Magistral-Small-2509

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 significantly better performance than Magistral Small 1.1 as seen in the benchmark results.
• Better tone and persona: You should experience 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

Model	AIME24 pass@1	AIME25 pass@1	GPQA Diamond	Livecodebench (v5)
Magistral Medium 1.2	91.82%	83.48%	76.26%	75.00%
Magistral Medium 1.1	72.03%	60.99%	71.46%	59.35%
Magistral Medium 1.0	73.59%	64.95%	70.83%	59.36%
Magistral Small 1.2	86.14%	77.34%	70.07%	70.88%
Magistral Small 1.1	70.52%	62.03%	65.78%	59.17%
Magistral Small 1.0	70.68%	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.

Usage

The model can be used with the following frameworks.

Inference

• vllm (recommended): See below
• transformers: See below
• llama.cpp: See https://huggingface.co/mistralai/Magistral-Small-2509-GGUF
• Unsloth GGUFs: See https://huggingface.co/unsloth/Magistral-Small-2509-GGUF
• Kaggle: See https://www.kaggle.com/models/mistral-ai/magistral-small-2509
• LM Studio: See https://lmstudio.ai/models/mistralai/magistral-small-2509

Fine-tuning

• Axolotl: See https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral
• Unsloth: See https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms/magistral-how-to-run-and-fine-tune

vLLM (recommended)

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

Installation

Make sure you install the latest vLLM code:

pip install --upgrade vllm

Doing so should automatically install mistral_common >= 1.8.5.

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.

Serve model as follows:

vllm serve mistralai/Magistral-Small-2509 \
  --reasoning-parser mistral \
  --tokenizer_mode mistral --config_format mistral \
  --load_format mistral --tool-call-parser mistral \
  --enable-auto-tool-choice --limit-mm-per-prompt '{"image":10}' \
  --tensor-parallel-size 2

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") and chunk.choices[0].delta.reasoning_content is not None:
        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") and chunk.choices[0].delta.reasoning_content is not None:
        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") and chunk.choices[0].delta.reasoning_content is not None:
        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") and chunk.choices[0].delta.reasoning_content is not None:
        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 Transfo