[!Note] To make our models more accesible to everyone, this repo provides a basic GGUF checkpoint compatible with llama.cpp and mistral-common.

In addition to using this GGUF checkpoint, we encourage the community to use other GGUF variants, e.g. from Unsloth, LM Studio, ...

If you encounter any problems with the provided checkpoints here, please open a discussion or pull request.

Magistral Small 1.2 (GGUF)

Building upon Mistral Small 3.2 (2506), with added reasoning capabilities, undergoing SFT from Magistral Medium traces and RL on top, it's a small, efficient reasoning model with 24B parameters.

Magistral Small can be deployed locally, fitting within a single RTX 4090 or a 32GB RAM MacBook once quantized.

This is the GGUF version of the Magistral-Small-2509 model. We released the BF16 weights as well as the following quantized format:

• Q8_0
• Q5_K_M
• Q4_K_M

We do not release alongside our GGUF files:

• An official chat template. Instead, we recommend using mistral-common, which serves as our source of truth for tokenization and detokenization. Llama.cpp automatically loads a chat template, but it is in most likelihood incorrect for Magistral.
• The vision encoder, since our recommended usage does not involve multimodality.

Updates compared with Magistral Small 1.1

• Multimodality: The model now has a vision encoder and can take multimodal inputs, extending its reasoning capabilities to vision.
• Performance upgrade: Magistral Small 1.2 should give you significatively better performance than Magistral Small 1.1 as seen in the benchmark results.
• Better tone and persona: You should experiment better LaTeX and Markdown formatting, and shorter answers on easy general prompts.
• Finite generation: The model is less likely to enter infinite generation loops.
• Special think tokens: [THINK] and [/THINK] special tokens encapsulate the reasoning content in a thinking chunk. This makes it easier to parse the reasoning trace and prevents confusion when the '[THINK]' token is given as a string in the prompt.
• Reasoning prompt: The reasoning prompt is given in the system prompt.

Key Features

• Reasoning: Capable of long chains of reasoning traces before providing an answer.
• Multilingual: Supports dozens of languages, including English, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Malay, Nepali, Polish, Portuguese, Romanian, Russian, Serbian, Spanish, Turkish, Ukrainian, Vietnamese, Arabic, Bengali, Chinese, and Farsi.
• Vision: Vision capabilities enable the model to analyze images and reason based on visual content in addition to text available with our main model Magistral-Small-2509.
• Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes.
• Context Window: A 128k context window. Performance might degrade past 40k but Magistral should still give good results. Hence we recommend to leave the maximum model length to 128k and only lower if you encounter low performance.

Usage

We recommend to use Magistral Small 1.2 GGUF with llama.cpp along with mistral-common >= 1.8.5 server. See here for the documentation of mistral-common server.

This recommended usage does not support vision.

[!Note] We do not believe we can guarantee correct behavior using the integrated, stringified chat template hence mistral-common should be used as a reference. However, we strongly encourage the community members to use this GGUF checkpoint and mistral_common as a reference implementation to build a correct integrated, stringified chat template.

Install

1. Install llama.cpp following their guidelines.

2. Install mistral-common >= 1.8.5 with its dependencies.

pip install --upgrade mistral-common[server,hf-hub]

3. Download the weights from huggingface.

pip install -U "huggingface_hub[cli]"

huggingface-cli download \
"mistralai/Magistral-Small-2509-GGUF" \
--include "Magistral-Small-2509-Q4_K_M.gguf" \
--local-dir "mistralai/Magistral-Small-2509-GGUF/"

Launch the servers

1. Launch the llama.cpp server

llama-server -m mistralai/Magistral-Small-2509-GGUF/Magistral-Small-2509-Q4_K_M.gguf -c 0

2. Launch the mistral-common server and pass the url of the llama.cpp server.

This is the server that will handle tokenization and detokenization and call the llama.cpp server for generations.

mistral_common serve mistralai/Magistral-Small-2509 \
--host localhost --port 6000 \
--engine-url http://localhost:8080 --engine-backend llama_cpp \
--timeout 300

Use the model

1. let's define the function to call the servers:

generate: call mistral-common that will tokenizer, call the llama.cpp server to generate new tokens and detokenize the output to an AssistantMessage with think chunk and tool calls parsed.

from mistral_common.protocol.instruct.messages import AssistantMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.experimental.app.models import OpenAIChatCompletionRequest
from fastapi.encoders import jsonable_encoder
import requests

mistral_common_url = "http://127.0.0.1:6000"

def generate(
    request: dict | ChatCompletionRequest | OpenAIChatCompletionRequest, url: str
) -> AssistantMessage:
    response = requests.post(
        f"{url}/v1/chat/completions", json=jsonable_encoder(request)
    )
    if response.status_code != 200:
        raise ValueError(f"Error: {response.status_code} - {response.text}")
    return AssistantMessage(**response.json())

2. Tokenize the input, call the model and detokenize

from typing import Any
from huggingface_hub import hf_hub_download


TEMP = 0.7
TOP_P = 0.95
MAX_TOK = 131072

def load_system_prompt(repo_id: str, filename: str) -> dict[str, Any]:
    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
    with open(file_path, "r") as file:
        system_prompt = file.read()

    index_begin_think = system_prompt.find("[THINK]")
    index_end_think = system_prompt.find("[/THINK]")

    return {
        "role": "system",
        "content": [
            {"type": "text", "text": system_prompt[:index_begin_think]},
            {
                "type": "thinking",
                "thinking": system_prompt[
                    index_begin_think + len("[THINK]") : index_end_think
                ],
                "closed": True,
            },
            {
                "type": "text",
                "text": system_prompt[index_end_think + len("[/THINK]") :],
            },
        ],
    }

SYSTEM_PROMPT = load_system_prompt("mistralai/Magistral-Small-2509", "SYSTEM_PROMPT.txt")

query = "Use each number in 2,5,6,3 exactly once, along with any combination of +, -, ×, ÷ (and parentheses for grouping), to make the number 24."

messages = [SYSTEM_PROMPT, {"role": "user", "content": [{"type": "text", "text": query}]}]

request = {"messages": messages, "temperature": TEMP, "top_p": TOP_P, "max_tokens": MAX_TOK}

generated_message = generate(request, mistral_common_url)
print(generated_message)