**Model Name: Qwen2 orca_mini_v7_72b** # Qwen2 orca_mini_v7_72b is trained with various SFT Datasets

Passionate about Generative AI? I help companies to privately train and deploy custom LLM/MLLM affordably. For startups, I can even assist with securing GPU grants to get you started. Let's chat! https://www.linkedin.com/in/pankajam Looking forward to connecting!
### NOTICE By providing proper credit and attribution, you are granted permission to use this model as a foundational base for further Full fine tuning, DPO, PPO or ORPO tuning and any kind of Merges. I actively encourage users to customize and enhance the model according to their specific needs, as this version is designed to be a comprehensive general model. Dive in and innovate! ### Example Usage Here is the ChatML prompt format ``` <|im_start|>system You are Orca Mini, a helpful AI assistant.<|im_end|> <|im_start|>user Hello Orca Mini, what can you do for me?<|im_end|> <|im_start|>assistant ``` Below shows a code example on how to use this model ```python from transformers import AutoModel, AutoTokenizer model_slug = "pankajmathur/orca_mini_v7_72b" model = AutoModel.from_pretrained(model_slug) tokenizer = AutoTokenizer.from_pretrained(model_slug) messages = [ {"role": "system", "content": "You are Orca Mini, a helpful AI assistant."}, {"role": "user", "content": "Hello Orca Mini, what can you do for me?"} ] gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt") model.generate(**gen_input) ``` **Quants** GGUF : Coming Soon AWQ: Coming Soon ### Processing Long Texts (Based upon Qwen2-7B-Instruct suggestions at https://huggingface.co/Qwen/Qwen2-7B-Instruct) To handle extensive inputs exceeding 32,768 tokens, we utilize [YARN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For deployment, we recommend using vLLM. You can enable the long-context capabilities by following these steps: 1. **Install vLLM**: You can install vLLM by running the following command. ```bash pip install "vllm>=0.4.3" ``` Or you can install vLLM from [source](https://github.com/vllm-project/vllm/). 2. **Configure Model Settings**: After downloading the model weights, modify the `config.json` file by including the below snippet: ```json { "architectures": [ "Qwen2ForCausalLM" ], // ... "vocab_size": 152064, // adding the following snippets "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } ``` This snippet enable YARN to support longer contexts. 3. **Model Deployment**: Utilize vLLM to deploy your model. For instance, you can set up an openAI-like server using the command: ```bash python -u -m vllm.entrypoints.openai.api_server --model pankajmathur/orca_mini_v7_72b ``` Then you can access the Chat API by: ```bash curl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "pankajmathur/orca_mini_v7_72b", "messages": [ {"role": "system", "content": "You are Orca Mini, a helpful AI assistant."}, {"role": "user", "content": "Hello Orca Mini, what can you do for me?"} ] }' ``` **Note**: Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**. We advise adding the `rope_scaling` configuration only when processing long contexts is required. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_pankajmathur__orca_mini_v7_72b) | Metric |Value| |-------------------|----:| |Avg. |39.06| |IFEval (0-Shot) |59.30| |BBH (3-Shot) |55.06| |MATH Lvl 5 (4-Shot)|26.44| |GPQA (0-shot) |18.01| |MuSR (0-shot) |24.21| |MMLU-PRO (5-shot) |51.35|