You can deploy this model and start earning money today!

unsloth/
Mistral-Small-3.1-24B-Base-2503

Name: Mistral-Small-3.1-24B-Base-2503
Availability: InStock

vllm

safetensors

mistral3

Views

No views yet

Model Card for Mistral-Small-3.1-24B-Base-2503

Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) adds state-of-the-art vision understanding and enhances long context capabilities up to 128k tokens without compromising text performance. With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks.
This model is the base model of Mistral-Small-3.1-24B-Instruct-2503.

For enterprises requiring specialized capabilities (increased context, specific modalities, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community.

Learn more about Mistral Small 3.1 in our blog post.

Key Features

Vision: Vision capabilities enable the model to analyze images and provide insights based on visual content in addition to text.
Multilingual: Supports dozens of languages, including English, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Malay, Nepali, Polish, Portuguese, Romanian, Russian, Serbian, Spanish, Swedish, Turkish, Ukrainian, Vietnamese, Arabic, Bengali, Chinese, Farshi.
Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes.
Context Window: A 128k context window.
Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size.

Benchmark Results

When available, we report numbers previously published by other model providers, otherwise we re-evaluate them using our own evaluation harness.

Pretrain Evals

Model	MMLU (5-shot)	MMLU Pro (5-shot CoT)	TriviaQA	GPQA Main (5-shot CoT)	MMMU
Small 3.1 24B Base	81.01%	56.03%	80.50%	37.50%	59.27%
Gemma 3 27B PT	78.60%	52.20%	81.30%	24.30%	56.10%

Usage Examples

vLLM (recommended)

We recommend using Mistral-Small 3.1 Base with the vLLM library. Note however that this is a pretrained-only checkpoint and thus not ready to work as an instruction model out-of-the-box. For a production-ready instruction model please use Mistral-Small-3.1-24B-Instruct-2503.

Installation

Make sure you install vLLM nightly:

pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly --upgrade

Doing so should automatically install mistral_common >= 1.5.4.

To check:

python -c "import mistral_common; print(mistral_common.__version__)"

You can also make use of a ready-to-go docker image or on the docker hub followed by a nightly install of vllm as shown above.

Example

from vllm import LLM
from vllm.sampling_params import SamplingParams
from vllm.inputs.data import TokensPrompt
import requests
from PIL import Image
from io import BytesIO
from vllm.multimodal import MultiModalDataBuiltins

from mistral_common.protocol.instruct.messages import TextChunk, ImageURLChunk

model_name = "mistralai/Mistral-Small-3.1-24B-Base-2503"
sampling_params = SamplingParams(max_tokens=8192)

llm = LLM(model=model_name, tokenizer_mode="mistral")

url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content))

prompt = "The image shows a"

user_content = [ImageURLChunk(image_url=url), TextChunk(text=prompt)]

tokenizer = llm.llm_engine.tokenizer.tokenizer.mistral.instruct_tokenizer
tokens, _ = tokenizer.encode_user_content(user_content, False)

prompt = TokensPrompt(
    prompt_token_ids=tokens, multi_modal_data=MultiModalDataBuiltins(image=[image])
)
outputs = llm.generate(prompt, sampling_params=sampling_params)

print(outputs[0].outputs[0].text)
# ' scene in Yosemite Valley and was taken at ISO 250 with an aperture of f/16 and a shutter speed of 1/18 second. ...'

Transformers (untested)

Transformers-compatible model weights are also uploaded (thanks a lot @cyrilvallez). However the transformers implementation was not throughly tested, but only on "vibe-checks". Hence, we can only ensure 100% correct behavior when using the original weight format with vllm (see above).

unsloth/Mistral-Small-3.1-24B-Base-2503