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

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

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

We recommend using this model with the vLLM library to implement production-ready inference pipelines.

Make sure you install vLLM >= 0.8.1:

pip install vllm --ugrade

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.

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).