--- license: llama3 language: - en library_name: transformers pipeline_tag: text2text-generation --- **Model Name: llama_3_orca_mini_v4_8b** # Llama-3-8b base model trained on Orca Style Mini 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 DPO/PPO tuning or 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, fully fine-tuned general model. Dive in and innovate! ### Evaluation We evaluated this model on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI. Here are the results on similar metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) | Metric |Value| |---------------------------------|----:| |Avg. |66.65| |AI2 Reasoning Challenge (25-Shot)|58.02| |HellaSwag (10-Shot) |81.65| |MMLU (5-Shot) |63.23| |TruthfulQA (0-shot) |55.78| |Winogrande (5-shot) |73.95| |GSM8k (5-shot) |67.25| ### 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_v4_8b" 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) ``` This model is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE) **Quants** GGUF : Coming Soon AWQ: Coming Soon