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Model Details
Model Provider
mistralai
Model Tree
mistralai/Mistral-Nemo-Base-2407
Input Modalities
Output Modalities
Supported Functionality
Mistral Nemo is a transformer model, with the following architecture choices:
| Benchmark | Score |
|---|---|
| HellaSwag (0-shot) | 83.5% |
| Winogrande (0-shot) | 76.8% |
| OpenBookQA (0-shot) | 60.6% |
| CommonSenseQA (0-shot) | 70.4% |
| TruthfulQA (0-shot) | 50.3% |
| MMLU (5-shot) | 68.0% |
| TriviaQA (5-shot) | 73.8% |
| NaturalQuestions (5-shot) | 31.2% |
| Language | Score |
|---|---|
| French | 62.3% |
| German | 62.7% |
| Spanish | 64.6% |
| Italian | 61.3% |
| Portuguese | 63.3% |
| Russian | 59.2% |
| Chinese | 59.0% |
| Japanese | 59.0% |
The model can be used with three different frameworks
mistral_inference: See heretransformers: See hereNeMo: See nvidia/Mistral-NeMo-12B-InstructIt is recommended to use mistralai/Mistral-Nemo-Instruct-2407 with mistral-inference. For HF transformers code snippets, please keep scrolling.
text
pip install mistral_inferencepy
from huggingface_hub import snapshot_downloadfrom pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Nemo-Instruct')mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Mistral-Nemo-Instruct-2407", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path)After installing mistral_inference, a mistral-chat CLI command should be available in your environment. You can chat with the model using
text
mistral-chat $HOME/mistral_models/Nemo-Instruct --instruct --max_tokens 256 --temperature 0.35E.g. Try out something like:
text
How expensive would it be to ask a window cleaner to clean all windows in Paris. Make a reasonable guess in US Dollar.py
from mistral_inference.transformer import Transformerfrom mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizerfrom mistral_common.protocol.instruct.messages import UserMessagefrom mistral_common.protocol.instruct.request import ChatCompletionRequest tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")model = Transformer.from_folder(mistral_models_path) prompt = "How expensive would it be to ask a window cleaner to clean all windows in Paris. Make a reasonable guess in US Dollar." completion_request = ChatCompletionRequest(messages=[UserMessage(content=prompt)]) tokens = tokenizer.encode_chat_completion(completion_request).tokens out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.35, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)result = tokenizer.decode(out_tokens[0]) print(result)py
from mistral_common.protocol.instruct.tool_calls import Function, Toolfrom mistral_inference.transformer import Transformerfrom mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizerfrom mistral_common.protocol.instruct.messages import UserMessagefrom mistral_common.protocol.instruct.request import ChatCompletionRequest tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")model = Transformer.from_folder(mistral_models_path) completion_request = ChatCompletionRequest( tools=[ Tool( function=Function( name="get_current_weather", description="Get the current weather", parameters={ "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use. Infer this from the users location.", }, }, "required": ["location", "format"], }, ) ) ], messages=[ UserMessage(content="What's the weather like today in Paris?"), ],) tokens = tokenizer.encode_chat_completion(completion_request).tokens out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.35, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)result = tokenizer.decode(out_tokens[0]) print(result)[!IMPORTANT] NOTE: Until a new release has been made, you need to install transformers from source:
sh
pip install git+https://github.com/huggingface/transformers.git
If you want to use Hugging Face transformers to generate text, you can do something like this.
py
from transformers import pipeline messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"},]chatbot = pipeline("text-generation", model="mistralai/Mistral-Nemo-Instruct-2407",max_new_tokens=128)chatbot(messages)transformersTo use this example, you'll need transformers version 4.42.0 or higher. Please see the
function calling guide
in the transformers docs for more information.
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_id = "mistralai/Mistral-Nemo-Instruct-2407"tokenizer = AutoTokenizer.from_pretrained(model_id) def get_current_weather(location: str, format: str): """ Get the current weather Args: location: The city and state, e.g. San Francisco, CA format: The temperature unit to use. Infer this from the users location. (choices: ["celsius", "fahrenheit"]) """ pass conversation = [{"role": "user", "content": "What's the weather like in Paris?"}]tools = [get_current_weather] # format and tokenize the tool use prompt inputs = tokenizer.apply_chat_template( conversation, tools=tools, add_generation_prompt=True, return_dict=True, return_tensors="pt",) model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto") inputs.to(model.device)outputs = model.generate(**inputs, max_new_tokens=1000)print(tokenizer.decode(outputs[0], skip_special_tokens=True))Note that, for reasons of space, this example does not show a complete cycle of calling a tool and adding the tool call and tool results to the chat history so that the model can use them in its next generation. For a full tool calling example, please see the function calling guide, and note that Mistral does use tool call IDs, so these must be included in your tool calls and tool results. They should be exactly 9 alphanumeric characters.
[!TIP] Unlike previous Mistral models, Mistral Nemo requires smaller temperatures. We recommend to use a temperature of 0.3.
The Mistral Nemo Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Alok Kothari, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Augustin Garreau, Austin Birky, Bam4d, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Carole Rambaud, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gaspard Blanchet, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Hichem Sattouf, Ian Mack, Jean-Malo Delignon, Jessica Chudnovsky, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickaël Seznec, Nicolas Schuhl, Niklas Muhs, Olivier de Garrigues, Patrick von Platen, Paul Jacob, Pauline Buche, Pavan Kumar Reddy, Perry Savas, Pierre Stock, Romain Sauvestre, Sagar Vaze, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibault Schueller, Thibaut Lavril, Thomas Wang, Théophile Gervet, Timothée Lacroix, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall
from huggingface_hub import snapshot_downloadfrom pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Nemo-Instruct')mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Mistral-Nemo-Instruct-2407", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path)from mistral_inference.transformer import Transformerfrom mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizerfrom mistral_common.protocol.instruct.messages import UserMessagefrom mistral_common.protocol.instruct.request import ChatCompletionRequest tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")model = Transformer.from_folder(mistral_models_path) prompt = "How expensive would it be to ask a window cleaner to clean all windows in Paris. Make a reasonable guess in US Dollar." completion_request = ChatCompletionRequest(messages=[UserMessage(content=prompt)]) tokens = tokenizer.encode_chat_completion(completion_request).tokens out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.35, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)result = tokenizer.decode(out_tokens[0]) print(result)from mistral_common.protocol.instruct.tool_calls import Function, Toolfrom mistral_inference.transformer import Transformerfrom mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizerfrom mistral_common.protocol.instruct.messages import UserMessagefrom mistral_common.protocol.instruct.request import ChatCompletionRequest tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")model = Transformer.from_folder(mistral_models_path) completion_request = ChatCompletionRequest( tools=[ Tool( function=Function( name="get_current_weather", description="Get the current weather", parameters={ "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use. Infer this from the users location.", }, }, "required": ["location", "format"], }, ) ) ], messages=[ UserMessage(content="What's the weather like today in Paris?"), ],) tokens = tokenizer.encode_chat_completion(completion_request).tokens out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.35, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)result = tokenizer.decode(out_tokens[0]) print(result)from transformers import pipeline messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"},]chatbot = pipeline("text-generation", model="mistralai/Mistral-Nemo-Instruct-2407",max_new_tokens=128)chatbot(messages)from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_id = "mistralai/Mistral-Nemo-Instruct-2407"tokenizer = AutoTokenizer.from_pretrained(model_id) def get_current_weather(location: str, format: str): """ Get the current weather Args: location: The city and state, e.g. San Francisco, CA format: The temperature unit to use. Infer this from the users location. (choices: ["celsius", "fahrenheit"]) """ pass conversation = [{"role": "user", "content": "What's the weather like in Paris?"}]tools = [get_current_weather] # format and tokenize the tool use prompt inputs = tokenizer.apply_chat_template( conversation, tools=tools, add_generation_prompt=True, return_dict=True, return_tensors="pt",) model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto") inputs.to(model.device)outputs = model.generate(**inputs, max_new_tokens=1000)print(tokenizer.decode(outputs[0], skip_special_tokens=True))