alt.freeweights.mistral / Magistral-Small-2509-unsloth-bnb-4bit
From: unsloth <huggingface.co/unsloth>Newsgroups: alt.freeweights.mistralSubject:

Magistral-Small-2509-unsloth-bnb-4bit

Date: 2026-11-15Commit: 5221529Signed: 2DC28CC7B26A283B ✓

Model with 24B parameters. Safetensors weights, 15.0 GB in all. Published by unsloth under Apache-2.0.

24B params15.0GBMagistral-Small-2509-unsloth-bnb-4bitsafetensorspermissive
FactsParams24BSize15.0 GBFormatssafetensorsTasknot taggedLicenseApache-2.0From sourcepermissive
$ fw get unsloth/Magistral-Small-2509-unsloth-bnb-4bit
# manifest sha256 0ee3c7fc6884… · 15.0 GB · 15 files
$ fw verify ./Magistral-Small-2509-unsloth-bnb-4bit unsloth/Magistral-Small-2509-unsloth-bnb-4bit
From sourceHosted (signed)unsloth/Magistral-Small-2509-unsloth-bnb-4bitLast check: 2026-11-15 05:02 UTC

Get help: custom fine-tune or private deploy. We review your setup before we quote.

Quantized of Magistral-Small-2509.

Works with

Runs with

Ranked by fit to this model (safetensors), then by GitHub stars.

Overview

Size
15.0 GB
Weights
15.0 GB (safetensors)
Parameters
24B
Formats
safetensors
Task
not tagged
License
Apache-2.0 (permissive)
Files
15, each with a sha256
Publisher
unsloth
Added
2026-11-15
Section
alt.freeweights.mistral
Lineage

Original release from the publisher.

Base model: mistralai/Magistral-Small-2509

Where these bytes come from
Publisher at the pinned commit→You

Sources are tried in order. A file counts only when its size and sha256 match the manifest. Sources are checked when you ask for them, not on a timer.

Sources
BadgeHostLast checkResult
From sourcehuggingface.co2026-11-15 05:02 UTCpassed

Model card

From the publisher at commit 5221529. Shown as plain text with links; images open at the publisher.

Learn to run Magistral 1.2 correctly - Read our Guide.

Unsloth Dynamic 2.0 achieves SOTA performance in model quantization.

https://github.com/unslothai/unsloth/ https://discord.gg/unsloth https://docs.unsloth.ai/basics/magistral

✨ How to Use Magistral 1.2:#

Run in llama.cpp:

./llama.cpp/llama-cli -hf unsloth/Magistral-Small-2509-GGUF:UD-Q4_K_XL --jinja --temp 0.7 --top-k -1 --top-p 0.95 -ngl 99

Run in Ollama:

ollama run hf.co/unsloth/Magistral-Small-2509-GGUF:UD-Q4_K_XL

Read our in-depth guide about Magistral 1.2: docs.unsloth.ai/basics/magistral

  • Fine-tune Magistral 1.2 for free using our Kaggle notebook here-Reasoning-Conversational.ipynb&accelerator=nvidiaTeslaT4)!
  • View the rest of our notebooks in our docs here.

Magistral Small 1.2#

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.

Learn more about Magistral in our blog post.

The model was presented in the paper Magistral.

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.
  • 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.

Benchmark Results#

ModelAIME24 pass@1AIME25 pass@1GPQA DiamondLivecodebench (v5)
Magistral Medium 1.291.82%83.48%76.26%75.00%
Magistral Medium 1.172.03%60.99%71.46%59.35%
Magistral Medium 1.073.59%64.95%70.83%59.36%
Magistral Small 1.286.14%77.34%70.07%70.88%
Magistral Small 1.170.52%62.03%65.78%59.17%
Magistral Small 1.070.68%62.76%68.18%55.84%

Sampling parameters#

Please make sure to use:

  • top_p: 0.95
  • temperature: 0.7
  • max_tokens: 131072

Basic Chat Template#

We highly recommend including the following system prompt for the best results, you can edit and customise it if needed for your specific use case.

First draft your thinking process (inner monologue) until you arrive at a response. Format your response using Markdown, and use LaTeX for any mathematical equations. Write both your thoughts and the response in the same language as the input.

Your thinking process must follow the template below:[THINK]Your thoughts or/and draft, like working through an exercise on scratch paper. Be as casual and as long as you want until you are confident to generate the response. Use the same language as the input.[/THINK]Here, provide a self-contained response.

The [THINK] and [/THINK] are special tokens that must be encoded as such.

Please make sure to use mistral-common as the source of truth. Find below examples from libraries supporting mistral-common.

We invite you to choose, depending on your use case and requirements, between keeping reasoning traces during multi-turn interactions or keeping only the final assistant response.

Ping model as follows:

Python text snippet

from typing import Any
from openai import OpenAI
from huggingface_hub import hf_hub_download

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

TEMP = 0.7
TOP_P = 0.95
MAX_TOK = 131072

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id

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(model, "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": query}
]
stream = client.chat.completions.create(
  model=model,
  messages=messages,
  stream=True,
  temperature=TEMP,
  top_p=TOP_P,
  max_tokens=MAX_TOK,
)

print("client: Start streaming chat completions...:\n")
printed_reasoning_content = False
answer = []

for chunk in stream:
    reasoning_content = None
    content = None
    # Check the content is reasoning_content or content
    if hasattr(chunk.choices[0].delta, "reasoning_content"):
        reasoning_content = chunk.choices[0].delta.reasoning_content
    elif hasattr(chunk.choices[0].delta, "content"):
        content = chunk.choices[0].delta.content

    if reasoning_content is not None:
        if not printed_reasoning_content:
            printed_reasoning_content = True
            print("Start reasoning:\n", end="", flush=True)
        print(reasoning_content, end="", flush=True)
    elif content is not None:
        # Extract and print the content
        if not reasoning_content and printed_reasoning_content:
            answer.extend(content)
        print(content, end="", flush=True)

if answer:
    print("\n\n=============\nAnswer\n=============\n")
    print("".join(answer))
else:
    print("\n\n=============\nNo Answer\n=============\n")
    print("No answer was generated by the model, probably because the maximum number of tokens was reached.")

# client: Start streaming chat completions...:
#
# Start reasoning:
# First, I need to ...
# ...
#
#
# =============
# Answer
# =============
# 
# Here's one way to use the numbers 2, 5, 6, 3 to make 24:
#
#\[
#(6 \div 2) \times (5 + 3) = 3 \times 8 = 24
#\]
#
#Alternatively, another solution is:
#
#\[
#6 \times (5 - 3 + 2) = 6 \times 4 = 24
#\]
#
#Both expressions use each of the numbers 2, 5, 6, 3 exactly once with the operations given.

Python text-image snippet: Pokemon

from typing import Any

from openai import OpenAI
from huggingface_hub import hf_hub_download

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

TEMP = 0.7
TOP_P = 0.95
MAX_TOK = 131072

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id


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]") :],
            },
        ],
    }


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

image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"

messages = [
    SYSTEM_PROMPT,
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "What action do you think I should take in this situation? List all the possible actions and explain why you think they are good or bad.",
            },
            {"type": "image_url", "image_url": {"url": image_url}},
        ],
    },
]


stream = client.chat.completions.create(
    model=model,
    messages=messages,
    stream=True,
    temperature=TEMP,
    top_p=TOP_P,
    max_tokens=MAX_TOK,
)

print("client: Start streaming chat completions...:\n")
printed_reasoning_content = False
answer = []

for chunk in stream:
    reasoning_content = None
    content = None
    # Check the content is reasoning_content or content
    if hasattr(chunk.choices[0].delta, "reasoning_content"):
        reasoning_content = chunk.choices[0].delta.reasoning_content
    elif hasattr(chunk.choices[0].delta, "content"):
        content = chunk.choices[0].delta.content

    if reasoning_content is not None:
        if not printed_reasoning_content:
            printed_reasoning_content = True
            print("Start reasoning:\n", end="", flush=True)
        print(reasoning_content, end="", flush=True)
    elif content is not None:
        # Extract and print the content
        if not reasoning_content and printed_reasoning_content:
            answer.extend(content)
        print(content, end="", flush=True)

if answer:
    print("\n\n=============\nAnswer\n=============\n")
    print("".join(answer))
else:
    print("\n\n=============\nNo Answer\n=============\n")
    print(
        "No answer was generated by the model, probably because the maximum number of tokens was reached."
    )

# client: Start streaming chat completions...:

# Start reasoning:
# In the image, we see a battle scene from a Pokémon game. The player's Pikachu is at full health (83/83 HP), and the opponent's Pidgey is at a lower level (level 17 compared to Pikachu's level 42). The possible actions available to the player are:

# 1. FIGHT: This allows the player to use one of Pikachu's moves to attack Pidgey. Given that Pikachu is at a higher level and has full HP, it is likely that Pikachu would be able to defeat Pidgey easily. This is a good option because it could potentially win the battle quickly and efficiently.

# 2. BAG: This allows the player to use an item from their bag. This could be useful if the player wants to heal Pikachu (though it's not necessary at full health) or use an item to weaken Pidgey. However, since Pikachu is at full health and Pidgey is at a lower level, this might not be necessary. It could be a good option if the player wants to use a special item, but generally, it might not be the best choice in this situation.

# 3. POKÉMON: This allows the player to switch the current Pokémon to another one in their team. Since Pikachu is at full health and at a higher level than Pidgey, switching might not be necessary. It could be useful if the player wants to train a different Pokémon, but it might not be the most efficient choice for winning the battle quickly.

# 4. RUN: This allows the player to flee from the battle. This could be a good option if the player wants to avoid the battle, but since Pikachu is at a clear advantage, running would not be the most efficient choice. It could be useful if the player wants to save time or if they are trying to avoid losing a Pokémon, but in this case, it seems unnecessary.

# Given the circumstances, the best action seems to be to FIGHT, as Pikachu is at a clear advantage in terms of level and health. The other options are not as efficient for winning the battle quickly.In the given scenario, the most appropriate action to take is to FIGHT. Here's why:

# 1. FIGHT: This is the best option because Pikachu is at a higher level and has full health, making it likely to defeat Pidgey quickly and efficiently. Using an attack move would be the most straightforward way to win the battle.

# 2. BAG: While this option could be useful for healing or using special items, it is not necessary since Pikachu is already at full health. This option is less efficient for winning the battle quickly.

# 3. POKÉMON: Switching to another Pokémon might be useful for training a different Pokémon, but it is not necessary since Pikachu is at a clear advantage. This option is not as efficient for winning the current battle.

# 4. RUN: Fleeing from the battle could be useful if the player wants to avoid the battle, but since Pikachu is at a clear advantage, running would not be the most efficient choice. It could be useful if the player wants to save time or avoid losing a Pokémon, but in this case, it seems unnecessary.

# Therefore, the best action to take in this situation is to FIGHT.

# FIGHT

# =============
# Answer
# =============

# In the given scenario, the most appropriate action to take is to FIGHT. Here's why:

# 1. FIGHT: This is the best option because Pikachu is at a higher level and has full health, making it likely to defeat Pidgey quickly and efficiently. Using an attack move would be the most straightforward way to win the battle.

# 2. BAG: While this option could be useful for healing or using special items, it is not necessary since Pikachu is already at full health. This option is less efficient for winning the battle quickly.

# 3. POKÉMON: Switching to another Pokémon might be useful for training a different Pokémon, but it is not necessary since Pikachu is at a clear advantage. This option is not as efficient for winning the current battle.

# 4. RUN: Fleeing from the battle could be useful if the player wants to avoid the battle, but since Pikachu is at a clear advantage, running would not be the most efficient choice. It could be useful if the player wants to save time or avoid losing a Pokémon, but in this case, it seems unnecessary.

# Therefore, the best action to take in this situation is to FIGHT.

# FIGHT

Python text-image snippet: Geo trivia

from typing import Any

from openai import OpenAI
from huggingface_hub import hf_hub_download

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

TEMP = 0.7
TOP_P = 0.95
MAX_TOK = 131072

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id


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]") :],
            },
        ],
    }


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

image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/d7/201806_Tianducheng_Bird-eye_View.jpg/1280px-201806_Tianducheng_Bird-eye_View.jpg"

messages = [
    SYSTEM_PROMPT,
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Where has this picture been taken ?",
            },
            {"type": "image_url", "image_url": {"url": image_url}},
        ],
    },
]


stream = client.chat.completions.create(
    model=model,
    messages=messages,
    stream=True,
    temperature=TEMP,
    top_p=TOP_P,
    max_tokens=MAX_TOK,
)

print("client: Start streaming chat completions...:\n")
printed_reasoning_content = False
answer = []

for chunk in stream:
    reasoning_content = None
    content = None
    # Check the content is reasoning_content or content
    if hasattr(chunk.choices[0].delta, "reasoning_content"):
        reasoning_content = chunk.choices[0].delta.reasoning_content
    elif hasattr(chunk.choices[0].delta, "content"):
        content = chunk.choices[0].delta.content

    if reasoning_content is not None:
        if not printed_reasoning_content:
            printed_reasoning_content = True
            print("Start reasoning:\n", end="", flush=True)
        print(reasoning_content, end="", flush=True)
    elif content is not None:
        # Extract and print the content
        if not reasoning_content and printed_reasoning_content:
            answer.extend(content)
        print(content, end="", flush=True)

if answer:
    print("\n\n=============\nAnswer\n=============\n")
    print("".join(answer))
else:
    print("\n\n=============\nNo Answer\n=============\n")
    print(
        "No answer was generated by the model, probably because the maximum number of tokens was reached."
    )

# client: Start streaming chat completions...:

# Start reasoning:
# The image shows a replica of the Eiffel Tower, but it's not in Paris. The background includes mountains, which are not present in Paris. The surrounding architecture appears to be more modern and dense, which is also not typical of Paris. The combination of the Eiffel Tower replica and the mountainous backdrop suggests that this is likely in a city in China, as China has several replicas of the Eiffel Tower, with the most famous one being in Shanghai. However, the dense residential buildings and the specific layout suggest that this might be in another city in China, possibly Shenzhen or another major city with a similar landscape.

# Given that the question is about identifying the location based on the visual clues, and considering the presence of the Eiffel Tower replica and the mountainous backdrop, it's likely that this is a well-known location in China.

# The most probable answer is that this is in Shenzhen, as it has a well-known Eiffel Tower replica in a park, but to be precise, this is the Eiffel Tower replica in Shenzhen, which is known as the "Shenzhen Park of Eiffel Tower."

# However, to be more accurate, this is likely the Eiffel Tower replica in Shenzhen, as it matches the description and visual elements.The image shows a replica of the Eiffel Tower, which is not in Paris but rather in a city with a mountainous backdrop and modern, dense architecture. This combination of elements is typical of a Chinese city, and the presence of the Eiffel Tower replica suggests a location like Shenzhen, which is known for having such a replica. The dense residential buildings and the specific layout further support this identification. Therefore, the most probable location for this image is Shenzhen, China.

# So, the answer is:

# Shenzhen

# =============
# Answer
# =============

# The image shows a replica of the Eiffel Tower, which is not in Paris but rather in a city with a mountainous backdrop and modern, dense architecture. This combination of elements is typical of a Chinese city, and the presence of the Eiffel Tower replica suggests a location like Shenzhen, which is known for having such a replica. The dense residential buildings and the specific layout further support this identification. Therefore, the most probable location for this image is Shenzhen, China.

# So, the answer is:

# Shenzhen

Python text-image snippet: Maths

from typing import Any

from openai import OpenAI
from huggingface_hub import hf_hub_download

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

TEMP = 0.7
TOP_P = 0.95
MAX_TOK = 131072

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id


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]") :],
            },
        ],
    }


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

image_url = "https://i.ytimg.com/vi/5Y3xLHeyKZU/hqdefault.jpg"

messages = [
    SYSTEM_PROMPT,
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Solve the equations. Answer in the language of the image.",
            },
            {"type": "image_url", "image_url": {"url": image_url}},
        ],
    },
]

stream = client.chat.completions.create(
    model=model,
    messages=messages,
    stream=True,
    temperature=TEMP,
    top_p=TOP_P,
    max_tokens=MAX_TOK,
)

print("client: Start streaming chat completions...:\n")
printed_reasoning_content = False
answer = []

for chunk in stream:
    reasoning_content = None
    content = None
    # Check the content is reasoning_content or content
    if hasattr(chunk.choices[0].delta, "reasoning_content"):
        reasoning_content = chunk.choices[0].delta.reasoning_content
    elif hasattr(chunk.choices[0].delta, "content"):
        content = chunk.choices[0].delta.content

    if reasoning_content is not None:
        if not printed_reasoning_content:
            printed_reasoning_content = True
            print("Start reasoning:\n", end="", flush=True)
        print(reasoning_content, end="", flush=True)
    elif content is not None:
        # Extract and print the content
        if not reasoning_content and printed_reasoning_content:
            answer.extend(content)
        print(content, end="", flush=True)

if answer:
    print("\n\n=============\nAnswer\n=============\n")
    print("".join(answer))
else:
    print("\n\n=============\nNo Answer\n=============\n")
    print(
        "No answer was generated by the model, probably because the maximum number of tokens was reached."
    )
# client: Start streaming chat completions...:

# Start reasoning:
# Je dois résoudre ce système d'équations. Voici les équations :

# 1. \(5x + 2y = -2\)
# 2. \(3x - 4y = 17\)

# D'abord, je pense que la méthode d'élimination pourrait être une bonne approche. Pour cela, je dois éliminer une des variables. Voyons comment.

# Je vais essayer d'éliminer y. Pour cela, je dois que les coefficients de y soient les mêmes (en valeur absolue) dans les deux équations.

# Le coefficient de y dans la première équation est 2, et dans la deuxième, c'est -4. Le plus petit multiple commun de 2 et 4 est 4. Donc, je vais multiplier la première équation par 2 pour que le coefficient de y devienne 4.

# Faisons cela :

# 1. \(2 \times (5x + 2y) = 2 \times (-2)\)
#    Ce qui donne : \(10x + 4y = -4\)

# Maintenant, les équations sont :

# 1. \(10x + 4y = -4\)
# 2. \(3x - 4y = 17\)

# Maintenant, si j'additionne ces deux équations, les termes avec y s'annuleront.

# Faisons l'addition :

# \( (10x + 4y) + (3x - 4y) = -4 + 17 \)

# Ce qui donne : \(13x = 13\)

# Donc, \(x = 1\).

# Maintenant que nous avons x, nous pouvons le substituer dans une des équations originales pour trouver y. Utilisons la première équation originale :

# \(5x + 2y = -2\)

# En substituant x = 1 :

# \(5(1) + 2y = -2\)

# Ce qui donne : \(5 + 2y = -2\)

# Soustraire 5 des deux côtés :

# ...

# Ce qui donne : \(5 + 2y = -2\)

# Soustraire 5 des deux côtés :

# \(2y = -2 - 5\)
# \(2y = -7\)

# Diviser par 2 :

# \(y = -\frac{7}{2}\)

# Donc, la solution est \(x = 1\) et \(y = -\frac{7}{2}\).

# $\boxed{x = 1,\ y = -\frac{7}{2}}$

# =============
# Answer
# =============

# Pour résoudre le système d'équations donné :

# 1. \(5x + 2y = -2\)
# 2. \(3x - 4y = 17\)

# Nous commençons par utiliser la méthode d'élimination pour éliminer une des variables. Nous choisissons d'éliminer \(y\) en rendant ses coefficients identiques en valeur absolue. Le coefficient de \(y\) dans la première équation est 2, et dans la deuxième, c'est -4. Le plus petit multiple commun de 2 et 4 est 4. Nous multiplions donc la première équation par 2 pour que le coefficient de \(y\) devienne 4.

# Faisons cela :

# 1. \(2 \times (5x + 2y) = 2 \times (-2)\)
#    Ce qui donne : \(10x + 4y = -4\)

# Maintenant, les équations sont :

# 1. \(10x + 4y = -4\)
# 2. \(3x - 4y = 17\)

# En additionnant ces deux équations, les termes avec \(y\) s'annuleront :

# \( (10x + 4y) + (3x - 4y) = -4 + 17 \)

# Ce qui donne : \(13x = 13\)

# Donc, \(x = 1\).

# Ensuite, nous substituons \(x = 1\) dans la première équation originale pour trouver \(y\) :

# \(5(1) + 2y = -2\)

# Ce qui donne : \(5 + 2y = -2\)

# Soustraire 5 des deux côtés :

# \(2y = -2 - 5\)
# \(2y = -7\)

# Diviser par 2 :

# \(y = -\frac{7}{2}\)

# Donc, la solution est \(x = 1\) et \(y = -\frac{7}{2}\).

# $\boxed{x = 1,\ y = -\frac{7}{2}}$
Transformers#

Make sure you install the latest Transformers version:

pip install --upgrade transformers[mistral-common]

This should also install mistral_common >= 1.8.5

To check:

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

Now you can use Transformers with Magistral:

Python snippet

from typing import Any
import torch

from huggingface_hub import hf_hub_download
from transformers import Mistral3ForConditionalGeneration
from transformers import AutoTokenizer


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]") :],
            },
        ],
    }


model_id = "mistralai/Magistral-Small-2509"

tokenizer = AutoTokenizer.from_pretrained(model_id, tokenizer_type="mistral")
model = Mistral3ForConditionalGeneration.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="auto"
).eval()

SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"
messages = [
    SYSTEM_PROMPT,
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "What action do you think I should take in this situation? List all the possible actions and explain why you think they are good or bad.",
            },
            {"type": "image_url", "image_url": {"url": image_url}},
        ],
    },
]

tokenized = tokenizer.apply_chat_template(messages, return_dict=True)

input_ids = torch.tensor(tokenized.input_ids, device="cuda").unsqueeze(0)
attention_mask = torch.tensor(tokenized.attention_mask, device="cuda").unsqueeze(0)
pixel_values = torch.tensor(
    tokenized.pixel_values[0], dtype=torch.bfloat16, device="cuda"
).unsqueeze(0)
image_sizes = torch.tensor(pixel_values.shape[-2:], device="cuda").unsqueeze(0)

with torch.inference_mode():
    output = model.generate(
        input_ids=input_ids,
        attention_mask=attention_mask,
        pixel_values=pixel_values,
        image_sizes=image_sizes,
    )[0]


decoded_output = tokenizer.decode(
    output[
        len(tokenized.input_ids) : (
            -1 if output[-1] == tokenizer.eos_token_id else len(output)
        )
    ]
)
print(decoded_output)
# [THINK]Alright, let's analyze the image carefully. It's a scene from a Pokémon game. The player is controlling Pikachu, which is at level 42 with full HP (83/83). The opponent is a Pidgey at level 17. The question is asking what action the player should take in this situation.

# First, let's list all the possible actions available. From the bottom of the screen, the options are:
# 1. FIGHT
# 2. BAG
# 3. POKÉMON
# 4. RUN

# Now, let's consider each option:

# 1. **FIGHT**: This means using Pikachu's moves to attack the Pidgey.
#    - Pros: Pikachu is at a higher level (42) compared to Pidgey (17), so it has a significant advantage. Pikachu's HP is full, so it's in good condition to fight. Fighting could potentially win the battle quickly.
#    - Cons: Even though Pikachu is stronger, there's always a risk of Pidgey landing a lucky hit or using a powerful move. However, given the level difference, this is less likely.

# 2. **BAG**: This means using items from the bag to help in the battle.
#    - Pros: Could use a potion to heal (though Pikachu is already at full HP), or use another item like a Poké Ball to try and catch Pidgey.
#    - Cons: Using items might be less efficient than just fighting, especially since Pikachu is already at full health. Also, if the goal is to catch Pidgey, using items to weaken it first might be useful, but the immediate advantage isn't clear.

# 3. **POKÉMON**: This means switching to another Pokémon from the team.
#    - Pros: If the player has another Pokémon that is stronger or has moves that are super effective against Pidgey, this could be useful.
#    - Cons: Pikachu is already at a significant level advantage and is at full health, so switching might not be necessary unless there's a strategic reason (e.g., leveling up another Pokémon).

# 4. **RUN**: This means attempting to flee from the battle.
#    - Pros: If the player wants to avoid the battle for some reason (e.g., saving time, or wanting to catch Pidgey without weakening it), running could be useful.
#    - Cons: If the goal is to catch or defeat Pidgey, running would not help in achieving that. Also, running might not always be successful, especially in a grass encounter where the chance to flee might be lower.

# Now, considering the context, if the goal is to defeat Pidgey, the best action would be to FIGHT since Pikachu is at a significant advantage in terms of level and health. Using the BAG could be useful if the goal is to catch Pidgey, as items like Poké Balls or berries could be used. Switching Pokémon doesn't seem necessary given Pikachu's current status. Running would only be useful if the player wants to avoid the battle, which seems unlikely if the goal is to progress in the game.

# However, the question asks for the best action without specifying the goal. Assuming the goal is to defeat Pidgey efficiently, the best action would be to FIGHT.

# But to list all possible actions and their pros and cons:

# 1. **FIGHT**
#    - Pros: High chance of winning quickly due to level and health advantage.
#    - Cons: Small risk of Pikachu getting damaged.

# 2. **BAG**
#    - Pros: Could use items to catch Pidgey or support Pikachu (though not necessary for healing).
#    - Cons: Might be less efficient if the goal is just to defeat Pidgey.

# 3. **POKÉMON**
#    - Pros: Could switch to another Pokémon for strategic reasons (e.g., type advantage, training a different Pokémon).
#    - Cons: Pikachu is already at a strong position, so switching might be unnecessary unless there's another goal.

# 4. **RUN**
#    - Pros: Escape the battle if needed.
#    - Cons: Does not contribute to progress if the goal is to defeat or catch Pidgey.

# Given that, if the goal is to defeat Pidgey, the best action is to FIGHT. If the goal is to catch Pidgey, using the BAG to throw a Poké Ball could be the best action.[/THINK]Here are the possible actions and their explanations:

# 1. **FIGHT**
#    - **Pros**: Pikachu is at a much higher level (42) compared to Pidgey (17) and has full HP. This makes it very likely to win quickly.
#    - **Cons**: There's always a small risk that Pidgey could land a lucky hit, but this is unlikely given the level difference.

# 2. **BAG**
#    - **Pros**: You could use items to catch Pidgey (e.g., Poké Ball) or support Pikachu (though Pikachu is already at full HP).
#    - **Cons**: Using items might be less efficient if the goal is simply to defeat Pidgey, as fighting directly could be quicker.

# 3. **POKÉMON**
#    - **Pros**: You could switch to another Pokémon that might have a type advantage or that you want to train.
#    - **Cons**: Pikachu is already in a strong position to defeat Pidgey, so switching might not be necessary unless there's another strategic reason.

# 4. **RUN**
#    - **Pros**: You can escape the battle if you need to, for example, if you want to preserve Pikachu's health for a tougher battle ahead.
#    - **Cons**: Running doesn't help you progress if your goal is to defeat or catch Pidgey. Additionally, the success rate for running might be lower in a grass encounter.

# Given these considerations, if your goal is to defeat Pidgey, the best action is likely to **FIGHT**, as Pikachu is at a significant advantage. If your goal is to catch Pidgey, using the **BAG** to throw a Poké Ball could be the best choice. If you're looking to train a different Pokémon, you might consider switching with **POKÉMON**, and if you need to preserve resources or Pikachu's health, **RUN** could be an option.

Files

15 files · 15.0 GBWeights 15.0 GB (safetensors). Sizes and sha256 from the signed manifest.
Hide all 15 files with their sha256
FileSizesha256
.gitattributes1.57 KB34448b82c17d60fec9b65b1f093c115ddbaadc04beb1b0140b6bfed2e012a930
README.md38.1 KB6defa1fb1f850b9b22b85c4d5f4837cab1ebe66298d8a817c0fe69fb60a3468e
chat_template.jinja4.69 KB817cbd470e260e28e69015cc34b1abfb2a7ab76500839c743ed0fee3d1a32c9e
config.json2.71 KB537b58e0c0b0821f9d967221f687899971aa79201d0dbd56ca08dd9d2c55a5c2
generation_config.json221 B59a3ff030f2e55005d6e41d90cf6ddf95026af905b9c171819d1b353cdeaee89
model-00001-of-00004.safetensors4.91 GB797a30293944e0d9d5895f90ec37197e214efcd6c00f6ab0e8819ca945517627
model-00002-of-00004.safetensors4.96 GBbe2cfaaaba5cac0748cc952940d5567abf86c6af9f233f30b2c28b33a0c4885d
model-00003-of-00004.safetensors3.81 GBe75f05fe0dfe32038b657e13de8b538a80156315680b558ea35e50f88829bc5e
model-00004-of-00004.safetensors1.34 GB487158e25eb90313f1f57a1894a260addf66dd6526984393fe8150abbe3c7a22
model.safetensors.index.json214 KB71c3b02b94d5ee3a270afef5f4e8c8586e157c32dab8b75641ed72a3eccd6703
preprocessor_config.json662 Be2281c57d4a9d3f5e43a1988a4b183792fa7172332ee33c7eba9583194b84cb4
processor_config.json189 B4ceef0d0cbf062ffd522d85fcbf5248c2db8659c589afcc47b2f26810e7d9a58
special_tokens_map.json21.4 KB1864ac9a603857f894d6cb47ac270f50685b05359128b8b9e59edf5ad56cdb49
tokenizer.json17.1 MB158915c7c928301e0d32f1da363e45c294d907bd4c64a8e855f0bd1a47b9e870
tokenizer_config.json203 KBbb0bbbbbe9fae009c439441a1c1a62c2759e930d97cf1a431a33d8ee81716881
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
5221529f5f pinned now2026-11-15 05:02 UTC15 files · 15.0 GB

Use it

$ fw get unsloth/Magistral-Small-2509-unsloth-bnb-4bit
# manifest sha256 0ee3c7fc6884… · 15.0 GB · 15 files
$ fw verify ./Magistral-Small-2509-unsloth-bnb-4bit unsloth/Magistral-Small-2509-unsloth-bnb-4bit
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/unsloth/Magistral-Small-2509-unsloth-bnb-4bit.json --dir ./Magistral-Small-2509-unsloth-bnb-4bit $ python3 fw.py verify ./Magistral-Small-2509-unsloth-bnb-4bit ./unsloth__Magistral-Small-2509-unsloth-bnb-4bit.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download unsloth/Magistral-Small-2509-unsloth-bnb-4bit --revision 5221529f5f581ba182b64977936491b2349be7a4 --local-dir ./Magistral-Small-2509-unsloth-bnb-4bit

License

Name
Apache License 2.0
Id
Apache-2.0
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-15
Text in
README.md
Text sha256
6defa1fb1f850b9b22b85c4d5f4837cab1ebe66298d8a817c0fe69fb60a3468e
Terms gate
No extra terms beyond the license.

Read the license text

Licenses are checked against the license table. A custom license passes only after a person reads it.

Provenance

From: unsloth <huggingface.co/unsloth>Newsgroups: alt.freeweights.mistralSubject: Magistral-Small-2509-unsloth-bnb-4bitDate: 2026-11-15Commit: 5221529Signed: 2DC28CC7B26A283B ✓
Upstream
unsloth/Magistral-Small-2509-unsloth-bnb-4bit
Pinned commit
5221529f5f581ba182b64977936491b2349be7a4
Manifest
Magistral-Small-2509-unsloth-bnb-4bit.json · .minisig
Manifest sha256
0ee3c7fc6884e0fcf1027b45e86d5a344fbad176db896e976bbc184c42c077e5
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 05:02 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/unsloth/Magistral-Small-2509-unsloth-bnb-4bit.json $ curl -O https://freeweights.allternit.com/manifests/unsloth/Magistral-Small-2509-unsloth-bnb-4bit.json.minisig $ minisign -Vm Magistral-Small-2509-unsloth-bnb-4bit.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

Model tree

Parent

mistralai/Magistral-Small-2509 · this model is a quantization of it

No model in the catalog names this one as its parent.

Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download unsloth/Magistral-Small-2509-unsloth-bnb-4bit
Open in Freeweights Studio

Pull, verify and run it in the Allternit app on Desktop, web or phone.

Signed manifest
minisign, key 2DC28CC7B26A283B. Magistral-Small-2509-unsloth-bnb-4bit.json. Provenance. Takedown history: none.
From: bartowski <huggingface.co/bartowski>Subject: alamios_Mistral-Small-3.1-DRAFT-0.5B-GGUF
From: microsoft <huggingface.co/microsoft>Subject: chatbench-mistral-7b
From: NousResearch <huggingface.co/NousResearch>Subject: DeepHermes-3-Mistral-24B-Preview
From: mistralai <huggingface.co/mistralai>Subject: Devstral-Small-2-24B-Instruct-2512
From: mlx-community <huggingface.co/mlx-community>Subject: Devstral-Small-2-24B-Instruct-2512-4bit
From: mlx-community <huggingface.co/mlx-community>Subject: Devstral-Small-2-24B-Instruct-2512-6bit