README.md

metadata

language:
  - en
  - fr
  - de
  - es
  - it
  - pt
  - zh
  - ja
  - ru
  - ko
library_name: vllm
license: other
license_name: mrl
license_link: https://mistral.ai/licenses/MRL-0.1.md
inference: false
extra_gated_prompt: >-
  # Mistral AI Research License

  If You want to use a Mistral Model, a Derivative or an Output for any purpose
  that is not expressly authorized under this Agreement, You must request a
  license from Mistral AI, which Mistral AI may grant to You in Mistral AI's
  sole discretion. To discuss such a license, please contact Mistral AI via the
  website contact form: https://mistral.ai/contact/

  ## 1. Scope and acceptance

  **1.1. Scope of the Agreement.** This Agreement applies to any use,
  modification, or Distribution of any Mistral Model by You, regardless of the
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  **1.2. Acceptance.** By accessing, using, modifying, Distributing a Mistral
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  **1.3. Acceptance on behalf of a third-party.** If You accept this Agreement
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  ## 2. License

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  **2.3. Distribution of Derivatives made by or for You.** Subject to Section 3
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  ## 3. Limitations

  **3.1. Misrepresentation.** You must not misrepresent or imply, through any
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  **3.2. Usage Limitation.** You shall only use the Mistral Models, Derivatives
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  ## 4. Intellectual Property

  **4.1. Trademarks.** No trademark licenses are granted under this Agreement,
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  **4.2. Outputs.** We claim no ownership rights in and to the Outputs. You are
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  restrictions set out in Section 3 of this Agreement.

  **4.3. Derivatives.** By entering into this Agreement, You accept that any
  Derivatives that You may create or that may be created for You shall be
  subject to the restrictions set out in Section 3 of this Agreement.

  ## 5. Liability

  **5.1. Limitation of liability.** In no event, unless required by applicable
  law (such as deliberate and grossly negligent acts) or agreed to in writing,
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  ## 6. Warranty

  **6.1. Disclaimer.** Unless required by applicable law or prior agreed to by
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  kind of result or generate any kind of content. You are solely responsible for
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  ## 7. Termination

  **7.1. Term.** This Agreement is effective as of the date of your acceptance
  of this Agreement or access to the concerned Mistral Models or Derivatives and
  will continue until terminated in accordance with the following terms.

  **7.2. Termination.** Mistral AI may terminate this Agreement at any time if
  You are in breach of this Agreement. Upon termination of this Agreement, You
  must cease to use all Mistral Models and Derivatives and shall permanently
  delete any copy thereof. The following provisions, in their relevant parts,
  will survive any termination or expiration of this Agreement, each for the
  duration necessary to achieve its own intended purpose (e.g. the liability
  provision will survive until the end of the applicable limitation
  period):Sections 5 (Liability), 6(Warranty), 7 (Termination) and 8 (General
  Provisions).

  **7.3. Litigation.** If You initiate any legal action or proceedings against
  Us or any other entity (including a cross-claim or counterclaim in a lawsuit),
  alleging that the Model or a Derivative, or any part thereof, infringe upon
  intellectual property or other rights owned or licensable by You, then any
  licenses granted to You under this Agreement will immediately terminate as of
  the date such legal action or claim is filed or initiated.

  ## 8. General provisions

  **8.1. Governing laws.** This Agreement will be governed by the laws of
  France, without regard to choice of law principles, and the UN Convention on
  Contracts for the International Sale of Goods does not apply to this
  Agreement.

  **8.2. Competent jurisdiction.** The courts of Paris shall have exclusive
  jurisdiction of any dispute arising out of this Agreement.

  **8.3. Severability.** If any provision of this Agreement is held to be
  invalid, illegal or unenforceable, the remaining provisions shall be
  unaffected thereby and remain valid as if such provision had not been set
  forth herein.

  ## 9. Definitions

  "Agreement": means this Mistral AI Research License agreement governing the
  access, use, and Distribution of the Mistral Models, Derivatives and Outputs.

  "Derivative": means any (i) modified version of the Mistral Model (including
  but not limited to any customized or fine-tuned version thereof), (ii) work
  based on the Mistral Model, or (iii) any other derivative work thereof.

  "Distribution", "Distributing", "Distribute" or "Distributed": means
  supplying, providing or making available, by any means, a copy of the Mistral
  Models and/or the Derivatives as the case may be, subject to Section 3 of this
  Agreement.

  "Mistral AI", "We" or "Us": means Mistral AI, a French société par actions
  simplifiée registered in the Paris commercial registry under the number 952
  418 325, and having its registered seat at 15, rue des Halles, 75001 Paris.

  "Mistral Model": means the foundational large language model(s), and its
  elements which include algorithms, software, instructed checkpoints,
  parameters, source code (inference code, evaluation code and, if applicable,
  fine-tuning code) and any other elements associated thereto made available by
  Mistral AI under this Agreement, including, if any, the technical
  documentation, manuals and instructions for the use and operation thereof.

  "Research Purposes": means any use of a Mistral Model,  Derivative, or Output
  that is solely for (a) personal, scientific or academic research, and (b) for
  non-profit and non-commercial purposes, and not directly or indirectly
  connected to any commercial activities or business operations. For
  illustration purposes, Research Purposes does not include (1) any usage of the
  Mistral Model, Derivative or Output by individuals or contractors employed in
  or engaged by companies in the context of (a) their daily tasks, or (b) any
  activity (including but not limited to any testing or proof-of-concept) that
  is intended to generate revenue, nor (2) any Distribution by a commercial
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  payment or free of charge, in any medium or form, including but not limited to
  through a hosted or managed service (e.g. SaaS, cloud instances, etc.), or
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  "Outputs": means any content generated by the operation of the Mistral Models
  or the Derivatives from  a prompt (i.e., text instructions) provided by users.
  For the avoidance of doubt, Outputs do not include any components of a Mistral
  Models, such as any fine-tuned versions of the Mistral Models, the weights, or
  parameters.

  "You": means the individual or entity entering into this Agreement with
  Mistral AI.


  *Mistral AI processes your personal data below to provide the model and
  enforce its license. If you are affiliated with a commercial entity, we may
  also send you communications about our models. For more information on your
  rights and data handling, please see our <a
  href="https://mistral.ai/terms/">privacy policy</a>.*
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  Mistral AI processes your personal data below to provide the model and enforce
  its license. If you are affiliated with a commercial entity, we may also send
  you communications about our models. For more information on your rights and
  data handling, please see our <a href="https://mistral.ai/terms/">privacy
  policy</a>.
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Model Card for Pixtral-Large-Instruct-2411

Pixtral-Large-Instruct-2411 is a 124B multimodal model built on top of Mistral Large 2, i.e., Mistral-Large-Instruct-2407. Pixtral Large is the second model in our multimodal family and demonstrates frontier-level image understanding. Particularly, the model is able to understand documents, charts and natural images, while maintaining the leading text-only understanding of Mistral Large 2.

For more details about this model please refer to the Pixtral Large blog post and the Pixtral 12B blog post.

❗ The Transformers implementation is not yet working (see here), please use the vLLM implementation as shown below.

Key features

• Frontier-class multimodal performance
• State-of-the-art on MathVista, DocVQA, VQAv2
• Extends Mistral Large 2 without compromising text performance
• 123B multimodal decoder, 1B parameter vision encoder
• 128K context window: fits minimum of 30 high-resolution images

System Prompt Handling

We appreciate the feedback received from our community regarding our system prompt handling.
In response, we have implemented stronger support for system prompts.
To achieve optimal results, we recommend always including a system prompt that clearly outlines the bot's purpose, even if it is minimal.

Basic Instruct Template (V7)

<s>[SYSTEM_PROMPT] <system prompt>[/SYSTEM_PROMPT][INST] <user message>[/INST] <assistant response></s>[INST] <user message>[/INST]

Be careful with subtle missing or trailing white spaces!

Please make sure to use mistral-common as the source of truth

Metrics

Model	MathVista (CoT)	MMMU (CoT)	ChartQA (CoT)	DocVQA (ANLS)	VQAv2 (VQA Match)	AI2D (BBox)	MM MT-Bench
Pixtral Large (124B)	69.4	64.0	88.1	93.3	80.9	93.8	7.4
Gemini-1.5 Pro (measured)	67.8	66.3	83.8	92.3	70.6	94.6	6.8
GPT-4o (measured)	65.4	68.6	85.2	88.5	76.4	93.2	6.7
Claude-3.5 Sonnet (measured)	67.1	68.4	89.1	88.6	69.5	76.9	7.3
Llama-3.2 90B (measured)	49.1	53.7	70.8	85.7	67.0	-	5.5

Specific model versions evaluated: Claude-3.5 Sonnet (new) [Oct 24], Gemini-1.5 Pro (002) [Sep 24], GPT-4o (2024-08-06) [Aug 24].

See mistral-evals for open-source MM MT-Bench evaluation scripts.

Usage

The model can be used with the following frameworks

• vllm: See here

vLLM

We recommend using Pixtral-Large-Instruct-2411 with the vLLM library to implement production-ready inference pipelines with Pixtral-Large-Instruct-2411.

Installation

Make sure you install vLLM >= v0.6.4.post1:

pip install --upgrade vllm

Also make sure you have mistral_common >= 1.5.0 installed:

pip install --upgrade mistral_common

You can also make use of a ready-to-go docker image or on the docker hub.

Server (Image)

We recommend to use Pixtral-Large-Instruct-2411 in a server/client setting.

1. Spin up a server:

vllm serve mistralai/Pixtral-Large-Instruct-2411 --config-format mistral --load-format mistral --tokenizer_mode mistral --limit_mm_per_prompt 'image=10' --tensor-parallel-size 8

2. And ping the client:

import requests
import json
from huggingface_hub import hf_hub_download
from datetime import datetime, timedelta

url = "http://<your-server-url>:8000/v1/chat/completions"
headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}

model = "mistralai/Pixtral-Large-Instruct-2411"


def load_system_prompt(repo_id: str, filename: str) -> str:
    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
    with open(file_path, "r") as file:
        system_prompt = file.read()
    today = datetime.today().strftime("%Y-%m-%d")
    yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
    model_name = repo_id.split("/")[-1]
    return system_prompt.format(name=model_name, today=today, yesterday=yesterday)


SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")

image_url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png"

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Which of the depicted countries has the best food? Which the second and third and fourth? Name the country, its color on the map and one its city that is visible on the map, but is not the capital. Make absolutely sure to only name a city that can be seen on the map.",
            },
            {"type": "image_url", "image_url": {"url": image_url}},
        ],
    },
]

data = {"model": model, "messages": messages}

response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json()["choices"][0]["message"]["content"])
# Determining which country has the "best" food can be subjective and depends on personal preferences. However, based on popular culinary reputations, here are some countries known for their cuisine:

#1. **Italy** (Brown) - Known for its pasta, pizza, and diverse regional dishes.
#   - City: Milan

#2. **France** (Dark Brown) - Renowned for its fine dining, pastries, and wine.
#   - City: Lyon

#3. **Spain** (Yellow) - Famous for tapas, paella, and a variety of seafood dishes.
#   - City: Barcelona

#4. **Greece** (Yellow) - Known for its Mediterranean cuisine, including moussaka, souvlaki, and fresh seafood.
#   - City: Thessaloniki

#These rankings are based on general culinary reputations and can vary widely depending on individual tastes.

Server (Text-only)

You can also ping the client with a text-only example. The following example shows how the system prompt can be used to make sure the model always knows the current date.

import requests
import json
from huggingface_hub import hf_hub_download
from datetime import datetime, timedelta

url = "http://<your-server-url>:8000/v1/chat/completions"
headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}

model = "mistralai/Pixtral-Large-Instruct-2411"


def load_system_prompt(repo_id: str, filename: str) -> str:
    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
    with open(file_path, "r") as file:
        system_prompt = file.read()
    today = datetime.today().strftime("%Y-%m-%d")
    yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
    model_name = repo_id.split("/")[-1]
    return system_prompt.format(name=model_name, today=today, yesterday=yesterday)


SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")

image_url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png"

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {
        "role": "user",
        "content": "Without browsing the web, how many days ago was Mistral founded?"
    },
]

data = {"model": model, "messages": messages}

response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json()["choices"][0]["message"]["content"])
# Mistral AI was founded in April 2023. Since the current date is November 18, 2024, we can calculate the number of days between April 2023 and November 18, 2024.

#First, calculate the days from April 2023 to the end of 2023:
#- April: 27 days (30 - 3)
#- May: 31 days
#- June: 30 days
#- July: 31 days
#- August: 31 days
#- September: 30 days
#- October: 31 days
#- November: 30 days
#- December: 31 days

#Total days from April 2023 to December 31, 2023: 27 + 31 + 30 + 31 + 31 + 30 + 31 + 30 + 31 = 272 days

#Next, calculate the days from January 1, 2024, to November 18, 2024:
#- January: 31 days
#- February: 29 days (2024 is a leap year)
#- March: 31 days
#- April: 30 days
#- May: 31 days
#- June: 30 days
#- July: 31 days
#- August: 31 days
#- September: 30 days
#- October: 31 days
#- November: 18 days

#Total days from January 1, 2024, to November 18, 2024: 31 + 29 + 31 + 30 + 31 + 30 + 31 + 31 + 30 + 31 + 18 = 323 days

#Adding the two periods together:
#272 days (from April 2023 to December 2023) + 323 days (from January 2024 to November 18, 2024) = 595 days

#Therefore, Mistral AI was founded 595 days ago from November 18, 2024.

Offline Example

from vllm import LLM
from vllm.sampling_params import SamplingParams
from huggingface_hub import hf_hub_download
from datetime import datetime, timedelta

model_name = "mistralai/Pixtral-Large-Instruct-2411"

def load_system_prompt(repo_id: str, filename: str) -> str:
    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
    with open(file_path, 'r') as file:
        system_prompt = file.read()
    today = datetime.today().strftime('%Y-%m-%d')
    yesterday = (datetime.today() - timedelta(days=1)).strftime('%Y-%m-%d')
    model_name = repo_id.split("/")[-1]
    return system_prompt.format(name=model_name, today=today, yesterday=yesterday)

SYSTEM_PROMPT = load_system_prompt(model_name, "SYSTEM_PROMPT.txt")

image_url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png"

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Which of the depicted countries has the best food? Which the second and third and fourth? Name the country, its color on the map and one its city that is visible on the map, but is not the capital. Make absolutely sure to only name a city that can be seen on the map.",
            },
            {"type": "image_url", "image_url": {"url": image_url}},
        ],
    },
]

sampling_params = SamplingParams(max_tokens=512)

# note that running this model on GPU requires over 300 GB of GPU RAM
llm = LLM(model=model_name, config_format="mistral", load_format="mistral", tokenizer_mode="mistral", tensor_parallel_size=8, limit_mm_per_prompt={"image": 4})

outputs = llm.chat(messages, sampling_params=sampling_params)

print(outputs[0].outputs[0].text)

The Mistral AI Team

Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Alok Kothari, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Augustin Garreau, Austin Birky, Bam4d, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Carole Rambaud, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Diogo Costa, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gaspard Blanchet, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Hichem Sattouf, Ian Mack, Jean-Malo Delignon, Jessica Chudnovsky, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickaël Seznec, Nicolas Schuhl, Niklas Muhs, Olivier de Garrigues, Patrick von Platen, Paul Jacob, Pauline Buche, Pavan Kumar Reddy, Perry Savas, Pierre Stock, Romain Sauvestre, Sagar Vaze, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibault Schueller, Thibaut Lavril, Thomas Wang, Théophile Gervet, Timothée Lacroix, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall