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2026-11-15 05:20 UTC
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Model card
From the publisher at commit 129d7a0. Shown as plain text with links; images open at the publisher.
Note. At Mistral, we don't yet have too much experience with providing GGUF-quantized checkpoints to the community, but want to help improving the ecosystem going forward. If you encounter any problems with the provided checkpoints here, please open a discussion or pull request
Building upon Mistral Small 3.1 (2503), 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-2507 model. We released the BF16 weights as well as the following quantized format:
Q8_0
Q5_K_M
Q4_K_M
Our format does not have a chat template and instead we recommend to use mistral-common.
Magistral Small 1.1 should give you about the same performance as Magistral Small 1.0 as seen in the benchmark results.
The update involves the following features:
Better tone and model behaviour. You should experiment better LaTeX and Markdown formatting, and shorter answers on easy general prompts.
The model is less likely to enter infinite generation loops.
[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.
The reasoning prompt is now given in the system prompt.
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 typing import Any
from huggingface_hub import hf_hub_download
TEMP = 0.7
TOP_P = 0.95
MAX_TOK = 40_960
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-2507", "SYSTEM_PROMPT.txt")
query = "Write 4 sentences, each with at least 8 words. Now make absolutely sure that every sentence has exactly one word less than the previous sentence."
# or try out other queries
# query = "Exactly how many days ago did the French Revolution start? Today is June 4th, 2025."
# query = "Think about 5 random numbers. Verify if you can combine them with addition, multiplication, subtraction or division to 133"
# query = "If it takes 30 minutes to dry 12 T-shirts in the sun, how long does it take to dry 33 T-shirts?"
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)
Files
6 files · 103 GBWeights 14.3 GB (smallest of 4 GGUF variants). Sizes and sha256 from the signed manifest.
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