language:
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license: other
license_name: mrl
license_link: https://mistral.ai/licenses/MRL-0.1.md
extra_gated_description: >-
If you want to learn more about how we process your personal data, please read
our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
library_name: transformers
base_model:
- mistralai/Mistral-Small-Instruct-2409
Model Card for Mistral-Small-Instruct-2409
Mistral-Small-Instruct-2409 is an instruct fine-tuned version with the following characteristics:
- 22B parameters
- Vocabulary to 32768
- Supports function calling
- 32k sequence length
Usage Examples
vLLM (recommended)
We recommend using this model with the vLLM library to implement production-ready inference pipelines.
Installation
Make sure you install vLLM >= v0.6.1.post1:
pip install --upgrade vllm
Also make sure you have mistral_common >= 1.4.1 installed:
pip install --upgrade mistral_common
You can also make use of a ready-to-go docker image.
Offline
from vllm import LLM
from vllm.sampling_params import SamplingParams
model_name = "mistralai/Mistral-Small-Instruct-2409"
sampling_params = SamplingParams(max_tokens=8192)
# note that running Mistral-Small on a single GPU requires at least 44 GB of GPU RAM
# If you want to divide the GPU requirement over multiple devices, please add *e.g.* `tensor_parallel=2`
llm = LLM(model=model_name, tokenizer_mode="mistral", config_format="mistral", load_format="mistral")
prompt = "How often does the letter r occur in Mistral?"
messages = [
{
"role": "user",
"content": prompt
},
]
outputs = llm.chat(messages, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
Server
You can also use Mistral Small in a server/client setting.
- Spin up a server:
vllm serve mistralai/Mistral-Small-Instruct-2409 --tokenizer_mode mistral --config_format mistral --load_format mistral
Note: Running Mistral-Small on a single GPU requires at least 44 GB of GPU RAM.
If you want to divide the GPU requirement over multiple devices, please add e.g. --tensor_parallel=2
- And ping the client:
curl --location 'http://<your-node-url>:8000/v1/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer token' \
--data '{
"model": "mistralai/Mistral-Small-Instruct-2409",
"messages": [
{
"role": "user",
"content": "How often does the letter r occur in Mistral?"
}
]
}'
Mistral-inference
We recommend using mistral-inference to quickly try out / "vibe-check" the model.
Install
Make sure to have mistral_inference >= 1.4.1 installed.
pip install mistral_inference --upgrade
Download
from huggingface_hub import snapshot_download
from pathlib import Path
mistral_models_path = Path.home().joinpath('mistral_models', '22B-Instruct-Small')
mistral_models_path.mkdir(parents=True, exist_ok=True)
snapshot_download(repo_id="mistralai/Mistral-Small-Instruct-2409", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)
Chat
After installing mistral_inference, a mistral-chat CLI command should be available in your environment. You can chat with the model using
mistral-chat $HOME/mistral_models/22B-Instruct-Small --instruct --max_tokens 256
Instruct following
from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest
tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3")
model = Transformer.from_folder(mistral_models_path)
completion_request = ChatCompletionRequest(messages=[UserMessage(content="How often does the letter r occur in Mistral?")])
tokens = tokenizer.encode_chat_completion(completion_request).tokens
out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])
print(result)
Function calling
from mistral_common.protocol.instruct.tool_calls import Function, Tool
from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest
tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3")
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=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])
print(result)
Usage in Hugging Face Transformers
You can also use Hugging Face transformers library to run inference using various chat templates, or fine-tune the model.
Example for inference:
from transformers import LlamaTokenizerFast, MistralForCausalLM
import torch
device = "cuda"
tokenizer = LlamaTokenizerFast.from_pretrained('mistralai/Mistral-Small-Instruct-2409')
tokenizer.pad_token = tokenizer.eos_token
model = MistralForCausalLM.from_pretrained('mistralai/Mistral-Small-Instruct-2409', torch_dtype=torch.bfloat16)
model = model.to(device)
prompt = "How often does the letter r occur in Mistral?"
messages = [
{"role": "user", "content": prompt},
]
model_input = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(device)
gen = model.generate(model_input, max_new_tokens=150)
dec = tokenizer.batch_decode(gen)
print(dec)
And you should obtain
<s>
[INST]
How often does the letter r occur in Mistral?
[/INST]
To determine how often the letter "r" occurs in the word "Mistral,"
we can simply count the instances of "r" in the word.
The word "Mistral" is broken down as follows:
- M
- i
- s
- t
- r
- a
- l
Counting the "r"s, we find that there is only one "r" in "Mistral."
Therefore, the letter "r" occurs once in the word "Mistral."
</s>
The Mistral AI Team
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, Diogo Costa, 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