All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
Model Card for Mistral-Small-24B-Instruct-2501
Mistral Small 3 ( 2501 ) sets a new benchmark in the "small" Large Language Models category below 70B, boasting 24B parameters and achieving state-of-the-art capabilities comparable to larger models!
This model is an instruction-fine-tuned version of the base model: Mistral-Small-24B-Base-2501.
Mistral Small can be deployed locally and is exceptionally "knowledge-dense", fitting in a single RTX 4090 or a 32GB RAM MacBook once quantized.
Perfect for:
Fast response conversational agents.
Low latency function calling.
Subject matter experts via fine-tuning.
Local inference for hobbyists and organizations handling sensitive data.
For enterprises that need specialized capabilities (increased context, particular modalities, domain specific knowledge, etc.), we will be releasing commercial models beyond what Mistral AI contributes to the community.
This release demonstrates our commitment to open source, serving as a strong base model.
Multilingual: Supports dozens of languages, including English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch, and Polish.
Agent-Centric: Offers best-in-class agentic capabilities with native function calling and JSON outputting.
Advanced Reasoning: State-of-the-art conversational and reasoning capabilities.
Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes.
Context Window: A 32k context window.
System Prompt: Maintains strong adherence and support for system prompts.
Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size.
Benchmark results
Human evaluated benchmarks
Category
Gemma-2-27B
Qwen-2.5-32B
Llama-3.3-70B
Gpt4o-mini
Mistral is better
0.536
0.496
0.192
0.200
Mistral is slightly better
0.196
0.184
0.164
0.204
Ties
0.052
0.060
0.236
0.160
Other is slightly better
0.060
0.088
0.112
0.124
Other is better
0.156
0.172
0.296
0.312
Note:
We conducted side by side evaluations with an external third-party vendor, on a set of over 1k proprietary coding and generalist prompts.
Evaluators were tasked with selecting their preferred model response from anonymized generations produced by Mistral Small 3 vs another model.
We are aware that in some cases the benchmarks on human judgement starkly differ from publicly available benchmarks, but have taken extra caution in verifying a fair evaluation. We are confident that the above benchmarks are valid.
Publicly accesible benchmarks
Reasoning & Knowledge
Evaluation
mistral-small-24B-instruct-2501
gemma-2b-27b
llama-3.3-70b
qwen2.5-32b
gpt-4o-mini-2024-07-18
mmlu_pro_5shot_cot_instruct
0.663
0.536
0.666
0.683
0.617
gpqa_main_cot_5shot_instruct
0.453
0.344
0.531
0.404
0.377
Math & Coding
Evaluation
mistral-small-24B-instruct-2501
gemma-2b-27b
llama-3.3-70b
qwen2.5-32b
gpt-4o-mini-2024-07-18
humaneval_instruct_pass@1
0.848
0.732
0.854
0.909
0.890
math_instruct
0.706
0.535
0.743
0.819
0.761
Instruction following
Evaluation
mistral-small-24B-instruct-2501
gemma-2b-27b
llama-3.3-70b
qwen2.5-32b
gpt-4o-mini-2024-07-18
------------
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--------------
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mtbench_dev
8.35
7.86
7.96
8.26
8.33
wildbench
52.27
48.21
50.04
52.73
56.13
arena_hard
0.873
0.788
0.840
0.860
0.897
ifeval
0.829
0.8065
0.8835
0.8401
0.8499
Note:
Performance accuracy on all benchmarks were obtained through the same internal evaluation pipeline - as such, numbers may vary slightly from previously reported performance
(Qwen2.5-32B-Instruct, Llama-3.3-70B-Instruct, Gemma-2-27B-IT).
Judge based evals such as Wildbench, Arena hard and MTBench were based on gpt-4o-2024-05-13.
We recommend using this model with the vLLM library
to implement production-ready inference pipelines.
Note 1: We recommond using a relatively low temperature, such as temperature=0.15.
Note 2: Make sure to add a system prompt to the model to best tailer it for your needs. If you want to use the model as a general assistant, we recommend the following
system prompt:
system_prompt = """You are Mistral Small 3, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.
Your knowledge base was last updated on 2023-10-01. The current date is 2025-01-30.
When you're not sure about some information, you say that you don't have the information and don't make up anything.
If the user's question is not clear, ambiguous, or does not provide enough context for you to accurately answer the question, you do not try to answer it right away and you rather ask the user to clarify their request (e.g. \"What are some good restaurants around me?\" => \"Where are you?\" or \"When is the next flight to Tokyo\" => \"Where do you travel from?\")"""
Note: Running Mistral-Small-24B-Instruct-2501 on GPU requires ~55 GB of GPU RAM in bf16 or fp16.
2. To ping the client you can use a simple Python snippet.
py
1import requests
2import json
3from datetime import datetime, timedelta
45url ="http://<your-server>:8000/v1/chat/completions"6headers ={"Content-Type":"application/json","Authorization":"Bearer token"}78model ="mistralai/Mistral-Small-24B-Instruct-2501"910messages =[11{12"role":"system",13"content":"You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."14},15{16"role":"user",17"content":"Give me 5 non-formal ways to say 'See you later' in French."18},19]20data ={"model": model,"messages": messages}2122response = requests.post(url, headers=headers, data=json.dumps(data))23print(response.json()["choices"][0]["message"]["content"])2425# Sure, here are five non-formal ways to say "See you later" in French:26#27# 1. À plus tard28# 2. À plus29# 3. Salut30# 4. À toute31# 5. Bisous32#33# ```34# /\_/\35# ( o.o )36# > ^ <37# ```
Function calling
Mistral-Small-24-Instruct-2501 is excellent at function / tool calling tasks via vLLM. E.g.:
Example
```py
import requests
import json
from huggingface_hub import hf_hub_download
from datetime import datetime, timedelta
tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "The city to find the weather for, e.g. 'San Francisco'",
},
"state": {
"type": "string",
"description": "The state abbreviation, e.g. 'CA' for California",
},
"unit": {
"type": "string",
"description": "The unit for temperature",
"enum": ["celsius", "fahrenheit"],
},
},
"required": ["city", "state", "unit"],
},
},
},
{
"type": "function",
"function": {
"name": "rewrite",
"description": "Rewrite a given text for improved clarity",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The input text to rewrite",
}
},
},
},
},
]
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": "Could you please make the below article more concise?\n\nOpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership.",
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "bbc5b7ede",
"type": "function",
"function": {
"name": "rewrite",
"arguments": '{"text": "OpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership."}',
},
}
],
},
{
"role": "tool",
"content": '{"action":"rewrite","outcome":"OpenAI is a FOR-profit company."}',
"tool_call_id": "bbc5b7ede",
"name": "rewrite",
},
{
"role": "assistant",
"content": "---\n\nOpenAI is a FOR-profit company.",
},
{
"role": "user",
"content": "Can you tell me what the temperature will be in Dallas, in Fahrenheit?",
},
]
data = {"model": model, "messages": messages, "tools": tools}
</details>
#### Offline
```py
from vllm import LLM
from vllm.sampling_params import SamplingParams
from datetime import datetime, timedelta
SYSTEM_PROMPT = "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."
user_prompt = "Give me 5 non-formal ways to say 'See you later' in French."
messages = [
{
"role": "system",
"content": SYSTEM_PROMPT
},
{
"role": "user",
"content": user_prompt
},
]
# note that running this model on GPU requires over 60 GB of GPU RAM
llm = LLM(model=model_name, tokenizer_mode="mistral", tensor_parallel_size=8)
sampling_params = SamplingParams(max_tokens=512, temperature=0.15)
outputs = llm.chat(messages, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
# Sure, here are five non-formal ways to say "See you later" in French:
#
# 1. À plus tard
# 2. À plus
# 3. Salut
# 4. À toute
# 5. Bisous
#
# ```
# /\_/\
# ( o.o )
# > ^ <
# ```
Transformers
If you want to use Hugging Face transformers to generate text, you can do something like this.
py
1from transformers import pipeline
2import torch
3messages =[4{"role":"user","content":"Give me 5 non-formal ways to say 'See you later' in French."},5]6chatbot = pipeline("text-generation", model="mistralai/Mistral-Small-24B-Instruct-2501", max_new_tokens=256, torch_dtype=torch.bfloat16)7chatbot(messages)
