--- license: other library_name: transformers base_model: nvidia/Mistral-NeMo-Minitron-8B-Base datasets: - teknium/OpenHermes-2.5 pipeline_tag: text-generation license_name: nvidia-open-model-license license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf model-index: - name: Mistral-NeMo-Minitron-8B-Chat results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 44.52 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rasyosef/Mistral-NeMo-Minitron-8B-Chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 26.04 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rasyosef/Mistral-NeMo-Minitron-8B-Chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 0.76 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rasyosef/Mistral-NeMo-Minitron-8B-Chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 3.47 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rasyosef/Mistral-NeMo-Minitron-8B-Chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 12.94 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rasyosef/Mistral-NeMo-Minitron-8B-Chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 15.6 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rasyosef/Mistral-NeMo-Minitron-8B-Chat name: Open LLM Leaderboard --- # Mistral-NeMo-Minitron-8B-Chat This is an instruction-tuned version of [nvidia/Mistral-NeMo-Minitron-8B-Base](https://huggingface.co/nvidia/Mistral-NeMo-Minitron-8B-Base) that has underwent **supervised fine-tuning** with 32k instruction-response pairs from the [teknium/OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) dataset. ## How to use ### Chat Format Given the nature of the training data, the Mistral-NeMo-Minitron-8B chat model is best suited for prompts using the chat format as follows. You can provide the prompt as a question with a generic template as follows: ```markdown <|im_start|>system You are a helpful assistant.<|im_end|> <|im_start|>user Question?<|im_end|> <|im_start|>assistant ``` For example: ```markdown <|im_start|>system You are a helpful assistant.<|im_end|> <|im_start|>user How to explain Internet for a medieval knight?<|im_end|> <|im_start|>assistant ``` where the model generates the text after `<|im_start|>assistant` . ### Sample inference code This code snippets show how to get quickly started with running the model on a GPU: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline torch.random.manual_seed(0) model_id = "rasyosef/Mistral-NeMo-Minitron-8B-Chat" model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", torch_dtype=torch.bfloat16 ) tokenizer = AutoTokenizer.from_pretrained(model_id) messages = [ {"role": "system", "content": "You are a helpful AI assistant."}, {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."}, {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"}, ] pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, ) generation_args = { "max_new_tokens": 256, "return_full_text": False, "temperature": 0.0, "do_sample": False, } output = pipe(messages, **generation_args) print(output[0]['generated_text']) ``` Note: If you want to use flash attention, call _AutoModelForCausalLM.from_pretrained()_ with _attn_implementation="flash_attention_2"_ ## Benchmarks These benchmarks were run using EleutherAI's [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) - **IFEval (Instruction Following Evaluation)**: IFEval is a fairly interesting dataset that tests the capability of models to clearly follow explicit instructions, such as “include keyword x” or “use format y”. The models are tested on their ability to strictly follow formatting instructions rather than the actual contents generated, allowing strict and rigorous metrics to be used. - Score: **45.83** ## Demo Here's a colab notebook with a chat interface, you can use this to interact with the chat model. https://huggingface.co/rasyosef/Mistral-NeMo-Minitron-8B-Chat/blob/main/Mistral_NeMo_Minitron_8B_chatbot.ipynb # [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_rasyosef__Mistral-NeMo-Minitron-8B-Chat) | Metric |Value| |-------------------|----:| |Avg. |17.22| |IFEval (0-Shot) |44.52| |BBH (3-Shot) |26.04| |MATH Lvl 5 (4-Shot)| 0.76| |GPQA (0-shot) | 3.47| |MuSR (0-shot) |12.94| |MMLU-PRO (5-shot) |15.60|