as instances where power consumption was higher than expected, potentially indicating faults (failures), or where the voltage supplied was below the safe operating level (failure) for the same period?"' - failure reasons for dysfunctional streetlights for geozone = 233 - '"Can you provide me with a comprehensive report for the past year showing the maximum load current, internal temperature, activation time, wireless signal strength of each streetlight in the group? Also, can you include metering component faults, instances where power under load was higher than normal and the threshold level for flickering? I''d like to understand the performance of the driver and instances where voltage under load was lower than expected. Finally, could you also specify the maximum latitude covered by this streetlight group?"' - source_sentence: Power consumption is lower than expected, possibly due to hardware issues (failure) sentences: - '"What is the total power consumption in watts of the ''Main Boulevard Group'' streetlights in the ''Grid B3'' Y-coordinate during the last 3 months, including instances where the supplied voltage was below the safe operating level causing failures? Also, how many complete power losses did this group of streetlights experience during this period due to supply issues or damage, and was there any instance where the ''Mode 4'' control switch or the ''Broadcast ID 9'' subscription was used for troubleshooting or restoring the lights?"' - '"What is the failure count for the last 3 months for the streetlight with ID ''unique streetlight identifier'' located in ''time zone where the streetlight is located'', specifically on ''Name of the street for the streetlight in error'', considering failures include lower than average power consumption affirming a hardware issue as identified by ''hardware version of the streetlight'' and difficulties in network connection for remote management? What is the IMEI number of the affected device and what''s the minimum current level it reached to be considered abnormal? Additionally, could you provide the maximum latitude of the geographic area covered by this group of streetlights?"' - '"Can you give me a breakdown of the total operational usage, active controller hours, and dimming controls type for each streetlight within a specified longitude range over the last six months, as well as any instances of relay status changes? And, could you also list the number of instances where the voltage under load was higher than expected, faults in the metering components, and any cases of complete power loss during this period? Additionally, can you inform me the strength of the wireless signal received by each streetlight''s communication module?"' - source_sentence: Upper voltage limit considered safe and efficient for streetlight operation sentences: - '"Can you pull a report detailing if there have been any instances where the power consumption was unusually high, potentially indicating faults over the last 3 months for the streetlight device with IMEI number XYZ, and provide the total hours of operation for its controller during that time? Include in this report the last timestamp when the threshold settings were updated, the level of that threshold for recording flickering, measured in occurrences, and the Y-coordinate for its location in the streetlight grid layout?"' - '"What is the current dimming level of the streetlight in operation, and have there been any instances where the voltage exceeded the safe operating level causing a failure? Additionally, I would like to know the total energy consumed, recorded in kilowatt-hours, for a specific group of streetlights and their efficiency of power usage represented by the power factor. Could you also tell me what type of DALI dimming protocol this group of streetlights is using, and the SIM card code used for their cellular network communication? Furthermore, what is the upper voltage limit considered safe and efficient for their operation and when was the latest data recorded or action performed by these streetlights?"' - '"What is the minimum load current that suggests suboptimal operation, the range of current indicating potential issues, the name of a group of streetlights, their drawn electrical current measure in amperes, details of faults in the link control mechanism, the geographic longitude range they cover, the identifier for their broadcast subscription, their maximum safe voltage under load conditions, minimum abnormal current level, and their linking rights for synchronized control?"' - source_sentence: IMEI number of the streetlight device sentences: - '"What is the failure count for the last 3 months for the streetlight with ID ''unique streetlight identifier'' located in ''time zone where the streetlight is located'', specifically on ''Name of the street for the streetlight in error'', considering failures include lower than average power consumption affirming a hardware issue as identified by ''hardware version of the streetlight'' and difficulties in network connection for remote management? What is the IMEI number of the affected device and what''s the minimum current level it reached to be considered abnormal? Additionally, could you provide the maximum latitude of the geographic area covered by this group of streetlights?"' - '"Can you provide the description of the group of streetlights, along with the geographic zone identifier of the streetlight that recently encountered a failure? Could you also include the last updated timestamp of the threshold settings, the maximum load current that indicates potential risk or overload, the frequency of the electricity supply measured in hertz, and the minimum power usage level below which it is considered abnormal for these streetlights?"' - '"What is the X-coordinate for the group of streetlights in a grid layout that have an operational age of more than 10,000 hours, registered a lower lux level below which additional lighting may be necessary and have had general faults related to light output, possibly including those that remain on during daylight hours due to sensor faults, despite the ambient light level detected being adequate? Also, could you provide the records of those which have the rights or permissions to synchronise control across multiple streetlights, the current electrical current drawn by each of these streetlights, the minimum operational voltage under their load conditions, and any instance where they reached the threshold levels to continue recording as flickering?"' - source_sentence: Count of how many times the streetlight has been switched on sentences: - '"What is the type of DALI dimming protocol used by our streetlights, do we have the necessary permissions to link multiple of these streetlights for synchronized control, how many times in the past 3 months has the supplied voltage dropped below the safe operating level causing failure, and what is the minimum current level below which the streetlight operation is considered abnormal?"' - '"Can you provide me with a comprehensive report for the past year showing the maximum load current, internal temperature, activation time, wireless signal strength of each streetlight in the group? Also, can you include metering component faults, instances where power under load was higher than normal and the threshold level for flickering? I''d like to understand the performance of the driver and instances where voltage under load was lower than expected. Finally, could you also specify the maximum latitude covered by this streetlight group?"' - '"What is the total count of times the streetlight has been switched on and what were the ambient light levels, measured in lux, at those instances?"' model-index: - name: BGE base Financial Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.022222222222222223 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.044444444444444446 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.17777777777777778 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.2222222222222222 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.022222222222222223 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.014814814814814814 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.035555555555555556 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.022222222222222223 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.022222222222222223 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.044444444444444446 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.17777777777777778 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.2222222222222222 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.10314853022641256 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.06666666666666668 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.09055451253282244 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.022222222222222223 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.044444444444444446 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.17777777777777778 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.2222222222222222 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.022222222222222223 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.014814814814814814 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.035555555555555556 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.022222222222222223 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.022222222222222223 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.044444444444444446 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.17777777777777778 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.2222222222222222 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.10314853022641256 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.06666666666666668 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.09055451253282244 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.022222222222222223 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.044444444444444446 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.13333333333333333 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.26666666666666666 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.022222222222222223 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.014814814814814814 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.026666666666666665 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.026666666666666672 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.022222222222222223 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.044444444444444446 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.13333333333333333 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.26666666666666666 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.11180419878864006 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.06644620811287479 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.0861674794441296 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.0 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.022222222222222223 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.13333333333333333 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.15555555555555556 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.0 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.007407407407407407 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.026666666666666665 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.015555555555555557 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.0 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.022222222222222223 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.13333333333333333 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.15555555555555556 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.06402667034388869 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.03574074074074074 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.05715693214212387 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.0 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.022222222222222223 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.08888888888888889 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.13333333333333333 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.0 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.007407407407407407 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.017777777777777778 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.013333333333333332 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.0 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.022222222222222223 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.08888888888888889 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.13333333333333333 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.0528696817100619 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.028518518518518516 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.0469896238659224 name: Cosine Map@100 --- # BGE base Financial Matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [SQAI/bge-embedding-model](https://huggingface.co/SQAI/bge-embedding-model). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [SQAI/bge-embedding-model](https://huggingface.co/SQAI/bge-embedding-model) <!-- at revision 9a9bc3f795ddfc56610a621b37aa077ae0653fa4 --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 384 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': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## 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("SQAI/bge-embedding-model3") # Run inference sentences = [ 'Count of how many times the streetlight has been switched on', '"What is the total count of times the streetlight has been switched on and what were the ambient light levels, measured in lux, at those instances?"', '"What is the type of DALI dimming protocol used by our streetlights, do we have the necessary permissions to link multiple of these streetlights for synchronized control, how many times in the past 3 months has the supplied voltage dropped below the safe operating level causing failure, and what is the minimum current level below which the streetlight operation is considered abnormal?"', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_768` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.0222 \| \| cosine_accuracy@3 \| 0.0444 \| \| cosine_accuracy@5 \| 0.1778 \| \| cosine_accuracy@10 \| 0.2222 \| \| cosine_precision@1 \| 0.0222 \| \| cosine_precision@3 \| 0.0148 \| \| cosine_precision@5 \| 0.0356 \| \| cosine_precision@10 \| 0.0222 \| \| cosine_recall@1 \| 0.0222 \| \| cosine_recall@3 \| 0.0444 \| \| cosine_recall@5 \| 0.1778 \| \| cosine_recall@10 \| 0.2222 \| \| cosine_ndcg@10 \| 0.1031 \| \| cosine_mrr@10 \| 0.0667 \| \| cosine_map@100 \| 0.0906 \| #### Information Retrieval * Dataset: `dim_512` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.0222 \| \| cosine_accuracy@3 \| 0.0444 \| \| cosine_accuracy@5 \| 0.1778 \| \| cosine_accuracy@10 \| 0.2222 \| \| cosine_precision@1 \| 0.0222 \| \| cosine_precision@3 \| 0.0148 \| \| cosine_precision@5 \| 0.0356 \| \| cosine_precision@10 \| 0.0222 \| \| cosine_recall@1 \| 0.0222 \| \| cosine_recall@3 \| 0.0444 \| \| cosine_recall@5 \| 0.1778 \| \| cosine_recall@10 \| 0.2222 \| \| cosine_ndcg@10 \| 0.1031 \| \| cosine_mrr@10 \| 0.0667 \| \| cosine_map@100 \| 0.0906 \| #### Information Retrieval * Dataset: `dim_256` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.0222 \| \| cosine_accuracy@3 \| 0.0444 \| \| cosine_accuracy@5 \| 0.1333 \| \| cosine_accuracy@10 \| 0.2667 \| \| cosine_precision@1 \| 0.0222 \| \| cosine_precision@3 \| 0.0148 \| \| cosine_precision@5 \| 0.0267 \| \| cosine_precision@10 \| 0.0267 \| \| cosine_recall@1 \| 0.0222 \| \| cosine_recall@3 \| 0.0444 \| \| cosine_recall@5 \| 0.1333 \| \| cosine_recall@10 \| 0.2667 \| \| cosine_ndcg@10 \| 0.1118 \| \| cosine_mrr@10 \| 0.0664 \| \| cosine_map@100 \| 0.0862 \| #### Information Retrieval * Dataset: `dim_128` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.0 \| \| cosine_accuracy@3 \| 0.0222 \| \| cosine_accuracy@5 \| 0.1333 \| \| cosine_accuracy@10 \| 0.1556 \| \| cosine_precision@1 \| 0.0 \| \| cosine_precision@3 \| 0.0074 \| \| cosine_precision@5 \| 0.0267 \| \| cosine_precision@10 \| 0.0156 \| \| cosine_recall@1 \| 0.0 \| \| cosine_recall@3 \| 0.0222 \| \| cosine_recall@5 \| 0.1333 \| \| cosine_recall@10 \| 0.1556 \| \| cosine_ndcg@10 \| 0.064 \| \| cosine_mrr@10 \| 0.0357 \| \| cosine_map@100 \| 0.0572 \| #### Information Retrieval * Dataset: `dim_64` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:----------\| \| cosine_accuracy@1 \| 0.0 \| \| cosine_accuracy@3 \| 0.0222 \| \| cosine_accuracy@5 \| 0.0889 \| \| cosine_accuracy@10 \| 0.1333 \| \| cosine_precision@1 \| 0.0 \| \| cosine_precision@3 \| 0.0074 \| \| cosine_precision@5 \| 0.0178 \| \| cosine_precision@10 \| 0.0133 \| \| cosine_recall@1 \| 0.0 \| \| cosine_recall@3 \| 0.0222 \| \| cosine_recall@5 \| 0.0889 \| \| cosine_recall@10 \| 0.1333 \| \| cosine_ndcg@10 \| 0.0529 \| \| cosine_mrr@10 \| 0.0285 \| \| cosine_map@100 \| 0.047 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 397 training samples * Columns: <code>positive</code> and <code>anchor</code> * Approximate statistics based on the first 1000 samples: \| \| positive \| anchor \| \|:--------\|:----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 5 tokens</li><li>mean: 13.89 tokens</li><li>max: 20 tokens</li></ul> \| <ul><li>min: 12 tokens</li><li>mean: 101.84 tokens</li><li>max: 175 tokens</li></ul> \| * Samples: \| positive \| anchor \| \|:------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>Electrical current drawn by the streetlight, measured in amperes</code> \| <code>"What is the minimum load current that suggests suboptimal operation, the range of current indicating potential issues, the name of a group of streetlights, their drawn electrical current measure in amperes, details of faults in the link control mechanism, the geographic longitude range they cover, the identifier for their broadcast subscription, their maximum safe voltage under load conditions, minimum abnormal current level, and their linking rights for synchronized control?"</code> \| \| <code>Faults in the link control mechanism managing multiple streetlights (failure)</code> \| <code>"Can you show me the data of the unique streetlight identifier in the geoZone with faults in the link control mechanism that manages multiple streetlights for the last three months, where the control mode setting of the streetlight was automated, also show the current dimming level of the streetlight in operation at that time, the maximum current level considered unsafe for the streetlight operation, the maximum load current indicating potential risk or overload, along with the time zone where the streetlight is located?"</code> \| \| <code>The relay responsible for turning the streetlight on and off is sticking (failure)</code> \| <code>"What is the network time synchronization receipt from the central control, maximum load current reading, whether the relay responsible for turning the streetlights on and off experienced any failures, the delta or height of the grid area occupied by this group of streetlights, the maximum load power level, and longitude of the streetlight that had a low power factor indicating inefficiency and possible reactive power issues in the last 3 months?"</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 384, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 45 evaluation samples * Columns: <code>positive</code> and <code>anchor</code> * Approximate statistics based on the first 1000 samples: \| \| positive \| anchor \| \|:--------\|:----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 8 tokens</li><li>mean: 14.27 tokens</li><li>max: 19 tokens</li></ul> \| <ul><li>min: 14 tokens</li><li>mean: 104.58 tokens</li><li>max: 167 tokens</li></ul> \| * Samples: \| positive \| anchor \| \|:---------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>Power factor of the streetlight, indicating the efficiency of power usage</code> \| <code>"Could you please provide the information on how many times the streetlight within the set geographical zone has been switched on? Can you also include details on its internal operating temperature, the status of the relay in the streetlight, any instances of it exceeding safe operating temperature limits, and the efficiency of its power usage or power factor, particularly when the operational voltage hit its minimum under load conditions? Lastly, provide insights into the width of the grid area it occupies. This information does not need to highlight any specific failure occurrences."</code> \| \| <code>General fault related to the light output of the streetlight (failure)</code> \| <code>"What is the X-coordinate for the group of streetlights in a grid layout that have an operational age of more than 10,000 hours, registered a lower lux level below which additional lighting may be necessary and have had general faults related to light output, possibly including those that remain on during daylight hours due to sensor faults, despite the ambient light level detected being adequate? Also, could you provide the records of those which have the rights or permissions to synchronise control across multiple streetlights, the current electrical current drawn by each of these streetlights, the minimum operational voltage under their load conditions, and any instance where they reached the threshold levels to continue recording as flickering?"</code> \| \| <code>Name of the street for the streetlight in error (failure)</code> \| <code>failure count for street name = Chestnut Street, Oak Avenue for time = last 55 days in streetlighting</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 384, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-06 - `weight_decay`: 0.03 - `num_train_epochs`: 100 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.2 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `learning_rate`: 2e-06 - `weight_decay`: 0.03 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 100 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.2 - `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`: True - `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`: True - `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_fused - `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 \| dim_128_cosine_map@100 \| dim_256_cosine_map@100 \| dim_512_cosine_map@100 \| dim_64_cosine_map@100 \| dim_768_cosine_map@100 \| \|:--------:\|:------:\|:-------------:\|:----------:\|:----------------------:\|:----------------------:\|:----------------------:\|:---------------------:\|:----------------------:\| \| 1.0 \| 1 \| 8.8958 \| 6.8979 \| 0.0490 \| 0.0530 \| 0.0604 \| 0.0457 \| 0.0604 \| \| 1.0769 \| 2 \| 0.8656 \| - \| - \| - \| - \| - \| - \| \| 2.0 \| 3 \| 7.9734 \| 6.8985 \| 0.0491 \| 0.0528 \| 0.0604 \| 0.0455 \| 0.0604 \| \| 2.1538 \| 4 \| 1.5652 \| - \| - \| - \| - \| - \| - \| \| 3.0 \| 5 \| 7.4072 \| 6.8893 \| 0.0491 \| 0.0532 \| 0.0605 \| 0.0448 \| 0.0605 \| \| 3.2308 \| 6 \| 2.2162 \| - \| - \| - \| - \| - \| - \| \| 4.0 \| 7 \| 6.6659 \| 6.8768 \| 0.0491 \| 0.0535 \| 0.0605 \| 0.0441 \| 0.0605 \| \| 4.3077 \| 8 \| 2.8917 \| - \| - \| - \| - \| - \| - \| \| 5.0 \| 9 \| 6.0505 \| 6.8666 \| 0.0497 \| 0.0539 \| 0.0608 \| 0.0440 \| 0.0608 \| \| 5.3846 \| 10 \| 3.5565 \| - \| - \| - \| - \| - \| - \| \| 6.0 \| 11 \| 5.4886 \| 6.8504 \| 0.0509 \| 0.0554 \| 0.0614 \| 0.0444 \| 0.0614 \| \| 6.4615 \| 12 \| 4.206 \| - \| - \| - \| - \| - \| - \| \| 7.0 \| 13 \| 4.6528 \| 6.8280 \| 0.0527 \| 0.0554 \| 0.0621 \| 0.0424 \| 0.0621 \| \| 7.5385 \| 14 \| 4.87 \| - \| - \| - \| - \| - \| - \| \| 8.0 \| 15 \| 3.9965 \| 6.8114 \| 0.0529 \| 0.0569 \| 0.0659 \| 0.0416 \| 0.0659 \| \| 8.6154 \| 16 \| 5.5005 \| - \| - \| - \| - \| - \| - \| \| 9.0 \| 17 \| 3.2411 \| 6.7791 \| 0.0532 \| 0.0551 \| 0.0661 \| 0.0421 \| 0.0661 \| \| 9.6923 \| 18 \| 6.1103 \| - \| - \| - \| - \| - \| - \| \| 10.0 \| 19 \| 2.6339 \| 6.7559 \| 0.0575 \| 0.0551 \| 0.0663 \| 0.0425 \| 0.0663 \| \| 10.7692 \| 20 \| 6.8119 \| - \| - \| - \| - \| - \| - \| \| 11.0 \| 21 \| 1.9097 \| 6.7235 \| 0.0572 \| 0.0617 \| 0.0629 \| 0.0439 \| 0.0629 \| \| 11.8462 \| 22 \| 7.4981 \| - \| - \| - \| - \| - \| - \| \| 12.0 \| 23 \| 1.2004 \| 6.6966 \| 0.0571 \| 0.0603 \| 0.0660 \| 0.0437 \| 0.0660 \| \| 12.9231 \| 24 \| 8.1134 \| - \| - \| - \| - \| - \| - \| \| 13.0 \| 25 \| 0.5338 \| 6.6707 \| 0.0557 \| 0.0607 \| 0.0645 \| 0.0428 \| 0.0645 \| \| 14.0 \| 26 \| 8.6211 \| 6.6592 \| 0.0558 \| 0.0610 \| 0.0651 \| 0.0458 \| 0.0651 \| \| 15.0 \| 27 \| 8.5679 \| - \| - \| - \| - \| - \| - \| \| 15.0769 \| 28 \| 0.1453 \| 6.6335 \| 0.0561 \| 0.0587 \| 0.0731 \| 0.0420 \| 0.0731 \| \| 16.0 \| 29 \| 8.3531 \| - \| - \| - \| - \| - \| - \| \| 16.1538 \| 30 \| 0.0862 \| 6.6090 \| 0.0560 \| 0.0593 \| 0.0725 \| 0.0443 \| 0.0725 \| \| 17.0 \| 31 \| 8.4562 \| 6.6064 \| 0.0564 \| 0.0616 \| 0.0747 \| 0.0445 \| 0.0747 \| \| 17.0769 \| 32 \| 0.8101 \| - \| - \| - \| - \| - \| - \| \| 18.0 \| 33 \| 7.578 \| 6.5912 \| 0.0555 \| 0.0597 \| 0.0730 \| 0.0470 \| 0.0730 \| \| 18.1538 \| 34 \| 1.4507 \| - \| - \| - \| - \| - \| - \| \| 19.0 \| 35 \| 6.996 \| 6.5713 \| 0.0560 \| 0.0617 \| 0.0764 \| 0.0474 \| 0.0764 \| \| 19.2308 \| 36 \| 2.0412 \| - \| - \| - \| - \| - \| - \| \| 20.0 \| 37 \| 6.4129 \| 6.5519 \| 0.061 \| 0.06 \| 0.0731 \| 0.0486 \| 0.0731 \| \| 20.3077 \| 38 \| 2.6771 \| - \| - \| - \| - \| - \| - \| \| 21.0 \| 39 \| 5.7615 \| 6.5315 \| 0.0605 \| 0.0656 \| 0.0722 \| 0.0474 \| 0.0722 \| \| 21.3846 \| 40 \| 3.2979 \| - \| - \| - \| - \| - \| - \| \| 22.0 \| 41 \| 5.1123 \| 6.5186 \| 0.0608 \| 0.0676 \| 0.0721 \| 0.0486 \| 0.0721 \| \| 22.4615 \| 42 \| 3.9339 \| - \| - \| - \| - \| - \| - \| \| 23.0 \| 43 \| 4.3093 \| 6.5020 \| 0.0607 \| 0.0816 \| 0.0626 \| 0.0473 \| 0.0626 \| \| 23.5385 \| 44 \| 4.6842 \| - \| - \| - \| - \| - \| - \| \| 24.0 \| 45 \| 3.7325 \| 6.4972 \| 0.0591 \| 0.0670 \| 0.0648 \| 0.0474 \| 0.0648 \| \| 24.6154 \| 46 \| 5.1717 \| - \| - \| - \| - \| - \| - \| \| 25.0 \| 47 \| 3.1253 \| 6.4821 \| 0.0584 \| 0.0676 \| 0.0648 \| 0.0473 \| 0.0648 \| \| 25.6923 \| 48 \| 5.7321 \| - \| - \| - \| - \| - \| - \| \| 26.0 \| 49 \| 2.4503 \| 6.4660 \| 0.0587 \| 0.0672 \| 0.0694 \| 0.0478 \| 0.0694 \| \| 26.7692 \| 50 \| 6.5409 \| - \| - \| - \| - \| - \| - \| \| 27.0 \| 51 \| 1.791 \| 6.4670 \| 0.0590 \| 0.0868 \| 0.0691 \| 0.0478 \| 0.0691 \| \| 27.8462 \| 52 \| 7.2565 \| - \| - \| - \| - \| - \| - \| \| 28.0 \| 53 \| 1.0513 \| 6.4549 \| 0.0590 \| 0.0674 \| 0.0689 \| 0.0478 \| 0.0689 \| \| 28.9231 \| 54 \| 7.7128 \| - \| - \| - \| - \| - \| - \| \| 29.0 \| 55 \| 0.4118 \| 6.4519 \| 0.0592 \| 0.0873 \| 0.0864 \| 0.0477 \| 0.0864 \| \| 30.0 \| 56 \| 8.15 \| 6.4472 \| 0.0589 \| 0.0872 \| 0.0890 \| 0.0477 \| 0.0890 \| \| 31.0 \| 57 \| 8.1077 \| - \| - \| - \| - \| - \| - \| \| 31.0769 \| 58 \| 0.0869 \| 6.4347 \| 0.0596 \| 0.0716 \| 0.0709 \| 0.0477 \| 0.0709 \| \| 32.0 \| 59 \| 7.9701 \| - \| - \| - \| - \| - \| - \| \| 32.1538 \| 60 \| 0.1293 \| 6.4306 \| 0.0580 \| 0.0897 \| 0.0880 \| 0.0474 \| 0.0880 \| \| 33.0 \| 61 \| 8.1437 \| 6.4254 \| 0.0561 \| 0.0895 \| 0.0889 \| 0.0474 \| 0.0889 \| \| 33.0769 \| 62 \| 0.7537 \| - \| - \| - \| - \| - \| - \| \| 34.0 \| 63 \| 7.4904 \| 6.4215 \| 0.0574 \| 0.0879 \| 0.0885 \| 0.0475 \| 0.0885 \| \| 34.1538 \| 64 \| 1.3546 \| - \| - \| - \| - \| - \| - \| \| 35.0 \| 65 \| 6.7289 \| 6.4152 \| 0.0555 \| 0.0888 \| 0.0701 \| 0.0474 \| 0.0701 \| \| 35.2308 \| 66 \| 1.9694 \| - \| - \| - \| - \| - \| - \| \| 36.0 \| 67 \| 6.1359 \| 6.4122 \| 0.0558 \| 0.0875 \| 0.0707 \| 0.0477 \| 0.0707 \| \| 36.3077 \| 68 \| 2.5335 \| - \| - \| - \| - \| - \| - \| \| 37.0 \| 69 \| 5.5274 \| 6.4068 \| 0.0558 \| 0.0694 \| 0.0718 \| 0.0470 \| 0.0718 \| \| 37.3846 \| 70 \| 3.1245 \| - \| - \| - \| - \| - \| - \| \| 38.0 \| 71 \| 4.8913 \| 6.4102 \| 0.0537 \| 0.0701 \| 0.0718 \| 0.0470 \| 0.0718 \| \| 38.4615 \| 72 \| 3.8402 \| - \| - \| - \| - \| - \| - \| \| 39.0 \| 73 \| 4.12 \| 6.4003 \| 0.0537 \| 0.0681 \| 0.0719 \| 0.0471 \| 0.0719 \| \| 39.5385 \| 74 \| 4.5087 \| - \| - \| - \| - \| - \| - \| \| 40.0 \| 75 \| 3.6038 \| 6.3984 \| 0.0575 \| 0.0878 \| 0.0890 \| 0.0471 \| 0.0890 \| \| 40.6154 \| 76 \| 5.1065 \| - \| - \| - \| - \| - \| - \| \| 41.0 \| 77 \| 3.042 \| 6.3966 \| 0.0577 \| 0.0684 \| 0.0715 \| 0.0469 \| 0.0715 \| \| 41.6923 \| 78 \| 5.6277 \| - \| - \| - \| - \| - \| - \| \| 42.0 \| 79 \| 2.3259 \| 6.3975 \| 0.0568 \| 0.0681 \| 0.0722 \| 0.0470 \| 0.0722 \| \| 42.7692 \| 80 \| 6.2878 \| - \| - \| - \| - \| - \| - \| \| 43.0 \| 81 \| 1.8184 \| 6.3983 \| 0.0571 \| 0.0680 \| 0.0728 \| 0.0470 \| 0.0728 \| \| 43.8462 \| 82 \| 6.9261 \| - \| - \| - \| - \| - \| - \| \| 44.0 \| 83 \| 1.0775 \| 6.3942 \| 0.0580 \| 0.0680 \| 0.0896 \| 0.0469 \| 0.0896 \| \| 44.9231 \| 84 \| 7.523 \| - \| - \| - \| - \| - \| - \| \| 45.0 \| 85 \| 0.3703 \| 6.3935 \| 0.0573 \| 0.0861 \| 0.0911 \| 0.0468 \| 0.0911 \| \| 46.0 \| 86 \| 7.945 \| 6.3961 \| 0.0555 \| 0.0855 \| 0.0898 \| 0.0468 \| 0.0898 \| \| 47.0 \| 87 \| 7.8871 \| - \| - \| - \| - \| - \| - \| \| 47.0769 \| 88 \| 0.1237 \| 6.3924 \| 0.0556 \| 0.0858 \| 0.0902 \| 0.0467 \| 0.0902 \| \| 48.0 \| 89 \| 7.9213 \| - \| - \| - \| - \| - \| - \| \| 48.1538 \| 90 \| 0.0809 \| 6.3922 \| 0.0600 \| 0.0684 \| 0.0727 \| 0.0467 \| 0.0727 \| \| 49.0 \| 91 \| 7.7954 \| 6.3901 \| 0.0601 \| 0.0858 \| 0.0907 \| 0.0467 \| 0.0907 \| \| 49.0769 \| 92 \| 0.7928 \| - \| - \| - \| - \| - \| - \| \| 50.0 \| 93 \| 7.3085 \| 6.3915 \| 0.0567 \| 0.0681 \| 0.0909 \| 0.0471 \| 0.0909 \| \| 50.1538 \| 94 \| 1.3327 \| - \| - \| - \| - \| - \| - \| \| 51.0 \| 95 \| 6.7179 \| 6.3970 \| 0.0597 \| 0.0677 \| 0.0725 \| 0.0466 \| 0.0725 \| \| 51.2308 \| 96 \| 1.9239 \| - \| - \| - \| - \| - \| - \| \| 52.0 \| 97 \| 6.0889 \| 6.3939 \| 0.0567 \| 0.0858 \| 0.0907 \| 0.0470 \| 0.0907 \| \| 52.3077 \| 98 \| 2.5265 \| - \| - \| - \| - \| - \| - \| \| 53.0 \| 99 \| 5.4464 \| 6.3943 \| 0.0539 \| 0.0677 \| 0.0732 \| 0.0466 \| 0.0732 \| \| 53.3846 \| 100 \| 3.0337 \| 6.3940 \| 0.0572 \| 0.0862 \| 0.0906 \| 0.0470 \| 0.0906 \| * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.31.0 - 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", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### 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. -->
de-coder/google-bert-bert-base-uncased-gguf	de-coder	2024-07-02T08:19:25Z	0	null	[ "region:us" ]	null	2024-07-02T08:19:25Z	Entry not found
Ryan-Pham/route_2	Ryan-Pham	2024-07-02T08:19:26Z	0	null	[ "region:us" ]	null	2024-07-02T08:19:26Z	Entry not found
abdfajar707/llama3_8B_lora_model_rkp_pn2025_v2	abdfajar707	2024-07-02T08:20:41Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T08:20:30Z	--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: abdfajar707 - 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)
bikeZero/my_awesome_model	bikeZero	2024-07-02T08:20:31Z	0	null	[ "region:us" ]	null	2024-07-02T08:20:31Z	Entry not found
PereLluis13/relik-reader-deberta-large-wikipedia-aida-full-interleave	PereLluis13	2024-07-02T08:21:25Z	0	transformers	[ "transformers", "pytorch", "relik-reader", "feature-extraction", "custom_code", "region:us" ]	feature-extraction	2024-07-02T08:20:32Z	Entry not found
PereLluis13/relik-entity-linking-large-wikipedia-aida-interleave	PereLluis13	2024-07-02T08:22:23Z	0	null	[ "region:us" ]	null	2024-07-02T08:21:40Z	Entry not found
jeromesky/prosodic_accuracy_v2	jeromesky	2024-07-02T08:58:23Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "audio-classification", "endpoints_compatible", "region:us" ]	audio-classification	2024-07-02T08:21:58Z	Entry not found
Thesiss/skin_llava	Thesiss	2024-07-02T13:06:51Z	0	transformers	[ "transformers", "safetensors", "llava_mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T08:22:04Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
SayedNabeel/Shorts_Writer_LLAMA3_8B_V0.2	SayedNabeel	2024-07-02T09:18:46Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "Story", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T08:23:57Z	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - Story --- # Uploaded model - Developed by: SayedNabeel - 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) # Story Generation - It can be used to Genertate YouTube Shorts - It is trained on 780 Steps on 6.1K rows of data
omartariq612/whisper-small-with-tajweed-tokens	omartariq612	2024-07-02T08:42:03Z	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "audio", "hf-asr-leaderboard", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "arxiv:2212.04356", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-07-02T08:25:17Z	--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - no - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition - hf-asr-leaderboard widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac model-index: - name: whisper-small results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (clean) type: librispeech_asr config: clean split: test args: language: en metrics: - name: Test WER type: wer value: 3.432213777886737 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (other) type: librispeech_asr config: other split: test args: language: en metrics: - name: Test WER type: wer value: 7.628304527060248 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: hi split: test args: language: hi metrics: - name: Test WER type: wer value: 87.3 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13.0 type: mozilla-foundation/common_voice_13_0 config: dv split: test args: language: dv metrics: - name: Wer type: wer value: 125.69809089960707 pipeline_tag: automatic-speech-recognition license: apache-2.0 --- # Whisper > This is the vanilla whisper small model with tajweed tokens, here is the [notebook](https://www.kaggle.com/code/omartariq612/whisper-small-with-tajweed-tokens/notebook) that generated that repo Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. Whisper was proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) by Alec Radford et al from OpenAI. The original code repository can be found [here](https://github.com/openai/whisper). Disclaimer: Content for this model card has partly been written by the Hugging Face team, and parts of it were copied and pasted from the original model card. ## Model details Whisper is a Transformer based encoder-decoder model, also referred to as a _sequence-to-sequence_ model. It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision. The models were trained on either English-only data or multilingual data. The English-only models were trained on the task of speech recognition. The multilingual models were trained on both speech recognition and speech translation. For speech recognition, the model predicts transcriptions in the same language as the audio. For speech translation, the model predicts transcriptions to a different language to the audio. Whisper checkpoints come in five configurations of varying model sizes. The smallest four are trained on either English-only or multilingual data. The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The checkpoints are summarised in the following table with links to the models on the Hub: \| Size \| Parameters \| English-only \| Multilingual \| \|----------\|------------\|------------------------------------------------------\|-----------------------------------------------------\| \| tiny \| 39 M \| [✓](https://huggingface.co/openai/whisper-tiny.en) \| [✓](https://huggingface.co/openai/whisper-tiny) \| \| base \| 74 M \| [✓](https://huggingface.co/openai/whisper-base.en) \| [✓](https://huggingface.co/openai/whisper-base) \| \| small \| 244 M \| [✓](https://huggingface.co/openai/whisper-small.en) \| [✓](https://huggingface.co/openai/whisper-small) \| \| medium \| 769 M \| [✓](https://huggingface.co/openai/whisper-medium.en) \| [✓](https://huggingface.co/openai/whisper-medium) \| \| large \| 1550 M \| x \| [✓](https://huggingface.co/openai/whisper-large) \| \| large-v2 \| 1550 M \| x \| [✓](https://huggingface.co/openai/whisper-large-v2) \| # Usage To transcribe audio samples, the model has to be used alongside a [`WhisperProcessor`](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperProcessor). The `WhisperProcessor` is used to: 1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model) 2. Post-process the model outputs (converting them from tokens to text) The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order: 1. The transcription always starts with the `<\|startoftranscript\|>` token 2. The second token is the language token (e.g. `<\|en\|>` for English) 3. The third token is the "task token". It can take one of two values: `<\|transcribe\|>` for speech recognition or `<\|translate\|>` for speech translation 4. In addition, a `<\|notimestamps\|>` token is added if the model should not include timestamp prediction Thus, a typical sequence of context tokens might look as follows: ``` <\|startoftranscript\|> <\|en\|> <\|transcribe\|> <\|notimestamps\|> ``` Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps. These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at each position. This allows one to control the output language and task for the Whisper model. If they are un-forced, the Whisper model will automatically predict the output langauge and task itself. The context tokens can be set accordingly: ```python model.config.forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="english", task="transcribe") ``` Which forces the model to predict in English under the task of speech recognition. ## Transcription ### English to English In this example, the context tokens are 'unforced', meaning the model automatically predicts the output language (English) and task (transcribe). ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") >>> model.config.forced_decoder_ids = None >>> # load dummy dataset and read audio files >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> sample = ds[0]["audio"] >>> input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False) ['<\|startoftranscript\|><\|en\|><\|transcribe\|><\|notimestamps\|> Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.<\|endoftext\|>'] >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.'] ``` The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`. ### French to French The following example demonstrates French to French transcription by setting the decoder ids appropriately. ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import Audio, load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") >>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="transcribe") >>> # load streaming dataset and read first audio sample >>> ds = load_dataset("common_voice", "fr", split="test", streaming=True) >>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) >>> input_speech = next(iter(ds))["audio"] >>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids) ['<\|startoftranscript\|><\|fr\|><\|transcribe\|><\|notimestamps\|> Un vrai travail intéressant va enfin être mené sur ce sujet.<\|endoftext\|>'] >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' Un vrai travail intéressant va enfin être mené sur ce sujet.'] ``` ## Translation Setting the task to "translate" forces the Whisper model to perform speech translation. ### French to English ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import Audio, load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") >>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="translate") >>> # load streaming dataset and read first audio sample >>> ds = load_dataset("common_voice", "fr", split="test", streaming=True) >>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) >>> input_speech = next(iter(ds))["audio"] >>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' A very interesting work, we will finally be given on this subject.'] ``` ## Evaluation This code snippet shows how to evaluate Whisper Small on [LibriSpeech test-clean](https://huggingface.co/datasets/librispeech_asr): ```python >>> from datasets import load_dataset >>> from transformers import WhisperForConditionalGeneration, WhisperProcessor >>> import torch >>> from evaluate import load >>> librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test") >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small").to("cuda") >>> def map_to_pred(batch): >>> audio = batch["audio"] >>> input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features >>> batch["reference"] = processor.tokenizer._normalize(batch['text']) >>> >>> with torch.no_grad(): >>> predicted_ids = model.generate(input_features.to("cuda"))[0] >>> transcription = processor.decode(predicted_ids) >>> batch["prediction"] = processor.tokenizer._normalize(transcription) >>> return batch >>> result = librispeech_test_clean.map(map_to_pred) >>> wer = load("wer") >>> print(100 * wer.compute(references=result["reference"], predictions=result["prediction"])) 3.432213777886737 ``` ## Long-Form Transcription The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) method. Chunking is enabled by setting `chunk_length_s=30` when instantiating the pipeline. With chunking enabled, the pipeline can be run with batched inference. It can also be extended to predict sequence level timestamps by passing `return_timestamps=True`: ```python >>> import torch >>> from transformers import pipeline >>> from datasets import load_dataset >>> device = "cuda:0" if torch.cuda.is_available() else "cpu" >>> pipe = pipeline( >>> "automatic-speech-recognition", >>> model="openai/whisper-small", >>> chunk_length_s=30, >>> device=device, >>> ) >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> sample = ds[0]["audio"] >>> prediction = pipe(sample.copy(), batch_size=8)["text"] " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel." >>> # we can also return timestamps for the predictions >>> prediction = pipe(sample.copy(), batch_size=8, return_timestamps=True)["chunks"] [{'text': ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.', 'timestamp': (0.0, 5.44)}] ``` Refer to the blog post [ASR Chunking](https://huggingface.co/blog/asr-chunking) for more details on the chunking algorithm. ## Fine-Tuning The pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However, its predictive capabilities can be improved further for certain languages and tasks through fine-tuning. The blog post [Fine-Tune Whisper with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper) provides a step-by-step guide to fine-tuning the Whisper model with as little as 5 hours of labelled data. ### Evaluated Use The primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research. The models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them. In particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes. ## Training Data The models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages. As discussed in [the accompanying paper](https://cdn.openai.com/papers/whisper.pdf), we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language. ## Performance and Limitations Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level. However, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself. Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in [the paper accompanying this release](https://cdn.openai.com/papers/whisper.pdf). In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in [the paper](https://cdn.openai.com/papers/whisper.pdf). It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages. ## Broader Implications We anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications. There are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects. ### BibTeX entry and citation info ```bibtex @misc{radford2022whisper, doi = {10.48550/ARXIV.2212.04356}, url = {https://arxiv.org/abs/2212.04356}, author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya}, title = {Robust Speech Recognition via Large-Scale Weak Supervision}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ```
apwic/summarization-lora-0	apwic	2024-07-02T12:00:03Z	0	null	[ "generated_from_trainer", "id", "base_model:LazarusNLP/IndoNanoT5-base", "license:apache-2.0", "region:us" ]	null	2024-07-02T08:25:33Z	--- language: - id license: apache-2.0 base_model: LazarusNLP/IndoNanoT5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: summarization-lora-0 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. --> # summarization-lora-0 This model is a fine-tuned version of [LazarusNLP/IndoNanoT5-base](https://huggingface.co/LazarusNLP/IndoNanoT5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5656 - Rouge1: 0.4188 - Rouge2: 0.0 - Rougel: 0.4161 - Rougelsum: 0.4157 - Gen Len: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 1.2271 \| 1.0 \| 1783 \| 0.6275 \| 0.491 \| 0.0 \| 0.4864 \| 0.4859 \| 1.0 \| \| 0.7893 \| 2.0 \| 3566 \| 0.5955 \| 0.4382 \| 0.0 \| 0.4358 \| 0.4345 \| 1.0 \| \| 0.7347 \| 3.0 \| 5349 \| 0.5738 \| 0.4461 \| 0.0 \| 0.4432 \| 0.4417 \| 1.0 \| \| 0.7084 \| 4.0 \| 7132 \| 0.5618 \| 0.4416 \| 0.0 \| 0.4409 \| 0.4389 \| 1.0 \| \| 0.6976 \| 5.0 \| 8915 \| 0.5656 \| 0.4188 \| 0.0 \| 0.4161 \| 0.4157 \| 1.0 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
chienweichang/Llama-3-Taiwan-8B-Instruct-128K-GGUF	chienweichang	2024-07-03T01:31:29Z	0	null	[ "gguf", "zhtw", "text-generation", "zh", "en", "arxiv:2403.20180", "arxiv:2311.17487", "base_model:yentinglin/Llama-3-Taiwan-8B-Instruct-128k", "license:llama3", "region:us" ]	text-generation	2024-07-02T08:25:36Z	Temporary Redirect. Redirecting to /chienweichang/Llama-3-Taiwan-8B-Instruct-128k-GGUF/resolve/main/README.md
camillop/phi-mini-company-classification-gguf-q4m	camillop	2024-07-02T08:27:39Z	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T08:25:40Z	--- base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf --- # Uploaded model - Developed by: camillop - License: apache-2.0 - Finetuned from model : unsloth/phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Dev372/HarshDev-whisper-tiny-English_2000	Dev372	2024-07-02T08:25:50Z	0	null	[ "region:us" ]	null	2024-07-02T08:25:50Z	Entry not found
vive0921/dog-sdxl	vive0921	2024-07-02T08:26:19Z	0	null	[ "region:us" ]	null	2024-07-02T08:26:19Z	Entry not found
Ram07/sri	Ram07	2024-07-02T20:20:52Z	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T08:27:20Z	--- license: mit ---
baxtos/bartik01-4	baxtos	2024-07-02T08:30:07Z	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T08:27:40Z	Entry not found
AlexWortega/bertoid	AlexWortega	2024-07-02T08:32:06Z	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "text-embeddings-inference", "region:us" ]	feature-extraction	2024-07-02T08:29:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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loginworks/Meta-Llama-3-8B-code	loginworks	2024-07-02T13:30:52Z	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T08:30:53Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
philnet/plantynet-mt5-en2kr	philnet	2024-07-02T08:36:06Z	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	2024-07-02T08:30:59Z	Entry not found
whizzzzkid/whizzzzkid_396_5	whizzzzkid	2024-07-02T08:32:20Z	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T08:31:59Z	Entry not found
nourheshamshaheen/llava_FEDPROX_8epochs_2000steps_2clients	nourheshamshaheen	2024-07-02T08:46:41Z	0	transformers	[ "transformers", "safetensors", "llava_llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T08:32:04Z	Entry not found
hflqf88888/OdysseyAgent-device	hflqf88888	2024-07-02T09:20:36Z	0	transformers	[ "transformers", "pytorch", "qwen", "text-generation", "GUI", "custom_code", "en", "zh", "dataset:OpenGVLab/GUI-Odyssey", "license:cc-by-4.0", "autotrain_compatible", "region:us" ]	text-generation	2024-07-02T08:32:25Z	--- license: cc-by-4.0 datasets: - OpenGVLab/GUI-Odyssey language: - en - zh tags: - GUI --- ## OdysseyAgent-device The OdysseyAgent fine-tuned on Train-Device split.
whizzzzkid/whizzzzkid_397_3	whizzzzkid	2024-07-02T08:33:19Z	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T08:32:59Z	Entry not found
baxtos/bartik02-4	baxtos	2024-07-02T08:35:45Z	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T08:33:16Z	Entry not found
whizzzzkid/whizzzzkid_398_4	whizzzzkid	2024-07-02T08:34:21Z	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T08:33:58Z	Entry not found
LarryAIDraw/ChamHarmoniePonyXL	LarryAIDraw	2024-07-02T08:41:24Z	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-07-02T08:34:04Z	--- license: creativeml-openrail-m --- https://civitai.com/models/543457/harmonie-3-outfits-or-arknights-or-pony-xl
whizzzzkid/whizzzzkid_399_1	whizzzzkid	2024-07-02T08:35:21Z	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T08:34:59Z	Entry not found
henrik-dra/paligemma-ft-energymeter	henrik-dra	2024-07-02T14:56:23Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-02T08:35:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
infinitymatter/llama-3-8b-chat_army	infinitymatter	2024-07-02T08:36:58Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-02T08:35:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
whizzzzkid/whizzzzkid_400_7	whizzzzkid	2024-07-02T08:36:31Z	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T08:36:10Z	Entry not found
whizzzzkid/whizzzzkid_401_6	whizzzzkid	2024-07-02T08:37:30Z	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T08:37:11Z	Entry not found
femiari/Qwen1.5-7B-Moe	femiari	2024-07-02T08:47:57Z	0	transformers	[ "transformers", "safetensors", "qwen2_moe", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T08:38:41Z	Entry not found
ZeroWw/glm-4-9b-chat-GGUF	ZeroWw	2024-07-02T09:27:44Z	0	null	[ "gguf", "en", "license:mit", "region:us" ]	null	2024-07-02T08:38:49Z	--- license: mit language: - en --- My own (ZeroWw) quantizations. output and embed tensors quantized to f16. all other tensors quantized to q5_k or q6_k. Result: both f16.q6 and f16.q5 are smaller than q8_0 standard quantization and they perform as well as the pure f16.
tamilanda/efedf	tamilanda	2024-07-02T08:38:59Z	0	null	[ "license:gpl-2.0", "region:us" ]	null	2024-07-02T08:38:59Z	--- license: gpl-2.0 ---

sakasaku/dqn-SpaceInvadersNoFrameskip-v4

sakasaku

2024-07-02T07:40:37Z

0

null

[ "region:us" ]

null

2024-07-02T07:40:37Z

Entry not found

SamagraDataGov/e2e_testtt

SamagraDataGov

2024-07-02T07:41:45Z

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-07-02T07:41:04Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

davidyu2023/Qwen-Qwen1.5-0.5B-1719906099

davidyu2023

2024-07-02T07:41:49Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen1.5-0.5B", "region:us" ]

null

2024-07-02T07:41:40Z

--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [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

Echelon-AI/Med-Qwen2-7B

Echelon-AI

2024-07-02T14:12:16Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "dataset:Malikeh1375/medical-question-answering-datasets", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-02T07:41:49Z

--- license: apache-2.0 datasets: - Malikeh1375/medical-question-answering-datasets --- Qwen2 7B, after finetuning on a medical dataset, demonstrates enhanced performance in medical text understanding and generation ### Model Description The model shows improved accuracy in diagnosing medical conditions, generating specialized medical texts, and responding to medical queries with contextually relevant information. This adaptation equips Med-Qwen2 to support advanced applications in healthcare, offering nuanced insights and precise language processing tailored for medical professionals and patients alike - **Finetuned from model :** [https://huggingface.co/Qwen/Qwen2-7B-Instruct](Qwen2-7B-Instruct) [GGUF](https://huggingface.co/Echelon-AI/Med-Qwen2-GGUF) ## Uses - Diagnosing medical conditions with improved accuracy. - Generating specialized medical texts and reports. - Providing contextually relevant responses to medical queries. - Supporting advanced applications in healthcare with precise language processing.

gnkbhuvan/phi-2-health

gnkbhuvan

2024-07-02T08:09:00Z

0

adapter-transformers

[ "adapter-transformers", "safetensors", "phi-msft", "medical", "text-generation", "custom_code", "en", "dataset:wangrongsheng/HealthCareMagic-100k-en", "license:apache-2.0", "region:us" ]

text-generation

2024-07-02T07:41:58Z

--- license: apache-2.0 datasets: - wangrongsheng/HealthCareMagic-100k-en language: - en metrics: - accuracy library_name: adapter-transformers pipeline_tag: text-generation tags: - medical ---

kapilrk04/indicbart_multiway_mt_model

kapilrk04

2024-07-02T22:31:24Z

0

transformers

[ "transformers", "safetensors", "mbart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-07-02T07:43:19Z

Entry not found

kazars24/wav2vec2-base-rus-golos-100h-farfield

kazars24

2024-07-02T15:22:40Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-base-960h", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-07-02T07:44:19Z

--- license: apache-2.0 base_model: facebook/wav2vec2-base-960h tags: - generated_from_trainer model-index: - name: wav2vec2-base-rus-golos-100h-farfield results: [] ---  # wav2vec2-base-rus-golos-100h-farfield This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1

mreza629/coca

mreza629

2024-07-02T07:47:50Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-07-02T07:45:13Z

--- license: apache-2.0 ---

Uphando-ng/naija_eng_female_accent_tts

Uphando-ng

2024-07-02T07:53:29Z

0

transformers

[ "transformers", "safetensors", "vits", "text-to-speech", "license:apache-2.0", "endpoints_compatible", "region:us" ]

text-to-speech

2024-07-02T07:45:15Z

--- license: apache-2.0 pipeline_tag: text-to-speech ---

Uphando-ng/naija_eng_male_accent_tts

Uphando-ng

2024-07-02T07:53:06Z

0

transformers

[ "transformers", "safetensors", "vits", "text-to-speech", "license:apache-2.0", "endpoints_compatible", "region:us" ]

text-to-speech

2024-07-02T07:45:36Z

--- license: apache-2.0 pipeline_tag: text-to-speech ---

habulaj/1832321302

habulaj

2024-07-02T07:45:43Z

0

null

[ "region:us" ]

null

2024-07-02T07:45:38Z

Entry not found

CatBarks/flant5small-lora-oasst1_model

CatBarks

2024-07-02T07:55:03Z

0

transformers

[ "transformers", "safetensors", "question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

question-answering

2024-07-02T07:45:50Z

--- library_name: transformers pipeline_tag: question-answering --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

CatBarks/flant5small-lora-oasst1_tokenizer

CatBarks

2024-07-02T07:45:56Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-07-02T07:45:54Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Dabitron/Qwen2-7B-Instruct-abliterated-Q4_K_M-GGUF

Dabitron

2024-07-02T07:46:18Z

0

null

[ "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:natong19/Qwen2-7B-Instruct-abliterated", "license:apache-2.0", "region:us" ]

text-generation

2024-07-02T07:45:58Z

--- base_model: natong19/Qwen2-7B-Instruct-abliterated language: - en license: apache-2.0 pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # Dabitron/Qwen2-7B-Instruct-abliterated-Q4_K_M-GGUF This model was converted to GGUF format from [`natong19/Qwen2-7B-Instruct-abliterated`](https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q4_K_M-GGUF --hf-file qwen2-7b-instruct-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q4_K_M-GGUF --hf-file qwen2-7b-instruct-abliterated-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q4_K_M-GGUF --hf-file qwen2-7b-instruct-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q4_K_M-GGUF --hf-file qwen2-7b-instruct-abliterated-q4_k_m.gguf -c 2048 ```

ThomasSimonini/dqn-SpaceInvadersNoFrameskip-v4BB

ThomasSimonini

2024-07-02T07:46:45Z

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2024-07-02T07:46:16Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 257.00 +/- 38.81 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ThomasSimonini -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ThomasSimonini -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ThomasSimonini ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

nbadrinath/ikea_room_designs_sd1.5_lora_full_finetuning_020720240714

nbadrinath

2024-07-02T10:09:43Z

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2024-07-02T07:47:21Z

--- base_model: runwayml/stable-diffusion-v1-5 library_name: diffusers license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora inference: true ---  # LoRA text2image fine-tuning - nbadrinath/ikea_room_designs_sd1.5_lora_full_finetuning_020720240714 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the nbadrinath/ikea_dataset_5.0 dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ## 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]

Echelon-AI/Med-Qwen2-GGUF

Echelon-AI

2024-07-02T08:08:41Z

0

null

[ "gguf", "license:apache-2.0", "region:us" ]

null

2024-07-02T07:48:32Z

--- license: apache-2.0 --- Qwen2 7B, after finetuning on a medical dataset, demonstrates enhanced performance in medical text understanding and generation ### Model Description The model shows improved accuracy in diagnosing medical conditions, generating specialized medical texts, and responding to medical queries with contextually relevant information. This adaptation equips Med-Qwen2 to support advanced applications in healthcare, offering nuanced insights and precise language processing tailored for medical professionals and patients alike - **Finetuned from model :** [https://huggingface.co/Qwen/Qwen2-7B-Instruct](Qwen2-7B-Instruct) ## Uses - Diagnosing medical conditions with improved accuracy. - Generating specialized medical texts and reports. - Providing contextually relevant responses to medical queries. - Supporting advanced applications in healthcare with precise language processing. ## Sample Outputs: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/658efeb9f7291078f9a0ef6e/cqMlPdOpLE1pQjJEbEB_G.png)

Poxios/STOCK_SOLAR-10.7B-Q4_K_M-GGUF

Poxios

2024-07-02T07:49:07Z

0

transformers

[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:kihoonlee/STOCK_SOLAR-10.7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-07-02T07:48:40Z

--- base_model: kihoonlee/STOCK_SOLAR-10.7B library_name: transformers license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # Poxios/STOCK_SOLAR-10.7B-Q4_K_M-GGUF This model was converted to GGUF format from [`kihoonlee/STOCK_SOLAR-10.7B`](https://huggingface.co/kihoonlee/STOCK_SOLAR-10.7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/kihoonlee/STOCK_SOLAR-10.7B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Poxios/STOCK_SOLAR-10.7B-Q4_K_M-GGUF --hf-file stock_solar-10.7b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Poxios/STOCK_SOLAR-10.7B-Q4_K_M-GGUF --hf-file stock_solar-10.7b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Poxios/STOCK_SOLAR-10.7B-Q4_K_M-GGUF --hf-file stock_solar-10.7b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Poxios/STOCK_SOLAR-10.7B-Q4_K_M-GGUF --hf-file stock_solar-10.7b-q4_k_m.gguf -c 2048 ```

TakuyaGemma/maki

TakuyaGemma

2024-07-02T07:49:31Z

0

null

[ "region:us" ]

null

2024-07-02T07:48:59Z

Entry not found

z3n7r4ck3r/filtered_dataset_20240702_095025

z3n7r4ck3r

2024-07-02T07:50:24Z

0

null

[ "region:us" ]

null

2024-07-02T07:50:24Z

Entry not found

YoSHiK/whisper-small-ja

YoSHiK

2024-07-02T09:47:46Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-07-02T07:50:35Z

Entry not found

flammenai/Mahou-1.3-spark-7B

flammenai

2024-07-02T12:00:59Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "dataset:flammenai/FlameMix-DPO-v1", "dataset:flammenai/MahouMix-v1", "dataset:flammenai/Grill-Flammen-v1_chatML", "base_model:arcee-ai/Arcee-Spark", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-02T07:50:47Z

--- library_name: transformers license: apache-2.0 base_model: - arcee-ai/Arcee-Spark datasets: - flammenai/FlameMix-DPO-v1 - flammenai/MahouMix-v1 - flammenai/Grill-Flammen-v1_chatML --- ![image/png](https://huggingface.co/flammenai/Mahou-1.0-mistral-7B/resolve/main/mahou1.png) # Mahou-1.3-spark-7B Mahou is designed to provide short messages in a conversational context. It is capable of casual conversation and character roleplay. ### Chat Format This model has been trained to use ChatML format. Note the additional tokens in [tokenizer_config.json](tokenizer_config.json). ``` <|im_start|>system {{system}}<|im_end|> <|im_start|>{{char}} {{message}}<|im_end|> <|im_start|>{{user}} {{message}}<|im_end|> ``` ### Roleplay Format - Speech without quotes. - Actions in `*asterisks*` ``` *leans against wall cooly* so like, i just casted a super strong spell at magician academy today, not gonna lie, felt badass. ``` ### SillyTavern Settings 1. Use ChatML for the Context Template. 2. Enable Instruct Mode. 3. Use the [Mahou preset](https://huggingface.co/datasets/flammenai/Mahou-ST-ChatML-Instruct/raw/main/Mahou.json). 4. *Recommended* Additonal stopping strings: `["\n", "<|", "</"]` ### Method Finetuned for 3 epochs using an A100 on Google Colab. [Fine-tune Llama 3 with ORPO](https://huggingface.co/blog/mlabonne/orpo-llama-3) - [Maxime Labonne](https://huggingface.co/mlabonne)

Dabitron/Qwen2-7B-Instruct-abliterated-Q6_K-GGUF

Dabitron

2024-07-02T07:51:27Z

0

null

[ "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:natong19/Qwen2-7B-Instruct-abliterated", "license:apache-2.0", "region:us" ]

text-generation

2024-07-02T07:51:01Z

--- base_model: natong19/Qwen2-7B-Instruct-abliterated language: - en license: apache-2.0 pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # Dabitron/Qwen2-7B-Instruct-abliterated-Q6_K-GGUF This model was converted to GGUF format from [`natong19/Qwen2-7B-Instruct-abliterated`](https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q6_K-GGUF --hf-file qwen2-7b-instruct-abliterated-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q6_K-GGUF --hf-file qwen2-7b-instruct-abliterated-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q6_K-GGUF --hf-file qwen2-7b-instruct-abliterated-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q6_K-GGUF --hf-file qwen2-7b-instruct-abliterated-q6_k.gguf -c 2048 ```

vgarg/fw_identification_model_e5_large_v7_02_07_2024

vgarg

2024-07-02T07:53:22Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "xlm-roberta", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2024-07-02T07:51:17Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # vgarg/fw_identification_model_e5_large_v7_02_07_2024 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("vgarg/fw_identification_model_e5_large_v7_02_07_2024") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

NeelShrimali/dir

NeelShrimali

2024-07-02T07:57:54Z

0

transformers

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-07-02T07:51:50Z

--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - **Developed by:** NeelShrimali - **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)

cyan2k/promptvieh_lora

cyan2k

2024-07-02T07:54:20Z

0

transformers

[ "transformers", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-07-02T07:54:19Z

--- base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - **Developed by:** cyan2k - **License:** apache-2.0 - **Finetuned from model :** unsloth/phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

z3n7r4ck3r/filtered_dataset_20240702_095438

z3n7r4ck3r

2024-07-02T07:54:37Z

0

null

[ "region:us" ]

null

2024-07-02T07:54:37Z

Entry not found

habulaj/8883165414

habulaj

2024-07-02T07:56:02Z

0

null

[ "region:us" ]

null

2024-07-02T07:56:00Z

Entry not found

Roooy/whisper-tiny-ko3

Roooy

2024-07-02T08:22:45Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ko", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-07-02T07:56:06Z

--- base_model: openai/whisper-tiny datasets: - mozilla-foundation/common_voice_11_0 language: - ko license: apache-2.0 metrics: - wer tags: - generated_from_trainer model-index: - name: Whisper Tiny Ko - Roooy results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Common Voice 17.0 type: mozilla-foundation/common_voice_11_0 config: ko split: None args: 'config: ko, split: test' metrics: - type: wer value: 55.80595874713522 name: Wer ---  # Whisper Tiny Ko - Roooy This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.8060 - Cer: 25.1580 - Wer: 55.8060 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | Wer | |:-------------:|:-------:|:----:|:---------------:|:-------:|:-------:| | 0.5449 | 2.2222 | 100 | 0.7823 | 25.3686 | 55.8442 | | 0.2663 | 4.4444 | 200 | 0.7768 | 25.5893 | 56.3407 | | 0.1466 | 6.6667 | 300 | 0.7857 | 25.1580 | 55.6532 | | 0.0953 | 8.8889 | 400 | 0.8016 | 25.3686 | 55.6150 | | 0.076 | 11.1111 | 500 | 0.8060 | 25.1580 | 55.8060 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

noake/segformer-b0-finetuned-segments-sidewalk-2

noake

2024-07-02T07:56:30Z

0

null

[ "region:us" ]

null

2024-07-02T07:56:30Z

Entry not found

gassirbek/wav2vec2-large-mms-1b-kaz-colab

gassirbek

2024-07-02T07:57:18Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-07-02T07:57:17Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

tinnguyen/falcon-openassistant-toxicity-increase-30-epochs

tinnguyen

2024-07-02T08:01:29Z

0

transformers

[ "transformers", "safetensors", "falcon", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-02T07:57:28Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_M-GGUF

Dabitron

2024-07-02T07:58:14Z

0

null

[ "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:natong19/Qwen2-7B-Instruct-abliterated", "license:apache-2.0", "region:us" ]

text-generation

2024-07-02T07:57:51Z

--- base_model: natong19/Qwen2-7B-Instruct-abliterated language: - en license: apache-2.0 pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_M-GGUF This model was converted to GGUF format from [`natong19/Qwen2-7B-Instruct-abliterated`](https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_M-GGUF --hf-file qwen2-7b-instruct-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_M-GGUF --hf-file qwen2-7b-instruct-abliterated-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_M-GGUF --hf-file qwen2-7b-instruct-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_M-GGUF --hf-file qwen2-7b-instruct-abliterated-q5_k_m.gguf -c 2048 ```

KasuleTrevor/wav2vec2-large-xls-r-300m-lg-cv-50hr-v1

KasuleTrevor

2024-07-02T11:23:53Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-07-02T07:58:01Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Yash0109/diaratechHf_llama49307095-fde8-4f40-b5ce-572eb6bcc729

Yash0109

2024-07-02T08:01:59Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "text-generation", "conversational", "dataset:generator", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]

text-generation

2024-07-02T07:59:36Z

--- base_model: mistralai/Mistral-7B-Instruct-v0.2 datasets: - generator library_name: peft license: apache-2.0 pipeline_tag: text-generation tags: - trl - sft - generated_from_trainer model-index: - name: diaratechHf_llama49307095-fde8-4f40-b5ce-572eb6bcc729 results: [] ---  # diaratechHf_llama49307095-fde8-4f40-b5ce-572eb6bcc729 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator 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: constant - training_steps: 2 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.39.3 - Pytorch 2.3.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

NgTMDuc/model_weight

NgTMDuc

2024-07-02T07:59:52Z

0

null

[ "region:us" ]

null

2024-07-02T07:59:52Z

Entry not found

adhityaprimandhika/fine-tuned-mdeberta-category-by-notes

adhityaprimandhika

2024-07-02T08:03:52Z

0

transformers

[ "transformers", "safetensors", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-07-02T08:00:09Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

lithiumice/smplx_blender_addon_data

lithiumice

2024-07-02T08:07:37Z

0

null

[ "region:us" ]

null

2024-07-02T08:00:54Z

Entry not found

gritli/distilbert-left

gritli

2024-07-02T08:01:54Z

0

transformers

[ "transformers", "zero-shot-classification", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

zero-shot-classification

2024-07-02T08:01:02Z

--- library_name: transformers pipeline_tag: zero-shot-classification --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

jameswilly991/worldtransdocuments

jameswilly991

2024-07-02T08:01:08Z

0

null

[ "license:other", "region:us" ]

null

2024-07-02T08:01:08Z

--- license: other license_name: worldtransdocuments license_link: LICENSE ---

Mayonnaisu/donut-kompetansebevis-v2

Mayonnaisu

2024-07-02T08:01:35Z

0

null

[ "region:us" ]

null

2024-07-02T08:01:35Z

Entry not found

QuantFactory/llama3-8B-DarkIdol-2.1-Uncensored-1048K-GGUF

QuantFactory

2024-07-02T08:54:37Z

0

null

[ "gguf", "region:us" ]

null

2024-07-02T08:01:39Z

Entry not found

hflqf88888/OdysseyAgent-task

hflqf88888

2024-07-02T09:20:15Z

0

transformers

[ "transformers", "pytorch", "qwen", "text-generation", "GUI", "custom_code", "en", "zh", "dataset:OpenGVLab/GUI-Odyssey", "license:cc-by-4.0", "autotrain_compatible", "region:us" ]

text-generation

2024-07-02T08:02:43Z

--- license: cc-by-4.0 datasets: - OpenGVLab/GUI-Odyssey language: - en - zh tags: - GUI --- ## OdysseyAgent-random The OdysseyAgent fine-tuned on Train-Task split.

AesopX/AA

AesopX

2024-07-02T08:03:03Z

0

null

[ "region:us" ]

null

2024-07-02T08:03:03Z

Entry not found

Moriacrafter/Qwen1.5-7B-4bit_DepressionDetection

Moriacrafter

2024-07-02T08:07:50Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-02T08:03:25Z

--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Raelina/Raemu-XL-V4

Raelina

2024-07-02T12:40:42Z

0

1

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "en", "base_model:Raelina/Rae-Diffusion-XL-V2", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

2024-07-02T08:04:11Z

--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en tags: - text-to-image - stable-diffusion - safetensors - stable-diffusion-xl base_model: Raelina/Rae-Diffusion-XL-V2 --- <style> .title-container { display: flex; justify-content: center; align-items: center; height: 100vh; /* Adjust this value to position the title vertically */ } .title { font-size: 2.5em; text-align: center; color: #333; font-family: 'Helvetica Neue', sans-serif; text-transform: uppercase; letter-spacing: 0.1em; padding: 0.5em 0; background: transparent; } .title span { background: -webkit-linear-gradient(45deg, #ff7a52, #a5cff0); -webkit-background-clip: text; -webkit-text-fill-color: transparent; } .custom-table { table-layout: fixed; width: 100%; border-collapse: collapse; margin-top: 2em; } .custom-table td { width: 50%; vertical-align: top; padding: 10px; box-shadow: 0px 0px 0px 0px rgba(0, 0, 0, 0.15); } .custom-image-container { position: relative; width: 100%; margin-bottom: 0em; overflow: hidden; border-radius: 10px; transition: transform .7s; } .custom-image-container:hover { transform: scale(1.05); } .custom-image { width: 100%; height: auto; object-fit: cover; border-radius: 10px; transition: transform .7s; margin-bottom: 0em; } .nsfw-filter { filter: blur(8px); transition: filter 0.3s ease; } .custom-image-container:hover .nsfw-filter { filter: none; } .overlay { position: absolute; bottom: 0; left: 0; right: 0; color: white; width: 100%; height: 40%; display: flex; flex-direction: column; justify-content: center; align-items: center; font-size: 1vw; font-style: bold; text-align: center; opacity: 0; background: linear-gradient(0deg, rgba(0, 0, 0, 0.8) 60%, rgba(0, 0, 0, 0) 100%); transition: opacity .5s; } .custom-image-container:hover .overlay { opacity: 1; } .overlay-text { background: linear-gradient(45deg, #F1F8E8, #F1F8E8); -webkit-background-clip: text; color: transparent; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.7); } .overlay-subtext { font-size: 0.75em; margin-top: 0.5em; font-style: italic; } .overlay, .overlay-subtext { text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.5); } </style> <h1 class="title"> <span>Raemu XL V4</span> </h1> <table class="custom-table"> <tr> <td> <div class="custom-image-container"> <img class="custom-image" src="https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/KmLBQhfvwNfPire0wsJ2r.png" alt="Sample Image 1"> <div class="overlay"> <div class="overlay-text">Mizuno Ai</div> </div> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/xzyMh8h90rZDVxpfZq0UD.png" alt="Sample Image 2"> <div class="overlay"> <div class="overlay-text">Cecillia Alcot</div> </div> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/wcga0-fZPfToZOc8ygpkN.png" alt="Sample Image 3"> <div class="overlay"> <div class="overlay-text">Miia</div> </div> </div> </td> </tr> <tr> <td> <div class="custom-image-container"> <img class="custom-image" src="https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/L2YsLdZsPZMlWrkEUPyvv.png" alt="Sample Image 4"> <div class="overlay"> <div class="overlay-text">Akaza Akari</div> </div> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/dhs2BM3HDZ9VOAzyb-6IJ.jpeg" alt="Sample Image 5"> <div class="overlay"> <div class="overlay-text">Lili</div> </div> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/pfH6AwI389cTm1KLyhnt2.png" alt="Sample Image 6"> <div class="overlay"> <div class="overlay-text">Lucy Heartfilia</div> </div> </div> </td> </tr> </table> ## Overview **Raemu XL V4** is a merged model that focused in 2.5D Anime ## Model Details - **Developed by**: [Raelina](https://civitai.com/user/Raelina) - **Model type**: Diffusion-based text-to-image generative model - **Model Description**: Generate high-quality anime images from textual prompts - **License**: [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/) - **Finetuned from**: [Rae Diffusion XL V2](https://huggingface.co/Raelina/Rae-Diffusion-XL-V2) ### Usage Guidelines ## Tag Ordering For optimal results, it's recommended to follow the structured prompt template because we train the model like this: ``` 1girl/1boy, character name, from which series, everything else in any order. ``` ## Special Tag Raemu XL inherits special tags from Rae DIffusion XL V2 to enhance image generation by steering results toward quality and aesthetic. While the model can generate images without these tags, using them helps achieve better results. - **Quality tags:** masterpiece, best quality, great quality, good quality, normal quality, low quality, worst quality - **Aesthetic tags:** very aesthetic, aesthetic, displeasing, very displeasing ## Recommended settings - **Positive prompts:** ``` masterpiece, best quality, very aesthetic, absurdres, ``` - **Negative prompts:** ``` (low quality, worst quality:1.2), very displeasing, ugly, poorly drawn, signature, watermark, ``` - **CFG:** 7 - **Sampling steps:** 25 to 35 - **Sampler:** Euler a - **Supported Resolution:** ``` 1024 x 1024, 1152 x 896, 896 x 1152, 1216 x 832, 832 x 1216, 1344 x 768, 768 x 1344, 1536 x 640, 640 x 1536 ``` ## Hires.fix Setting - **Upscaler:** [4x_NMKD-YandereNeoXL](https://nmkd.de/?esrgan) - **Hires step:** 10-15 - **Denoising:** 0.1-0.3 or 0.55 for latent upscaler ## Merge Parameter 1. Rae Diffusion XL V2 merged to RealCartoonXL V6 using MBW (0.0,1.0,0.8,0.5,0.25,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.5,0.71,1.0,0.56,0.71,1.0,0.83,0.1,0) 2. (1) merged with Blue Pencil XL v3.1.0 using MBW (0.0,0.11,0.22,0.33,0.44,0.55,0.44,0.33,0.22,0.11,0.0,0.11,0.22,0.33,0.44,0.55,0.44,0.33,0.22,0.11,0) 3. Raemu XL V4 ## License Raemu XL V4 uses the [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/) inherited from Rae Diffusion XL V2, compatible with Stable Diffusion models. Key points: 1. **Modification Sharing:** If you modify Raemu XL V4, you must share both your changes and the original license. 2. **Source Code Accessibility:** If your modified version is network-accessible, provide a way (like a download link) for others to get the source code. This applies to derived models too. 3. **Distribution Terms:** Any distribution must be under this license or another with similar rules. 4. **Compliance:** Non-compliance must be fixed within 30 days to avoid license termination, emphasizing transparency and adherence to open-source values.

anushaporwal/wav2vec2-common_voice-tr-demo-mini-multiGPU-tr

anushaporwal

2024-07-02T08:04:31Z

0

null

[ "region:us" ]

null

2024-07-02T08:04:31Z

Entry not found

necrobradley/face_predict

necrobradley

2024-07-02T08:33:00Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-07-02T08:04:55Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: face_predict results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train[:800] args: default metrics: - name: Accuracy type: accuracy value: 0.5625 ---  # face_predict This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2322 - Accuracy: 0.5625 ## 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: 6 - total_train_batch_size: 192 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 40 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.9 | 3 | 2.0747 | 0.1187 | | No log | 1.8 | 6 | 2.0728 | 0.1375 | | 2.0713 | 3.0 | 10 | 2.0449 | 0.2 | | 2.0713 | 3.9 | 13 | 2.0225 | 0.2562 | | 2.0713 | 4.8 | 16 | 1.9779 | 0.2938 | | 1.9642 | 6.0 | 20 | 1.8985 | 0.3688 | | 1.9642 | 6.9 | 23 | 1.8440 | 0.4188 | | 1.9642 | 7.8 | 26 | 1.7593 | 0.4437 | | 1.7442 | 9.0 | 30 | 1.6551 | 0.4875 | | 1.7442 | 9.9 | 33 | 1.5996 | 0.4875 | | 1.7442 | 10.8 | 36 | 1.5324 | 0.5188 | | 1.5402 | 12.0 | 40 | 1.5053 | 0.525 | | 1.5402 | 12.9 | 43 | 1.4543 | 0.5188 | | 1.5402 | 13.8 | 46 | 1.4335 | 0.5188 | | 1.4064 | 15.0 | 50 | 1.3768 | 0.5938 | | 1.4064 | 15.9 | 53 | 1.3583 | 0.6 | | 1.4064 | 16.8 | 56 | 1.3464 | 0.575 | | 1.2844 | 18.0 | 60 | 1.3245 | 0.6125 | | 1.2844 | 18.9 | 63 | 1.3265 | 0.5563 | | 1.2844 | 19.8 | 66 | 1.2899 | 0.5813 | | 1.1834 | 21.0 | 70 | 1.2863 | 0.5625 | | 1.1834 | 21.9 | 73 | 1.2939 | 0.5687 | | 1.1834 | 22.8 | 76 | 1.2508 | 0.5938 | | 1.1046 | 24.0 | 80 | 1.2604 | 0.5563 | | 1.1046 | 24.9 | 83 | 1.2344 | 0.6062 | | 1.1046 | 25.8 | 86 | 1.2124 | 0.6125 | | 1.0379 | 27.0 | 90 | 1.2053 | 0.6312 | | 1.0379 | 27.9 | 93 | 1.3067 | 0.5375 | | 1.0379 | 28.8 | 96 | 1.2247 | 0.5875 | | 1.0064 | 30.0 | 100 | 1.2060 | 0.625 | | 1.0064 | 30.9 | 103 | 1.2308 | 0.575 | | 1.0064 | 31.8 | 106 | 1.1936 | 0.6188 | | 0.9611 | 33.0 | 110 | 1.2257 | 0.5938 | | 0.9611 | 33.9 | 113 | 1.2302 | 0.5563 | | 0.9611 | 34.8 | 116 | 1.2172 | 0.6 | | 0.9351 | 36.0 | 120 | 1.2355 | 0.55 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

QuantFactory/llama3-8B-DarkIdol-2.0-Uncensored-GGUF

QuantFactory

2024-07-02T08:56:45Z

0

null

[ "gguf", "region:us" ]

null

2024-07-02T08:05:52Z

Entry not found

purelife/XV8

purelife

2024-07-03T00:47:39Z

0

null

[ "license:openrail", "region:us" ]

null

2024-07-02T08:06:20Z

--- license: openrail ---

snowian/emotion

snowian

2024-07-02T08:09:18Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-07-02T08:08:39Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: 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: 0.9285 - name: F1 type: f1 value: 0.92867427809199 ---  # 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: 0.1702 - Accuracy: 0.9285 - F1: 0.9287 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8067 | 1.0 | 250 | 0.2883 | 0.9115 | 0.9115 | | 0.2204 | 2.0 | 500 | 0.1883 | 0.9295 | 0.9299 | | 0.1495 | 3.0 | 750 | 0.1702 | 0.9285 | 0.9287 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

hflqf88888/OdysseyAgent-app

hflqf88888

2024-07-02T09:22:35Z

0

transformers

[ "transformers", "pytorch", "qwen", "text-generation", "GUI", "custom_code", "en", "zh", "dataset:OpenGVLab/GUI-Odyssey", "license:cc-by-4.0", "autotrain_compatible", "region:us" ]

text-generation

2024-07-02T08:09:18Z

--- license: cc-by-4.0 datasets: - OpenGVLab/GUI-Odyssey language: - en - zh tags: - GUI --- ## OdysseyAgent-app The OdysseyAgent fine-tuned on Train-App split.

prasunjeet/falcon-7b-sharded-bf16-finetuned-treccast

prasunjeet

2024-07-02T13:28:55Z

0

null

[ "text-generation", "region:us" ]

text-generation

2024-07-02T08:09:36Z

--- pipeline_tag: text-generation ---

Stable-Diffusion-PT/Stable-image-transformer-weight

Stable-Diffusion-PT

2024-07-02T08:09:51Z

0

null

[ "region:us" ]

null

2024-07-02T08:09:51Z

Entry not found

glfarnold/results

glfarnold

2024-07-02T12:12:16Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "summarization", "generated_from_trainer", "base_model:t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

summarization

2024-07-02T08:09:52Z

--- license: apache-2.0 base_model: t5-small tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: results results: [] ---  # results This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7600 - Rouge1: 0.1317 - Rouge2: 0.1118 - Rougel: 0.1232 - Rougelsum: 0.1232 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | No log | 1.0 | 250 | 1.8935 | 0.04 | 0.0359 | 0.0375 | 0.0375 | | 2.8412 | 2.0 | 500 | 1.7864 | 0.1089 | 0.0934 | 0.0954 | 0.0954 | | 2.8412 | 3.0 | 750 | 1.7600 | 0.1317 | 0.1118 | 0.1232 | 0.1232 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

FPHam/L3-8B-Everything-COT

FPHam

2024-07-02T18:18:09Z

0

2

transformers

[ "transformers", "safetensors", "llama", "text-generation", "llm", "llama3", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-02T08:10:03Z

--- tags: - llm - llama - llama3 ---  <div style="display: flex; flex-direction: column; align-items: center;"> </div> <div style="width: 100%;"> <img src="https://huggingface.co/FPHam/L3-8B-Everything-COT/resolve/main/cot2.png" alt="COT EVERYTHING" style="width: 40%; min-width: 200px; display: block; margin: auto;"> </div> <div style="display: flex; flex-direction: column; align-items: center;"> <p><a href="https://ko-fi.com/Q5Q5MOB4M">Buy me Ko-fi</a></p> </div>  Legal: This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](https://llama.meta.com/llama3/license/) **Everything COT** is an investigative self-reflecting general model that uses Chain of Thought for everything. And I mean everything. Instead of confidently proclaiming something (or confidently hallucinating other things) like most models, it caries an internal dialogue with itself and often cast doubts over uncertain topics while looking at it from various sides. >What's your name? >To answer this question, we must first understand the context. The question "What's your name?" is typically addressed to a human being. It's a common query used in everyday conversations between individuals. > >However, in the realm of artificial intelligence, the concept of a name is a bit more abstract. Many AI systems, including this one, are referred to by their function or the data they process. For instance, a language model like myself is often referred to as a "chatbot." > >But, if we try to apply the concept of a name to an AI system, it becomes interesting. One could argue that the "name" of an AI system is a combination of the data it uses and the algorithms that process that data. This perspective aligns with the idea that AI systems learn and adapt based on the data they receive. > >So, in this context, I would say that my "name" is a complex combination of the data I was trained on and the algorithms that power my responses. **Everything COT uses Llama 3 instruct template** The correct jinja chat_template is in tokenizer_config.json It was NOT trained with a system message, amd you can further use various system messages to steer the model. **Parameters** It's up to you to discover the best parameters that works. I tested it in oobabooga WebUi using very off-the-shelf min_p preset: Temperature: 1, Top_p: 1, Top_k: 0, Typical_p: 1, min_p: 0.05, repetition_penalty: 1 Different parameters, like temperature will affect the models talkativnes and self-reflecting properties. If you find something really good, let me know and I'll post it here.

AesopX/123

AesopX

2024-07-02T08:11:36Z

0

null

[ "region:us" ]

null

2024-07-02T08:10:25Z

# Use a pipeline as a high-level helper from transformers import pipeline messages = [ {"role": "user", "content": "Who are you?"}, ] pipe = pipeline("text-generation", model="shenzhi-wang/Llama3-8B-Chinese-Chat") pipe(messages) # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("shenzhi-wang/Llama3-8B-Chinese-Chat") model = AutoModelForCausalLM.from_pretrained("shenzhi-wang/Llama3-8B-Chinese-Chat")

RULES007/test

RULES007

2024-07-02T08:11:48Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-07-02T08:11:48Z

--- license: apache-2.0 ---

NgTMDuc/weight

NgTMDuc

2024-07-02T12:17:39Z

0

null

[ "region:us" ]

null

2024-07-02T08:12:03Z

Entry not found

quissuiven/donut-ktp-v2-test-2

quissuiven

2024-07-02T08:22:47Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]

null

2024-07-02T08:13:06Z

--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-ktp-v2-test-2 results: [] ---  # donut-ktp-v2-test-2 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder 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: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

sarahai/whisper-medium-uzbek

sarahai

2024-07-02T08:13:24Z

0

null

[ "region:us" ]

null

2024-07-02T08:13:24Z

Entry not found

rbhatia46/bge-base-financial-nvidia-matryoshka

rbhatia46

2024-07-02T08:16:31Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:6300", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:BAAI/bge-base-en-v1.5", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-embeddings-inference", "region:us" ]

sentence-similarity

2024-07-02T08:16:22Z

--- base_model: BAAI/bge-base-en-v1.5 datasets: [] language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6300 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: As of December 31, 2023, deferred revenues for unsatisfied performance obligations consisted of $769 million related to Hilton Honors that will be recognized as revenue over approximately the next two years. sentences: - How many shares of common stock were issued in both 2022 and 2023? - What is the projected timeline for recognizing revenue from deferred revenues related to Hilton Honors as of December 31, 2023? - What acquisitions did CVS Health Corporation complete in 2023 to enhance their care delivery strategy? - source_sentence: If a good or service does not qualify as distinct, it is combined with the other non-distinct goods or services within the arrangement and these combined goods or services are treated as a single performance obligation for accounting purposes. The arrangement's transaction price is then allocated to each performance obligation based on the relative standalone selling price of each performance obligation. sentences: - What does the summary table indicate about the company's activities at the end of 2023? - What governs the treatment of goods or services that are not distinct within a contractual arrangement? - What is the basis for the Company to determine the Standalone Selling Price (SSP) for each distinct performance obligation in contracts with multiple performance obligations? - source_sentence: As of January 2023, the maximum daily borrowing capacity under the commercial paper program was approximately $2.75 billion. sentences: - What is the maximum daily borrowing capacity under the commercial paper program as of January 2023? - When does the Company's fiscal year end? - How much cash did acquisition activities use in 2023? - source_sentence: Federal Home Loan Bank borrowings had an interest rate of 4.59% in 2022, which increased to 5.14% in 2023. sentences: - By what percentage did the company's capital expenditures increase in fiscal 2023 compared to fiscal 2022? - What is the significance of Note 13 in the context of legal proceedings described in the Annual Report on Form 10-K? - How much did the Federal Home Loan Bank borrowings increase in terms of interest rates from 2022 to 2023? - source_sentence: The design of the Annual Report, with the consolidated financial statements placed immediately after Part IV, enhances the integration of financial data by maintaining a coherent structure. sentences: - How does the structure of the Annual Report on Form 10-K facilitate the integration of the consolidated financial statements? - Where can one find the Glossary of Terms and Acronyms in Item 8? - What part of the annual report contains the consolidated financial statements and accompanying notes? model-index: - name: BGE base Financial Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.6957142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8171428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8628571428571429 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6957142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2723809523809524 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17257142857142854 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08999999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6957142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8171428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8628571428571429 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7971144469297426 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7641831065759639 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7681728985040082 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6942857142857143 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.81 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8514285714285714 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6942857142857143 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17028571428571426 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6942857142857143 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.81 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8514285714285714 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7951260604161544 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7617998866213151 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7658003405075238 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.7014285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7971428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.85 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8885714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7014285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26571428571428574 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16999999999999998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08885714285714284 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7014285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7971428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.85 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8885714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.793266992460996 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7629580498866213 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7678096436855835 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6957142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8014285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8357142857142857 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8842857142857142 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6957142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2671428571428571 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16714285714285712 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08842857142857141 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6957142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8014285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8357142857142857 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8842857142857142 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.787378246207931 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7566984126984126 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7613545312565108 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6571428571428571 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7871428571428571 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8285714285714286 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8757142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6571428571428571 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2623809523809524 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1657142857142857 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08757142857142856 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6571428571428571 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7871428571428571 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8285714285714286 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8757142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7655516319615892 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7303951247165531 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7349875161463472 name: Cosine Map@100 --- # BGE base Financial Matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)  - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity  - **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': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## 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("rbhatia46/bge-base-financial-nvidia-matryoshka") # Run inference sentences = [ 'The design of the Annual Report, with the consolidated financial statements placed immediately after Part IV, enhances the integration of financial data by maintaining a coherent structure.', 'How does the structure of the Annual Report on Form 10-K facilitate the integration of the consolidated financial statements?', 'Where can one find the Glossary of Terms and Acronyms in Item 8?', ] 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] ```    ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_768` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.6957 | | cosine_accuracy@3 | 0.8171 | | cosine_accuracy@5 | 0.8629 | | cosine_accuracy@10 | 0.9 | | cosine_precision@1 | 0.6957 | | cosine_precision@3 | 0.2724 | | cosine_precision@5 | 0.1726 | | cosine_precision@10 | 0.09 | | cosine_recall@1 | 0.6957 | | cosine_recall@3 | 0.8171 | | cosine_recall@5 | 0.8629 | | cosine_recall@10 | 0.9 | | cosine_ndcg@10 | 0.7971 | | cosine_mrr@10 | 0.7642 | | **cosine_map@100** | **0.7682** | #### Information Retrieval * Dataset: `dim_512` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.6943 | | cosine_accuracy@3 | 0.81 | | cosine_accuracy@5 | 0.8514 | | cosine_accuracy@10 | 0.9 | | cosine_precision@1 | 0.6943 | | cosine_precision@3 | 0.27 | | cosine_precision@5 | 0.1703 | | cosine_precision@10 | 0.09 | | cosine_recall@1 | 0.6943 | | cosine_recall@3 | 0.81 | | cosine_recall@5 | 0.8514 | | cosine_recall@10 | 0.9 | | cosine_ndcg@10 | 0.7951 | | cosine_mrr@10 | 0.7618 | | **cosine_map@100** | **0.7658** | #### Information Retrieval * Dataset: `dim_256` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.7014 | | cosine_accuracy@3 | 0.7971 | | cosine_accuracy@5 | 0.85 | | cosine_accuracy@10 | 0.8886 | | cosine_precision@1 | 0.7014 | | cosine_precision@3 | 0.2657 | | cosine_precision@5 | 0.17 | | cosine_precision@10 | 0.0889 | | cosine_recall@1 | 0.7014 | | cosine_recall@3 | 0.7971 | | cosine_recall@5 | 0.85 | | cosine_recall@10 | 0.8886 | | cosine_ndcg@10 | 0.7933 | | cosine_mrr@10 | 0.763 | | **cosine_map@100** | **0.7678** | #### Information Retrieval * Dataset: `dim_128` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.6957 | | cosine_accuracy@3 | 0.8014 | | cosine_accuracy@5 | 0.8357 | | cosine_accuracy@10 | 0.8843 | | cosine_precision@1 | 0.6957 | | cosine_precision@3 | 0.2671 | | cosine_precision@5 | 0.1671 | | cosine_precision@10 | 0.0884 | | cosine_recall@1 | 0.6957 | | cosine_recall@3 | 0.8014 | | cosine_recall@5 | 0.8357 | | cosine_recall@10 | 0.8843 | | cosine_ndcg@10 | 0.7874 | | cosine_mrr@10 | 0.7567 | | **cosine_map@100** | **0.7614** | #### Information Retrieval * Dataset: `dim_64` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:----------| | cosine_accuracy@1 | 0.6571 | | cosine_accuracy@3 | 0.7871 | | cosine_accuracy@5 | 0.8286 | | cosine_accuracy@10 | 0.8757 | | cosine_precision@1 | 0.6571 | | cosine_precision@3 | 0.2624 | | cosine_precision@5 | 0.1657 | | cosine_precision@10 | 0.0876 | | cosine_recall@1 | 0.6571 | | cosine_recall@3 | 0.7871 | | cosine_recall@5 | 0.8286 | | cosine_recall@10 | 0.8757 | | cosine_ndcg@10 | 0.7656 | | cosine_mrr@10 | 0.7304 | | **cosine_map@100** | **0.735** |   ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 6,300 training samples * Columns: <code>positive</code> and <code>anchor</code> * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 6 tokens</li><li>mean: 45.53 tokens</li><li>max: 222 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.3 tokens</li><li>max: 45 tokens</li></ul> | * Samples: | positive | anchor | |:---------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------| | <code>Acquisition activity used cash of $765 million in 2023, primarily related to a Beauty acquisition.</code> | <code>How much cash did acquisition activities use in 2023?</code> | | <code>In a financial report, Part IV Item 15 includes Exhibits and Financial Statement Schedules as mentioned.</code> | <code>What content can be expected under Part IV Item 15 in a financial report?</code> | | <code>we had more than 8.3 million fiber consumer wireline broadband customers, adding 1.1 million during the year.</code> | <code>How many fiber consumer wireline broadband customers did the company have at the end of the year?</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `learning_rate`: 2e-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`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `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`: True - `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`: True - `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_fused - `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 | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.8122 | 10 | 1.5751 | - | - | - | - | - | | 0.9746 | 12 | - | - | - | - | - | 0.7580 | | 0.8122 | 10 | 0.6362 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7503 | 0.7576 | 0.7653 | 0.7282 | 0.7638 | | 1.6244 | 20 | 0.4426 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7544 | 0.7662 | 0.7640 | 0.7311 | 0.7676 | | 2.4365 | 30 | 0.3217 | - | - | - | - | - | | 2.9239 | 36 | - | 0.7608 | 0.7684 | 0.7662 | 0.7341 | 0.7686 | | 3.2487 | 40 | 0.2761 | - | - | - | - | - | | **3.8985** | **48** | **-** | **0.7614** | **0.7678** | **0.7658** | **0.735** | **0.7682** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.6 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.31.0 - 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", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### 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} } ```

Yash0109/diaratechHf_llama188fe336-1221-4050-af76-a84e95bc6450

Yash0109

2024-07-02T08:18:52Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "text-generation", "conversational", "dataset:generator", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]

text-generation

2024-07-02T08:16:30Z

--- base_model: mistralai/Mistral-7B-Instruct-v0.2 datasets: - generator library_name: peft license: apache-2.0 pipeline_tag: text-generation tags: - trl - sft - generated_from_trainer model-index: - name: diaratechHf_llama188fe336-1221-4050-af76-a84e95bc6450 results: [] ---  # diaratechHf_llama188fe336-1221-4050-af76-a84e95bc6450 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator 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: constant - training_steps: 2 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.39.3 - Pytorch 2.3.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

lysandre/test-4

lysandre

2024-07-02T08:40:10Z

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-02T08:18:36Z

--- license: mit --- This one with a custom `config.head_dim` as allowed by the architecture (see 7b model).

SQAI/bge-embedding-model3

SQAI

2024-07-02T08:19:43Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:397", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:SQAI/bge-embedding-model", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-embeddings-inference", "region:us" ]

sentence-similarity

2024-07-02T08:19:22Z

--- base_model: SQAI/bge-embedding-model datasets: [] language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:397 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: Time taken for the streetlight to activate or light up from the command sentences: - '"Can you provide a report showing each unique streetlight identifier along with its power usage recorded in watts for the last three months, as well as instances where power consumption was higher than expected, potentially indicating faults (failures), or where the voltage supplied was below the safe operating level (failure) for the same period?"' - failure reasons for dysfunctional streetlights for geozone = 233 - '"Can you provide me with a comprehensive report for the past year showing the maximum load current, internal temperature, activation time, wireless signal strength of each streetlight in the group? Also, can you include metering component faults, instances where power under load was higher than normal and the threshold level for flickering? I''d like to understand the performance of the driver and instances where voltage under load was lower than expected. Finally, could you also specify the maximum latitude covered by this streetlight group?"' - source_sentence: Power consumption is lower than expected, possibly due to hardware issues (failure) sentences: - '"What is the total power consumption in watts of the ''Main Boulevard Group'' streetlights in the ''Grid B3'' Y-coordinate during the last 3 months, including instances where the supplied voltage was below the safe operating level causing failures? Also, how many complete power losses did this group of streetlights experience during this period due to supply issues or damage, and was there any instance where the ''Mode 4'' control switch or the ''Broadcast ID 9'' subscription was used for troubleshooting or restoring the lights?"' - '"What is the failure count for the last 3 months for the streetlight with ID ''unique streetlight identifier'' located in ''time zone where the streetlight is located'', specifically on ''Name of the street for the streetlight in error'', considering failures include lower than average power consumption affirming a hardware issue as identified by ''hardware version of the streetlight'' and difficulties in network connection for remote management? What is the IMEI number of the affected device and what''s the minimum current level it reached to be considered abnormal? Additionally, could you provide the maximum latitude of the geographic area covered by this group of streetlights?"' - '"Can you give me a breakdown of the total operational usage, active controller hours, and dimming controls type for each streetlight within a specified longitude range over the last six months, as well as any instances of relay status changes? And, could you also list the number of instances where the voltage under load was higher than expected, faults in the metering components, and any cases of complete power loss during this period? Additionally, can you inform me the strength of the wireless signal received by each streetlight''s communication module?"' - source_sentence: Upper voltage limit considered safe and efficient for streetlight operation sentences: - '"Can you pull a report detailing if there have been any instances where the power consumption was unusually high, potentially indicating faults over the last 3 months for the streetlight device with IMEI number XYZ, and provide the total hours of operation for its controller during that time? Include in this report the last timestamp when the threshold settings were updated, the level of that threshold for recording flickering, measured in occurrences, and the Y-coordinate for its location in the streetlight grid layout?"' - '"What is the current dimming level of the streetlight in operation, and have there been any instances where the voltage exceeded the safe operating level causing a failure? Additionally, I would like to know the total energy consumed, recorded in kilowatt-hours, for a specific group of streetlights and their efficiency of power usage represented by the power factor. Could you also tell me what type of DALI dimming protocol this group of streetlights is using, and the SIM card code used for their cellular network communication? Furthermore, what is the upper voltage limit considered safe and efficient for their operation and when was the latest data recorded or action performed by these streetlights?"' - '"What is the minimum load current that suggests suboptimal operation, the range of current indicating potential issues, the name of a group of streetlights, their drawn electrical current measure in amperes, details of faults in the link control mechanism, the geographic longitude range they cover, the identifier for their broadcast subscription, their maximum safe voltage under load conditions, minimum abnormal current level, and their linking rights for synchronized control?"' - source_sentence: IMEI number of the streetlight device sentences: - '"What is the failure count for the last 3 months for the streetlight with ID ''unique streetlight identifier'' located in ''time zone where the streetlight is located'', specifically on ''Name of the street for the streetlight in error'', considering failures include lower than average power consumption affirming a hardware issue as identified by ''hardware version of the streetlight'' and difficulties in network connection for remote management? What is the IMEI number of the affected device and what''s the minimum current level it reached to be considered abnormal? Additionally, could you provide the maximum latitude of the geographic area covered by this group of streetlights?"' - '"Can you provide the description of the group of streetlights, along with the geographic zone identifier of the streetlight that recently encountered a failure? Could you also include the last updated timestamp of the threshold settings, the maximum load current that indicates potential risk or overload, the frequency of the electricity supply measured in hertz, and the minimum power usage level below which it is considered abnormal for these streetlights?"' - '"What is the X-coordinate for the group of streetlights in a grid layout that have an operational age of more than 10,000 hours, registered a lower lux level below which additional lighting may be necessary and have had general faults related to light output, possibly including those that remain on during daylight hours due to sensor faults, despite the ambient light level detected being adequate? Also, could you provide the records of those which have the rights or permissions to synchronise control across multiple streetlights, the current electrical current drawn by each of these streetlights, the minimum operational voltage under their load conditions, and any instance where they reached the threshold levels to continue recording as flickering?"' - source_sentence: Count of how many times the streetlight has been switched on sentences: - '"What is the type of DALI dimming protocol used by our streetlights, do we have the necessary permissions to link multiple of these streetlights for synchronized control, how many times in the past 3 months has the supplied voltage dropped below the safe operating level causing failure, and what is the minimum current level below which the streetlight operation is considered abnormal?"' - '"Can you provide me with a comprehensive report for the past year showing the maximum load current, internal temperature, activation time, wireless signal strength of each streetlight in the group? Also, can you include metering component faults, instances where power under load was higher than normal and the threshold level for flickering? I''d like to understand the performance of the driver and instances where voltage under load was lower than expected. Finally, could you also specify the maximum latitude covered by this streetlight group?"' - '"What is the total count of times the streetlight has been switched on and what were the ambient light levels, measured in lux, at those instances?"' model-index: - name: BGE base Financial Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.022222222222222223 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.044444444444444446 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.17777777777777778 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.2222222222222222 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.022222222222222223 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.014814814814814814 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.035555555555555556 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.022222222222222223 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.022222222222222223 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.044444444444444446 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.17777777777777778 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.2222222222222222 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.10314853022641256 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.06666666666666668 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.09055451253282244 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.022222222222222223 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.044444444444444446 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.17777777777777778 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.2222222222222222 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.022222222222222223 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.014814814814814814 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.035555555555555556 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.022222222222222223 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.022222222222222223 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.044444444444444446 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.17777777777777778 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.2222222222222222 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.10314853022641256 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.06666666666666668 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.09055451253282244 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.022222222222222223 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.044444444444444446 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.13333333333333333 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.26666666666666666 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.022222222222222223 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.014814814814814814 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.026666666666666665 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.026666666666666672 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.022222222222222223 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.044444444444444446 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.13333333333333333 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.26666666666666666 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.11180419878864006 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.06644620811287479 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.0861674794441296 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.0 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.022222222222222223 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.13333333333333333 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.15555555555555556 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.0 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.007407407407407407 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.026666666666666665 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.015555555555555557 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.0 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.022222222222222223 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.13333333333333333 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.15555555555555556 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.06402667034388869 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.03574074074074074 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.05715693214212387 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.0 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.022222222222222223 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.08888888888888889 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.13333333333333333 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.0 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.007407407407407407 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.017777777777777778 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.013333333333333332 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.0 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.022222222222222223 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.08888888888888889 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.13333333333333333 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.0528696817100619 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.028518518518518516 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.0469896238659224 name: Cosine Map@100 --- # BGE base Financial Matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [SQAI/bge-embedding-model](https://huggingface.co/SQAI/bge-embedding-model). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [SQAI/bge-embedding-model](https://huggingface.co/SQAI/bge-embedding-model)  - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 384 tokens - **Similarity Function:** Cosine Similarity  - **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': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## 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("SQAI/bge-embedding-model3") # Run inference sentences = [ 'Count of how many times the streetlight has been switched on', '"What is the total count of times the streetlight has been switched on and what were the ambient light levels, measured in lux, at those instances?"', '"What is the type of DALI dimming protocol used by our streetlights, do we have the necessary permissions to link multiple of these streetlights for synchronized control, how many times in the past 3 months has the supplied voltage dropped below the safe operating level causing failure, and what is the minimum current level below which the streetlight operation is considered abnormal?"', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```    ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_768` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.0222 | | cosine_accuracy@3 | 0.0444 | | cosine_accuracy@5 | 0.1778 | | cosine_accuracy@10 | 0.2222 | | cosine_precision@1 | 0.0222 | | cosine_precision@3 | 0.0148 | | cosine_precision@5 | 0.0356 | | cosine_precision@10 | 0.0222 | | cosine_recall@1 | 0.0222 | | cosine_recall@3 | 0.0444 | | cosine_recall@5 | 0.1778 | | cosine_recall@10 | 0.2222 | | cosine_ndcg@10 | 0.1031 | | cosine_mrr@10 | 0.0667 | | **cosine_map@100** | **0.0906** | #### Information Retrieval * Dataset: `dim_512` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.0222 | | cosine_accuracy@3 | 0.0444 | | cosine_accuracy@5 | 0.1778 | | cosine_accuracy@10 | 0.2222 | | cosine_precision@1 | 0.0222 | | cosine_precision@3 | 0.0148 | | cosine_precision@5 | 0.0356 | | cosine_precision@10 | 0.0222 | | cosine_recall@1 | 0.0222 | | cosine_recall@3 | 0.0444 | | cosine_recall@5 | 0.1778 | | cosine_recall@10 | 0.2222 | | cosine_ndcg@10 | 0.1031 | | cosine_mrr@10 | 0.0667 | | **cosine_map@100** | **0.0906** | #### Information Retrieval * Dataset: `dim_256` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.0222 | | cosine_accuracy@3 | 0.0444 | | cosine_accuracy@5 | 0.1333 | | cosine_accuracy@10 | 0.2667 | | cosine_precision@1 | 0.0222 | | cosine_precision@3 | 0.0148 | | cosine_precision@5 | 0.0267 | | cosine_precision@10 | 0.0267 | | cosine_recall@1 | 0.0222 | | cosine_recall@3 | 0.0444 | | cosine_recall@5 | 0.1333 | | cosine_recall@10 | 0.2667 | | cosine_ndcg@10 | 0.1118 | | cosine_mrr@10 | 0.0664 | | **cosine_map@100** | **0.0862** | #### Information Retrieval * Dataset: `dim_128` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.0 | | cosine_accuracy@3 | 0.0222 | | cosine_accuracy@5 | 0.1333 | | cosine_accuracy@10 | 0.1556 | | cosine_precision@1 | 0.0 | | cosine_precision@3 | 0.0074 | | cosine_precision@5 | 0.0267 | | cosine_precision@10 | 0.0156 | | cosine_recall@1 | 0.0 | | cosine_recall@3 | 0.0222 | | cosine_recall@5 | 0.1333 | | cosine_recall@10 | 0.1556 | | cosine_ndcg@10 | 0.064 | | cosine_mrr@10 | 0.0357 | | **cosine_map@100** | **0.0572** | #### Information Retrieval * Dataset: `dim_64` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:----------| | cosine_accuracy@1 | 0.0 | | cosine_accuracy@3 | 0.0222 | | cosine_accuracy@5 | 0.0889 | | cosine_accuracy@10 | 0.1333 | | cosine_precision@1 | 0.0 | | cosine_precision@3 | 0.0074 | | cosine_precision@5 | 0.0178 | | cosine_precision@10 | 0.0133 | | cosine_recall@1 | 0.0 | | cosine_recall@3 | 0.0222 | | cosine_recall@5 | 0.0889 | | cosine_recall@10 | 0.1333 | | cosine_ndcg@10 | 0.0529 | | cosine_mrr@10 | 0.0285 | | **cosine_map@100** | **0.047** |   ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 397 training samples * Columns: <code>positive</code> and <code>anchor</code> * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 5 tokens</li><li>mean: 13.89 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 101.84 tokens</li><li>max: 175 tokens</li></ul> | * Samples: | positive | anchor | |:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Electrical current drawn by the streetlight, measured in amperes</code> | <code>"What is the minimum load current that suggests suboptimal operation, the range of current indicating potential issues, the name of a group of streetlights, their drawn electrical current measure in amperes, details of faults in the link control mechanism, the geographic longitude range they cover, the identifier for their broadcast subscription, their maximum safe voltage under load conditions, minimum abnormal current level, and their linking rights for synchronized control?"</code> | | <code>Faults in the link control mechanism managing multiple streetlights (failure)</code> | <code>"Can you show me the data of the unique streetlight identifier in the geoZone with faults in the link control mechanism that manages multiple streetlights for the last three months, where the control mode setting of the streetlight was automated, also show the current dimming level of the streetlight in operation at that time, the maximum current level considered unsafe for the streetlight operation, the maximum load current indicating potential risk or overload, along with the time zone where the streetlight is located?"</code> | | <code>The relay responsible for turning the streetlight on and off is sticking (failure)</code> | <code>"What is the network time synchronization receipt from the central control, maximum load current reading, whether the relay responsible for turning the streetlights on and off experienced any failures, the delta or height of the grid area occupied by this group of streetlights, the maximum load power level, and longitude of the streetlight that had a low power factor indicating inefficiency and possible reactive power issues in the last 3 months?"</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 384, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 45 evaluation samples * Columns: <code>positive</code> and <code>anchor</code> * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 8 tokens</li><li>mean: 14.27 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 104.58 tokens</li><li>max: 167 tokens</li></ul> | * Samples: | positive | anchor | |:---------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Power factor of the streetlight, indicating the efficiency of power usage</code> | <code>"Could you please provide the information on how many times the streetlight within the set geographical zone has been switched on? Can you also include details on its internal operating temperature, the status of the relay in the streetlight, any instances of it exceeding safe operating temperature limits, and the efficiency of its power usage or power factor, particularly when the operational voltage hit its minimum under load conditions? Lastly, provide insights into the width of the grid area it occupies. This information does not need to highlight any specific failure occurrences."</code> | | <code>General fault related to the light output of the streetlight (failure)</code> | <code>"What is the X-coordinate for the group of streetlights in a grid layout that have an operational age of more than 10,000 hours, registered a lower lux level below which additional lighting may be necessary and have had general faults related to light output, possibly including those that remain on during daylight hours due to sensor faults, despite the ambient light level detected being adequate? Also, could you provide the records of those which have the rights or permissions to synchronise control across multiple streetlights, the current electrical current drawn by each of these streetlights, the minimum operational voltage under their load conditions, and any instance where they reached the threshold levels to continue recording as flickering?"</code> | | <code>Name of the street for the streetlight in error (failure)</code> | <code>failure count for street name = Chestnut Street, Oak Avenue for time = last 55 days in streetlighting</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 384, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-06 - `weight_decay`: 0.03 - `num_train_epochs`: 100 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.2 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `learning_rate`: 2e-06 - `weight_decay`: 0.03 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 100 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.2 - `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`: True - `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`: True - `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_fused - `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 | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |:--------:|:------:|:-------------:|:----------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 1.0 | 1 | 8.8958 | 6.8979 | 0.0490 | 0.0530 | 0.0604 | 0.0457 | 0.0604 | | 1.0769 | 2 | 0.8656 | - | - | - | - | - | - | | 2.0 | 3 | 7.9734 | 6.8985 | 0.0491 | 0.0528 | 0.0604 | 0.0455 | 0.0604 | | 2.1538 | 4 | 1.5652 | - | - | - | - | - | - | | 3.0 | 5 | 7.4072 | 6.8893 | 0.0491 | 0.0532 | 0.0605 | 0.0448 | 0.0605 | | 3.2308 | 6 | 2.2162 | - | - | - | - | - | - | | 4.0 | 7 | 6.6659 | 6.8768 | 0.0491 | 0.0535 | 0.0605 | 0.0441 | 0.0605 | | 4.3077 | 8 | 2.8917 | - | - | - | - | - | - | | 5.0 | 9 | 6.0505 | 6.8666 | 0.0497 | 0.0539 | 0.0608 | 0.0440 | 0.0608 | | 5.3846 | 10 | 3.5565 | - | - | - | - | - | - | | 6.0 | 11 | 5.4886 | 6.8504 | 0.0509 | 0.0554 | 0.0614 | 0.0444 | 0.0614 | | 6.4615 | 12 | 4.206 | - | - | - | - | - | - | | 7.0 | 13 | 4.6528 | 6.8280 | 0.0527 | 0.0554 | 0.0621 | 0.0424 | 0.0621 | | 7.5385 | 14 | 4.87 | - | - | - | - | - | - | | 8.0 | 15 | 3.9965 | 6.8114 | 0.0529 | 0.0569 | 0.0659 | 0.0416 | 0.0659 | | 8.6154 | 16 | 5.5005 | - | - | - | - | - | - | | 9.0 | 17 | 3.2411 | 6.7791 | 0.0532 | 0.0551 | 0.0661 | 0.0421 | 0.0661 | | 9.6923 | 18 | 6.1103 | - | - | - | - | - | - | | 10.0 | 19 | 2.6339 | 6.7559 | 0.0575 | 0.0551 | 0.0663 | 0.0425 | 0.0663 | | 10.7692 | 20 | 6.8119 | - | - | - | - | - | - | | 11.0 | 21 | 1.9097 | 6.7235 | 0.0572 | 0.0617 | 0.0629 | 0.0439 | 0.0629 | | 11.8462 | 22 | 7.4981 | - | - | - | - | - | - | | 12.0 | 23 | 1.2004 | 6.6966 | 0.0571 | 0.0603 | 0.0660 | 0.0437 | 0.0660 | | 12.9231 | 24 | 8.1134 | - | - | - | - | - | - | | 13.0 | 25 | 0.5338 | 6.6707 | 0.0557 | 0.0607 | 0.0645 | 0.0428 | 0.0645 | | 14.0 | 26 | 8.6211 | 6.6592 | 0.0558 | 0.0610 | 0.0651 | 0.0458 | 0.0651 | | 15.0 | 27 | 8.5679 | - | - | - | - | - | - | | 15.0769 | 28 | 0.1453 | 6.6335 | 0.0561 | 0.0587 | 0.0731 | 0.0420 | 0.0731 | | 16.0 | 29 | 8.3531 | - | - | - | - | - | - | | 16.1538 | 30 | 0.0862 | 6.6090 | 0.0560 | 0.0593 | 0.0725 | 0.0443 | 0.0725 | | 17.0 | 31 | 8.4562 | 6.6064 | 0.0564 | 0.0616 | 0.0747 | 0.0445 | 0.0747 | | 17.0769 | 32 | 0.8101 | - | - | - | - | - | - | | 18.0 | 33 | 7.578 | 6.5912 | 0.0555 | 0.0597 | 0.0730 | 0.0470 | 0.0730 | | 18.1538 | 34 | 1.4507 | - | - | - | - | - | - | | 19.0 | 35 | 6.996 | 6.5713 | 0.0560 | 0.0617 | 0.0764 | 0.0474 | 0.0764 | | 19.2308 | 36 | 2.0412 | - | - | - | - | - | - | | **20.0** | **37** | **6.4129** | **6.5519** | **0.061** | **0.06** | **0.0731** | **0.0486** | **0.0731** | | 20.3077 | 38 | 2.6771 | - | - | - | - | - | - | | 21.0 | 39 | 5.7615 | 6.5315 | 0.0605 | 0.0656 | 0.0722 | 0.0474 | 0.0722 | | 21.3846 | 40 | 3.2979 | - | - | - | - | - | - | | 22.0 | 41 | 5.1123 | 6.5186 | 0.0608 | 0.0676 | 0.0721 | 0.0486 | 0.0721 | | 22.4615 | 42 | 3.9339 | - | - | - | - | - | - | | 23.0 | 43 | 4.3093 | 6.5020 | 0.0607 | 0.0816 | 0.0626 | 0.0473 | 0.0626 | | 23.5385 | 44 | 4.6842 | - | - | - | - | - | - | | 24.0 | 45 | 3.7325 | 6.4972 | 0.0591 | 0.0670 | 0.0648 | 0.0474 | 0.0648 | | 24.6154 | 46 | 5.1717 | - | - | - | - | - | - | | 25.0 | 47 | 3.1253 | 6.4821 | 0.0584 | 0.0676 | 0.0648 | 0.0473 | 0.0648 | | 25.6923 | 48 | 5.7321 | - | - | - | - | - | - | | 26.0 | 49 | 2.4503 | 6.4660 | 0.0587 | 0.0672 | 0.0694 | 0.0478 | 0.0694 | | 26.7692 | 50 | 6.5409 | - | - | - | - | - | - | | 27.0 | 51 | 1.791 | 6.4670 | 0.0590 | 0.0868 | 0.0691 | 0.0478 | 0.0691 | | 27.8462 | 52 | 7.2565 | - | - | - | - | - | - | | 28.0 | 53 | 1.0513 | 6.4549 | 0.0590 | 0.0674 | 0.0689 | 0.0478 | 0.0689 | | 28.9231 | 54 | 7.7128 | - | - | - | - | - | - | | 29.0 | 55 | 0.4118 | 6.4519 | 0.0592 | 0.0873 | 0.0864 | 0.0477 | 0.0864 | | 30.0 | 56 | 8.15 | 6.4472 | 0.0589 | 0.0872 | 0.0890 | 0.0477 | 0.0890 | | 31.0 | 57 | 8.1077 | - | - | - | - | - | - | | 31.0769 | 58 | 0.0869 | 6.4347 | 0.0596 | 0.0716 | 0.0709 | 0.0477 | 0.0709 | | 32.0 | 59 | 7.9701 | - | - | - | - | - | - | | 32.1538 | 60 | 0.1293 | 6.4306 | 0.0580 | 0.0897 | 0.0880 | 0.0474 | 0.0880 | | 33.0 | 61 | 8.1437 | 6.4254 | 0.0561 | 0.0895 | 0.0889 | 0.0474 | 0.0889 | | 33.0769 | 62 | 0.7537 | - | - | - | - | - | - | | 34.0 | 63 | 7.4904 | 6.4215 | 0.0574 | 0.0879 | 0.0885 | 0.0475 | 0.0885 | | 34.1538 | 64 | 1.3546 | - | - | - | - | - | - | | 35.0 | 65 | 6.7289 | 6.4152 | 0.0555 | 0.0888 | 0.0701 | 0.0474 | 0.0701 | | 35.2308 | 66 | 1.9694 | - | - | - | - | - | - | | 36.0 | 67 | 6.1359 | 6.4122 | 0.0558 | 0.0875 | 0.0707 | 0.0477 | 0.0707 | | 36.3077 | 68 | 2.5335 | - | - | - | - | - | - | | 37.0 | 69 | 5.5274 | 6.4068 | 0.0558 | 0.0694 | 0.0718 | 0.0470 | 0.0718 | | 37.3846 | 70 | 3.1245 | - | - | - | - | - | - | | 38.0 | 71 | 4.8913 | 6.4102 | 0.0537 | 0.0701 | 0.0718 | 0.0470 | 0.0718 | | 38.4615 | 72 | 3.8402 | - | - | - | - | - | - | | 39.0 | 73 | 4.12 | 6.4003 | 0.0537 | 0.0681 | 0.0719 | 0.0471 | 0.0719 | | 39.5385 | 74 | 4.5087 | - | - | - | - | - | - | | 40.0 | 75 | 3.6038 | 6.3984 | 0.0575 | 0.0878 | 0.0890 | 0.0471 | 0.0890 | | 40.6154 | 76 | 5.1065 | - | - | - | - | - | - | | 41.0 | 77 | 3.042 | 6.3966 | 0.0577 | 0.0684 | 0.0715 | 0.0469 | 0.0715 | | 41.6923 | 78 | 5.6277 | - | - | - | - | - | - | | 42.0 | 79 | 2.3259 | 6.3975 | 0.0568 | 0.0681 | 0.0722 | 0.0470 | 0.0722 | | 42.7692 | 80 | 6.2878 | - | - | - | - | - | - | | 43.0 | 81 | 1.8184 | 6.3983 | 0.0571 | 0.0680 | 0.0728 | 0.0470 | 0.0728 | | 43.8462 | 82 | 6.9261 | - | - | - | - | - | - | | 44.0 | 83 | 1.0775 | 6.3942 | 0.0580 | 0.0680 | 0.0896 | 0.0469 | 0.0896 | | 44.9231 | 84 | 7.523 | - | - | - | - | - | - | | 45.0 | 85 | 0.3703 | 6.3935 | 0.0573 | 0.0861 | 0.0911 | 0.0468 | 0.0911 | | 46.0 | 86 | 7.945 | 6.3961 | 0.0555 | 0.0855 | 0.0898 | 0.0468 | 0.0898 | | 47.0 | 87 | 7.8871 | - | - | - | - | - | - | | 47.0769 | 88 | 0.1237 | 6.3924 | 0.0556 | 0.0858 | 0.0902 | 0.0467 | 0.0902 | | 48.0 | 89 | 7.9213 | - | - | - | - | - | - | | 48.1538 | 90 | 0.0809 | 6.3922 | 0.0600 | 0.0684 | 0.0727 | 0.0467 | 0.0727 | | 49.0 | 91 | 7.7954 | 6.3901 | 0.0601 | 0.0858 | 0.0907 | 0.0467 | 0.0907 | | 49.0769 | 92 | 0.7928 | - | - | - | - | - | - | | 50.0 | 93 | 7.3085 | 6.3915 | 0.0567 | 0.0681 | 0.0909 | 0.0471 | 0.0909 | | 50.1538 | 94 | 1.3327 | - | - | - | - | - | - | | 51.0 | 95 | 6.7179 | 6.3970 | 0.0597 | 0.0677 | 0.0725 | 0.0466 | 0.0725 | | 51.2308 | 96 | 1.9239 | - | - | - | - | - | - | | 52.0 | 97 | 6.0889 | 6.3939 | 0.0567 | 0.0858 | 0.0907 | 0.0470 | 0.0907 | | 52.3077 | 98 | 2.5265 | - | - | - | - | - | - | | 53.0 | 99 | 5.4464 | 6.3943 | 0.0539 | 0.0677 | 0.0732 | 0.0466 | 0.0732 | | 53.3846 | 100 | 3.0337 | 6.3940 | 0.0572 | 0.0862 | 0.0906 | 0.0470 | 0.0906 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.31.0 - 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", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### 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} } ```

de-coder/google-bert-bert-base-uncased-gguf

de-coder

2024-07-02T08:19:25Z

0

null

[ "region:us" ]

null

2024-07-02T08:19:25Z

Entry not found

Ryan-Pham/route_2

Ryan-Pham

2024-07-02T08:19:26Z

0

null

[ "region:us" ]

null

2024-07-02T08:19:26Z

Entry not found

abdfajar707/llama3_8B_lora_model_rkp_pn2025_v2

abdfajar707

2024-07-02T08:20:41Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-07-02T08:20:30Z

--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** abdfajar707 - **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)

bikeZero/my_awesome_model

bikeZero

2024-07-02T08:20:31Z

0

null

[ "region:us" ]

null

2024-07-02T08:20:31Z

Entry not found

PereLluis13/relik-reader-deberta-large-wikipedia-aida-full-interleave

PereLluis13

2024-07-02T08:21:25Z

0

transformers

[ "transformers", "pytorch", "relik-reader", "feature-extraction", "custom_code", "region:us" ]

feature-extraction

2024-07-02T08:20:32Z

Entry not found

PereLluis13/relik-entity-linking-large-wikipedia-aida-interleave

PereLluis13

2024-07-02T08:22:23Z

0

null

[ "region:us" ]

null

2024-07-02T08:21:40Z

Entry not found

jeromesky/prosodic_accuracy_v2

jeromesky

2024-07-02T08:58:23Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "audio-classification", "endpoints_compatible", "region:us" ]

audio-classification

2024-07-02T08:21:58Z

Entry not found

Thesiss/skin_llava

Thesiss

2024-07-02T13:06:51Z

0

transformers

[ "transformers", "safetensors", "llava_mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T08:22:04Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

SayedNabeel/Shorts_Writer_LLAMA3_8B_V0.2

SayedNabeel

2024-07-02T09:18:46Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "Story", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-07-02T08:23:57Z

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - Story --- # Uploaded model - **Developed by:** SayedNabeel - **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) # Story Generation - **It can be used to Genertate YouTube Shorts** - **It is trained on 780 Steps on 6.1K rows of data**

omartariq612/whisper-small-with-tajweed-tokens

omartariq612

2024-07-02T08:42:03Z

0

transformers

[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "audio", "hf-asr-leaderboard", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "arxiv:2212.04356", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-07-02T08:25:17Z

--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - no - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition - hf-asr-leaderboard widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac model-index: - name: whisper-small results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (clean) type: librispeech_asr config: clean split: test args: language: en metrics: - name: Test WER type: wer value: 3.432213777886737 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (other) type: librispeech_asr config: other split: test args: language: en metrics: - name: Test WER type: wer value: 7.628304527060248 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: hi split: test args: language: hi metrics: - name: Test WER type: wer value: 87.3 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13.0 type: mozilla-foundation/common_voice_13_0 config: dv split: test args: language: dv metrics: - name: Wer type: wer value: 125.69809089960707 pipeline_tag: automatic-speech-recognition license: apache-2.0 --- # Whisper > This is the vanilla whisper small model with tajweed tokens, here is the [notebook](https://www.kaggle.com/code/omartariq612/whisper-small-with-tajweed-tokens/notebook) that generated that repo Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains **without** the need for fine-tuning. Whisper was proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) by Alec Radford et al from OpenAI. The original code repository can be found [here](https://github.com/openai/whisper). **Disclaimer**: Content for this model card has partly been written by the Hugging Face team, and parts of it were copied and pasted from the original model card. ## Model details Whisper is a Transformer based encoder-decoder model, also referred to as a _sequence-to-sequence_ model. It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision. The models were trained on either English-only data or multilingual data. The English-only models were trained on the task of speech recognition. The multilingual models were trained on both speech recognition and speech translation. For speech recognition, the model predicts transcriptions in the *same* language as the audio. For speech translation, the model predicts transcriptions to a *different* language to the audio. Whisper checkpoints come in five configurations of varying model sizes. The smallest four are trained on either English-only or multilingual data. The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The checkpoints are summarised in the following table with links to the models on the Hub: | Size | Parameters | English-only | Multilingual | |----------|------------|------------------------------------------------------|-----------------------------------------------------| | tiny | 39 M | [✓](https://huggingface.co/openai/whisper-tiny.en) | [✓](https://huggingface.co/openai/whisper-tiny) | | base | 74 M | [✓](https://huggingface.co/openai/whisper-base.en) | [✓](https://huggingface.co/openai/whisper-base) | | small | 244 M | [✓](https://huggingface.co/openai/whisper-small.en) | [✓](https://huggingface.co/openai/whisper-small) | | medium | 769 M | [✓](https://huggingface.co/openai/whisper-medium.en) | [✓](https://huggingface.co/openai/whisper-medium) | | large | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large) | | large-v2 | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large-v2) | # Usage To transcribe audio samples, the model has to be used alongside a [`WhisperProcessor`](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperProcessor). The `WhisperProcessor` is used to: 1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model) 2. Post-process the model outputs (converting them from tokens to text) The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order: 1. The transcription always starts with the `<|startoftranscript|>` token 2. The second token is the language token (e.g. `<|en|>` for English) 3. The third token is the "task token". It can take one of two values: `<|transcribe|>` for speech recognition or `<|translate|>` for speech translation 4. In addition, a `<|notimestamps|>` token is added if the model should not include timestamp prediction Thus, a typical sequence of context tokens might look as follows: ``` <|startoftranscript|> <|en|> <|transcribe|> <|notimestamps|> ``` Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps. These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at each position. This allows one to control the output language and task for the Whisper model. If they are un-forced, the Whisper model will automatically predict the output langauge and task itself. The context tokens can be set accordingly: ```python model.config.forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="english", task="transcribe") ``` Which forces the model to predict in English under the task of speech recognition. ## Transcription ### English to English In this example, the context tokens are 'unforced', meaning the model automatically predicts the output language (English) and task (transcribe). ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") >>> model.config.forced_decoder_ids = None >>> # load dummy dataset and read audio files >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> sample = ds[0]["audio"] >>> input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False) ['<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.<|endoftext|>'] >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.'] ``` The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`. ### French to French The following example demonstrates French to French transcription by setting the decoder ids appropriately. ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import Audio, load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") >>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="transcribe") >>> # load streaming dataset and read first audio sample >>> ds = load_dataset("common_voice", "fr", split="test", streaming=True) >>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) >>> input_speech = next(iter(ds))["audio"] >>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids) ['<|startoftranscript|><|fr|><|transcribe|><|notimestamps|> Un vrai travail intéressant va enfin être mené sur ce sujet.<|endoftext|>'] >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' Un vrai travail intéressant va enfin être mené sur ce sujet.'] ``` ## Translation Setting the task to "translate" forces the Whisper model to perform speech translation. ### French to English ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import Audio, load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") >>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="translate") >>> # load streaming dataset and read first audio sample >>> ds = load_dataset("common_voice", "fr", split="test", streaming=True) >>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) >>> input_speech = next(iter(ds))["audio"] >>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' A very interesting work, we will finally be given on this subject.'] ``` ## Evaluation This code snippet shows how to evaluate Whisper Small on [LibriSpeech test-clean](https://huggingface.co/datasets/librispeech_asr): ```python >>> from datasets import load_dataset >>> from transformers import WhisperForConditionalGeneration, WhisperProcessor >>> import torch >>> from evaluate import load >>> librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test") >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small").to("cuda") >>> def map_to_pred(batch): >>> audio = batch["audio"] >>> input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features >>> batch["reference"] = processor.tokenizer._normalize(batch['text']) >>> >>> with torch.no_grad(): >>> predicted_ids = model.generate(input_features.to("cuda"))[0] >>> transcription = processor.decode(predicted_ids) >>> batch["prediction"] = processor.tokenizer._normalize(transcription) >>> return batch >>> result = librispeech_test_clean.map(map_to_pred) >>> wer = load("wer") >>> print(100 * wer.compute(references=result["reference"], predictions=result["prediction"])) 3.432213777886737 ``` ## Long-Form Transcription The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) method. Chunking is enabled by setting `chunk_length_s=30` when instantiating the pipeline. With chunking enabled, the pipeline can be run with batched inference. It can also be extended to predict sequence level timestamps by passing `return_timestamps=True`: ```python >>> import torch >>> from transformers import pipeline >>> from datasets import load_dataset >>> device = "cuda:0" if torch.cuda.is_available() else "cpu" >>> pipe = pipeline( >>> "automatic-speech-recognition", >>> model="openai/whisper-small", >>> chunk_length_s=30, >>> device=device, >>> ) >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> sample = ds[0]["audio"] >>> prediction = pipe(sample.copy(), batch_size=8)["text"] " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel." >>> # we can also return timestamps for the predictions >>> prediction = pipe(sample.copy(), batch_size=8, return_timestamps=True)["chunks"] [{'text': ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.', 'timestamp': (0.0, 5.44)}] ``` Refer to the blog post [ASR Chunking](https://huggingface.co/blog/asr-chunking) for more details on the chunking algorithm. ## Fine-Tuning The pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However, its predictive capabilities can be improved further for certain languages and tasks through *fine-tuning*. The blog post [Fine-Tune Whisper with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper) provides a step-by-step guide to fine-tuning the Whisper model with as little as 5 hours of labelled data. ### Evaluated Use The primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research. The models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them. In particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes. ## Training Data The models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages. As discussed in [the accompanying paper](https://cdn.openai.com/papers/whisper.pdf), we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language. ## Performance and Limitations Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level. However, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself. Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in [the paper accompanying this release](https://cdn.openai.com/papers/whisper.pdf). In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in [the paper](https://cdn.openai.com/papers/whisper.pdf). It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages. ## Broader Implications We anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications. There are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects. ### BibTeX entry and citation info ```bibtex @misc{radford2022whisper, doi = {10.48550/ARXIV.2212.04356}, url = {https://arxiv.org/abs/2212.04356}, author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya}, title = {Robust Speech Recognition via Large-Scale Weak Supervision}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ```

apwic/summarization-lora-0

apwic

2024-07-02T12:00:03Z

0

null

[ "generated_from_trainer", "id", "base_model:LazarusNLP/IndoNanoT5-base", "license:apache-2.0", "region:us" ]

null

2024-07-02T08:25:33Z

--- language: - id license: apache-2.0 base_model: LazarusNLP/IndoNanoT5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: summarization-lora-0 results: [] ---  # summarization-lora-0 This model is a fine-tuned version of [LazarusNLP/IndoNanoT5-base](https://huggingface.co/LazarusNLP/IndoNanoT5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5656 - Rouge1: 0.4188 - Rouge2: 0.0 - Rougel: 0.4161 - Rougelsum: 0.4157 - Gen Len: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 1.2271 | 1.0 | 1783 | 0.6275 | 0.491 | 0.0 | 0.4864 | 0.4859 | 1.0 | | 0.7893 | 2.0 | 3566 | 0.5955 | 0.4382 | 0.0 | 0.4358 | 0.4345 | 1.0 | | 0.7347 | 3.0 | 5349 | 0.5738 | 0.4461 | 0.0 | 0.4432 | 0.4417 | 1.0 | | 0.7084 | 4.0 | 7132 | 0.5618 | 0.4416 | 0.0 | 0.4409 | 0.4389 | 1.0 | | 0.6976 | 5.0 | 8915 | 0.5656 | 0.4188 | 0.0 | 0.4161 | 0.4157 | 1.0 | ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

chienweichang/Llama-3-Taiwan-8B-Instruct-128K-GGUF

chienweichang

2024-07-03T01:31:29Z

0

null

[ "gguf", "zhtw", "text-generation", "zh", "en", "arxiv:2403.20180", "arxiv:2311.17487", "base_model:yentinglin/Llama-3-Taiwan-8B-Instruct-128k", "license:llama3", "region:us" ]

text-generation

2024-07-02T08:25:36Z

Temporary Redirect. Redirecting to /chienweichang/Llama-3-Taiwan-8B-Instruct-128k-GGUF/resolve/main/README.md

camillop/phi-mini-company-classification-gguf-q4m

camillop

2024-07-02T08:27:39Z

0

transformers

[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-07-02T08:25:40Z

--- base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf --- # Uploaded model - **Developed by:** camillop - **License:** apache-2.0 - **Finetuned from model :** unsloth/phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Dev372/HarshDev-whisper-tiny-English_2000

Dev372

2024-07-02T08:25:50Z

0

null

[ "region:us" ]

null

2024-07-02T08:25:50Z

Entry not found

vive0921/dog-sdxl

vive0921

2024-07-02T08:26:19Z

0

null

[ "region:us" ]

null

2024-07-02T08:26:19Z

Entry not found

Ram07/sri

Ram07

2024-07-02T20:20:52Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-02T08:27:20Z

--- license: mit ---

baxtos/bartik01-4

baxtos

2024-07-02T08:30:07Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T08:27:40Z

Entry not found

AlexWortega/bertoid

AlexWortega

2024-07-02T08:32:06Z

0

transformers

[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "text-embeddings-inference", "region:us" ]

feature-extraction

2024-07-02T08:29:39Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

loginworks/Meta-Llama-3-8B-code

loginworks

2024-07-02T13:30:52Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-02T08:30:53Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

philnet/plantynet-mt5-en2kr

philnet

2024-07-02T08:36:06Z

0

transformers

[ "transformers", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text2text-generation

2024-07-02T08:30:59Z

Entry not found

whizzzzkid/whizzzzkid_396_5

whizzzzkid

2024-07-02T08:32:20Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T08:31:59Z

Entry not found

nourheshamshaheen/llava_FEDPROX_8epochs_2000steps_2clients

nourheshamshaheen

2024-07-02T08:46:41Z

0

transformers

[ "transformers", "safetensors", "llava_llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T08:32:04Z

Entry not found

hflqf88888/OdysseyAgent-device

hflqf88888

2024-07-02T09:20:36Z

0

transformers

[ "transformers", "pytorch", "qwen", "text-generation", "GUI", "custom_code", "en", "zh", "dataset:OpenGVLab/GUI-Odyssey", "license:cc-by-4.0", "autotrain_compatible", "region:us" ]

text-generation

2024-07-02T08:32:25Z

--- license: cc-by-4.0 datasets: - OpenGVLab/GUI-Odyssey language: - en - zh tags: - GUI --- ## OdysseyAgent-device The OdysseyAgent fine-tuned on Train-Device split.

whizzzzkid/whizzzzkid_397_3

whizzzzkid

2024-07-02T08:33:19Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T08:32:59Z

Entry not found

baxtos/bartik02-4

baxtos

2024-07-02T08:35:45Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T08:33:16Z

Entry not found

whizzzzkid/whizzzzkid_398_4

whizzzzkid

2024-07-02T08:34:21Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T08:33:58Z

Entry not found

LarryAIDraw/ChamHarmoniePonyXL

LarryAIDraw

2024-07-02T08:41:24Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2024-07-02T08:34:04Z

--- license: creativeml-openrail-m --- https://civitai.com/models/543457/harmonie-3-outfits-or-arknights-or-pony-xl

whizzzzkid/whizzzzkid_399_1

whizzzzkid

2024-07-02T08:35:21Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T08:34:59Z

Entry not found

henrik-dra/paligemma-ft-energymeter

henrik-dra

2024-07-02T14:56:23Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-07-02T08:35:03Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

infinitymatter/llama-3-8b-chat_army

infinitymatter

2024-07-02T08:36:58Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-07-02T08:35:12Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

whizzzzkid/whizzzzkid_400_7

whizzzzkid

2024-07-02T08:36:31Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T08:36:10Z

Entry not found

whizzzzkid/whizzzzkid_401_6

whizzzzkid

2024-07-02T08:37:30Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T08:37:11Z

Entry not found

femiari/Qwen1.5-7B-Moe

femiari

2024-07-02T08:47:57Z

0

transformers

[ "transformers", "safetensors", "qwen2_moe", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T08:38:41Z

Entry not found

ZeroWw/glm-4-9b-chat-GGUF

ZeroWw

2024-07-02T09:27:44Z

0

null

[ "gguf", "en", "license:mit", "region:us" ]

null

2024-07-02T08:38:49Z

--- license: mit language: - en --- My own (ZeroWw) quantizations. output and embed tensors quantized to f16. all other tensors quantized to q5_k or q6_k. Result: both f16.q6 and f16.q5 are smaller than q8_0 standard quantization and they perform as well as the pure f16.

tamilanda/efedf

tamilanda

2024-07-02T08:38:59Z

0

null

[ "license:gpl-2.0", "region:us" ]

null

2024-07-02T08:38:59Z

--- license: gpl-2.0 ---