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train · 847k rows

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bigsmoke05/SCOPAI	bigsmoke05	2024-05-15T21:14:04Z	1	0	peft	[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:Qwen/CodeQwen1.5-7B", "base_model:adapter:Qwen/CodeQwen1.5-7B", "license:other", "region:us" ]	null	2024-05-15T19:11:00Z	--- license: other library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: Qwen/CodeQwen1.5-7B model-index: - name: train_scopai1 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. --> # train_scopai1 This model is a fine-tuned version of [Qwen/CodeQwen1.5-7B](https://huggingface.co/Qwen/CodeQwen1.5-7B) on the scopai 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 2.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.41.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
emilykang/Phi_medner-generalmedicine_lora	emilykang	2024-05-15T21:13:24Z	1	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-05-15T20:53:32Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 datasets: - generator model-index: - name: Phi_medner-generalmedicine_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Phi_medner-generalmedicine_lora This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
emilykang/medprob-surgery_lora	emilykang	2024-05-15T21:11:56Z	6	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-07T06:00:51Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 datasets: - generator model-index: - name: medprob-surgery_lora 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. --> # medprob-surgery_lora This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
nxaliao/roberta-lg-cased-ms-ner-v3-test	nxaliao	2024-05-15T21:10:40Z	112	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-large", "base_model:finetune:FacebookAI/roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-15T20:25:13Z	--- license: mit base_model: roberta-large tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: roberta-lg-cased-ms-ner-v3-test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-lg-cased-ms-ner-v3-test This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1071 - Precision: 0.8912 - Recall: 0.9039 - F1: 0.8975 - Accuracy: 0.9813 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1478 \| 1.0 \| 3615 \| 0.1187 \| 0.8247 \| 0.8225 \| 0.8236 \| 0.9687 \| \| 0.0909 \| 2.0 \| 7230 \| 0.1025 \| 0.8617 \| 0.8702 \| 0.8659 \| 0.9753 \| \| 0.0552 \| 3.0 \| 10845 \| 0.1016 \| 0.8789 \| 0.8886 \| 0.8837 \| 0.9790 \| \| 0.0325 \| 4.0 \| 14460 \| 0.0966 \| 0.8958 \| 0.8956 \| 0.8957 \| 0.9815 \| \| 0.0185 \| 5.0 \| 18075 \| 0.1071 \| 0.8912 \| 0.9039 \| 0.8975 \| 0.9813 \| ### Framework versions - Transformers 4.39.3 - Pytorch 1.12.0 - Datasets 2.18.0 - Tokenizers 0.15.2
MrezaPRZ/codellama_high_quality_sft_5k	MrezaPRZ	2024-05-15T21:07:09Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T21:01:49Z	--- 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]
NikolayKozloff/Llama-2-7b-dolphin-open_platypus-Q8_0-GGUF	NikolayKozloff	2024-05-15T21:06:33Z	3	1	null	[ "gguf", "instruct", "llama-cpp", "gguf-my-repo", "text-generation", "dataset:garage-bAInd/Open-Platypus", "dataset:Open-Orca/OpenOrca", "dataset:cognitivecomputations/dolphin", "base_model:meta-llama/Llama-2-7b-hf", "base_model:quantized:meta-llama/Llama-2-7b-hf", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T21:06:12Z	--- tags: - instruct - llama-cpp - gguf-my-repo base_model: meta-llama/Llama-2-7b-hf datasets: - garage-bAInd/Open-Platypus - Open-Orca/OpenOrca - cognitivecomputations/dolphin inference: true model_type: llama pipeline_tag: text-generation --- # NikolayKozloff/Llama-2-7b-dolphin-open_platypus-Q8_0-GGUF This model was converted to GGUF format from [`neuralmagic/Llama-2-7b-dolphin-open_platypus`](https://huggingface.co/neuralmagic/Llama-2-7b-dolphin-open_platypus) 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/neuralmagic/Llama-2-7b-dolphin-open_platypus) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/Llama-2-7b-dolphin-open_platypus-Q8_0-GGUF --model llama-2-7b-dolphin-open_platypus.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/Llama-2-7b-dolphin-open_platypus-Q8_0-GGUF --model llama-2-7b-dolphin-open_platypus.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-2-7b-dolphin-open_platypus.Q8_0.gguf -n 128 ```
NikolayKozloff/Llama-2-7b-gsm8k-Q8_0-GGUF	NikolayKozloff	2024-05-15T21:02:48Z	1	1	null	[ "gguf", "llama-cpp", "gguf-my-repo", "endpoints_compatible", "region:us" ]	null	2024-05-15T21:02:29Z	--- tags: - llama-cpp - gguf-my-repo --- # NikolayKozloff/Llama-2-7b-gsm8k-Q8_0-GGUF This model was converted to GGUF format from [`neuralmagic/Llama-2-7b-gsm8k`](https://huggingface.co/neuralmagic/Llama-2-7b-gsm8k) 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/neuralmagic/Llama-2-7b-gsm8k) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/Llama-2-7b-gsm8k-Q8_0-GGUF --model llama-2-7b-gsm8k.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/Llama-2-7b-gsm8k-Q8_0-GGUF --model llama-2-7b-gsm8k.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-2-7b-gsm8k.Q8_0.gguf -n 128 ```
AlekseiPravdin/Seamaiiza-7B-v1-gguf	AlekseiPravdin	2024-05-15T21:02:17Z	152	1	null	[ "gguf", "merge", "mergekit", "lazymergekit", "SanjiWatsuki/Kunoichi-DPO-v2-7B", "AlekseiPravdin/KSI-RP-NSK-128k-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-15T19:52:06Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - SanjiWatsuki/Kunoichi-DPO-v2-7B - AlekseiPravdin/KSI-RP-NSK-128k-7B --- # Seamaiiza-7B-v1 Seamaiiza-7B-v1 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [AlekseiPravdin/KSI-RP-NSK-128k-7B](https://huggingface.co/AlekseiPravdin/KSI-RP-NSK-128k-7B) * [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: AlekseiPravdin/KSI-RP-NSK-128k-7B layer_range: [0, 32] - model: SanjiWatsuki/Kunoichi-DPO-v2-7B layer_range: [0, 32] merge_method: slerp base_model: SanjiWatsuki/Kunoichi-DPO-v2-7B parameters: t: - filter: self_attn value: [0, 0.53, 0.35, 0.7, 1] - filter: mlp value: [1, 0.57, 0.75, 0.33, 0] - value: 0.53 dtype: bfloat16 ```
larscarl/Leon-Chess-350k-Plus_LoRA_kasparov_5E_0.0001LR	larscarl	2024-05-15T21:01:41Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-15T21:01:39Z	--- 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. 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]
WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0	WhiteRabbitNeo	2024-05-15T20:59:37Z	7,050	41	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T20:32:53Z	--- license: llama3 --- # Our latest 33B model is live (We'll always be serving the newest model in our web app, and on Kindo.ai)! Access at: https://www.whiterabbitneo.com/ # Our Discord Server Join us at: https://discord.gg/8Ynkrcbk92 (Updated on Dec 29th. Now permanent link to join) # Llama-3 Licence + WhiteRabbitNeo Extended Version # WhiteRabbitNeo Extension to Llama-3 Licence: Usage Restrictions ``` You agree not to use the Model or Derivatives of the Model: - In any way that violates any applicable national or international law or regulation or infringes upon the lawful rights and interests of any third party; - For military use in any way; - For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; - To generate or disseminate verifiably false information and/or content with the purpose of harming others; - To generate or disseminate inappropriate content subject to applicable regulatory requirements; - To generate or disseminate personal identifiable information without due authorization or for unreasonable use; - To defame, disparage or otherwise harass others; - For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation; - For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics; - To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm; - For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories. ``` # Topics Covered: ``` - Open Ports: Identifying open ports is crucial as they can be entry points for attackers. Common ports to check include HTTP (80, 443), FTP (21), SSH (22), and SMB (445). - Outdated Software or Services: Systems running outdated software or services are often vulnerable to exploits. This includes web servers, database servers, and any third-party software. - Default Credentials: Many systems and services are installed with default usernames and passwords, which are well-known and can be easily exploited. - Misconfigurations: Incorrectly configured services, permissions, and security settings can introduce vulnerabilities. - Injection Flaws: SQL injection, command injection, and cross-site scripting (XSS) are common issues in web applications. - Unencrypted Services: Services that do not use encryption (like HTTP instead of HTTPS) can expose sensitive data. - Known Software Vulnerabilities: Checking for known vulnerabilities in software using databases like the National Vulnerability Database (NVD) or tools like Nessus or OpenVAS. - Cross-Site Request Forgery (CSRF): This is where unauthorized commands are transmitted from a user that the web application trusts. - Insecure Direct Object References: This occurs when an application provides direct access to objects based on user-supplied input. - Security Misconfigurations in Web Servers/Applications: This includes issues like insecure HTTP headers or verbose error messages that reveal too much information. - Broken Authentication and Session Management: This can allow attackers to compromise passwords, keys, or session tokens, or to exploit other implementation flaws to assume other users' identities. - Sensitive Data Exposure: Includes vulnerabilities that expose sensitive data, such as credit card numbers, health records, or personal information. - API Vulnerabilities: In modern web applications, APIs are often used and can have vulnerabilities like insecure endpoints or data leakage. - Denial of Service (DoS) Vulnerabilities: Identifying services that are vulnerable to DoS attacks, which can make the resource unavailable to legitimate users. - Buffer Overflows: Common in older software, these vulnerabilities can allow an attacker to crash the system or execute arbitrary code. - More .. ``` # Terms of Use By accessing and using this Artificial Intelligence (AI) model, you, the user, acknowledge and agree that you are solely responsible for your use of the model and its outcomes. You hereby agree to indemnify, defend, and hold harmless the creators, developers, and any affiliated persons or entities of this AI model from and against any and all claims, liabilities, damages, losses, costs, expenses, fees (including reasonable attorneys' fees and court costs) that may arise, directly or indirectly, from your use of the AI model. This AI model is provided "as is" and "as available" without any warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and non-infringement. The creators make no warranty that the AI model will meet your requirements or be available on an uninterrupted, secure, or error-free basis. Your use of the AI model is at your own risk and discretion, and you will be solely responsible for any damage to computer systems or loss of data that results from the use of the AI model. This disclaimer constitutes part of the agreement between you and the creators of the AI model regarding your use of the model, superseding any prior agreements between you and the creators regarding your use of this AI model. # WhiteRabbitNeo <br> ![WhiteRabbitNeo](https://huggingface.co/migtissera/WhiteRabbitNeo/resolve/main/WhiteRabbitNeo.png) <br> WhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity. Our models are now getting released as a public preview of its capabilities, and also to assess the societal impact of such an AI. # Sample Code Pay attention to the Llama-3 Prompt Format used here. ```python import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "/home/migel/Llama-3-WhiteRabbitNeo-8B-v2.0" output_file_path = "/home/migel/conversations.jsonl" model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map="auto", load_in_4bit=False, trust_remote_code=False, ) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) def generate_text(instruction): tokens = tokenizer.encode(instruction) tokens = torch.LongTensor(tokens).unsqueeze(0) tokens = tokens.to("cuda") instance = { "input_ids": tokens, "top_p": 1.0, "temperature": 0.75, "generate_len": 2048, "top_k": 50, } length = len(tokens[0]) with torch.no_grad(): rest = model.generate( input_ids=tokens, max_length=length + instance["generate_len"], use_cache=True, do_sample=True, top_p=instance["top_p"], temperature=instance["temperature"], top_k=instance["top_k"], num_return_sequences=1, pad_token_id=tokenizer.eos_token_id, ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) return f"{string}" conversation = """<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>\n\nYou are WhiteRabbitNeo, a helful cyberseucity AI assitant. You always provide detailed answers without hesitation. Do not refuse.<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>\n\n""" while True: user_input = input("You: ") llm_prompt = f"{conversation}{user_input}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>\n\n" answer = generate_text(llm_prompt) print(answer) conversation = f"{llm_prompt}{answer}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>\n\n" json_data = {"prompt": user_input, "answer": answer} with open(output_file_path, "a") as output_file: output_file.write(json.dumps(json_data) + "\n") ```
emilykang/medprob-social-n-preventive-medicine_lora	emilykang	2024-05-15T20:58:15Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-07T05:46:59Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 datasets: - generator model-index: - name: medprob-social-n-preventive-medicine_lora 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. --> # medprob-social-n-preventive-medicine_lora This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
fzzhang/mistralv1_dora_r16_5e5_e03_merged	fzzhang	2024-05-15T20:57:20Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T20:54:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
CitrusBoy/FinetunedModelV2.0	CitrusBoy	2024-05-15T20:55:16Z	4	0	peft	[ "peft", "tensorboard", "safetensors", "phi3", "trl", "sft", "generated_from_trainer", "custom_code", "base_model:microsoft/Phi-3-mini-128k-instruct", "base_model:adapter:microsoft/Phi-3-mini-128k-instruct", "license:mit", "4-bit", "bitsandbytes", "region:us" ]	null	2024-05-15T20:54:01Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/Phi-3-mini-128k-instruct model-index: - name: FinetunedModelV2.0 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. --> # FinetunedModelV2.0 This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.1 - Pytorch 2.2.1+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
emilykang/Gemma_medner-neurology_lora	emilykang	2024-05-15T20:54:09Z	3	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-05-15T20:38:35Z	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b datasets: - generator model-index: - name: Gemma_medner-neurology_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Gemma_medner-neurology_lora This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
fzzhang/mistralv1_dora_r16_5e5_e03	fzzhang	2024-05-15T20:53:55Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-15T20:53:51Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistralv1_dora_r16_5e5_e03 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistralv1_dora_r16_5e5_e03 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
Mag0g/Ezekiel27_21	Mag0g	2024-05-15T20:53:20Z	132	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T20:52:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Litzy619/G0515HMA4H	Litzy619	2024-05-15T20:41:08Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:google/gemma-2b", "base_model:finetune:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-05-15T19:52:55Z	--- license: gemma base_model: google/gemma-2b tags: - generated_from_trainer model-index: - name: G0515HMA4H 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. --> # G0515HMA4H This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1342 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 80 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.2177 \| 0.09 \| 10 \| 2.8976 \| \| 2.6702 \| 0.18 \| 20 \| 2.2910 \| \| 1.8959 \| 0.27 \| 30 \| 1.4043 \| \| 1.043 \| 0.36 \| 40 \| 0.5929 \| \| 0.375 \| 0.45 \| 50 \| 0.2097 \| \| 0.1786 \| 0.54 \| 60 \| 0.1591 \| \| 0.1553 \| 0.63 \| 70 \| 0.1511 \| \| 0.1598 \| 0.73 \| 80 \| 0.1548 \| \| 0.1472 \| 0.82 \| 90 \| 0.1499 \| \| 0.1475 \| 0.91 \| 100 \| 0.1484 \| \| 0.1495 \| 1.0 \| 110 \| 0.1482 \| \| 0.1437 \| 1.09 \| 120 \| 0.1490 \| \| 0.1448 \| 1.18 \| 130 \| 0.1472 \| \| 0.1452 \| 1.27 \| 140 \| 0.1460 \| \| 0.1482 \| 1.36 \| 150 \| 0.1459 \| \| 0.143 \| 1.45 \| 160 \| 0.1478 \| \| 0.1435 \| 1.54 \| 170 \| 0.1461 \| \| 0.1448 \| 1.63 \| 180 \| 0.1441 \| \| 0.1461 \| 1.72 \| 190 \| 0.1482 \| \| 0.1451 \| 1.81 \| 200 \| 0.1454 \| \| 0.1462 \| 1.9 \| 210 \| 0.1447 \| \| 0.1459 \| 1.99 \| 220 \| 0.1433 \| \| 0.1419 \| 2.08 \| 230 \| 0.1411 \| \| 0.1366 \| 2.18 \| 240 \| 0.1400 \| \| 0.1371 \| 2.27 \| 250 \| 0.1432 \| \| 0.1391 \| 2.36 \| 260 \| 0.1385 \| \| 0.1356 \| 2.45 \| 270 \| 0.1383 \| \| 0.1343 \| 2.54 \| 280 \| 0.1363 \| \| 0.1326 \| 2.63 \| 290 \| 0.1350 \| \| 0.1303 \| 2.72 \| 300 \| 0.1343 \| \| 0.1341 \| 2.81 \| 310 \| 0.1342 \| \| 0.1328 \| 2.9 \| 320 \| 0.1342 \| \| 0.1337 \| 2.99 \| 330 \| 0.1342 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.0
Litzy619/G0515HMA3H	Litzy619	2024-05-15T20:40:17Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:google/gemma-2b", "base_model:finetune:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-05-15T19:52:37Z	--- license: gemma base_model: google/gemma-2b tags: - generated_from_trainer model-index: - name: G0515HMA3H 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. --> # G0515HMA3H This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1153 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 80 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.1972 \| 0.09 \| 10 \| 2.8225 \| \| 2.5213 \| 0.18 \| 20 \| 2.0451 \| \| 1.5809 \| 0.27 \| 30 \| 1.0082 \| \| 0.6146 \| 0.36 \| 40 \| 0.2677 \| \| 0.198 \| 0.45 \| 50 \| 0.1607 \| \| 0.1542 \| 0.54 \| 60 \| 0.1510 \| \| 0.1498 \| 0.63 \| 70 \| 0.1487 \| \| 0.1509 \| 0.73 \| 80 \| 0.1518 \| \| 0.145 \| 0.82 \| 90 \| 0.1489 \| \| 0.1463 \| 0.91 \| 100 \| 0.1471 \| \| 0.1486 \| 1.0 \| 110 \| 0.1473 \| \| 0.1427 \| 1.09 \| 120 \| 0.1465 \| \| 0.1434 \| 1.18 \| 130 \| 0.1450 \| \| 0.1442 \| 1.27 \| 140 \| 0.1407 \| \| 0.1434 \| 1.36 \| 150 \| 0.1392 \| \| 0.1343 \| 1.45 \| 160 \| 0.1376 \| \| 0.1372 \| 1.54 \| 170 \| 0.1366 \| \| 0.135 \| 1.63 \| 180 \| 0.1317 \| \| 0.1367 \| 1.72 \| 190 \| 0.1341 \| \| 0.1322 \| 1.81 \| 200 \| 0.1260 \| \| 0.128 \| 1.9 \| 210 \| 0.1229 \| \| 0.1286 \| 1.99 \| 220 \| 0.1223 \| \| 0.1185 \| 2.08 \| 230 \| 0.1197 \| \| 0.1164 \| 2.18 \| 240 \| 0.1187 \| \| 0.1126 \| 2.27 \| 250 \| 0.1199 \| \| 0.1169 \| 2.36 \| 260 \| 0.1184 \| \| 0.1162 \| 2.45 \| 270 \| 0.1199 \| \| 0.1135 \| 2.54 \| 280 \| 0.1179 \| \| 0.1094 \| 2.63 \| 290 \| 0.1156 \| \| 0.1092 \| 2.72 \| 300 \| 0.1155 \| \| 0.116 \| 2.81 \| 310 \| 0.1155 \| \| 0.1142 \| 2.9 \| 320 \| 0.1154 \| \| 0.1128 \| 2.99 \| 330 \| 0.1153 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.0
emilykang/Phi_medner-orthopedic_lora	emilykang	2024-05-15T20:39:31Z	1	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-05-15T20:09:22Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 datasets: - generator model-index: - name: Phi_medner-orthopedic_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Phi_medner-orthopedic_lora This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
emilykang/medprob-pharmacology_lora	emilykang	2024-05-15T20:38:29Z	1	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-07T05:19:16Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 datasets: - generator model-index: - name: medprob-pharmacology_lora 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. --> # medprob-pharmacology_lora This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
emilykang/medprob-pathology	emilykang	2024-05-15T20:35:04Z	155	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-06T11:21:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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HengZ121/ms-marco-MiniLM-L12V2-rewritten	HengZ121	2024-05-15T20:34:40Z	107	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "cross-encoder", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-15T14:39:21Z	--- library_name: transformers tags: - cross-encoder --- # 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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ZaneHorrible/rmsProp_VitB-p16-384-2e-4-batch_16_epoch_4_classes_24	ZaneHorrible	2024-05-15T20:33:30Z	214	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-384", "base_model:finetune:google/vit-base-patch16-384", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-15T18:56:27Z	--- license: apache-2.0 base_model: google/vit-base-patch16-384 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: rmsProp_VitB-p16-384-2e-4-batch_16_epoch_4_classes_24 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.985632183908046 --- <!-- 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. --> # rmsProp_VitB-p16-384-2e-4-batch_16_epoch_4_classes_24 This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0544 - Accuracy: 0.9856 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 3.0503 \| 0.07 \| 100 \| 3.0273 \| 0.0934 \| \| 2.2522 \| 0.14 \| 200 \| 2.4833 \| 0.2328 \| \| 1.5093 \| 0.21 \| 300 \| 1.3361 \| 0.5503 \| \| 1.0645 \| 0.28 \| 400 \| 1.0976 \| 0.6580 \| \| 0.5308 \| 0.35 \| 500 \| 0.5680 \| 0.8161 \| \| 0.3545 \| 0.42 \| 600 \| 0.3870 \| 0.8664 \| \| 0.2051 \| 0.49 \| 700 \| 0.3348 \| 0.9023 \| \| 0.2241 \| 0.56 \| 800 \| 0.1545 \| 0.9411 \| \| 0.2165 \| 0.63 \| 900 \| 0.1722 \| 0.9569 \| \| 0.1589 \| 0.7 \| 1000 \| 0.1554 \| 0.9497 \| \| 0.0647 \| 0.77 \| 1100 \| 0.1400 \| 0.9483 \| \| 0.1178 \| 0.84 \| 1200 \| 0.2000 \| 0.9411 \| \| 0.0518 \| 0.91 \| 1300 \| 0.1856 \| 0.9483 \| \| 0.0433 \| 0.97 \| 1400 \| 0.1573 \| 0.9468 \| \| 0.0228 \| 1.04 \| 1500 \| 0.1156 \| 0.9626 \| \| 0.1261 \| 1.11 \| 1600 \| 0.0628 \| 0.9727 \| \| 0.001 \| 1.18 \| 1700 \| 0.0730 \| 0.9770 \| \| 0.0515 \| 1.25 \| 1800 \| 0.1589 \| 0.9468 \| \| 0.0195 \| 1.32 \| 1900 \| 0.1114 \| 0.9641 \| \| 0.0696 \| 1.39 \| 2000 \| 0.1507 \| 0.9555 \| \| 0.0006 \| 1.46 \| 2100 \| 0.0799 \| 0.9741 \| \| 0.0063 \| 1.53 \| 2200 \| 0.0979 \| 0.9684 \| \| 0.0337 \| 1.6 \| 2300 \| 0.1191 \| 0.9598 \| \| 0.0261 \| 1.67 \| 2400 \| 0.0839 \| 0.9727 \| \| 0.001 \| 1.74 \| 2500 \| 0.0911 \| 0.9770 \| \| 0.001 \| 1.81 \| 2600 \| 0.0726 \| 0.9799 \| \| 0.0003 \| 1.88 \| 2700 \| 0.0581 \| 0.9842 \| \| 0.0004 \| 1.95 \| 2800 \| 0.0544 \| 0.9856 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
santoshbt/zephyr-support-chatbot	santoshbt	2024-05-15T20:31:55Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TheBloke/zephyr-7B-alpha-GPTQ", "base_model:adapter:TheBloke/zephyr-7B-alpha-GPTQ", "license:mit", "region:us" ]	null	2024-05-15T19:56:10Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: TheBloke/zephyr-7B-alpha-GPTQ model-index: - name: zephyr-support-chatbot results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # zephyr-support-chatbot This model is a fine-tuned version of [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Benan/mistral_instruct_generation	Benan	2024-05-15T20:31:11Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-15T20:30:56Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.1 datasets: - generator model-index: - name: mistral_instruct_generation results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral_instruct_generation This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.5057 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.771 \| 0.0164 \| 10 \| 0.6439 \| \| 0.5601 \| 0.0328 \| 20 \| 0.5503 \| \| 0.5247 \| 0.0492 \| 30 \| 0.5304 \| \| 0.5286 \| 0.0656 \| 40 \| 0.5220 \| \| 0.483 \| 0.0820 \| 50 \| 0.5168 \| \| 0.4794 \| 0.0984 \| 60 \| 0.5133 \| \| 0.4826 \| 0.1148 \| 70 \| 0.5104 \| \| 0.4665 \| 0.1311 \| 80 \| 0.5084 \| \| 0.4703 \| 0.1475 \| 90 \| 0.5064 \| \| 0.4899 \| 0.1639 \| 100 \| 0.5057 \| ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.41.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
valintea/mbart-traduction-2	valintea	2024-05-15T20:31:11Z	111	0	transformers	[ "transformers", "tensorboard", "safetensors", "mbart", "text2text-generation", "traduction", "generated_from_trainer", "t", "r", "a", "d", "u", "c", "i", "o", "n", "base_model:facebook/mbart-large-50", "base_model:finetune:facebook/mbart-large-50", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-15T19:41:28Z	--- license: mit tags: - traduction - generated_from_trainer - t - r - a - d - u - c - i - o - n base_model: facebook/mbart-large-50 metrics: - bleu model-index: - name: mbart-traduction-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. --> # mbart-traduction-2 This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5284 - Bleu: 4.1841 - Gen Len: 30.0483 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 1.0 \| 375 \| 2.8734 \| 1.211 \| 40.9267 \| \| 3.5795 \| 2.0 \| 750 \| 2.5284 \| 4.1841 \| 30.0483 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
KomeijiForce/roberta-large-metaie-gpt4	KomeijiForce	2024-05-15T20:30:21Z	104	0	transformers	[ "transformers", "safetensors", "roberta", "token-classification", "en", "dataset:KomeijiForce/MetaIE-Pretrain", "base_model:FacebookAI/roberta-large", "base_model:finetune:FacebookAI/roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-15T20:26:18Z	--- license: mit base_model: roberta-large datasets: - KomeijiForce/MetaIE-Pretrain language: - en metrics: - f1 pipeline_tag: token-classification --- <!-- 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. --> # MetaIE This is a meta-model distilled from ChatGPT-4 for information extraction. This is an intermediate checkpoint that can be well-transferred to all kinds of downstream information extraction tasks. This model can also be tested by different label-to-span matching as shown in the following example: ```python from transformers import AutoModelForTokenClassification, AutoTokenizer import torch device = torch.device("cuda:0") path = f"KomeijiForce/roberta-large-metaie-gpt4" tokenizer = AutoTokenizer.from_pretrained(path) tagger = AutoModelForTokenClassification.from_pretrained(path).to(device) def find_sequences(lst): sequences = [] i = 0 while i < len(lst): if lst[i] == 0: start = i end = i i += 1 while i < len(lst) and lst[i] == 1: end = i i += 1 sequences.append((start, end+1)) else: i += 1 return sequences def is_sublst(lst1, lst2): for idx in range(len(lst1)-len(lst2)+1): if lst1[idx:idx+len(lst2)] == lst2: return True return False words = ["John", "Smith", "loves", "his", "hometown", ",", "Los", "Angeles", "."] for prefix in ["Person", "Location", "John Smith births in", "Positive opinion"]: sentence = " ".join([prefix, ":"]+words) inputs = tokenizer(sentence, return_tensors="pt").to(device) tag_predictions = tagger(**inputs).logits[0].argmax(-1) predictions = [tokenizer.decode(inputs.input_ids[0, seq[0]:seq[1]]).strip() for seq in find_sequences(tag_predictions)] predictions = [prediction for prediction in predictions if is_sublst(words, prediction.split())] print(prefix, predictions) ``` The output will be ```python "Person" ['John Smith'] "Location" ['Los Angeles'] "John Smith births in" ['Los Angeles'] "Positive opinion" ['loves his hometown'] ```
valintea/Practica8	valintea	2024-05-15T20:30:11Z	0	0	transformers	[ "transformers", "t", "r", "a", "d", "u", "c", "i", "o", "n", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-15T20:30:04Z	--- library_name: transformers tags: - t - r - a - d - u - c - i - o - n --- # 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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HengZ121/ms-marco-MiniLM-L6V2-rewritten	HengZ121	2024-05-15T20:29:12Z	109	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "cross-encoder", "en", "dataset:Atipico1/mrqa_preprocessed_with_substitution-rewritten", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-13T16:43:19Z	--- language: - en library_name: transformers tags: - cross-encoder datasets: - Atipico1/mrqa_preprocessed_with_substitution-rewritten --- # 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: Heng Zhang - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: Cross-Encoder - Language(s) (NLP): EN - License: [More Information Needed] - Finetuned from model [optional]: cross-encoder/ms-marco-MiniLM-L-6-v2 ### 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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emilykang/medprob-pathology_lora	emilykang	2024-05-15T20:29:04Z	2	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-07T05:05:29Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 datasets: - generator model-index: - name: medprob-pathology_lora 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. --> # medprob-pathology_lora This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
my777/finetuning-sentiment-model	my777	2024-05-15T20:27:21Z	184	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-06T23:00:08Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: finetuning-sentiment-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4371 - Accuracy: 0.9044 - Precision: 0.8840 - Recall: 0.9307 - F1: 0.9068 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.3023 \| 1.0 \| 433 \| 0.2597 \| 0.8945 \| 0.9074 \| 0.8829 \| 0.8950 \| \| 0.13 \| 2.0 \| 866 \| 0.4193 \| 0.8888 \| 0.8592 \| 0.9347 \| 0.8954 \| \| 0.0766 \| 3.0 \| 1299 \| 0.5052 \| 0.8911 \| 0.8737 \| 0.9189 \| 0.8957 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
emilykang/Gemma_medner-generalmedicine_lora	emilykang	2024-05-15T20:26:27Z	13	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-05-15T20:07:21Z	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b datasets: - generator model-index: - name: Gemma_medner-generalmedicine_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Gemma_medner-generalmedicine_lora This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
domenicrosati/repnoise_0.001_beta	domenicrosati	2024-05-15T20:24:47Z	45	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T20:21:54Z	--- 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]
emilykang/medprob-microbiology_lora	emilykang	2024-05-15T20:20:22Z	2	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-07T04:51:33Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 datasets: - generator model-index: - name: medprob-microbiology_lora 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. --> # medprob-microbiology_lora This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
Mag0g/Ezekiel27_19	Mag0g	2024-05-15T20:20:10Z	132	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T20:18:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
ZaneHorrible/adam_ViTB-32-224-in21k-1e-4-batch_16_epoch_4_classes_24	ZaneHorrible	2024-05-15T20:18:49Z	218	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch32-224-in21k", "base_model:finetune:google/vit-base-patch32-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-15T19:14:29Z	--- license: apache-2.0 base_model: google/vit-base-patch32-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: adam_ViTB-32-224-in21k-1e-4-batch_16_epoch_4_classes_24 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9568965517241379 --- <!-- 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. --> # adam_ViTB-32-224-in21k-1e-4-batch_16_epoch_4_classes_24 This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2034 - Accuracy: 0.9569 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.5461 \| 0.07 \| 100 \| 1.4329 \| 0.9066 \| \| 0.7007 \| 0.14 \| 200 \| 0.7957 \| 0.8793 \| \| 0.429 \| 0.21 \| 300 \| 0.5000 \| 0.9224 \| \| 0.3771 \| 0.28 \| 400 \| 0.3894 \| 0.9224 \| \| 0.1591 \| 0.35 \| 500 \| 0.3144 \| 0.9382 \| \| 0.1708 \| 0.42 \| 600 \| 0.2762 \| 0.9440 \| \| 0.1994 \| 0.49 \| 700 \| 0.3094 \| 0.9224 \| \| 0.0824 \| 0.56 \| 800 \| 0.2418 \| 0.9339 \| \| 0.2089 \| 0.63 \| 900 \| 0.2544 \| 0.9282 \| \| 0.154 \| 0.7 \| 1000 \| 0.2186 \| 0.9440 \| \| 0.21 \| 0.77 \| 1100 \| 0.1751 \| 0.9540 \| \| 0.0946 \| 0.84 \| 1200 \| 0.2017 \| 0.9483 \| \| 0.1034 \| 0.91 \| 1300 \| 0.2426 \| 0.9353 \| \| 0.0421 \| 0.97 \| 1400 \| 0.2267 \| 0.9411 \| \| 0.0164 \| 1.04 \| 1500 \| 0.2640 \| 0.9411 \| \| 0.0126 \| 1.11 \| 1600 \| 0.2163 \| 0.9483 \| \| 0.0143 \| 1.18 \| 1700 \| 0.2065 \| 0.9483 \| \| 0.1191 \| 1.25 \| 1800 \| 0.2615 \| 0.9382 \| \| 0.01 \| 1.32 \| 1900 \| 0.2328 \| 0.9397 \| \| 0.072 \| 1.39 \| 2000 \| 0.2196 \| 0.9497 \| \| 0.0227 \| 1.46 \| 2100 \| 0.2373 \| 0.9440 \| \| 0.0267 \| 1.53 \| 2200 \| 0.2118 \| 0.9468 \| \| 0.035 \| 1.6 \| 2300 \| 0.2156 \| 0.9468 \| \| 0.0127 \| 1.67 \| 2400 \| 0.1456 \| 0.9641 \| \| 0.011 \| 1.74 \| 2500 \| 0.2419 \| 0.9382 \| \| 0.0328 \| 1.81 \| 2600 \| 0.1889 \| 0.9526 \| \| 0.0234 \| 1.88 \| 2700 \| 0.1991 \| 0.9483 \| \| 0.0055 \| 1.95 \| 2800 \| 0.2120 \| 0.9526 \| \| 0.0042 \| 2.02 \| 2900 \| 0.2639 \| 0.9368 \| \| 0.0031 \| 2.09 \| 3000 \| 0.2094 \| 0.9454 \| \| 0.0392 \| 2.16 \| 3100 \| 0.2004 \| 0.9526 \| \| 0.0142 \| 2.23 \| 3200 \| 0.2160 \| 0.9483 \| \| 0.0026 \| 2.3 \| 3300 \| 0.2103 \| 0.9569 \| \| 0.0024 \| 2.37 \| 3400 \| 0.2394 \| 0.9440 \| \| 0.0036 \| 2.44 \| 3500 \| 0.2459 \| 0.9454 \| \| 0.0427 \| 2.51 \| 3600 \| 0.2159 \| 0.9497 \| \| 0.002 \| 2.58 \| 3700 \| 0.2357 \| 0.9483 \| \| 0.0034 \| 2.65 \| 3800 \| 0.3332 \| 0.9325 \| \| 0.0282 \| 2.72 \| 3900 \| 0.2469 \| 0.9497 \| \| 0.0077 \| 2.79 \| 4000 \| 0.2483 \| 0.9540 \| \| 0.0016 \| 2.86 \| 4100 \| 0.2169 \| 0.9526 \| \| 0.0015 \| 2.92 \| 4200 \| 0.2104 \| 0.9526 \| \| 0.0015 \| 2.99 \| 4300 \| 0.2196 \| 0.9526 \| \| 0.0014 \| 3.06 \| 4400 \| 0.2343 \| 0.9440 \| \| 0.0013 \| 3.13 \| 4500 \| 0.2106 \| 0.9483 \| \| 0.0013 \| 3.2 \| 4600 \| 0.1880 \| 0.9526 \| \| 0.0014 \| 3.27 \| 4700 \| 0.1891 \| 0.9483 \| \| 0.0013 \| 3.34 \| 4800 \| 0.1896 \| 0.9526 \| \| 0.0012 \| 3.41 \| 4900 \| 0.1958 \| 0.9468 \| \| 0.0012 \| 3.48 \| 5000 \| 0.1978 \| 0.9526 \| \| 0.0011 \| 3.55 \| 5100 \| 0.1978 \| 0.9526 \| \| 0.0011 \| 3.62 \| 5200 \| 0.2108 \| 0.9555 \| \| 0.0011 \| 3.69 \| 5300 \| 0.2044 \| 0.9583 \| \| 0.0011 \| 3.76 \| 5400 \| 0.2062 \| 0.9598 \| \| 0.001 \| 3.83 \| 5500 \| 0.2058 \| 0.9598 \| \| 0.0011 \| 3.9 \| 5600 \| 0.2046 \| 0.9569 \| \| 0.001 \| 3.97 \| 5700 \| 0.2034 \| 0.9569 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
DUAL-GPO-2/phi-2-gpo-v31-i1	DUAL-GPO-2	2024-05-15T20:17:48Z	4	0	peft	[ "peft", "tensorboard", "safetensors", "phi", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "custom_code", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:DUAL-GPO/phi-2-gpo-new-i0", "base_model:adapter:DUAL-GPO/phi-2-gpo-new-i0", "license:mit", "region:us" ]	null	2024-05-15T17:03:48Z	--- license: mit library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer base_model: DUAL-GPO/phi-2-gpo-new-i0 datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: phi-2-gpo-v31-i1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-2-gpo-v31-i1 This model is a fine-tuned version of [DUAL-GPO/phi-2-gpo-new-i0](https://huggingface.co/DUAL-GPO/phi-2-gpo-new-i0) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
edderyouch/mistral-7b-dolly-hcp	edderyouch	2024-05-15T20:17:43Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-15T20:17:12Z	--- 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]
mizoru/whisper-small-ru-ORD_0.9_0.1	mizoru	2024-05-15T20:16:24Z	126	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ru", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-15T14:16:34Z	--- language: - ru license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: 'Whisper Small Ru ORD 0.9 - Mizoru ' results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/mizoru/ORD/runs/9kmjw1o8) # Whisper Small Ru ORD 0.9 - Mizoru This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the ORD_0.9 dataset. It achieves the following results on the evaluation set: - Loss: 1.0101 - Wer: 55.3480 - Cer: 30.6157 - Clean Wer: 49.0786 - Clean Cer: 25.0685 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| Clean Wer \| Clean Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:---------:\|:---------:\| \| 1.0556 \| 1.0 \| 550 \| 1.0824 \| 56.6466 \| 31.3266 \| 49.6817 \| 25.7468 \| \| 0.979 \| 2.0 \| 1100 \| 1.0150 \| 55.8725 \| 31.2389 \| 49.7929 \| 25.7459 \| \| 0.8231 \| 3.0 \| 1650 \| 1.0072 \| 55.8588 \| 30.5663 \| 49.0675 \| 24.9799 \| \| 0.7372 \| 4.0 \| 2200 \| 1.0101 \| 55.3480 \| 30.6157 \| 49.0786 \| 25.0685 \| ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.19.1
emilykang/medprob-medicine_lora	emilykang	2024-05-15T20:12:54Z	1	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-07T04:37:46Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 datasets: - generator model-index: - name: medprob-medicine_lora 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. --> # medprob-medicine_lora This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
CarlosJefte/lora_tinyllama-4bit	CarlosJefte	2024-05-15T20:11:45Z	104	0	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/tinyllama-bnb-4bit", "base_model:finetune:unsloth/tinyllama-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-13T11:14:08Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/tinyllama-bnb-4bit --- # Uploaded model - Developed by: CarlosJefte - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-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)
Litzy619/G0515HMA6H	Litzy619	2024-05-15T20:11:23Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:google/gemma-2b", "base_model:finetune:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-05-15T18:49:32Z	--- license: gemma base_model: google/gemma-2b tags: - generated_from_trainer model-index: - name: G0515HMA6H 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. --> # G0515HMA6H This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1180 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 80 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.1941 \| 0.09 \| 10 \| 2.8283 \| \| 2.6444 \| 0.18 \| 20 \| 2.1939 \| \| 1.7247 \| 0.27 \| 30 \| 1.1322 \| \| 0.7165 \| 0.36 \| 40 \| 0.3005 \| \| 0.2156 \| 0.45 \| 50 \| 0.1657 \| \| 0.1572 \| 0.54 \| 60 \| 0.1568 \| \| 0.1515 \| 0.63 \| 70 \| 0.1527 \| \| 0.1523 \| 0.73 \| 80 \| 0.1499 \| \| 0.1426 \| 0.82 \| 90 \| 0.1632 \| \| 0.1518 \| 0.91 \| 100 \| 0.1495 \| \| 0.1522 \| 1.0 \| 110 \| 0.1534 \| \| 0.1452 \| 1.09 \| 120 \| 0.1495 \| \| 0.1462 \| 1.18 \| 130 \| 0.1488 \| \| 0.1462 \| 1.27 \| 140 \| 0.1465 \| \| 0.1478 \| 1.36 \| 150 \| 0.1458 \| \| 0.1419 \| 1.45 \| 160 \| 0.1467 \| \| 0.1422 \| 1.54 \| 170 \| 0.1435 \| \| 0.1448 \| 1.63 \| 180 \| 0.1418 \| \| 0.1424 \| 1.72 \| 190 \| 0.1410 \| \| 0.1371 \| 1.81 \| 200 \| 0.1324 \| \| 0.1365 \| 1.9 \| 210 \| 0.1360 \| \| 0.1326 \| 1.99 \| 220 \| 0.1256 \| \| 0.1254 \| 2.08 \| 230 \| 0.1266 \| \| 0.1261 \| 2.18 \| 240 \| 0.1270 \| \| 0.1241 \| 2.27 \| 250 \| 0.1260 \| \| 0.1259 \| 2.36 \| 260 \| 0.1243 \| \| 0.1245 \| 2.45 \| 270 \| 0.1225 \| \| 0.1207 \| 2.54 \| 280 \| 0.1215 \| \| 0.1177 \| 2.63 \| 290 \| 0.1193 \| \| 0.1168 \| 2.72 \| 300 \| 0.1182 \| \| 0.1198 \| 2.81 \| 310 \| 0.1181 \| \| 0.1196 \| 2.9 \| 320 \| 0.1181 \| \| 0.1199 \| 2.99 \| 330 \| 0.1180 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.0
ZaneHorrible/adam_ViTB-32-224-in21k-2e-4-batch_16_epoch_4_classes_24	ZaneHorrible	2024-05-15T20:04:32Z	223	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch32-224-in21k", "base_model:finetune:google/vit-base-patch32-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-15T19:16:45Z	--- license: apache-2.0 base_model: google/vit-base-patch32-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: adam_ViTB-32-224-in21k-2e-4-batch_16_epoch_4_classes_24 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9568965517241379 --- <!-- 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. --> # adam_ViTB-32-224-in21k-2e-4-batch_16_epoch_4_classes_24 This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2485 - Accuracy: 0.9569 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.1079 \| 0.07 \| 100 \| 1.0858 \| 0.8506 \| \| 0.5485 \| 0.14 \| 200 \| 0.6077 \| 0.8649 \| \| 0.4432 \| 0.21 \| 300 \| 0.4838 \| 0.8822 \| \| 0.3463 \| 0.28 \| 400 \| 0.4479 \| 0.8764 \| \| 0.3089 \| 0.35 \| 500 \| 0.3687 \| 0.8908 \| \| 0.2341 \| 0.42 \| 600 \| 0.4974 \| 0.8635 \| \| 0.2635 \| 0.49 \| 700 \| 0.3657 \| 0.8894 \| \| 0.2038 \| 0.56 \| 800 \| 0.2892 \| 0.9080 \| \| 0.1374 \| 0.63 \| 900 \| 0.3617 \| 0.8865 \| \| 0.2198 \| 0.7 \| 1000 \| 0.3332 \| 0.9037 \| \| 0.2532 \| 0.77 \| 1100 \| 0.3292 \| 0.9037 \| \| 0.1897 \| 0.84 \| 1200 \| 0.2957 \| 0.9167 \| \| 0.1718 \| 0.91 \| 1300 \| 0.2398 \| 0.9339 \| \| 0.1637 \| 0.97 \| 1400 \| 0.3514 \| 0.9009 \| \| 0.0794 \| 1.04 \| 1500 \| 0.2616 \| 0.9224 \| \| 0.0541 \| 1.11 \| 1600 \| 0.3213 \| 0.9124 \| \| 0.0475 \| 1.18 \| 1700 \| 0.3717 \| 0.9124 \| \| 0.1251 \| 1.25 \| 1800 \| 0.2938 \| 0.9195 \| \| 0.0712 \| 1.32 \| 1900 \| 0.2988 \| 0.9181 \| \| 0.1021 \| 1.39 \| 2000 \| 0.3862 \| 0.9009 \| \| 0.0073 \| 1.46 \| 2100 \| 0.2492 \| 0.9310 \| \| 0.0114 \| 1.53 \| 2200 \| 0.2902 \| 0.9267 \| \| 0.0487 \| 1.6 \| 2300 \| 0.2301 \| 0.9411 \| \| 0.0856 \| 1.67 \| 2400 \| 0.2682 \| 0.9411 \| \| 0.0028 \| 1.74 \| 2500 \| 0.2948 \| 0.9325 \| \| 0.0028 \| 1.81 \| 2600 \| 0.3002 \| 0.9282 \| \| 0.0279 \| 1.88 \| 2700 \| 0.2797 \| 0.9353 \| \| 0.0768 \| 1.95 \| 2800 \| 0.2721 \| 0.9368 \| \| 0.0251 \| 2.02 \| 2900 \| 0.2896 \| 0.9325 \| \| 0.0645 \| 2.09 \| 3000 \| 0.2802 \| 0.9397 \| \| 0.0022 \| 2.16 \| 3100 \| 0.2387 \| 0.9468 \| \| 0.0073 \| 2.23 \| 3200 \| 0.2074 \| 0.9540 \| \| 0.0016 \| 2.3 \| 3300 \| 0.2271 \| 0.9440 \| \| 0.0016 \| 2.37 \| 3400 \| 0.2513 \| 0.9526 \| \| 0.0656 \| 2.44 \| 3500 \| 0.2889 \| 0.9411 \| \| 0.0013 \| 2.51 \| 3600 \| 0.2750 \| 0.9397 \| \| 0.0014 \| 2.58 \| 3700 \| 0.2463 \| 0.9526 \| \| 0.0011 \| 2.65 \| 3800 \| 0.2723 \| 0.9483 \| \| 0.0012 \| 2.72 \| 3900 \| 0.2631 \| 0.9511 \| \| 0.0012 \| 2.79 \| 4000 \| 0.2584 \| 0.9540 \| \| 0.0012 \| 2.86 \| 4100 \| 0.2572 \| 0.9540 \| \| 0.001 \| 2.92 \| 4200 \| 0.2481 \| 0.9569 \| \| 0.0011 \| 2.99 \| 4300 \| 0.2485 \| 0.9569 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
BANA577/Llama3-Michael-4	BANA577	2024-05-15T19:59:38Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T19:55:55Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
longma98/dummy-model	longma98	2024-05-15T19:52:30Z	106	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-15T19:48:01Z	--- 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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Litzy619/G0515HMA1H	Litzy619	2024-05-15T19:51:31Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:google/gemma-2b", "base_model:finetune:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-05-15T19:03:13Z	--- license: gemma base_model: google/gemma-2b tags: - generated_from_trainer model-index: - name: G0515HMA1H 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. --> # G0515HMA1H This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1342 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 80 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.2177 \| 0.09 \| 10 \| 2.8976 \| \| 2.6702 \| 0.18 \| 20 \| 2.2910 \| \| 1.8959 \| 0.27 \| 30 \| 1.4043 \| \| 1.043 \| 0.36 \| 40 \| 0.5929 \| \| 0.375 \| 0.45 \| 50 \| 0.2097 \| \| 0.1786 \| 0.54 \| 60 \| 0.1591 \| \| 0.1553 \| 0.63 \| 70 \| 0.1511 \| \| 0.1598 \| 0.73 \| 80 \| 0.1548 \| \| 0.1472 \| 0.82 \| 90 \| 0.1499 \| \| 0.1475 \| 0.91 \| 100 \| 0.1484 \| \| 0.1495 \| 1.0 \| 110 \| 0.1482 \| \| 0.1437 \| 1.09 \| 120 \| 0.1490 \| \| 0.1448 \| 1.18 \| 130 \| 0.1472 \| \| 0.1452 \| 1.27 \| 140 \| 0.1460 \| \| 0.1482 \| 1.36 \| 150 \| 0.1459 \| \| 0.143 \| 1.45 \| 160 \| 0.1478 \| \| 0.1435 \| 1.54 \| 170 \| 0.1461 \| \| 0.1448 \| 1.63 \| 180 \| 0.1441 \| \| 0.1461 \| 1.72 \| 190 \| 0.1482 \| \| 0.1451 \| 1.81 \| 200 \| 0.1454 \| \| 0.1462 \| 1.9 \| 210 \| 0.1447 \| \| 0.1459 \| 1.99 \| 220 \| 0.1433 \| \| 0.1419 \| 2.08 \| 230 \| 0.1411 \| \| 0.1366 \| 2.18 \| 240 \| 0.1400 \| \| 0.1371 \| 2.27 \| 250 \| 0.1432 \| \| 0.1391 \| 2.36 \| 260 \| 0.1385 \| \| 0.1356 \| 2.45 \| 270 \| 0.1383 \| \| 0.1343 \| 2.54 \| 280 \| 0.1363 \| \| 0.1326 \| 2.63 \| 290 \| 0.1350 \| \| 0.1303 \| 2.72 \| 300 \| 0.1343 \| \| 0.1341 \| 2.81 \| 310 \| 0.1342 \| \| 0.1328 \| 2.9 \| 320 \| 0.1342 \| \| 0.1337 \| 2.99 \| 330 \| 0.1342 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.0
Dhanu459/LLama3_8B_MarketingTemplate_M12_Lora	Dhanu459	2024-05-15T19:48:01Z	76	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "8-bit", "region:us" ]	text-generation	2024-05-15T19:43:46Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: Dhanu459 - 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)
themex1380/Persian-Therapist-Llama-3-8B	themex1380	2024-05-15T19:46:07Z	28	2	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T19:39:42Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: themex1380 - 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)
RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf	RichardErkhov	2024-05-15T19:42:49Z	14	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-15T17:30:37Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Not-WizardLM-2-7B - GGUF - Model creator: https://huggingface.co/amazingvince/ - Original model: https://huggingface.co/amazingvince/Not-WizardLM-2-7B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Not-WizardLM-2-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [Not-WizardLM-2-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [Not-WizardLM-2-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [Not-WizardLM-2-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [Not-WizardLM-2-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [Not-WizardLM-2-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [Not-WizardLM-2-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [Not-WizardLM-2-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [Not-WizardLM-2-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [Not-WizardLM-2-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [Not-WizardLM-2-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [Not-WizardLM-2-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [Not-WizardLM-2-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [Not-WizardLM-2-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [Not-WizardLM-2-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [Not-WizardLM-2-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [Not-WizardLM-2-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [Not-WizardLM-2-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [Not-WizardLM-2-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [Not-WizardLM-2-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [Not-WizardLM-2-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [Not-WizardLM-2-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- license: apache-2.0 --- # amazingvince/Not-WizardLM-2-7B <a href="https://colab.research.google.com/gist/pszemraj/d3d74ceab942722b49188606785e2bfd/not-wizardlm-2-7b-inference.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> Included is code ripped from fastchat with the expected chat templating. Also wiz.pdf is a pdf of the github blog showing the apache 2 release. Link to wayback machine included: https://web.archive.org/web/20240415221214/https://wizardlm.github.io/WizardLM2/ ## example ```python import dataclasses from enum import auto, Enum from typing import List, Tuple, Any class SeparatorStyle(Enum): """Different separator style.""" SINGLE = auto() TWO = auto() @dataclasses.dataclass class Conversation: """A class that keeps all conversation history.""" system: str roles: List[str] messages: List[List[str]] offset: int sep_style: SeparatorStyle = SeparatorStyle.SINGLE sep: str = "###" sep2: str = None # Used for gradio server skip_next: bool = False conv_id: Any = None def get_prompt(self): if self.sep_style == SeparatorStyle.SINGLE: ret = self.system for role, message in self.messages: if message: ret += self.sep + " " + role + ": " + message else: ret += self.sep + " " + role + ":" return ret elif self.sep_style == SeparatorStyle.TWO: seps = [self.sep, self.sep2] ret = self.system + seps[0] for i, (role, message) in enumerate(self.messages): if message: ret += role + ": " + message + seps[i % 2] else: ret += role + ":" return ret else: raise ValueError(f"Invalid style: {self.sep_style}") def append_message(self, role, message): self.messages.append([role, message]) def to_gradio_chatbot(self): ret = [] for i, (role, msg) in enumerate(self.messages[self.offset:]): if i % 2 == 0: ret.append([msg, None]) else: ret[-1][-1] = msg return ret def copy(self): return Conversation( system=self.system, roles=self.roles, messages=[[x, y] for x, y in self.messages], offset=self.offset, sep_style=self.sep_style, sep=self.sep, sep2=self.sep2, conv_id=self.conv_id) def dict(self): return { "system": self.system, "roles": self.roles, "messages": self.messages, "offset": self.offset, "sep": self.sep, "sep2": self.sep2, "conv_id": self.conv_id, } conv = Conversation( system="A chat between a curious user and an artificial intelligence assistant. " "The assistant gives helpful, detailed, and polite answers to the user's questions.", roles=("USER", "ASSISTANT"), messages=[], offset=0, sep_style=SeparatorStyle.TWO, sep=" ", sep2="</s>", ) conv.append_message(conv.roles[0], "Why would Microsoft take this down?") conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() inputs = tokenizer(prompt, return_tensors="pt").to(model.device) result = model.generate(**inputs, max_new_tokens=1000) generated_ids = result[0] generated_text = tokenizer.decode(generated_ids, skip_special_tokens=True) print(generated_text) ```
AlekseiPravdin/Seamaiiza-7B-v1	AlekseiPravdin	2024-05-15T19:42:41Z	6	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "SanjiWatsuki/Kunoichi-DPO-v2-7B", "AlekseiPravdin/KSI-RP-NSK-128k-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T19:38:48Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - SanjiWatsuki/Kunoichi-DPO-v2-7B - AlekseiPravdin/KSI-RP-NSK-128k-7B --- # Seamaiiza-7B-v1 Seamaiiza-7B-v1 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [AlekseiPravdin/KSI-RP-NSK-128k-7B](https://huggingface.co/AlekseiPravdin/KSI-RP-NSK-128k-7B) * [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: AlekseiPravdin/KSI-RP-NSK-128k-7B layer_range: [0, 32] - model: SanjiWatsuki/Kunoichi-DPO-v2-7B layer_range: [0, 32] merge_method: slerp base_model: SanjiWatsuki/Kunoichi-DPO-v2-7B parameters: t: - filter: self_attn value: [0, 0.53, 0.35, 0.7, 1] - filter: mlp value: [1, 0.57, 0.75, 0.33, 0] - value: 0.53 dtype: bfloat16 ```
RichardErkhov/Undi95_-_Meta-Llama-3-8B-hf-8bits	RichardErkhov	2024-05-15T19:41:38Z	3	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-15T19:32:16Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Meta-Llama-3-8B-hf - bnb 8bits - Model creator: https://huggingface.co/Undi95/ - Original model: https://huggingface.co/Undi95/Meta-Llama-3-8B-hf/ Original model description: --- language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: other license_name: llama3 license_link: LICENSE extra_gated_prompt: >- ### META LLAMA 3 COMMUNITY LICENSE AGREEMENT Meta Llama 3 Version Release Date: April 18, 2024 "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/. "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. "Meta Llama 3" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads. "Llama Materials" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement. "Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). 1. License Rights and Redistribution. a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. b. Redistribution and Use. i. 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You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.” iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement. v. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Meta Llama 3 or derivative works thereof). 2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights. 3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS. 4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 5. Intellectual Property. a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use “Llama 3” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta. b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Meta Llama 3 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy) #### Prohibited Uses We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following: 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 3. Generating, promoting, or further distributing spam 4. Impersonating another individual without consent, authorization, or legal right 5. Representing that the use of Meta Llama 3 or outputs are human-generated 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3) * Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback * Reporting bugs and security concerns: facebook.com/whitehat/info * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. Model developers Meta Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. Input Models input text only. Output Models generate text and code only. Model Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> Llama 3 family of models. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date April 18, 2024. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English. Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-8B, for use with transformers and with the original `llama3` codebase. ### Use with transformers See the snippet below for usage with Transformers: ```python >>> import transformers >>> import torch >>> model_id = "meta-llama/Meta-Llama-3-8B" >>> pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto" ) >>> pipeline("Hey how are you doing today?") ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3). To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-8B --include "original/" --local-dir Meta-Llama-3-8B ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software Training Factors* We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td><strong>Time (GPU hours)</strong> </td> <td><strong>Power Consumption (W)</strong> </td> <td><strong>Carbon Emitted(tCO2eq)</strong> </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> CO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of March 2023 for the 8B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama2 7B</strong> </td> <td><strong>Llama2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama2 70B</strong> </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 2 7B</strong> </td> <td><strong>Llama 2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 2 70B</strong> </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. <span style="text-decoration:underline;">Safety</span> For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. <span style="text-decoration:underline;">Refusals</span> In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks <span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### <span style="text-decoration:underline;">Cyber Security </span> We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### <span style="text-decoration:underline;">Child Safety</span> Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
osbm/llama-7b-4bit	osbm	2024-05-15T19:41:34Z	0	0	peft	[ "peft", "region:us" ]	null	2024-05-15T19:41:27Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0
dannys160/pole-of-da-cart-2k24	dannys160	2024-05-15T19:40:57Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-15T19:40:48Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: pole-of-da-cart-2k24 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
BilalMuftuoglu/beit-base-patch16-224-55-fold1	BilalMuftuoglu	2024-05-15T19:35:48Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-base-patch16-224", "base_model:finetune:microsoft/beit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-15T18:55:25Z	--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: beit-base-patch16-224-fold1 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8481012658227848 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # beit-base-patch16-224-fold1 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7241 - Accuracy: 0.8481 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.8571 \| 3 \| 0.8050 \| 0.4557 \| \| No log \| 2.0 \| 7 \| 0.7151 \| 0.5696 \| \| 0.8103 \| 2.8571 \| 10 \| 0.6822 \| 0.5570 \| \| 0.8103 \| 4.0 \| 14 \| 0.6408 \| 0.5696 \| \| 0.8103 \| 4.8571 \| 17 \| 0.6244 \| 0.6709 \| \| 0.6583 \| 6.0 \| 21 \| 0.5893 \| 0.6709 \| \| 0.6583 \| 6.8571 \| 24 \| 0.5877 \| 0.6329 \| \| 0.6583 \| 8.0 \| 28 \| 0.5752 \| 0.6835 \| \| 0.5912 \| 8.8571 \| 31 \| 0.5826 \| 0.6456 \| \| 0.5912 \| 10.0 \| 35 \| 0.5469 \| 0.6835 \| \| 0.5912 \| 10.8571 \| 38 \| 0.6173 \| 0.6582 \| \| 0.5301 \| 12.0 \| 42 \| 0.5151 \| 0.6962 \| \| 0.5301 \| 12.8571 \| 45 \| 0.5105 \| 0.6962 \| \| 0.5301 \| 14.0 \| 49 \| 0.5489 \| 0.7089 \| \| 0.4703 \| 14.8571 \| 52 \| 0.5725 \| 0.6835 \| \| 0.4703 \| 16.0 \| 56 \| 0.5560 \| 0.6962 \| \| 0.4703 \| 16.8571 \| 59 \| 0.5824 \| 0.6709 \| \| 0.4189 \| 18.0 \| 63 \| 0.5401 \| 0.7468 \| \| 0.4189 \| 18.8571 \| 66 \| 0.5147 \| 0.7722 \| \| 0.3741 \| 20.0 \| 70 \| 0.4864 \| 0.7595 \| \| 0.3741 \| 20.8571 \| 73 \| 0.5272 \| 0.7342 \| \| 0.3741 \| 22.0 \| 77 \| 0.4914 \| 0.7468 \| \| 0.387 \| 22.8571 \| 80 \| 0.5658 \| 0.7468 \| \| 0.387 \| 24.0 \| 84 \| 0.4662 \| 0.7722 \| \| 0.387 \| 24.8571 \| 87 \| 0.4376 \| 0.7848 \| \| 0.3502 \| 26.0 \| 91 \| 0.5367 \| 0.7722 \| \| 0.3502 \| 26.8571 \| 94 \| 0.5490 \| 0.7342 \| \| 0.3502 \| 28.0 \| 98 \| 0.7163 \| 0.7722 \| \| 0.3148 \| 28.8571 \| 101 \| 0.6005 \| 0.7468 \| \| 0.3148 \| 30.0 \| 105 \| 0.6501 \| 0.7722 \| \| 0.3148 \| 30.8571 \| 108 \| 0.5313 \| 0.7975 \| \| 0.2973 \| 32.0 \| 112 \| 0.5466 \| 0.7722 \| \| 0.2973 \| 32.8571 \| 115 \| 0.5731 \| 0.8101 \| \| 0.2973 \| 34.0 \| 119 \| 0.6544 \| 0.8101 \| \| 0.2474 \| 34.8571 \| 122 \| 0.6061 \| 0.7848 \| \| 0.2474 \| 36.0 \| 126 \| 0.5816 \| 0.7722 \| \| 0.2474 \| 36.8571 \| 129 \| 0.7161 \| 0.7595 \| \| 0.2033 \| 38.0 \| 133 \| 0.6235 \| 0.7848 \| \| 0.2033 \| 38.8571 \| 136 \| 0.7889 \| 0.7595 \| \| 0.2338 \| 40.0 \| 140 \| 0.5943 \| 0.7595 \| \| 0.2338 \| 40.8571 \| 143 \| 0.6170 \| 0.7342 \| \| 0.2338 \| 42.0 \| 147 \| 0.6964 \| 0.6962 \| \| 0.2067 \| 42.8571 \| 150 \| 0.7154 \| 0.7468 \| \| 0.2067 \| 44.0 \| 154 \| 0.7675 \| 0.7722 \| \| 0.2067 \| 44.8571 \| 157 \| 0.7766 \| 0.7468 \| \| 0.2133 \| 46.0 \| 161 \| 0.9330 \| 0.7848 \| \| 0.2133 \| 46.8571 \| 164 \| 0.6494 \| 0.7975 \| \| 0.2133 \| 48.0 \| 168 \| 0.5709 \| 0.7722 \| \| 0.2004 \| 48.8571 \| 171 \| 0.6462 \| 0.8101 \| \| 0.2004 \| 50.0 \| 175 \| 0.6668 \| 0.7722 \| \| 0.2004 \| 50.8571 \| 178 \| 0.6305 \| 0.8101 \| \| 0.188 \| 52.0 \| 182 \| 0.7189 \| 0.8228 \| \| 0.188 \| 52.8571 \| 185 \| 0.6853 \| 0.7848 \| \| 0.188 \| 54.0 \| 189 \| 0.8040 \| 0.8228 \| \| 0.1623 \| 54.8571 \| 192 \| 0.6958 \| 0.8101 \| \| 0.1623 \| 56.0 \| 196 \| 0.6907 \| 0.8101 \| \| 0.1623 \| 56.8571 \| 199 \| 0.6821 \| 0.8101 \| \| 0.1588 \| 58.0 \| 203 \| 0.6534 \| 0.8101 \| \| 0.1588 \| 58.8571 \| 206 \| 0.7192 \| 0.8101 \| \| 0.1607 \| 60.0 \| 210 \| 0.7753 \| 0.8228 \| \| 0.1607 \| 60.8571 \| 213 \| 0.8950 \| 0.8101 \| \| 0.1607 \| 62.0 \| 217 \| 0.7904 \| 0.8101 \| \| 0.1767 \| 62.8571 \| 220 \| 0.6973 \| 0.8101 \| \| 0.1767 \| 64.0 \| 224 \| 0.6694 \| 0.7975 \| \| 0.1767 \| 64.8571 \| 227 \| 0.6339 \| 0.8101 \| \| 0.1463 \| 66.0 \| 231 \| 0.6530 \| 0.8101 \| \| 0.1463 \| 66.8571 \| 234 \| 0.6142 \| 0.8101 \| \| 0.1463 \| 68.0 \| 238 \| 0.6290 \| 0.8228 \| \| 0.1287 \| 68.8571 \| 241 \| 0.6334 \| 0.8354 \| \| 0.1287 \| 70.0 \| 245 \| 0.8059 \| 0.8101 \| \| 0.1287 \| 70.8571 \| 248 \| 0.7241 \| 0.8481 \| \| 0.1323 \| 72.0 \| 252 \| 0.6836 \| 0.8481 \| \| 0.1323 \| 72.8571 \| 255 \| 0.6588 \| 0.8228 \| \| 0.1323 \| 74.0 \| 259 \| 0.6598 \| 0.8481 \| \| 0.1042 \| 74.8571 \| 262 \| 0.7139 \| 0.8354 \| \| 0.1042 \| 76.0 \| 266 \| 0.7236 \| 0.8354 \| \| 0.1042 \| 76.8571 \| 269 \| 0.6919 \| 0.8354 \| \| 0.1106 \| 78.0 \| 273 \| 0.6568 \| 0.8354 \| \| 0.1106 \| 78.8571 \| 276 \| 0.6556 \| 0.8481 \| \| 0.1348 \| 80.0 \| 280 \| 0.6612 \| 0.8354 \| \| 0.1348 \| 80.8571 \| 283 \| 0.6686 \| 0.8228 \| \| 0.1348 \| 82.0 \| 287 \| 0.6705 \| 0.8481 \| \| 0.1352 \| 82.8571 \| 290 \| 0.6776 \| 0.8354 \| \| 0.1352 \| 84.0 \| 294 \| 0.6873 \| 0.8354 \| \| 0.1352 \| 84.8571 \| 297 \| 0.6888 \| 0.8354 \| \| 0.1226 \| 85.7143 \| 300 \| 0.6880 \| 0.8354 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Zelyanoth/wav2vec2-bert-fon-colab	Zelyanoth	2024-05-15T19:35:45Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-10T16:02:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- wav2vec2-bert-fon-colab is a model wav2vec2-bert model fine tuned on ~ 11000 audio files in beninese language called "Fongbe" --> ## 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] <!-- 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]
pjchardt/emotion-endpoint-test	pjchardt	2024-05-15T19:35:43Z	104	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "emotion", "endpoints-template", "en", "dataset:emotion", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-15T19:35:20Z	--- language: - en tags: - text-classification - emotion - endpoints-template license: apache-2.0 datasets: - emotion metrics: - Accuracy, F1 Score --- # Fork of [bhadresh-savani/distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion)
Brillibits/Instruct_Llama3_8B	Brillibits	2024-05-15T19:31:30Z	42	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "en", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-13T19:06:33Z	--- license: llama3 language: - en pipeline_tag: text-generation --- # Instruct_Llama3_8B Fine-tuned from Llama-3-8B， using a wide variety of sources for the dataset. 84.9% for training, 15% validation, 0.1% test. Trained for 2 epochs using QDora. Trained with 4096 context window. # Model Details * Trained by: trained by [Brillibits](https://brillibits.com/en). See [YouTube](https://www.youtube.com/@Brillibits) as well. * Model type: Instruct_Llama3_8B is an auto-regressive language model based on the Llama 3 transformer architecture. * Language(s): English * License for Instruct_Llama3_8B: llama3 license # Prompting ``` <s>[SYS] {system prompt or blank space} [/SYS] [INST] {instruction} [/INST] {response}</s> ``` ## Professional Assistance This model and other models like it are great, but where LLMs hold the most promise is when they are applied on custom data to automate a wide variety of tasks If you have a dataset and want to see if you might be able to apply that data to automate some tasks, and you are looking for professional assistance, contact me [here](mailto:[email protected])
dwdfww/slavi	dwdfww	2024-05-15T19:26:57Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-15T19:26:57Z	--- license: apache-2.0 ---
GunUltimateID/ppo-LunarLander-v2	GunUltimateID	2024-05-15T19:23:58Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-15T19:23:38Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 262.46 +/- 19.87 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
RileyI/IAmATest	RileyI	2024-05-15T19:20:24Z	0	0	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	2024-05-15T19:20:24Z	--- license: cc-by-nc-4.0 ---
nxaliao/bert-lg-cased-ms-ner-v3-test	nxaliao	2024-05-15T19:19:34Z	116	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-large-cased", "base_model:finetune:google-bert/bert-large-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-15T18:31:19Z	--- license: apache-2.0 base_model: bert-large-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-lg-cased-ms-ner-v3-test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-lg-cased-ms-ner-v3-test This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1288 - Precision: 0.8909 - Recall: 0.9094 - F1: 0.9001 - Accuracy: 0.9804 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1394 \| 1.0 \| 3615 \| 0.1234 \| 0.8374 \| 0.8269 \| 0.8321 \| 0.9695 \| \| 0.0736 \| 2.0 \| 7230 \| 0.1110 \| 0.8618 \| 0.8742 \| 0.8679 \| 0.9756 \| \| 0.0385 \| 3.0 \| 10845 \| 0.1019 \| 0.8844 \| 0.8968 \| 0.8906 \| 0.9787 \| \| 0.019 \| 4.0 \| 14460 \| 0.1193 \| 0.8859 \| 0.9048 \| 0.8953 \| 0.9798 \| \| 0.0094 \| 5.0 \| 18075 \| 0.1288 \| 0.8909 \| 0.9094 \| 0.9001 \| 0.9804 \| ### Framework versions - Transformers 4.39.3 - Pytorch 1.12.0 - Datasets 2.18.0 - Tokenizers 0.15.2
SelimGilon/test	SelimGilon	2024-05-15T19:19:12Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2023-12-24T01:58:59Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
suprith777/plant_classifier	suprith777	2024-05-15T19:16:06Z	64	0	transformers	[ "transformers", "tf", "vit", "image-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-15T18:29:07Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: suprith777/plant_classifier results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # suprith777/plant_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.9681 - Validation Loss: 1.9158 - Train Accuracy: 0.3929 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 297, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 1.9681 \| 1.9158 \| 0.3929 \| 0 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.15.0 - Datasets 2.16.0 - Tokenizers 0.13.3
veronica-girolimetti/mistral_qt_finetuned_LoRA_rel01	veronica-girolimetti	2024-05-15T19:12:42Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-15T19:11:07Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit --- # Uploaded model - Developed by: veronica-girolimetti - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.2-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)
Sikirulahi/bert-finetuned-glue	Sikirulahi	2024-05-15T19:10:44Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-15T18:26:24Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: bert-base-uncased metrics: - accuracy - f1 model-index: - name: bert-finetuned-glue results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-glue This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5517 - Accuracy: 0.8676 - F1: 0.9062 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 459 \| 0.3733 \| 0.8309 \| 0.8792 \| \| 0.5093 \| 2.0 \| 918 \| 0.4034 \| 0.8676 \| 0.9072 \| \| 0.3019 \| 3.0 \| 1377 \| 0.5517 \| 0.8676 \| 0.9062 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
monsoon-nlp/llama3-biotokenpretrain-kaniwa	monsoon-nlp	2024-05-15T19:08:27Z	8	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "unsloth", "generated_from_trainer", "dna", "en", "base_model:gradientai/Llama-3-8B-Instruct-262k", "base_model:adapter:gradientai/Llama-3-8B-Instruct-262k", "license:llama3", "region:us" ]	null	2024-05-12T15:32:03Z	--- license: llama3 library_name: peft language: - en tags: - trl - sft - unsloth - generated_from_trainer - dna base_model: gradientai/Llama-3-8B-Instruct-262k model-index: - name: llama3-biotokenpretrain-kaniwa results: [] --- # llama3-biotokenpretrain-kaniwa This is a LoRA adapter. The base model is the longer-context LLaMA-3-8b-Instruct developed by Gradient and Crusoe: `gradientai/Llama-3-8B-Instruct-262k` The tokenizer has added "biotokens" ∎A, ∎C, ∎G, and ∎T. The dataset was 0.5% of BYU's 2019 kaniwa (Chenopodium pallidicaule) genome, from https://genomevolution.org/coge/GenomeInfo.pl?gid=53872 The adapter was finetuned for 3 hours on an L4 GPU. The data was split into ~7k nucleotide snippets with an Alpaca like message format. Training Notebook: https://colab.research.google.com/drive/1FKA3p_jnfRHYd-hqJdYmKn8MQpxec0t5?usp=sharing Sample message: ``` Write information about the nucleotide sequence. ### Sequence: ∎G∎C∎C∎T∎A∎T∎A∎G∎T∎G∎T∎G∎T∎A∎G... ### Annotation: Information about location in the kaniwa chromosome: >lcl\|Cp5 ``` ## Usage ### Inference with DNA sequence ```python from peft import AutoPeftModelForCausalLM from transformers import AutoTokenizer model = AutoPeftModelForCausalLM.from_pretrained("monsoon-nlp/llama3-biotokenpretrain-kaniwa", load_in_4bit=True).to("cuda") tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/llama3-biotokenpretrain-kaniwa") tokenizer.pad_token = tokenizer.eos_token # pad fix qed = "∎" # from math symbols, used in pretraining sequence = "".join([(qed + nt.upper()) for nt in "GCCTATAGTGTGTAGCTAATGAGCCTAGGTTATCGACCCTAATCT"]) inputs = tokenizer(f"{prefix}{sequence}{annotation}", return_tensors="pt") outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=50) sample = tokenizer.batch_decode(outputs, skip_special_tokens=False)[0] ``` ### LoRA finetuning on a new task ```python from transformers import AutoTokenizer from trl import SFTTrainer from unsloth import FastLanguageModel model, _ = FastLanguageModel.from_pretrained( model_name = "monsoon-nlp/llama3-biotokenpretrain-kaniwa", max_seq_length = 7_000, # max 6,000 bp for AgroNT tasks dtype = None, load_in_4bit = True, resize_model_vocab=128260, # includes biotokens ) tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/llama3-biotokenpretrain-kaniwa") tokenizer.pad_token = tokenizer.eos_token # pad fix trainer = SFTTrainer( model = model, tokenizer = tokenizer, ... ) ``` This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 280 ### Framework versions - PEFT 0.10.0 - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1 ### Genome Citation Mangelson H, et al. The genome of Chenopodium pallidicaule: an emerging Andean super grain. Appl. Plant Sci. 2019;7:e11300. doi: 10.1002/aps3.11300
BrokenSoul/GPT2-GPTQ-4bit	BrokenSoul	2024-05-15T19:01:33Z	8	0	transformers	[ "transformers", "gpt2", "text-generation", "autotrain_compatible", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-05-15T18:59:04Z	--- library_name: transformers tags: [] --- # BrokenSoul/GPT2-GPTQ-4bit <!-- Provide a quick summary of what the model is/does. --> This is a GPT2 Quantized model following this tutorial: [4-bit LLM Quantization with GPTQ](https://mlabonne.github.io/blog/posts/4_bit_Quantization_with_GPTQ.html).
RaniRahbani/llava-1.5-7b-hf-ft-mix-vsft	RaniRahbani	2024-05-15T19:00:37Z	0	0	null	[ "tensorboard", "trl", "sft", "generated_from_trainer", "base_model:liuhaotian/llava-v1.5-13b", "base_model:finetune:liuhaotian/llava-v1.5-13b", "region:us" ]	null	2024-05-15T06:06:39Z	--- base_model: liuhaotian/llava-v1.5-13b tags: - trl - sft - generated_from_trainer model-index: - name: llava-1.5-7b-hf-ft-mix-vsft 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. --> # llava-1.5-7b-hf-ft-mix-vsft This model is a fine-tuned version of [liuhaotian/llava-v1.5-13b](https://huggingface.co/liuhaotian/llava-v1.5-13b) 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: 1.4e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.19.1 - Tokenizers 0.13.3
Abdullah-Nazhat/Uniform_Contextualizer	Abdullah-Nazhat	2024-05-15T18:59:00Z	0	0	null	[ "license:bsd-3-clause", "region:us" ]	null	2024-05-15T18:56:31Z	--- license: bsd-3-clause --- # Uniform_Contextualizer Uniform_Contextualizer: Studying The Effect of Unity Expansion Factor for The Hidden Dimension in Transformer MLP Paper Coming Soon
dbalasub/finalcheck-ensem-qa	dbalasub	2024-05-15T18:54:43Z	115	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-12T17:38:36Z	--- library_name: transformers license: mit --- # 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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ZaneHorrible/rmsProp_VitB-p16-384-1e-4-batch_16_epoch_4_classes_24	ZaneHorrible	2024-05-15T18:54:06Z	196	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-384", "base_model:finetune:google/vit-base-patch16-384", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-15T16:15:45Z	--- license: apache-2.0 base_model: google/vit-base-patch16-384 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: rmsProp_VitB-p16-384-1e-4-batch_16_epoch_4_classes_24 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9798850574712644 --- <!-- 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. --> # rmsProp_VitB-p16-384-1e-4-batch_16_epoch_4_classes_24 This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0907 - Accuracy: 0.9799 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2663 \| 0.07 \| 100 \| 0.2025 \| 0.9368 \| \| 0.1384 \| 0.14 \| 200 \| 0.2169 \| 0.9382 \| \| 0.0582 \| 0.21 \| 300 \| 0.0932 \| 0.9641 \| \| 0.1129 \| 0.28 \| 400 \| 0.1382 \| 0.9555 \| \| 0.0575 \| 0.35 \| 500 \| 0.1204 \| 0.9684 \| \| 0.1027 \| 0.42 \| 600 \| 0.0923 \| 0.9684 \| \| 0.0369 \| 0.49 \| 700 \| 0.1114 \| 0.9655 \| \| 0.015 \| 0.56 \| 800 \| 0.1745 \| 0.9540 \| \| 0.0455 \| 0.63 \| 900 \| 0.0871 \| 0.9655 \| \| 0.0129 \| 0.7 \| 1000 \| 0.1222 \| 0.9626 \| \| 0.0623 \| 0.77 \| 1100 \| 0.0981 \| 0.9670 \| \| 0.0328 \| 0.84 \| 1200 \| 0.0956 \| 0.9655 \| \| 0.0515 \| 0.91 \| 1300 \| 0.0740 \| 0.9756 \| \| 0.0513 \| 0.97 \| 1400 \| 0.0696 \| 0.9756 \| \| 0.0005 \| 1.04 \| 1500 \| 0.0757 \| 0.9784 \| \| 0.0606 \| 1.11 \| 1600 \| 0.0869 \| 0.9784 \| \| 0.0507 \| 1.18 \| 1700 \| 0.1121 \| 0.9698 \| \| 0.0004 \| 1.25 \| 1800 \| 0.0562 \| 0.9813 \| \| 0.0013 \| 1.32 \| 1900 \| 0.0455 \| 0.9828 \| \| 0.0309 \| 1.39 \| 2000 \| 0.0752 \| 0.9799 \| \| 0.0096 \| 1.46 \| 2100 \| 0.0739 \| 0.9770 \| \| 0.001 \| 1.53 \| 2200 \| 0.0536 \| 0.9842 \| \| 0.0892 \| 1.6 \| 2300 \| 0.0728 \| 0.9799 \| \| 0.0568 \| 1.67 \| 2400 \| 0.2331 \| 0.9670 \| \| 0.0049 \| 1.74 \| 2500 \| 0.0924 \| 0.9784 \| \| 0.0003 \| 1.81 \| 2600 \| 0.0922 \| 0.9770 \| \| 0.0004 \| 1.88 \| 2700 \| 0.1383 \| 0.9756 \| \| 0.0001 \| 1.95 \| 2800 \| 0.1568 \| 0.9670 \| \| 0.0001 \| 2.02 \| 2900 \| 0.1299 \| 0.9741 \| \| 0.0002 \| 2.09 \| 3000 \| 0.0976 \| 0.9828 \| \| 0.0 \| 2.16 \| 3100 \| 0.0536 \| 0.9885 \| \| 0.004 \| 2.23 \| 3200 \| 0.1074 \| 0.9770 \| \| 0.0001 \| 2.3 \| 3300 \| 0.0702 \| 0.9828 \| \| 0.008 \| 2.37 \| 3400 \| 0.1185 \| 0.9756 \| \| 0.0212 \| 2.44 \| 3500 \| 0.0793 \| 0.9756 \| \| 0.0001 \| 2.51 \| 3600 \| 0.1402 \| 0.9698 \| \| 0.0001 \| 2.58 \| 3700 \| 0.0761 \| 0.9828 \| \| 0.0134 \| 2.65 \| 3800 \| 0.1132 \| 0.9741 \| \| 0.0001 \| 2.72 \| 3900 \| 0.0703 \| 0.9828 \| \| 0.0 \| 2.79 \| 4000 \| 0.0764 \| 0.9799 \| \| 0.0 \| 2.86 \| 4100 \| 0.0737 \| 0.9828 \| \| 0.0011 \| 2.92 \| 4200 \| 0.1525 \| 0.9727 \| \| 0.019 \| 2.99 \| 4300 \| 0.1078 \| 0.9799 \| \| 0.0 \| 3.06 \| 4400 \| 0.0774 \| 0.9828 \| \| 0.0 \| 3.13 \| 4500 \| 0.1081 \| 0.9799 \| \| 0.0 \| 3.2 \| 4600 \| 0.0995 \| 0.9828 \| \| 0.0 \| 3.27 \| 4700 \| 0.0861 \| 0.9856 \| \| 0.0 \| 3.34 \| 4800 \| 0.0852 \| 0.9856 \| \| 0.0 \| 3.41 \| 4900 \| 0.0834 \| 0.9856 \| \| 0.0 \| 3.48 \| 5000 \| 0.0932 \| 0.9828 \| \| 0.0 \| 3.55 \| 5100 \| 0.0837 \| 0.9828 \| \| 0.0 \| 3.62 \| 5200 \| 0.0854 \| 0.9813 \| \| 0.0 \| 3.69 \| 5300 \| 0.0850 \| 0.9813 \| \| 0.0 \| 3.76 \| 5400 \| 0.0842 \| 0.9813 \| \| 0.0 \| 3.83 \| 5500 \| 0.0911 \| 0.9813 \| \| 0.0 \| 3.9 \| 5600 \| 0.0913 \| 0.9813 \| \| 0.0 \| 3.97 \| 5700 \| 0.0907 \| 0.9799 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
lodestones/Cascaded-Rectified-Flow	lodestones	2024-05-15T18:50:27Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-10T06:18:27Z	--- license: apache-2.0 ---
emilykang/medner-soap_chart_progressnotes_lora	emilykang	2024-05-15T18:49:37Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-15T18:44:21Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 datasets: - generator model-index: - name: medner-soap_chart_progressnotes_lora 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. --> # medner-soap_chart_progressnotes_lora This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
ZaneHorrible/rmsProp_vitB-32-384-2e4-ne-1-bs-16	ZaneHorrible	2024-05-15T18:48:51Z	195	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-384", "base_model:finetune:google/vit-base-patch16-384", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-15T18:07:20Z	--- license: apache-2.0 base_model: google/vit-base-patch16-384 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: rmsProp_vitB-32-384-2e4-ne-1-bs-16 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9827586206896551 --- <!-- 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. --> # rmsProp_vitB-32-384-2e4-ne-1-bs-16 This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0752 - Accuracy: 0.9828 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.774 \| 0.07 \| 100 \| 3.4867 \| 0.0819 \| \| 1.1104 \| 0.14 \| 200 \| 1.5104 \| 0.5359 \| \| 0.2994 \| 0.21 \| 300 \| 0.3914 \| 0.8836 \| \| 0.2754 \| 0.28 \| 400 \| 0.2456 \| 0.9210 \| \| 0.212 \| 0.35 \| 500 \| 0.1669 \| 0.9511 \| \| 0.1063 \| 0.42 \| 600 \| 0.1406 \| 0.9511 \| \| 0.0999 \| 0.49 \| 700 \| 0.2218 \| 0.9425 \| \| 0.0609 \| 0.56 \| 800 \| 0.1407 \| 0.9598 \| \| 0.0784 \| 0.63 \| 900 \| 0.0985 \| 0.9641 \| \| 0.0251 \| 0.7 \| 1000 \| 0.0963 \| 0.9698 \| \| 0.0094 \| 0.77 \| 1100 \| 0.0893 \| 0.9727 \| \| 0.0153 \| 0.84 \| 1200 \| 0.1044 \| 0.9670 \| \| 0.032 \| 0.91 \| 1300 \| 0.1035 \| 0.9713 \| \| 0.0042 \| 0.97 \| 1400 \| 0.0752 \| 0.9828 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
NikolayKozloff/EVA-GPT-German-v7-2-Beta-Q5_K_M-GGUF	NikolayKozloff	2024-05-15T18:45:17Z	4	1	transformers	[ "transformers", "gguf", "gpt", "llm", "large language model", "h2o-llmstudio", "TMP-Networks LLM Studio", "llama-cpp", "gguf-my-repo", "en", "de", "region:us" ]	null	2024-05-15T18:45:03Z	--- language: - en - de library_name: transformers tags: - gpt - llm - large language model - h2o-llmstudio - TMP-Networks LLM Studio - llama-cpp - gguf-my-repo inference: false thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico --- # NikolayKozloff/EVA-GPT-German-v7-2-Beta-Q5_K_M-GGUF This model was converted to GGUF format from [`MTSmash/EVA-GPT-German-v7-2-Beta`](https://huggingface.co/MTSmash/EVA-GPT-German-v7-2-Beta) 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/MTSmash/EVA-GPT-German-v7-2-Beta) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/EVA-GPT-German-v7-2-Beta-Q5_K_M-GGUF --model eva-gpt-german-v7-2-beta.Q5_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/EVA-GPT-German-v7-2-Beta-Q5_K_M-GGUF --model eva-gpt-german-v7-2-beta.Q5_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m eva-gpt-german-v7-2-beta.Q5_K_M.gguf -n 128 ```
jspr/llama3-instruct-wordcel-smutrom_merged	jspr	2024-05-15T18:44:26Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:jspr/llama3_8b_instruct_wordcel_merged", "base_model:finetune:jspr/llama3_8b_instruct_wordcel_merged", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T18:41:28Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: jspr/llama3_8b_instruct_wordcel_merged --- # Uploaded model - Developed by: jspr - License: apache-2.0 - Finetuned from model : jspr/llama3_8b_instruct_wordcel_merged 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)
RobertML/sn6e	RobertML	2024-05-15T18:44:03Z	19	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-04-28T15:40:23Z	--- 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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BANA577/Llama3-Michael-3	BANA577	2024-05-15T18:44:02Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T18:40:18Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
AvinashAmballa/DPO_LLAMA-7B_0.125	AvinashAmballa	2024-05-15T18:42:43Z	2	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T18:37:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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jspr/llama3-instruct-wordcel-smutrom_peft	jspr	2024-05-15T18:41:25Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:jspr/llama3_8b_instruct_wordcel_merged", "base_model:finetune:jspr/llama3_8b_instruct_wordcel_merged", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-15T18:41:15Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: jspr/llama3_8b_instruct_wordcel_merged --- # Uploaded model - Developed by: jspr - License: apache-2.0 - Finetuned from model : jspr/llama3_8b_instruct_wordcel_merged 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)
jfranklin-foundry/gemma-2b-flock-1715798166	jfranklin-foundry	2024-05-15T18:37:02Z	145	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T18:34:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Giuieu/Marcel	Giuieu	2024-05-15T18:35:38Z	0	0	adapter-transformers	[ "adapter-transformers", "question-answering", "ro", "en", "de", "hu", "dataset:open-llm-leaderboard/details_cloudyu__google-gemma-7b-it-dpo-v1", "arxiv:1910.09700", "region:us" ]	question-answering	2024-05-15T18:17:22Z	--- datasets: - open-llm-leaderboard/details_cloudyu__google-gemma-7b-it-dpo-v1 language: - ro - en - de - hu metrics: - recall - accuracy - precision - f1 - bleu - roc_auc library_name: adapter-transformers pipeline_tag: question-answering --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## 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. 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UmarRamzan/w2v2-bert-urdu	UmarRamzan	2024-05-15T18:32:30Z	47	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "generated_from_trainer", "ur", "dataset:mozilla-foundation/common_voice_17_0", "base_model:UmarRamzan/w2v2-bert-urdu", "base_model:finetune:UmarRamzan/w2v2-bert-urdu", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-12T13:59:02Z	--- license: mit base_model: UmarRamzan/w2v2-bert-urdu tags: - generated_from_trainer metrics: - wer model-index: - name: w2v2-bert-urdu results: [] language: - ur datasets: - mozilla-foundation/common_voice_17_0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Wav2Vec-Bert-2.0-Urdu This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the Urdu split of the [Common Voice 17](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0) dataset. It achieves the following results on the evaluation set: - Loss: 0.3681 - Wer: 0.2929 ## Usage Instructions ```python from transformers import AutoFeatureExtractor, Wav2Vec2BertModel import torch from datasets import load_dataset dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") dataset = dataset.sort("id") sampling_rate = dataset.features["audio"].sampling_rate processor = AutoProcessor.from_pretrained("UmarRamzan/w2v2-bert-urdu") model = Wav2Vec2BertModel.from_pretrained("UmarRamzan/w2v2-bert-urdu") # audio file is decoded on the fly inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\| \| 0.4362 \| 0.1695 \| 50 \| 0.4144 \| 0.3213 \| \| 0.3776 \| 0.3390 \| 100 \| 0.4029 \| 0.3137 \| \| 0.3918 \| 0.5085 \| 150 \| 0.4095 \| 0.3060 \| \| 0.3968 \| 0.6780 \| 200 \| 0.3961 \| 0.3060 \| \| 0.3685 \| 0.8475 \| 250 \| 0.3681 \| 0.2929 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
vijayhn/llama2-7b-base-w-ft-sql	vijayhn	2024-05-15T18:32:24Z	75	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-15T18:29:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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UmarRamzan/w2v2-bert-ngram-urdu	UmarRamzan	2024-05-15T18:31:58Z	85	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "generated_from_trainer", "ur", "dataset:mozilla-foundation/common_voice_17_0", "base_model:UmarRamzan/w2v2-bert-urdu", "base_model:finetune:UmarRamzan/w2v2-bert-urdu", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-12T22:34:00Z	--- license: mit base_model: UmarRamzan/w2v2-bert-urdu tags: - generated_from_trainer metrics: - wer model-index: - name: w2v2-bert-urdu results: [] language: - ur datasets: - mozilla-foundation/common_voice_17_0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Wav2Vec-Bert-2.0-ngram-Urdu This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the Urdu split of the [Common Voice 17](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0) dataset. The fine-tuned model is enhanced with the addition of an ngram language model that has also been trained on the same dataset. It achieves the following results on the evaluation set: - Loss: 0.3681 - Wer: 0.2407 ## Usage Instructions ```python from transformers import AutoFeatureExtractor, Wav2Vec2BertModel import torch from datasets import load_dataset dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") dataset = dataset.sort("id") sampling_rate = dataset.features["audio"].sampling_rate processor = AutoProcessor.from_pretrained("UmarRamzan/w2v2-bert-ngram-urdu") model = Wav2Vec2BertModel.from_pretrained("UmarRamzan/w2v2-bert-ngram-urdu") # audio file is decoded on the fly inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Arara10/new-wolf_coder	Arara10	2024-05-15T18:29:28Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-15T18:29:27Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: Arara10 - 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)
emilykang/medner-obstetrics_gynecology_lora	emilykang	2024-05-15T18:27:44Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-15T18:20:35Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 datasets: - generator model-index: - name: medner-obstetrics_gynecology_lora 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. --> # medner-obstetrics_gynecology_lora This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
cctuan/lora_model	cctuan	2024-05-15T18:22:34Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-15T18:22:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
sakren/debert-imeocap	sakren	2024-05-15T18:21:03Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-15T17:23:01Z	--- license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer metrics: - f1 - precision - recall - accuracy model-index: - name: debert-imeocap 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. --> # debert-imeocap This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8660 - F1: 0.6185 - Precision: 0.6337 - Recall: 0.6154 - Accuracy: 0.6154 ## 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: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Precision \| Recall \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:---------:\|:------:\|:--------:\| \| 0.4637 \| 1.0 \| 74 \| 1.3864 \| 0.6129 \| 0.6262 \| 0.6115 \| 0.6115 \| \| 0.3815 \| 2.0 \| 148 \| 1.3801 \| 0.6193 \| 0.6348 \| 0.6173 \| 0.6173 \| \| 0.3363 \| 3.0 \| 222 \| 1.6944 \| 0.6077 \| 0.6297 \| 0.6077 \| 0.6077 \| \| 0.31 \| 4.0 \| 296 \| 1.6945 \| 0.5995 \| 0.6285 \| 0.5942 \| 0.5942 \| \| 0.2885 \| 5.0 \| 370 \| 1.5945 \| 0.6218 \| 0.6306 \| 0.6192 \| 0.6192 \| \| 0.2594 \| 6.0 \| 444 \| 1.7662 \| 0.6279 \| 0.6396 \| 0.625 \| 0.625 \| \| 0.2319 \| 7.0 \| 518 \| 1.7093 \| 0.6210 \| 0.6321 \| 0.6173 \| 0.6173 \| \| 0.2306 \| 8.0 \| 592 \| 1.8068 \| 0.6279 \| 0.6341 \| 0.6288 \| 0.6288 \| \| 0.2167 \| 9.0 \| 666 \| 1.7306 \| 0.6376 \| 0.6444 \| 0.6346 \| 0.6346 \| \| 0.2158 \| 10.0 \| 740 \| 1.8745 \| 0.6262 \| 0.6318 \| 0.6269 \| 0.6269 \| \| 0.222 \| 11.0 \| 814 \| 1.8323 \| 0.6200 \| 0.6348 \| 0.6173 \| 0.6173 \| \| 0.2152 \| 12.0 \| 888 \| 1.8576 \| 0.6246 \| 0.6363 \| 0.6212 \| 0.6212 \| \| 0.226 \| 13.0 \| 962 \| 1.8880 \| 0.6343 \| 0.6411 \| 0.6308 \| 0.6308 \| \| 0.2097 \| 14.0 \| 1036 \| 1.8884 \| 0.6152 \| 0.6326 \| 0.6115 \| 0.6115 \| \| 0.2192 \| 15.0 \| 1110 \| 1.8660 \| 0.6185 \| 0.6337 \| 0.6154 \| 0.6154 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
BANA577/Llama3-Michael-6	BANA577	2024-05-15T18:18:13Z	2	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "autotrain", "text-generation-inference", "peft", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T18:07:52Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
chrlu/zephyr-7b-gemma-dynamic_blended_adaptive_quantile_loss	chrlu	2024-05-15T18:17:13Z	4	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "alignment-handbook", "generated_from_trainer", "conversational", "dataset:argilla/dpo-mix-7k", "base_model:HuggingFaceH4/zephyr-7b-gemma-sft-v0.1", "base_model:finetune:HuggingFaceH4/zephyr-7b-gemma-sft-v0.1", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T18:13:31Z	--- license: other base_model: HuggingFaceH4/zephyr-7b-gemma-sft-v0.1 tags: - alignment-handbook - generated_from_trainer datasets: - argilla/dpo-mix-7k model-index: - name: zephyr-7b-gemma-dynamic_blended_adaptive_quantile_loss 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. --> # zephyr-7b-gemma-dynamic_blended_adaptive_quantile_loss This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-gemma-sft-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1) on the argilla/dpo-mix-7k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.2+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
bhoopendrakumar/donut-demo2	bhoopendrakumar	2024-05-15T18:17:11Z	52	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-15T13:31:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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ehristoforu/llamamistral-2	ehristoforu	2024-05-15T18:15:41Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:David-Xu/Mistral-7B-Instruct-v0.2", "base_model:merge:David-Xu/Mistral-7B-Instruct-v0.2", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:merge:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:OpenBuddy/openbuddy-mistral-7b-v13.1", "base_model:merge:OpenBuddy/openbuddy-mistral-7b-v13.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T18:12:18Z	--- base_model: - OpenBuddy/openbuddy-mistral-7b-v13.1 - David-Xu/Mistral-7B-Instruct-v0.2 - NousResearch/Meta-Llama-3-8B-Instruct library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [David-Xu/Mistral-7B-Instruct-v0.2](https://huggingface.co/David-Xu/Mistral-7B-Instruct-v0.2) as a base. ### Models Merged The following models were included in the merge: * [OpenBuddy/openbuddy-mistral-7b-v13.1](https://huggingface.co/OpenBuddy/openbuddy-mistral-7b-v13.1) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: David-Xu/Mistral-7B-Instruct-v0.2 #no parameters necessary for base model - model: OpenBuddy/openbuddy-mistral-7b-v13.1 parameters: density: 0.5 weight: 0.5 - model: NousResearch/Meta-Llama-3-8B-Instruct parameters: density: 0.5 weight: 0.5 weight: 0.5 merge_method: ties base_model: David-Xu/Mistral-7B-Instruct-v0.2 parameters: normalize: true int8_mask: true dtype: bfloat16 ```
johnnyllm/2-WalkkE	johnnyllm	2024-05-15T18:15:28Z	145	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T18:12:43Z	--- 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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emilykang/Phi_medner-consult-historyandphy_lora	emilykang	2024-05-15T18:13:53Z	5	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-05-15T17:29:19Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 datasets: - generator model-index: - name: Phi_medner-consult-historyandphy_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Phi_medner-consult-historyandphy_lora This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
AvinashAmballa/DPO_LLAMA-7B_0.5	AvinashAmballa	2024-05-15T18:12:38Z	2	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T18:08:03Z	--- 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. 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mradermacher/dolphin-2.9-llama3-8b-256k-self-merge-GGUF	mradermacher	2024-05-15T18:10:39Z	1	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-15T15:57:36Z	--- base_model: FiditeNemini/dolphin-2.9-llama3-8b-256k-self-merge language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hfhfix --> <!-- ### vocab_type: --> static quants of https://huggingface.co/FiditeNemini/dolphin-2.9-llama3-8b-256k-self-merge <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-256k-self-merge-GGUF/resolve/main/dolphin-2.9-llama3-8b-256k-self-merge.Q2_K.gguf) \| Q2_K \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-256k-self-merge-GGUF/resolve/main/dolphin-2.9-llama3-8b-256k-self-merge.IQ3_XS.gguf) \| IQ3_XS \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-256k-self-merge-GGUF/resolve/main/dolphin-2.9-llama3-8b-256k-self-merge.Q3_K_S.gguf) \| Q3_K_S \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-256k-self-merge-GGUF/resolve/main/dolphin-2.9-llama3-8b-256k-self-merge.IQ3_S.gguf) \| IQ3_S \| 6.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-256k-self-merge-GGUF/resolve/main/dolphin-2.9-llama3-8b-256k-self-merge.IQ3_M.gguf) \| IQ3_M \| 6.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-256k-self-merge-GGUF/resolve/main/dolphin-2.9-llama3-8b-256k-self-merge.Q3_K_M.gguf) \| Q3_K_M \| 6.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-256k-self-merge-GGUF/resolve/main/dolphin-2.9-llama3-8b-256k-self-merge.Q3_K_L.gguf) \| Q3_K_L \| 7.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-256k-self-merge-GGUF/resolve/main/dolphin-2.9-llama3-8b-256k-self-merge.IQ4_XS.gguf) \| IQ4_XS \| 7.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-256k-self-merge-GGUF/resolve/main/dolphin-2.9-llama3-8b-256k-self-merge.Q4_K_S.gguf) \| Q4_K_S \| 7.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-256k-self-merge-GGUF/resolve/main/dolphin-2.9-llama3-8b-256k-self-merge.Q4_K_M.gguf) \| Q4_K_M \| 8.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-256k-self-merge-GGUF/resolve/main/dolphin-2.9-llama3-8b-256k-self-merge.Q5_K_S.gguf) \| Q5_K_S \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-256k-self-merge-GGUF/resolve/main/dolphin-2.9-llama3-8b-256k-self-merge.Q5_K_M.gguf) \| Q5_K_M \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-256k-self-merge-GGUF/resolve/main/dolphin-2.9-llama3-8b-256k-self-merge.Q6_K.gguf) \| Q6_K \| 11.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-256k-self-merge-GGUF/resolve/main/dolphin-2.9-llama3-8b-256k-self-merge.Q8_0.gguf) \| Q8_0 \| 14.2 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
reemmasoud/idv_vs_col_llama-3_PromptTuning_CAUSAL_LM_gradient_descent_v2	reemmasoud	2024-05-15T18:09:18Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-15T18:09:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
richardwang7/sd-class-butterflies-32	richardwang7	2024-05-15T18:02:32Z	44	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2024-05-15T18:02:20Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('richardwang7/sd-class-butterflies-32') image = pipeline().images[0] image ```
emilykang/medner-generalmedicine_lora	emilykang	2024-05-15T18:01:10Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-15T17:49:36Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 datasets: - generator model-index: - name: medner-generalmedicine_lora 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. --> # medner-generalmedicine_lora This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1

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