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modelId stringlengths 5 122	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]	likes int64 0 11k	library_name stringclasses 245 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 48 values	createdAt timestamp[us, tz=UTC]	card stringlengths 1 901k
Wenboz/phi-3-dpo-noise-0.0	Wenboz	2024-07-02T10:19:15Z	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "custom_code", "base_model:microsoft/Phi-3-mini-4k-instruct", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T04:55:46Z	--- license: mit base_model: microsoft/Phi-3-mini-4k-instruct tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer model-index: - name: phi-3-dpo-noise-0.0 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. --> [<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/causal/huggingface/runs/5nzzd8kk) # phi-3-dpo-noise-0.0 This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - total_eval_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 - Transformers 4.42.3 - Pytorch 2.3.0+cu121 - Datasets 2.14.6 - Tokenizers 0.19.1
RyotaKadoya1993/phi3_math_jp	RyotaKadoya1993	2024-07-02T05:06:31Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:RyotaKadoya1993/phi3_translator_merged3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T04:58:16Z	--- base_model: RyotaKadoya1993/phi3_translator_merged3 language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - Developed by: RyotaKadoya1993 - License: apache-2.0 - Finetuned from model : RyotaKadoya1993/phi3_translator_merged3 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)
Nooman/myhuggigngfacemodel	Nooman	2024-07-02T05:51:37Z	0	null	[ "region:us" ]	null	2024-07-02T04:59:20Z	This is my first Hugging Face Model --- license: mit ---
NestNeuro/new_model	NestNeuro	2024-07-02T05:26:07Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T04:59:30Z	--- base_model: unsloth/mistral-7b-v0.3-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - Developed by: NestNeuro - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
NoodieKnoodie/bob	NoodieKnoodie	2024-07-02T05:07:09Z	0	null	[ "region:us" ]	null	2024-07-02T05:02:06Z	Entry not found
rinogrego/GritLM-BioMedLM-4-bit	rinogrego	2024-07-02T05:03:50Z	0	null	[ "region:us" ]	null	2024-07-02T05:03:50Z	Entry not found
TomEijkelenkamp/renaissance-deepseek-focus	TomEijkelenkamp	2024-07-02T05:05:49Z	0	null	[ "region:us" ]	null	2024-07-02T05:05:49Z	Entry not found
shivanikerai/Llama-2-7b-chat-hf-feature-extraction-function-calling-v1.0	shivanikerai	2024-07-02T05:11:36Z	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T05:06:13Z	Entry not found
maltrz/tools-mistral-4bit-lora-adapter	maltrz	2024-07-02T05:09:13Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-02T05:06:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
sperfu/XiXiLM-14b	sperfu	2024-07-02T05:48:08Z	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T05:06:43Z	--- pipeline_tag: text-generation license: other --- # XiXiLM-14b <div align="center"> <img src="https://github.com/AI4Bread/GouMang/blob/main/assets/goumang_logoallnew.png?raw=true" width="600"/> <div> </div> <div align="center"> <!-- <b><font size="5">XiXiLM</font></b> --> <sup> <a href="http://www.ai4bread.com"> </a> </sup> <div> </div> </div> [💻Github Repo](https://github.com/AI4Bread/GouMang) • [🤔Reporting Issues](https://github.com/AI4Bread/GouMang/issues) • [📜Technical Report](https://github.com/AI4Bread) </div> <p align="center"> 👋 join us on <a href="https://github.com/AI4Bread/GouMang" target="_blank">Github</a> </p> ## Introduction XiXiLM-14b（GouMang LLM-14b） has open-sourced a 14 billion parameter base model and a chat model tailored for agricultural scenarios. The model has the following characteristics: 1. High Professionalism: XiXiLM focuses on the agricultural field, providing professional and accurate answers especially in areas such as tuber crop cultivation, pest and disease control, and soil management. 2. Academic Support: The model is based on the latest agricultural research findings, capable of providing academic-level answers to help researchers and agricultural practitioners gain a deeper understanding of agricultural issues. 3. Multilingual Support: Supports both Chinese and English languages, making it convenient for users both domestically and internationally. 4. Free Commercial Use: The model weights are fully open, supporting not only academic research but also allowing free commercial usage. Users can use the model in commercial projects for free, lowering the usage threshold. 5. Efficient Training: Employs advanced training algorithms and techniques, enabling the model to respond quickly to user inquiries and provide efficient Q&A services. 6. Continuous Optimization: The model will be continuously optimized based on user feedback and the latest research findings, constantly improving the quality and coverage of its answers. ## XiXiLM-Qwen-14B Limitations: Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information. ### Import from Transformers To load the XiXiLM model using Transformers, use the following code: ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AI4Bread/XiXi_Qwen_base_14b", trust_remote_code=True) # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and cause OOM Error. model = AutoModelForCausalLM.from_pretrained("AI4Bread/XiXi_Qwen_base_14b", torch_dtype=torch.float16, trust_remote_code=True).cuda() model = model.eval() response, history = model.chat(tokenizer, "你好", history=[]) print(response) # Hello! How can I help you today? response, history = model.chat(tokenizer, "马铃薯育种有什么注意事项？需要注意什么呢？", history=history) print(response) ``` The responses can be streamed using `stream_chat`: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "AI4Bread/XiXi_Qwen_base_14b" model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, trust_remote_code=True).cuda() tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) model = model.eval() length = 0 for response, history in model.stream_chat(tokenizer, "Hello", history=[]): print(response[length:], flush=True, end="") length = len(response) ``` ## Deployment ### LMDeploy LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the MMRazor and MMDeploy teams. ```bash pip install lmdeploy ``` Or you can launch an OpenAI compatible server with the following command: ```bash lmdeploy serve api_server internlm/internlm2-chat-7b --model-name internlm2-chat-7b --server-port 23333 ``` Then you can send a chat request to the server: ```bash curl http://localhost:23333/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "internlm2-chat-7b", "messages": [ {"role": "system", "content": "你是一个专业的农业专家"}, {"role": "user", "content": "马铃薯种植的时候有哪些注意事项？"} ] }' ``` The output be like: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/658a3c4cbbb04840e3ce7e2c/NPdRr5Y5l5E0m0URCVZ1f.png) Find more details in the [LMDeploy documentation](https://lmdeploy.readthedocs.io/en/latest/) ### vLLM Launch OpenAI compatible server with `vLLM>=0.3.2`: ```bash pip install vllm ``` ```bash python -m vllm.entrypoints.openai.api_server --model internlm/internlm2-chat-7b --served-model-name internlm2-chat-7b --trust-remote-code ``` Then you can send a chat request to the server: ```bash curl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "internlm2-chat-7b", "messages": [ {"role": "system", "content": "You are a professional agriculture expert."}, {"role": "user", "content": "Introduce potato farming to me."} ] }' ``` Find more details in the [vLLM documentation](https://docs.vllm.ai/en/latest/index.html) ## Used local trained model ### First: Convert lmdeploy TurboMind Here, we will use our pre-trained model file and execute the conversion in the user's root directory, as shown below. ```bash # Converting Model to TurboMind (FastTransformer Format) lmdeploy convert internlm2-chat-7b /root/autodl-tmp/agri_intern/XiXiLM --tokenizer-path ./GouMang/tokenizer.json ``` After execution, a workspace folder will be generated in the current directory. This folder contains the necessary files for TurboMind and Triton "Model Inference." as shown below: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/658a3c4cbbb04840e3ce7e2c/CqdwhshIL8xxjog_WD_St.png) ### Second: Chat Locally ```bash lmdeploy chat turbomind ./workspace ``` ### Third(Optional): TurboMind Inference + API Service In the previous section, we tried starting the Client directly using the command line. Now, we will attempt to use lmdeploy for service deployment. The "Model Inference/Service" currently offers two service deployment methods: TurboMind and TritonServer. In this case, the Server is either TurboMind or TritonServer, and the API Server can provide external API services. We recommend using TurboMind. First, start the service with the following command: ```bash # ApiServer+Turbomind api_server => AsyncEngine => TurboMind lmdeploy serve api_server ./workspace \ --server-name 0.0.0.0 \ --server-port 23333 \ --tp 1 ``` In the above parameters, `server_name` and `server_port` indicate the service address and port, respectively. The `tp` parameter, as mentioned earlier, stands for Tensor Parallelism. After this, users can start the Web Service as described in [TurboMind Service as the Backend](#--turbomind-service-as-the-backend). ## Web Service Startup Method 1: ### Starting the Service with Gradio This section demonstrates using Gradio as a front-end demo. > Since Gradio requires local access to display the interface, > you also need to forward the data to your local machine via SSH. The command is as follows: > > ssh -CNg -L 6006:127.0.0.1:6006 [email protected] -p <your ssh port> #### --TurboMind Service as the Backend The API Server is started the same way as in the previous section. Here, we directly start Gradio as the front-end. ```bash # Gradio+ApiServer. The Server must be started first, and Gradio acts as the Client lmdeploy serve gradio http://0.0.0.0:23333 --server-port 6006 ``` #### --Other way(Recommended!!!) Of course, Gradio can also connect directly with TurboMind, as shown below ```bash # Gradio+Turbomind(local) lmdeploy serve gradio ./workspace ``` You can start Gradio directly. In this case, there is no API Server, and TurboMind communicates directly with Gradio. ## Web Service Startup Method 2: ### Starting the Service with Streamlit ```bash pip install streamlit==1.24.0 ``` Download the [GouMang](https://huggingface.co/AI4Bread/GouMang) project model (please Star if you like it) ```bash git clone https://github.com/AI4Bread/GouMang.git cd GouMang ``` Replace the model path in `web_demo.py` with the path where the downloaded parameters of `GouMang` are stored Run the `web_demo.py` file in the directory, and after entering the following command, [check this tutorial 5.2 for local port configuration](https://github.com/InternLM/tutorial/blob/main/helloworld/hello_world.md#52-%E9%85%8D%E7%BD%AE%E6%9C%AC%E5%9C%B0%E7%AB%AF%E5%8F%A3)，to map the port to your local machine. Enter `http://127.0.0.1:6006` in your local browser. ``` streamlit run web_demo.py --server.address 127.0.0.1 --server.port 6006 ``` Note: The model will load only after you open the `http://127.0.0.1:6006` page in your browser. Once the model is loaded, you can start conversing with GouMang like this. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/658a3c4cbbb04840e3ce7e2c/VcuSpAKrRGY1HP1mwLGI6.png) ## Open Source License The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow free commercial usage. To apply for a commercial license, please fill in the <a href="https://wj.qq.com/s2/14897739/e871/" target="_blank">申请表（中文）</a>. For other questions or collaborations, please contact <[email protected]>. ## Citation ## 简介 XiXiLM-14b ，即西西大模型（又名：句芒大模型），开源了面向农业问答的大模型。模型具有以下特点： 1. 专业性强：XiXiLM 专注于农业领域，特别是薯类作物的种植、病虫害防治、土壤管理等方面，提供专业、精准的解答。 2. 学术化支持：模型基于最新的农业研究成果，能够提供学术化的回答，帮助研究人员和农业从业者深入理解农业问题。 3. 多语言支持：支持中文和英文两种语言，方便国内外用户使用。 4. 免费商业使用：模型权重完全开放，不仅支持学术研究，还允许申请商业使用。用户可以在商业项目中免费使用该模型，降低了使用门槛。 5. 高效训练：采用先进的训练算法和技术，使得模型能够快速响应用户提问，提供高效的问答服务。 6. 持续优化：模型会根据用户反馈和最新研究成果进行持续优化，不断提升问答质量和覆盖面。 ## XiXiLM-14B 局限性：尽管在训练过程中我们非常注重模型的安全性，尽力促使模型输出符合伦理和法律要求的文本，但受限于模型大小以及概率生成范式，模型可能会产生各种不符合预期的输出，例如回复内容包含偏见、歧视等有害内容，请勿传播这些内容。由于传播不良信息导致的任何后果，本项目不承担责任。 ### 通过 Transformers 加载通过以下的代码加载 XiXiLM-14b Chat 模型 ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AI4Bread/XiXiLM_14b", trust_remote_code=True) # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and cause OOM Error. model = AutoModelForCausalLM.from_pretrained("AI4Bread/XiXiLM_14b", torch_dtype=torch.float16, trust_remote_code=True).cuda() model = model.eval() response, history = model.chat(tokenizer, "你好", history=[]) print(response) # Hello! How can I help you today? response, history = model.chat(tokenizer, "马铃薯育种有什么注意事项？需要注意什么呢？", history=history) print(response) ``` 如果想进行流式生成，则可以使用 `stream_chat` 接口： ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "AI4Bread/XiXi_Qwen_base_14b" model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, trust_remote_code=True).cuda() tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) model = model.eval() length = 0 for response, history in model.stream_chat(tokenizer, "马铃薯育种有什么注意事项？需要注意什么呢？", history=[]): print(response[length:], flush=True, end="") length = len(response) ``` ## 部署 ### LMDeploy LMDeploy 由 MMDeploy 和 MMRazor 团队联合开发，是涵盖了 LLM 任务的全套轻量化、部署和服务解决方案。 ```bash pip install lmdeploy ``` 你可以使用以下命令启动兼容 OpenAI API 的服务: ```bash lmdeploy serve api_server internlm/internlm2-chat-7b --server-port 23333 ``` 然后你可以向服务端发起一个聊天请求: ```bash curl http://localhost:23333/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "internlm2-chat-7b", "messages": [ {"role": "system", "content": "你是一个专业的农业专家"}, {"role": "user", "content": "马铃薯种植的时候有哪些注意事项？"} ] }' ``` 更多信息请查看 [LMDeploy 文档](https://lmdeploy.readthedocs.io/en/latest/) ### vLLM 使用`vLLM>=0.3.2`启动兼容 OpenAI API 的服务: ```bash pip install vllm ``` ```bash python -m vllm.entrypoints.openai.api_server --model internlm/internlm2-chat-7b --trust-remote-code ``` 然后你可以向服务端发起一个聊天请求: ```bash curl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "internlm2-chat-7b", "messages": [ {"role": "system", "content": "你是一个专业的农业专家."}, {"role": "user", "content": "请给我介绍一下马铃薯育种."} ] }' ``` 更多信息请查看 [vLLM 文档](https://docs.vllm.ai/en/latest/index.html) ## 使用本地训练模型 ### 第一步：转换为 lmdeploy TurboMind 格式这里，我们将使用预训练的模型文件，并在用户的根目录下执行转换，如下所示。 ```bash # 将模型转换为 TurboMind (FastTransformer 格式) lmdeploy convert internlm2-chat-7b /root/autodl-tmp/agri_intern/XiXiLM --tokenizer-path ./GouMang/tokenizer.json ``` 执行完毕后，当前目录下将生成一个 workspace 文件夹。这个文件夹包含 TurboMind 和 Triton “模型推理”所需的文件，如下所示： ![image/png](https://cdn-uploads.huggingface.co/production/uploads/658a3c4cbbb04840e3ce7e2c/CqdwhshIL8xxjog_WD_St.png) ### 第二步：本地聊天 ```bash lmdeploy chat turbomind ./workspace ``` ### 第三步（可选）：TurboMind 推理 + API 服务在前一部分中，我们尝试通过命令行直接启动客户端。现在，我们将尝试使用 lmdeploy 进行服务部署。 “模型推理/服务”目前提供两种服务部署方式：TurboMind 和 TritonServer。在这种情况下，服务器可以是 TurboMind 或 TritonServer，而 API 服务器可以提供外部 API 服务。我们推荐使用 TurboMind。首先，使用以下命令启动服务： ```bash # ApiServer+Turbomind api_server => AsyncEngine => TurboMind lmdeploy serve api_server ./workspace \ --server-name 0.0.0.0 \ --server-port 23333 \ --tp 1 ``` 在上述参数中，server_name 和 server_port 分别表示服务地址和端口。tp 参数如前所述代表 Tensor 并行性。之后，用户可以按照[TurboMind Service as the Backend](#--turbomind-service-as-the-backend) 中描述的启动 Web 服务。 ## 网页服务启动方式1: ### Gradio 方式启动服务这一部分主要是将 Gradio 作为前端 Demo 演示。在上一节的基础上，我们不执行后面的 `api_client` 或 `triton_client`，而是执行 `gradio`。请参考[LMDeploy](#lmdeploy)部分获取详细信息。 > 由于 Gradio 需要本地访问展示界面，因此也需要通过 ssh 将数据转发到本地。命令如下： > > ssh -CNg -L 6006:127.0.0.1:6006 [email protected] -p <你的 ssh 端口号> #### --TurboMind 服务作为后端直接启动作为前端的 Gradio。 ```bash # Gradio+ApiServer。必须先开启 Server，此时 Gradio 为 Client lmdeploy serve gradio http://0.0.0.0:23333 --server-port 6006 ``` #### --其他方式(推荐!!!) 当然，Gradio 也可以直接和 TurboMind 连接，如下所示。 ```bash # Gradio+Turbomind(local) lmdeploy serve gradio ./workspace ``` 可以直接启动 Gradio，此时没有 API Server，TurboMind 直接与 Gradio 通信。 ## 网页服务启动方式2: ### Streamlit 方式启动服务：下载 [GouMang](https://huggingface.co/AI4Bread/GouMang) 项目模型（如果喜欢请给个 Star） ```bash git clone https://github.com/AI4Bread/GouMang.git cd GouMang ``` 将 `web_demo.py` 中的模型路径替换为下载的 `GouMang` 参数存储路径在目录中运行 `web_demo.py` 文件，并在输入以下命令后，[查看本教程 5.2 以配置本地端口](https://github.com/InternLM/tutorial/blob/main/helloworld/hello_world.md#52-%E9%85%8D%E7%BD%AE%E6%9C%AC%E5%9C%B0%E7%AB%AF%E5%8F%A3)，将端口映射到本地。在本地浏览器中输入 `http://127.0.0.1:6006`。 ``` streamlit run /root/personal_assistant/code/InternLM/web_demo.py --server.address 127.0.0.1 --server.port 6006 ``` 注意：只有在浏览器中打开 `http://127.0.0.1:6006` 页面后，模型才会加载。模型加载完成后，您就可以开始与西西（句芒）进行对话了。 ## 开源许可证本仓库的代码依照 Apache-2.0 协议开源。模型权重对学术研究完全开放，也可申请免费的商业使用授权（<a href="https://wj.qq.com/s2/14897739/e871/" target="_blank">申请表（中文）</a>）。其他问题与合作请联系 <[email protected]>。 ## 引用
Maxivi/d	Maxivi	2024-07-02T12:35:52Z	0	null	[ "safetensors", "region:us" ]	null	2024-07-02T05:07:15Z	Entry not found
tz3/finetune_v4	tz3	2024-07-02T06:06:00Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-07-02T05:08:06Z	--- license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer metrics: - wer model-index: - name: finetune_v4 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. --> # finetune_v4 This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2085 - Wer: 14.5161 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 80 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\| \| No log \| 6.6667 \| 10 \| 0.2449 \| 19.4700 \| \| No log \| 13.3333 \| 20 \| 0.1970 \| 14.5161 \| \| No log \| 20.0 \| 30 \| 0.1805 \| 11.6359 \| \| No log \| 26.6667 \| 40 \| 0.1826 \| 14.4009 \| \| 0.0538 \| 33.3333 \| 50 \| 0.1930 \| 22.1198 \| \| 0.0538 \| 40.0 \| 60 \| 0.1967 \| 36.5207 \| \| 0.0538 \| 46.6667 \| 70 \| 0.2035 \| 35.3687 \| \| 0.0538 \| 53.3333 \| 80 \| 0.2085 \| 14.5161 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.2.0 - Datasets 2.20.0 - Tokenizers 0.19.1
BilalKhan1/llama-urdu-tokenizer	BilalKhan1	2024-07-02T05:11:49Z	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-02T05:11:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
briggers/omega_a2a_test2	briggers	2024-07-03T01:12:46Z	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	null	2024-07-02T05:12:27Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
Homie0609/MatchTime	Homie0609	2024-07-02T06:26:07Z	0	null	[ "license:cc-by-sa-4.0", "region:us" ]	null	2024-07-02T05:12:39Z	--- license: cc-by-sa-4.0 datasets: - Homie0609/MatchTime language: - en tags: - sports - soccer --- ## Requirements - Python >= 3.8 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html)) - [PyTorch >= 2.0.0](https://pytorch.org/) (If use A100) - transformers >= 4.42.3 - pycocoevalcap >= 1.2 A suitable [conda](https://conda.io/) environment named `matchtime` can be created and activated with: ``` cd MatchTime conda env create -f environment.yaml conda activate matchtime ``` ## Training Before training, make sure you have prepared [features](https://pypi.org/project/SoccerNet/) and caption [data]((https://drive.google.com/drive/folders/14tb6lV2nlTxn3VygwAPdmtKm7v0Ss8wG)), and put them into according folders. The structure after collating should be like: `````` └─ MatchTime ├─ dataset │ ├─ MatchTime │ │ ├─ valid │ │ └─ train │ │ ├─ england_epl_2014-2015 │ │ ... ├─ 2015-02-21 - 18-00 Chelsea 1 - 1 Burnley │ │ ... └─ Labels-caption.json │ │ │ ├─ SN-Caption │ └─ SN-Caption-test-align │ ├─ england_epl_2015-2016 │ ... ├─ 2015-08-16 - 18-00 Manchester City 3 - 0 Chelsea │ ... └─ Labels-caption_with_gt.json │ ├─ features │ ├─ baidu_soccer_embeddings │ │ ├─ england_epl_2014-2015 ... │ ... ├─ 2015-02-21 - 18-00 Chelsea 1 - 1 Burnley │ ... ├─ 1_baidu_soccer_embeddings.npy │ └─ 2_baidu_soccer_embeddings.npy ├─ C3D_PCA512 ... `````` with the format of features is adjusted by ``` python ./features/preprocess.py directory_path_of_feature ``` After preparing the data and features, you can pre-train (or finetune) with the following terminal command (Check hyper-parameters at the bottom of train.py): ``` python train.py ``` ## Inference We provide two types of inference: #### For all test set You can generate a .csv file with the following code to test the *MatchVoice* model with the following code (Check hyper-parameters at the bottom of inference.py) ``` python inference.py ``` There is a sample of this type of inference in ./inference_result/sample.csv. #### For Single Video We also provide a version for predict the commentary single video (for our checkpoints, use 30s video) ``` python inference_single_video_CLIP.py single_video_path ``` Here we only provide the version of CLIP feature (using VIT/B-32), for crop the CLIP feature, please check [here](https://github.com/openai/CLIP). CLIP features are not the one with best performance but are the most friendly for new new videos. ## Alignment Before doing alignment, you should download videos from [here](https://www.soccer-net.org/data) (224p is enough) and make it in the following format: `````` └─ MatchTime ├─ videos_224p ... ├─ england_epl_2014-2015 ... ├─ 2015-02-21 - 18-00 Chelsea 1 - 1 Burnley ... ├─ 1_224.mkv └─ 2_224p.mkv `````` ### Pre-process (Coarse Align) We need to use [WhisperX](https://github.com/m-bain/whisperX) and [LLaMA3](https://huggingface.co/docs/transformers/model_doc/llama3)(as agent) to finish coarse alignment with following steps: WhisperX ASR: ``` python ./alignment/soccer_whisperx.py --process_directory video_folder(eg. ./videos_224p/england_epl_2014-2015) --output_directory output_folder(eg. ./ASR_results/england_epl_2014-2015) ``` Transform to Events: ``` python ./alignment/soccer_asr2events.py --base_path ASR_results_folder(eg. ./ASR_results/england_epl_2014-2015) --output_dir envent_results_folder(eg. ./event_results/england_epl_2014-2015) ``` Align from Events: ``` python ./alignment/soccer_align_from_event.py --event_path envent_results_folder(eg. ./event_results/england_epl_2014-2015) --output_dir output_directory(eg. ./pre-processed/england_epl_2014-2015) ``` More details could be checked in paper. ### Contrastive Learning (Fine-grained Align) After downloading checkpoints from [here](https://huggingface.co/Homie0609/MatchTime/tree/main). Use the following code to finish alignment with contrastive learning: ``` python ./alignment/do_alignment.py ``` By changing the hyper-parameter *finding_words*, you can freely align from ASR, enent, or original SN-Caption. Also, you can directly use alignment model by ``` from alignment.matchtime_model import ContrastiveLearningModel ``` ## Evaluation We provide codes for evaluate the prediction results: ``` # for single csv file python ./evaluation/scoer_single.py --csv_path ./inference_result/sample.csv # for many csv files to record scores in a new csv file python ./evaluation/scoer_group.py # for gpt score (need OpenAI API Key) python ./evaluation/scoer_gpt.py ./inference_result/sample.csv ```
Sana28/LLM_tut	Sana28	2024-07-02T05:13:50Z	0	null	[ "region:us" ]	null	2024-07-02T05:13:50Z	Entry not found
briggers/omega_a2a_test3	briggers	2024-07-02T05:15:32Z	0	null	[ "region:us" ]	null	2024-07-02T05:15:32Z	Entry not found
briggers/omega_a2a_test4	briggers	2024-07-02T05:25:28Z	0	null	[ "region:us" ]	null	2024-07-02T05:15:37Z	Entry not found
himanishprak23/lstm_rnn	himanishprak23	2024-07-02T05:34:14Z	0	null	[ "region:us" ]	null	2024-07-02T05:16:07Z	Entry not found
rithikachowta/phi-3-medium	rithikachowta	2024-07-02T05:20:59Z	0	null	[ "license:mit", "region:us" ]	null	2024-07-02T05:20:59Z	--- license: mit ---
yihanwang617/tinyllama-sft-ultrachat-93k-processed-more-0.6	yihanwang617	2024-07-02T07:23:36Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "dataset:yihanwang617/ultrachat_93k_processed_more_0.6", "base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T05:21:53Z	--- license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - yihanwang617/ultrachat_93k_processed_more_0.6 model-index: - name: tinyllama-sft-ultrachat-93k-processed-more-0.6 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. --> # tinyllama-sft-ultrachat-93k-processed-more-0.6 This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the yihanwang617/ultrachat_93k_processed_more_0.6 dataset. It achieves the following results on the evaluation set: - Loss: 1.0749 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.0754 \| 1.0 \| 727 \| 1.0749 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
Team-Coffee-Gym/DS-Coder-7B-PPO-CoffeeEval	Team-Coffee-Gym	2024-07-02T08:52:57Z	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T05:22:35Z	--- pipeline_tag: text-generation --- This is the official checkpoint of feedback model trained using COFFEE-GYM with PPO strategy. This model generates natural language feedback given an erroneous code. For further detials, please see our paper. https://huggingface.co/spaces/Coffee-Gym/Project-Coffee-Gym
CashClubCorporation/public-models-backup	CashClubCorporation	2024-07-02T05:26:54Z	0	null	[ "region:us" ]	null	2024-07-02T05:23:16Z	Entry not found
sinsa12/beomi_llama	sinsa12	2024-07-02T05:23:32Z	0	null	[ "license:cc-by-nc-sa-4.0", "region:us" ]	null	2024-07-02T05:23:32Z	--- license: cc-by-nc-sa-4.0 ---
jpark011/test	jpark011	2024-07-02T05:36:21Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-07-02T05:23:35Z	--- license: apache-2.0 --- # Hello world!
RichardErkhov/quantumaikr_-_llama-2-70b-fb16-orca-chat-10k-gguf	RichardErkhov	2024-07-02T05:23:37Z	0	null	[ "region:us" ]	null	2024-07-02T05:23:37Z	Entry not found
cedricclyburn/insurance-trained-model	cedricclyburn	2024-07-02T05:23:49Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-07-02T05:23:49Z	--- license: apache-2.0 ---
Auart/DMR	Auart	2024-07-02T05:25:42Z	0	null	[ "license:openrail", "region:us" ]	null	2024-07-02T05:24:54Z	--- license: openrail ---
ram36/git_ipl	ram36	2024-07-02T05:38:18Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-07-02T05:25:03Z	--- license: apache-2.0 --- # Inference API for Question Answering This repository contains the implementation for a question-answering system using a custom knowledge base and a transformer model. ## Setup 1. Install the dependencies: ```sh pip install -r requirements.txt
cosmoboy/venons	cosmoboy	2024-07-02T05:26:23Z	0	null	[ "license:afl-3.0", "region:us" ]	null	2024-07-02T05:26:23Z	--- license: afl-3.0 ---
ImagineIt/beta-test-checkpoint-11000	ImagineIt	2024-07-02T05:28:26Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:27:11Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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] ### Framework versions - PEFT 0.11.1
ImagineIt/beta-test-checkpoint-17000	ImagineIt	2024-07-02T05:29:29Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:28:29Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
ImagineIt/beta-test-checkpoint-15000	ImagineIt	2024-07-02T05:30:40Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:29:32Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
ImagineIt/beta-test-checkpoint-12000	ImagineIt	2024-07-02T05:31:50Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:30:42Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
ImagineIt/beta-test-checkpoint-2000	ImagineIt	2024-07-02T05:32:52Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:31:52Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
ImagineIt/beta-test-checkpoint-18000	ImagineIt	2024-07-02T05:33:58Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:32:54Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
ethedeltae/gemma-7b-oig-unsloth-iitg-final	ethedeltae	2024-07-02T05:33:44Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T05:33:18Z	--- base_model: unsloth/gemma-7b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl --- # Uploaded model - Developed by: ethedeltae - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
yihanwang617/tinyllama-sft-vicuna-processed-more-0.5-full	yihanwang617	2024-07-02T07:22:51Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "dataset:yihanwang617/vicuna_clean_processed_more_0.5", "base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T05:33:18Z	--- license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - yihanwang617/vicuna_clean_processed_more_0.5 model-index: - name: tinyllama-sft-vicuna-processed-more-0.5-full 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. --> # tinyllama-sft-vicuna-processed-more-0.5-full This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the yihanwang617/vicuna_clean_processed_more_0.5 dataset. It achieves the following results on the evaluation set: - Loss: 0.8800 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9117 \| 1.0 \| 732 \| 0.8800 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
ImagineIt/beta-test-checkpoint-8000	ImagineIt	2024-07-02T05:35:01Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:34:00Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
ethedeltae/gemma-7b-oig-unsloth-merged-iitg-final	ethedeltae	2024-07-02T05:42:22Z	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/gemma-7b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T05:34:11Z	--- base_model: unsloth/gemma-7b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl - sft --- # Uploaded model - Developed by: ethedeltae - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
pooja-k/donut-base-sroie	pooja-k	2024-07-02T06:30:38Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "endpoints_compatible", "region:us" ]	null	2024-07-02T05:34:43Z	Entry not found
ImagineIt/beta-test-checkpoint-16000	ImagineIt	2024-07-02T05:36:07Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:35:03Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
mamamamamaasdasd/yolov10	mamamamamaasdasd	2024-07-02T06:17:41Z	0	ultralytics	[ "ultralytics", "safetensors", "object-detection", "computer-vision", "yolov10", "dataset:detection-datasets/coco", "arxiv:2405.14458", "license:agpl-3.0", "region:us" ]	object-detection	2024-07-02T05:35:38Z	--- license: agpl-3.0 library_name: ultralytics tags: - object-detection - computer-vision - yolov10 datasets: - detection-datasets/coco repo_url: https://github.com/THU-MIG/yolov10 inference: false --- ### Model Description [YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458v1) - arXiv: https://arxiv.org/abs/2405.14458v1 - github: https://github.com/THU-MIG/yolov10 ### Installation ``` pip install git+https://github.com/THU-MIG/yolov10.git ``` ### Training and validation ```python from ultralytics import YOLOv10 model = YOLOv10.from_pretrained('jameslahm/yolov10n') # Training model.train(...) # after training, one can push to the hub model.push_to_hub("your-hf-username/yolov10-finetuned") # Validation model.val(...) ``` ### Inference Here's an end-to-end example showcasing inference on a cats image: ```python from ultralytics import YOLOv10 model = YOLOv10.from_pretrained('jameslahm/yolov10n') source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) ``` which shows: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/628ece6054698ce61d1e7be3/tBwAsKcQA_96HCYQp7BRr.png) ### BibTeX Entry and Citation Info ``` @article{wang2024yolov10, title={YOLOv10: Real-Time End-to-End Object Detection}, author={Wang, Ao and Chen, Hui and Liu, Lihao and Chen, Kai and Lin, Zijia and Han, Jungong and Ding, Guiguang}, journal={arXiv preprint arXiv:2405.14458}, year={2024} } ```
NexaAIDev/octo-planner-gguf	NexaAIDev	2024-07-02T16:55:18Z	2	transformers	[ "transformers", "gguf", "phi3", "conversational", "custom_code", "text-generation", "en", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T05:35:47Z	--- license: cc-by-nc-4.0 language: - en library_name: transformers pipeline_tag: text-generation tags: - phi3 - conversational - custom_code --- # Quantized Octo-planner: On-device Language Model for Planner-Action Agents Framework This repo includes GGUF quantized models, for our Octo-planner model at [NexaAIDev/octopus-planning](https://huggingface.co/NexaAIDev/octopus-planning) # GGUF Quantization To run the models, please download them to your local machine using either git clone or [Hugging Face Hub](https://huggingface.co/docs/huggingface_hub/en/guides/download) ``` git clone https://huggingface.co/NexaAIDev/octo-planner-gguf ``` ## Run with [llama.cpp](https://github.com/ggerganov/llama.cpp) (Recommended) 1. Clone and compile: ```bash git clone https://github.com/ggerganov/llama.cpp cd llama.cpp # Compile the source code: make ``` 2. Execute the Model: Run the following command in the terminal: ```bash ./llama-cli -m ./path/to/octopus-planning-Q4_K_M.gguf -p "<\|user\|>Find my presentation for tomorrow's meeting, connect to the conference room projector via Bluetooth, increase the screen brightness, take a screenshot of the final summary slide, and email it to all participants<\|end\|><\|assistant\|>" ``` ## Run with [Ollama](https://github.com/ollama/ollama) Since our models have not been uploaded to the Ollama server, please download the models and manually import them into Ollama by following these steps: 1. Install Ollama on your local machine. You can also following the guide from [Ollama GitHub repository](https://github.com/ollama/ollama/blob/main/docs/import.md) ```bash git clone https://github.com/ollama/ollama.git ollama ``` 2. Locate the local Ollama directory: ```bash cd ollama ``` 3. Create a `Modelfile` in your directory ```bash touch Modelfile ``` 4. In the Modelfile, include a `FROM` statement with the path to your local model, and the default parameters: ```bash FROM ./path/to/octopus-planning-Q4_K_M.gguf ``` 5. Use the following command to add the model to Ollama: ```bash ollama create octopus-planning-Q4_K_M -f Modelfile ``` 6. Verify that the model has been successfully imported: ```bash ollama ls ``` 7. Run the mode ```bash ollama run octopus-planning-Q4_K_M "<\|user\|>Find my presentation for tomorrow's meeting, connect to the conference room projector via Bluetooth, increase the screen brightness, take a screenshot of the final summary slide, and email it to all participants<\|end\|><\|assistant\|>" ``` # Quantized GGUF Models Benchmark \| Name \| Quant method \| Bits \| Size \| Use Cases \| \| ---------------------------- \| ------------ \| ---- \| -------- \| ----------------------------------- \| \| octopus-planning-Q2_K.gguf \| Q2_K \| 2 \| 1.42 GB \| fast but high loss, not recommended \| \| octopus-planning-Q3_K.gguf \| Q3_K \| 3 \| 1.96 GB \| extremely not recommended \| \| octopus-planning-Q3_K_S.gguf \| Q3_K_S \| 3 \| 1.68 GB \| extremely not recommended \| \| octopus-planning-Q3_K_M.gguf \| Q3_K_M \| 3 \| 1.96 GB \| moderate loss, not very recommended \| \| octopus-planning-Q3_K_L.gguf \| Q3_K_L \| 3 \| 2.09 GB \| not very recommended \| \| octopus-planning-Q4_0.gguf \| Q4_0 \| 4 \| 2.18 GB \| moderate speed, recommended \| \| octopus-planning-Q4_1.gguf \| Q4_1 \| 4 \| 2.41 GB \| moderate speed, recommended \| \| octopus-planning-Q4_K.gguf \| Q4_K \| 4 \| 2.39 GB \| moderate speed, recommended \| \| octopus-planning-Q4_K_S.gguf \| Q4_K_S \| 4 \| 2.19 GB \| fast and accurate, very recommended \| \| octopus-planning-Q4_K_M.gguf \| Q4_K_M \| 4 \| 2.39 GB \| fast, recommended \| \| octopus-planning-Q5_0.gguf \| Q5_0 \| 5 \| 2.64 GB \| fast, recommended \| \| octopus-planning-Q5_1.gguf \| Q5_1 \| 5 \| 2.87 GB \| very big, prefer Q4 \| \| octopus-planning-Q5_K.gguf \| Q5_K \| 5 \| 2.82 GB \| big, recommended \| \| octopus-planning-Q5_K_S.gguf \| Q5_K_S \| 5 \| 2.64 GB \| big, recommended \| \| octopus-planning-Q5_K_M.gguf \| Q5_K_M \| 5 \| 2.82 GB \| big, recommended \| \| octopus-planning-Q6_K.gguf \| Q6_K \| 6 \| 3.14 GB \| very big, not very recommended \| \| octopus-planning-Q8_0.gguf \| Q8_0 \| 8 \| 4.06 GB \| very big, not very recommended \| \| octopus-planning-F16.gguf \| F16 \| 16 \| 7.64 GB \| extremely big \| _Quantized with llama.cpp_
ImagineIt/beta-test-checkpoint-13000	ImagineIt	2024-07-02T05:37:16Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:36:10Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Noodle-bg/BERT_Text_classification_noisy_FULL	Noodle-bg	2024-07-02T07:09:40Z	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-02T05:37:05Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: BERT_Text_classification_noisy_FULL 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_Text_classification_noisy_FULL 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: 0.5701 - Accuracy: 0.8552 - F1: 0.8469 - Precision: 0.8499 - Recall: 0.8466 ## 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 2.8254 \| 0.14 \| 100 \| 2.1467 \| 0.4831 \| 0.4014 \| 0.4638 \| 0.4628 \| \| 1.7026 \| 0.28 \| 200 \| 1.0972 \| 0.7123 \| 0.6713 \| 0.6981 \| 0.6939 \| \| 1.207 \| 0.42 \| 300 \| 0.9107 \| 0.7524 \| 0.7200 \| 0.7407 \| 0.7315 \| \| 1.0468 \| 0.56 \| 400 \| 0.7801 \| 0.7861 \| 0.7586 \| 0.7829 \| 0.7676 \| \| 1.0953 \| 0.71 \| 500 \| 0.8004 \| 0.7852 \| 0.7649 \| 0.7778 \| 0.7685 \| \| 1.0857 \| 0.85 \| 600 \| 0.8224 \| 0.7678 \| 0.7367 \| 0.7775 \| 0.7507 \| \| 0.9787 \| 0.99 \| 700 \| 0.6742 \| 0.8111 \| 0.7968 \| 0.8069 \| 0.7976 \| \| 0.7944 \| 1.13 \| 800 \| 0.6627 \| 0.8222 \| 0.8068 \| 0.8162 \| 0.8077 \| \| 0.8078 \| 1.27 \| 900 \| 0.6222 \| 0.8306 \| 0.8194 \| 0.8260 \| 0.8187 \| \| 0.8101 \| 1.41 \| 1000 \| 0.6620 \| 0.8279 \| 0.8158 \| 0.8276 \| 0.8155 \| \| 0.7668 \| 1.55 \| 1100 \| 0.5905 \| 0.8439 \| 0.8361 \| 0.8400 \| 0.8356 \| \| 0.7133 \| 1.69 \| 1200 \| 0.6085 \| 0.8328 \| 0.8231 \| 0.8297 \| 0.8225 \| \| 0.7621 \| 1.84 \| 1300 \| 0.6039 \| 0.8375 \| 0.8293 \| 0.8368 \| 0.8283 \| \| 0.6461 \| 1.98 \| 1400 \| 0.6176 \| 0.8392 \| 0.8298 \| 0.8356 \| 0.8292 \| \| 0.5791 \| 2.12 \| 1500 \| 0.5925 \| 0.8524 \| 0.8439 \| 0.8497 \| 0.8440 \| \| 0.602 \| 2.26 \| 1600 \| 0.5877 \| 0.8473 \| 0.8389 \| 0.8441 \| 0.8382 \| \| 0.5459 \| 2.4 \| 1700 \| 0.5888 \| 0.8534 \| 0.8460 \| 0.8528 \| 0.8459 \| \| 0.569 \| 2.54 \| 1800 \| 0.6118 \| 0.8467 \| 0.8393 \| 0.8448 \| 0.8387 \| \| 0.5138 \| 2.68 \| 1900 \| 0.5797 \| 0.8561 \| 0.8474 \| 0.8520 \| 0.8474 \| \| 0.5427 \| 2.82 \| 2000 \| 0.5678 \| 0.8578 \| 0.8498 \| 0.8531 \| 0.8493 \| \| 0.5437 \| 2.97 \| 2100 \| 0.5701 \| 0.8552 \| 0.8469 \| 0.8499 \| 0.8466 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2
ImagineIt/beta-test-checkpoint-10000	ImagineIt	2024-07-02T05:38:21Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:37:17Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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YuvrajSingh9886/phi3-mini-fine-tuned-agricultural-irrigation-QnA	YuvrajSingh9886	2024-07-02T05:39:33Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T05:38:01Z	--- base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - Developed by: YuvrajSingh9886 - License: apache-2.0 - Finetuned from model : unsloth/phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ImagineIt/beta-test-checkpoint-1000	ImagineIt	2024-07-02T05:39:25Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:38:24Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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ImagineIt/beta-test-checkpoint-5000	ImagineIt	2024-07-02T05:40:28Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:39:27Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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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ImagineIt/beta-test-checkpoint-3000	ImagineIt	2024-07-02T05:41:33Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:40:30Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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] ### Framework versions - PEFT 0.11.1
sharmadhruv/qa_by_bird_lora_weights	sharmadhruv	2024-07-02T19:23:58Z	0	null	[ "region:us" ]	null	2024-07-02T05:41:14Z	Entry not found
richardkelly/Qwen-Qwen1.5-0.5B-1719898892	richardkelly	2024-07-02T05:41:38Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen1.5-0.5B", "region:us" ]	null	2024-07-02T05:41:32Z	--- library_name: peft base_model: Qwen/Qwen1.5-0.5B --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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ImagineIt/beta-test-checkpoint-4000	ImagineIt	2024-07-02T05:42:39Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:41:35Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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Noodle-bg/open_llama_3b_v2-20_newsgroup_full	Noodle-bg	2024-07-03T01:31:31Z	0	null	[ "tensorboard", "safetensors", "region:us" ]	null	2024-07-02T05:42:13Z	Entry not found
ImagineIt/beta-test-checkpoint-9000	ImagineIt	2024-07-02T05:43:43Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:42:41Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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ImagineIt/beta-test-checkpoint-6000	ImagineIt	2024-07-02T05:44:52Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:43:46Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Moriacrafter/Qwen1.5-14B-8bit_DepressionDetection	Moriacrafter	2024-07-02T05:53:24Z	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T05:44:06Z	--- 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. 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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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ImagineIt/beta-test-checkpoint-7000	ImagineIt	2024-07-02T05:46:07Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:44:55Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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GraydientPlatformAPI/loras-july2	GraydientPlatformAPI	2024-07-02T05:48:15Z	0	null	[ "region:us" ]	null	2024-07-02T05:45:20Z	Entry not found
richardkelly/Qwen-Qwen1.5-1.8B-1719899135	richardkelly	2024-07-02T05:45:41Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen1.5-1.8B", "region:us" ]	null	2024-07-02T05:45:35Z	--- library_name: peft base_model: Qwen/Qwen1.5-1.8B --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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ImagineIt/beta-test-checkpoint-19000	ImagineIt	2024-07-02T05:47:10Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:46:10Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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ImagineIt/beta-test-checkpoint-14000	ImagineIt	2024-07-02T05:48:16Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-07-02T05:47:13Z	--- base_model: NousResearch/Meta-Llama-3-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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richardkelly/google-gemma-2b-1719899362	richardkelly	2024-07-02T05:49:41Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/gemma-2b", "region:us" ]	null	2024-07-02T05:49:22Z	--- library_name: peft base_model: google/gemma-2b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
LafouCC/civil-comments-PEM-merged	LafouCC	2024-07-02T05:50:01Z	0	null	[ "region:us" ]	null	2024-07-02T05:50:01Z	Entry not found
Kavin1701/whisper-small-tamil	Kavin1701	2024-07-02T10:19:19Z	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-07-02T05:52:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
percymamedy/reuters-falcon-qlora	percymamedy	2024-07-02T06:19:20Z	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:tiiuae/falcon-7b", "license:apache-2.0", "region:us" ]	null	2024-07-02T05:53:03Z	--- base_model: tiiuae/falcon-7b library_name: peft license: apache-2.0 tags: - generated_from_trainer model-index: - name: reuters-falcon-qlora 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. --> # reuters-falcon-qlora This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1135 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2695 \| 0.96 \| 15 \| 2.1135 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
RyanLee1229/LlaMA3_Model	RyanLee1229	2024-07-02T07:11:13Z	0	null	[ "gguf", "license:apache-2.0", "region:us" ]	null	2024-07-02T06:00:25Z	--- license: apache-2.0 ---
lewy666/ChartInsturct	lewy666	2024-07-02T06:01:17Z	0	null	[ "region:us" ]	null	2024-07-02T06:01:17Z	Invalid username or password.
KasuleTrevor/wav2vec2-large-xls-r-300m-lg-20hr-v3	KasuleTrevor	2024-07-02T06:38:41Z	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-07-02T06:01:38Z	--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-large-xls-r-300m-lg-20hr-v3 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/asr-africa-research-team/ASR%20Africa/runs/ed6zbieq) # wav2vec2-large-xls-r-300m-lg-20hr-v3 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5269 - Wer: 0.6523 - Cer: 0.1443 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\|:------:\| \| No log \| 0.9948 \| 95 \| 4.7296 \| 1.0 \| 1.0 \| \| 10.707 \| 2.0 \| 191 \| 3.2782 \| 1.0 \| 1.0 \| \| 3.6293 \| 2.9948 \| 286 \| 2.9509 \| 1.0 \| 1.0 \| \| 3.0251 \| 4.0 \| 382 \| 2.5502 \| 1.0 \| 0.8614 \| \| 2.7179 \| 4.9948 \| 477 \| 0.8813 \| 0.9115 \| 0.2460 \| \| 1.0647 \| 6.0 \| 573 \| 0.6062 \| 0.8192 \| 0.1791 \| \| 0.5597 \| 6.9948 \| 668 \| 0.4977 \| 0.7163 \| 0.1474 \| \| 0.3926 \| 8.0 \| 764 \| 0.4691 \| 0.6665 \| 0.1396 \| \| 0.292 \| 8.9948 \| 859 \| 0.4583 \| 0.6417 \| 0.1298 \| \| 0.2443 \| 9.9476 \| 950 \| 0.4634 \| 0.6453 \| 0.1309 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.2.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
Supuntd/text2image-GAN-with-diffusion	Supuntd	2024-07-02T06:01:57Z	0	null	[ "region:us" ]	null	2024-07-02T06:01:57Z	Entry not found
liminerity/sara-30m-gf-1.58-bit	liminerity	2024-07-02T06:04:09Z	0	null	[ "region:us" ]	null	2024-07-02T06:04:09Z	Entry not found
LoveTk/Sms	LoveTk	2024-07-02T11:21:09Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-07-02T06:05:05Z	--- license: apache-2.0 ---
lunarlist/mt5-summarize	lunarlist	2024-07-02T17:03:45Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:google/mt5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-07-02T06:05:32Z	--- license: apache-2.0 base_model: google/mt5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: mt5-summarize 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. --> # mt5-summarize This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1534 - Rouge1: 0.3153 - Rouge2: 0.1594 - Rougel: 0.2511 - Rougelsum: 0.3397 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - 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: 90 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| 4.3806 \| 1.0667 \| 100 \| 3.4709 \| 0.2568 \| 0.1258 \| 0.2214 \| 0.2662 \| \| 3.7757 \| 2.1333 \| 200 \| 3.2899 \| 0.2759 \| 0.1388 \| 0.2381 \| 0.2946 \| \| 3.5195 \| 3.2 \| 300 \| 3.1951 \| 0.2951 \| 0.1523 \| 0.2466 \| 0.3217 \| \| 3.4319 \| 4.2667 \| 400 \| 3.1715 \| 0.2813 \| 0.1323 \| 0.2331 \| 0.3011 \| \| 3.2402 \| 5.3333 \| 500 \| 3.1704 \| 0.3058 \| 0.1548 \| 0.2513 \| 0.3366 \| \| 3.2313 \| 6.4 \| 600 \| 3.1657 \| 0.3077 \| 0.1534 \| 0.2461 \| 0.3335 \| \| 3.1444 \| 7.4667 \| 700 \| 3.1719 \| 0.2957 \| 0.1453 \| 0.2378 \| 0.3191 \| \| 3.116 \| 8.5333 \| 800 \| 3.1639 \| 0.3144 \| 0.1540 \| 0.2501 \| 0.3453 \| \| 2.9937 \| 9.6 \| 900 \| 3.1534 \| 0.3153 \| 0.1594 \| 0.2511 \| 0.3397 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
yuvraj108c/Depth-Anything-2-Onnx	yuvraj108c	2024-07-02T06:08:37Z	1	null	[ "onnx", "region:us" ]	null	2024-07-02T06:07:13Z	Entry not found
gaurav-raul/toyota_corolla_cross_xle_2022_LoRA	gaurav-raul	2024-07-02T20:43:03Z	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-07-02T06:07:16Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers instance_prompt: a photo of Toyota Corolla Cross XLE 2022 car widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - gaurav-raul/toyota_corolla_cross_xle_2022_LoRA <Gallery /> ## Model description These are gaurav-raul/toyota_corolla_cross_xle_2022_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of Toyota Corolla Cross XLE 2022 car to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](gaurav-raul/toyota_corolla_cross_xle_2022_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
madhavipuliraju/gemma-Code-Instruct-Finetune-test	madhavipuliraju	2024-07-02T06:13:52Z	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T06:09:01Z	--- 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. 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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maltrz/mz_tools-mistral-4bit-lora-adapter	maltrz	2024-07-02T09:55:49Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-02T06:09:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Yseenn/Ggtvb	Yseenn	2024-07-02T06:09:55Z	0	null	[ "region:us" ]	null	2024-07-02T06:09:55Z	Entry not found
FudanDNN-NLP/llama3-8b-instruct-ragga-disturb	FudanDNN-NLP	2024-07-02T08:06:28Z	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T06:10:26Z	--- license: apache-2.0 ---
hflqf88888/OdysseyAgent-random	hflqf88888	2024-07-02T09:19:56Z	0	transformers	[ "transformers", "pytorch", "qwen", "text-generation", "GUI", "custom_code", "en", "zh", "dataset:OpenGVLab/GUI-Odyssey", "license:cc-by-4.0", "autotrain_compatible", "region:us" ]	text-generation	2024-07-02T06:13:13Z	--- license: cc-by-4.0 datasets: - OpenGVLab/GUI-Odyssey language: - en - zh tags: - GUI --- ## OdysseyAgent-task The OdysseyAgent fine-tuned on Train-Random split.
VectorZhao/q-FrozenLake-v1-4x4-noSlippery	VectorZhao	2024-07-02T06:15:06Z	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-07-02T06:15:03Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="VectorZhao/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
sjfhsajkf/11111	sjfhsajkf	2024-07-02T06:16:13Z	0	null	[ "region:us" ]	null	2024-07-02T06:16:13Z	Entry not found
ductai199x/sam_hq_vit_large	ductai199x	2024-07-02T06:18:42Z	0	transformers	[ "transformers", "safetensors", "sam_hq", "feature-extraction", "mask-generation", "custom_code", "arxiv:2306.01567", "license:apache-2.0", "region:us" ]	mask-generation	2024-07-02T06:16:45Z	--- license: apache-2.0 pipeline_tag: mask-generation --- # SAM-HQ: Segment Anything in High Quality (ViT Large) Directly converted weights from [https://github.com/SysCV/sam-hq/tree/main](https://github.com/SysCV/sam-hq/tree/main) to huggingface format. This work does not belong to me. Please checkout the authors' github for more information and updates. > [Segment Anything in High Quality](https://arxiv.org/abs/2306.01567) > NeurIPS 2023 > ETH Zurich & HKUST
tz3/finetune_v5	tz3	2024-07-02T06:17:20Z	0	null	[ "region:us" ]	null	2024-07-02T06:17:20Z	Entry not found
Subha07/Hjj	Subha07	2024-07-02T06:17:55Z	0	null	[ "region:us" ]	null	2024-07-02T06:17:55Z	Entry not found
humbl-pi/dummy-camembert-model	humbl-pi	2024-07-02T06:19:31Z	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-07-02T06:19:16Z	--- 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]
WALIDALI/faisal	WALIDALI	2024-07-02T17:26:57Z	0	null	[ "region:us" ]	null	2024-07-02T06:20:00Z	Entry not found
VectorZhao/Taxi-v3	VectorZhao	2024-07-02T06:21:51Z	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-07-02T06:21:49Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.76 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="VectorZhao/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
sherryliu987/esm2_t12_35M_UR50D-finetuned-localization	sherryliu987	2024-07-02T06:55:10Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "esm", "text-classification", "generated_from_trainer", "base_model:facebook/esm2_t12_35M_UR50D", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-02T06:22:21Z	--- license: mit base_model: facebook/esm2_t12_35M_UR50D tags: - generated_from_trainer metrics: - accuracy model-index: - name: esm2_t12_35M_UR50D-finetuned-localization 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. --> # esm2_t12_35M_UR50D-finetuned-localization This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1841 - Accuracy: 0.9488 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 483 \| 0.2038 \| 0.9356 \| \| 0.2382 \| 2.0 \| 966 \| 0.1799 \| 0.9441 \| \| 0.1423 \| 3.0 \| 1449 \| 0.1841 \| 0.9488 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
chohtet/qwen2_7b_instruct_lora_45706_test	chohtet	2024-07-02T10:36:24Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:unsloth/Qwen2-7B-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T06:23:30Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl base_model: unsloth/Qwen2-7B-Instruct-bnb-4bit --- # Uploaded model - Developed by: chohtet - License: apache-2.0 - Finetuned from model : unsloth/Qwen2-7B-Instruct-bnb-4bit This qwen2 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)
chohtet/qwen2_7b_instruct_4bit_45706_test	chohtet	2024-07-02T10:37:02Z	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Qwen2-7B-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-02T06:24:13Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft base_model: unsloth/Qwen2-7B-Instruct-bnb-4bit --- # Uploaded model - Developed by: chohtet - License: apache-2.0 - Finetuned from model : unsloth/Qwen2-7B-Instruct-bnb-4bit This qwen2 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)
quissuiven/donut-ktp-v3-test-again	quissuiven	2024-07-02T06:57:32Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-07-02T06:27:53Z	--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-ktp-v3-test-again results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # donut-ktp-v3-test-again This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
YaGiNA/my_awesome_model	YaGiNA	2024-07-02T08:10:32Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-02T06:29:03Z	Entry not found
habulaj/283521252438	habulaj	2024-07-02T06:29:21Z	0	null	[ "region:us" ]	null	2024-07-02T06:29:12Z	Entry not found
VKapseln475/WLoss441	VKapseln475	2024-07-02T06:30:33Z	0	null	[ "region:us" ]	null	2024-07-02T06:30:06Z	# W - Loss France Expériences : W-Loss Commentaires Avantages Dose et apport, acheter W-Loss France Expériences W-Loss est un supplément 100 % naturel pour perdre du poids. Son complexe d'actifs d'origine végétale. Ce produit doit être considéré comme faisant partie d'un plan global de gestion du poids et doit être associé à une alimentation équilibrée et à une activité physique. Renforcez l'effet de votre alimentation avec des extraits naturels. Extrait d'ananas, extrait de kumquat, extrait de papaye, extrait de fruit de la passion, extrait de feuille de thé vert, vitamine B6, chrome, iode. ## [Cliquez ici pour acheter maintenant sur le site officiel de W - Loss](https://adtocart.xyz/w-loss-fr) ## Brûleurs de graisse Les brûleurs de graisse sont généralement une combinaison de stimulants dérivés de plantes, d'acides gras essentiels, de picolinate de chrome, de pyruvate et/ou d'acide hydroxycitrique. Les stimulants dérivés des herbes comprennent la caféine et l'éphédrine ainsi que les herbes guarana et ma huang. Deux ou trois de ces stimulants sont généralement « empilés » dans un seul produit amaigrissant, souvent avec de l'aspirine ou de l'écorce de saule. Ils sont censés augmenter l’énergie tout en stimulant la combustion des graisses. La plupart des experts conviennent qu’ils fonctionnent lorsqu’ils sont combinés à de l’exercice. Mais leur sécurité est une autre affaire. "L'éphédra [qui vient du ma huang] est efficace... particulièrement lorsqu'elle est combinée avec de l'aspirine et d'autres ingrédients", explique Heymsfield. "Mais cela augmente la tension artérielle et peut provoquer des crises cardiaques mortelles, des arythmies [cardiaques] et des accidents vasculaires cérébraux. … Les publicités sont très trompeuses pour Ma Huang en ce qui concerne la sécurité." Klauer et ses collègues ont étudié le potentiel de perte de poids de l'éphédra l'année dernière. "Les gens ont effectivement perdu plus de poids grâce à ce médicament", dit-elle, "mais leur élévation de la tension artérielle est restée élevée même après avoir arrêté le médicament. … Donc, je ne recommande vraiment pas l'éphédra." Selon Klauer, l'éphédra et la caféine agissent toutes deux en augmentant votre taux métabolique au repos, ce qui peut être fait de manière plus sûre et à moindre coût simplement en faisant plus d'exercice et en mangeant moins. Malgré des antécédents familiaux de maladie cardiaque, le triathlète potentiel Matthew Martin a choisi une combinaison de caféine et d'éphédrine comme programme pour brûler les graisses. Il a remarqué que son rythme cardiaque s'accélérait après avoir pris une pilule, mais cela ne le dérangeait pas. Il a constaté que la prise de pilules augmentait suffisamment son énergie pour lui donner envie de s'entraîner davantage et d'atteindre ses objectifs de remise en forme plus tôt. Un mois après l'arrêt du programme, il reste en forme, mais il est également toujours en pleine formation de triathlon. Les acides gras essentiels comprennent l’acide linoléique conjugué (CLA) et l’huile de graines de lin. Ils sont parfois utilisés en association avec l’ail pour augmenter la masse musculaire et brûler les graisses. Ils semblent utiles chez les animaux, et de nouvelles recherches s’avèrent prometteuses chez les humains. Michael W. Pariza, PhD, directeur du Food Research Institute de l'Université du Wisconsin à Madison, a étudié le CLA. "Des preuves cliniques émergent certainement selon lesquelles cela peut être utile, en particulier pour contrôler la prise de graisse et de poids", a-t-il déclaré à WebMD. "Nous avons également de très bonnes preuves que cela soulage de nombreux effets indésirables [tels que les étourdissements et les problèmes d'estomac] que les gens ressentent lorsqu'ils suivent un régime." ## [Cliquez ici pour acheter maintenant sur le site officiel de W - Loss](https://adtocart.xyz/w-loss-fr)
makhataei/Wav2vec2-xlsr-Shemo	makhataei	2024-07-02T06:31:56Z	0	null	[ "region:us" ]	null	2024-07-02T06:31:56Z	Entry not found
itay-nakash/model_387dff9370_sweep_lunar-dragon-1172	itay-nakash	2024-07-02T06:32:05Z	0	null	[ "region:us" ]	null	2024-07-02T06:32:05Z	Entry not found
sudip2003/Phi-2-PetGPT	sudip2003	2024-07-02T06:43:30Z	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T06:32:40Z	--- 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]
abdfajar707/llama3_8B_lora_model_rkp_pn2025_v1	abdfajar707	2024-07-02T06:34:08Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T06:33:57Z	--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: abdfajar707 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

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