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--- |
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language: |
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library_name: vllm |
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license: other |
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license_name: mrl |
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license_link: https://mistral.ai/licenses/MRL-0.1.md |
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inference: false |
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extra_gated_prompt: '# Mistral AI Research License |
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## 1. Scope and acceptance |
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**1.1. Scope of the Agreement.** This Agreement applies to any use, modification, |
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## 2. License |
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## 3. Limitations |
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## 5. Liability |
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## 6. Warranty |
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**6.1. Disclaimer.** Unless required by applicable law or prior agreed to by Mistral |
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## 7. Termination |
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**7.1. Term.** This Agreement is effective as of the date of your acceptance of |
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**7.2. Termination.** Mistral AI may terminate this Agreement at any time if You |
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and 8 (General Provisions). |
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**7.3. Litigation.** If You initiate any legal action or proceedings against Us |
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## 8. General provisions |
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**8.1. Governing laws.** This Agreement will be governed by the laws of France, |
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without regard to choice of law principles, and the UN Convention on Contracts for |
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**8.2. Competent jurisdiction.** The courts of Paris shall have exclusive jurisdiction |
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**8.3. Severability.** If any provision of this Agreement is held to be invalid, |
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remain valid as if such provision had not been set forth herein. |
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## 9. Definitions |
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"Agreement": means this Mistral AI Research License agreement governing the access, |
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use, and Distribution of the Mistral Models, Derivatives and Outputs. |
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"Derivative": means any (i) modified version of the Mistral Model (including but |
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"Distribution", "Distributing", "Distribute" or "Distributed": means supplying, |
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"Mistral AI", "We" or "Us": means Mistral AI, a French société par actions simplifiée |
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its registered seat at 15, rue des Halles, 75001 Paris. |
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"Mistral Model": means the foundational large language model(s), and its elements |
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if any, the technical documentation, manuals and instructions for the use and operation |
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"Research Purposes": means any use of a Mistral Model, Derivative, or Output that |
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"Outputs": means any content generated by the operation of the Mistral Models or |
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the avoidance of doubt, Outputs do not include any components of a Mistral Models, |
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"You": means the individual or entity entering into this Agreement with Mistral |
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AI. |
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*Mistral AI processes your personal data below to provide the model and enforce |
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please see our <a href="https://mistral.ai/terms/">privacy policy</a>.*' |
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--- |
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|
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# Model Card for Pixtral-Large-Instruct-2411 |
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Pixtral-Large-Instruct-2411 is a 124B multimodal model built on top of Mistral Large 2, i.e., [Mistral-Large-Instruct-2407](https://huggingface.co/mistralai/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. |
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For more details about this model please refer to the [Pixtral Large blog post](https://mistral.ai/news/pixtral-large/) and the [Pixtral 12B blog post](https://mistral.ai/news/pixtral-12b/). |
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> [!IMPORTANT] |
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> ❗ |
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> The Transformers implementation is not yet working (see [here](https://huggingface.co/mistralai/Pixtral-Large-Instruct-2411/discussions/3#673b8bfe55cb1761c8d50ce2)), please use the vLLM implementation |
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> as shown below. |
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## Key features |
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- Frontier-class multimodal performance |
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- State-of-the-art on MathVista, DocVQA, VQAv2 |
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- Extends Mistral Large 2 without compromising text performance |
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- 123B multimodal decoder, 1B parameter vision encoder |
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- 128K context window: fits minimum of 30 high-resolution images |
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<!-- |
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- **Multi-lingual by design:** Dozens of languages supported, including English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch and Polish. |
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- **Proficient in coding:** Trained on 80+ coding languages such as Python, Java, C, C++, Javacsript, and Bash. Also trained on more specific languages such as Swift and Fortran. |
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- **Agentic-centric:** Best-in-class agentic capabilities with native function calling and JSON outputting. |
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- **Advanced Reasoning:** State-of-the-art mathematical and reasoning capabilities. |
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- **Mistral Research License:** Allows usage and modification for research and non-commercial usages. |
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- **Large Context:** A large 128k context window. |
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- **System Prompt:** Maintains strong adherence and support for more reliable system prompts. |
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- **Vision:** A 1B parameter Vision Encoder achieving SOTA vision capabilities. |
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--> |
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### System Prompt Handling |
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We appreciate the feedback received from our community regarding our system prompt handling. |
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In response, we have implemented stronger support for system prompts. |
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To achieve optimal results, we recommend always including a system prompt that clearly outlines the bot's purpose, even if it is minimal. |
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### Basic Instruct Template (V7) |
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``` |
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<s>[SYSTEM_PROMPT] <system prompt>[/SYSTEM_PROMPT][INST] <user message>[/INST] <assistant response></s>[INST] <user message>[/INST] |
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``` |
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**Be careful with subtle missing or trailing white spaces!** |
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*Please make sure to use [mistral-common](https://github.com/mistralai/mistral-common) as the source of truth* |
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## Metrics |
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| Model | MathVista (CoT) | MMMU (CoT) | ChartQA (CoT) | DocVQA (ANLS) | VQAv2 (VQA Match) | AI2D (BBox) | MM MT-Bench | |
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|:----------------------------:|:---------------:|:----------:|:-------------:|:--------------:|:-----------------:|:-----------:|:-----------:| |
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| **Pixtral Large (124B)** | **<u>69.4</u>** | 64.0 | 88.1 | **<u>93.3</u>**| **<u>80.9</u>** | 93.8 | **<u>7.4</u>**| |
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| Gemini-1.5 Pro (measured) | 67.8 | 66.3 | 83.8 | 92.3 | 70.6 | **<u>94.6</u>**| 6.8 | |
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| GPT-4o (measured) | 65.4 | **<u>68.6</u>**| 85.2 | 88.5 | 76.4 | 93.2 | 6.7 | |
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| Claude-3.5 Sonnet (measured) | 67.1 | 68.4 | **<u>89.1</u>**| 88.6 | 69.5 | 76.9 | 7.3 | |
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| Llama-3.2 90B (measured) | 49.1 | 53.7 | 70.8 | 85.7 | 67.0 | - | 5.5 | |
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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]. |
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See [mistral-evals](https://github.com/mistralai/mistral-evals) for open-source MM MT-Bench evaluation scripts. |
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## Usage |
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The model can be used with the following frameworks |
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- [`vllm`](https://github.com/vllm-project/vllm): See [here](#vLLM) |
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### vLLM |
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We recommend using Pixtral-Large-Instruct-2411 with the [vLLM library](https://github.com/vllm-project/vllm) |
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to implement production-ready inference pipelines with Pixtral-Large-Instruct-2411. |
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**_Installation_** |
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Make sure you install [`vLLM >= v0.6.4.post1`](https://github.com/vllm-project/vllm/releases/tag/v0.6.4.post1): |
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``` |
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pip install --upgrade vllm |
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``` |
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Also make sure you have [`mistral_common >= 1.5.0`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.0) installed: |
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``` |
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pip install --upgrade mistral_common |
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``` |
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You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-55a88146a4da0b6e193431b5b1d3492dfd7bebdc16919df4d031273e85a6157c?context=explore). |
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#### Server (Image) |
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We recommend to use Pixtral-Large-Instruct-2411 in a server/client setting. |
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1. Spin up a server: |
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``` |
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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 |
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``` |
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2. And ping the client: |
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```py |
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import requests |
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import json |
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from huggingface_hub import hf_hub_download |
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from datetime import datetime, timedelta |
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url = "http://<your-server-url>:8000/v1/chat/completions" |
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headers = {"Content-Type": "application/json", "Authorization": "Bearer token"} |
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model = "mistralai/Pixtral-Large-Instruct-2411" |
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def load_system_prompt(repo_id: str, filename: str) -> str: |
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file_path = hf_hub_download(repo_id=repo_id, filename=filename) |
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with open(file_path, "r") as file: |
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system_prompt = file.read() |
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today = datetime.today().strftime("%Y-%m-%d") |
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yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d") |
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model_name = repo_id.split("/")[-1] |
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return system_prompt.format(name=model_name, today=today, yesterday=yesterday) |
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SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt") |
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image_url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png" |
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messages = [ |
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{"role": "system", "content": SYSTEM_PROMPT}, |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "text", |
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"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.", |
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}, |
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{"type": "image_url", "image_url": {"url": image_url}}, |
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], |
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}, |
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] |
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data = {"model": model, "messages": messages} |
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response = requests.post(url, headers=headers, data=json.dumps(data)) |
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print(response.json()["choices"][0]["message"]["content"]) |
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# 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: |
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#1. **Italy** (Brown) - Known for its pasta, pizza, and diverse regional dishes. |
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# - City: Milan |
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#2. **France** (Dark Brown) - Renowned for its fine dining, pastries, and wine. |
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# - City: Lyon |
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#3. **Spain** (Yellow) - Famous for tapas, paella, and a variety of seafood dishes. |
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# - City: Barcelona |
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#4. **Greece** (Yellow) - Known for its Mediterranean cuisine, including moussaka, souvlaki, and fresh seafood. |
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# - City: Thessaloniki |
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#These rankings are based on general culinary reputations and can vary widely depending on individual tastes. |
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``` |
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#### Server (Text-only) |
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You can also ping the client with a text-only example. The following example |
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shows how the system prompt can be used to make sure the model always knows |
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the current date. |
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|
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```py |
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import requests |
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import json |
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from huggingface_hub import hf_hub_download |
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from datetime import datetime, timedelta |
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url = "http://<your-server-url>:8000/v1/chat/completions" |
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headers = {"Content-Type": "application/json", "Authorization": "Bearer token"} |
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model = "mistralai/Pixtral-Large-Instruct-2411" |
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def load_system_prompt(repo_id: str, filename: str) -> str: |
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file_path = hf_hub_download(repo_id=repo_id, filename=filename) |
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with open(file_path, "r") as file: |
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system_prompt = file.read() |
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today = datetime.today().strftime("%Y-%m-%d") |
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yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d") |
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model_name = repo_id.split("/")[-1] |
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return system_prompt.format(name=model_name, today=today, yesterday=yesterday) |
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SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt") |
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image_url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png" |
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messages = [ |
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{"role": "system", "content": SYSTEM_PROMPT}, |
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{ |
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"role": "user", |
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"content": "Without browsing the web, how many days ago was Mistral founded?" |
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}, |
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] |
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data = {"model": model, "messages": messages} |
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response = requests.post(url, headers=headers, data=json.dumps(data)) |
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print(response.json()["choices"][0]["message"]["content"]) |
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# 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. |
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|
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#First, calculate the days from April 2023 to the end of 2023: |
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#- April: 27 days (30 - 3) |
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#- May: 31 days |
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#- June: 30 days |
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#- July: 31 days |
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#- August: 31 days |
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#- September: 30 days |
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#- October: 31 days |
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#- November: 30 days |
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#- December: 31 days |
|
|
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#Total days from April 2023 to December 31, 2023: 27 + 31 + 30 + 31 + 31 + 30 + 31 + 30 + 31 = 272 days |
|
|
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#Next, calculate the days from January 1, 2024, to November 18, 2024: |
|
#- January: 31 days |
|
#- February: 29 days (2024 is a leap year) |
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#- March: 31 days |
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#- April: 30 days |
|
#- May: 31 days |
|
#- June: 30 days |
|
#- July: 31 days |
|
#- August: 31 days |
|
#- September: 30 days |
|
#- October: 31 days |
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#- November: 18 days |
|
|
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#Total days from January 1, 2024, to November 18, 2024: 31 + 29 + 31 + 30 + 31 + 30 + 31 + 31 + 30 + 31 + 18 = 323 days |
|
|
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#Adding the two periods together: |
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#272 days (from April 2023 to December 2023) + 323 days (from January 2024 to November 18, 2024) = 595 days |
|
|
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#Therefore, Mistral AI was founded 595 days ago from November 18, 2024. |
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``` |
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|
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#### Offline Example |
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```py |
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from vllm import LLM |
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from vllm.sampling_params import SamplingParams |
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from huggingface_hub import hf_hub_download |
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from datetime import datetime, timedelta |
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|
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model_name = "mistralai/Pixtral-Large-Instruct-2411" |
|
|
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def load_system_prompt(repo_id: str, filename: str) -> str: |
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file_path = hf_hub_download(repo_id=repo_id, filename=filename) |
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with open(file_path, 'r') as file: |
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system_prompt = file.read() |
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today = datetime.today().strftime('%Y-%m-%d') |
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yesterday = (datetime.today() - timedelta(days=1)).strftime('%Y-%m-%d') |
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model_name = repo_id.split("/")[-1] |
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return system_prompt.format(name=model_name, today=today, yesterday=yesterday) |
|
|
|
SYSTEM_PROMPT = load_system_prompt(model_name, "SYSTEM_PROMPT.txt") |
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|
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image_url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png" |
|
|
|
messages = [ |
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{"role": "system", "content": SYSTEM_PROMPT}, |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "text", |
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"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.", |
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}, |
|
{"type": "image_url", "image_url": {"url": image_url}}, |
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], |
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}, |
|
] |
|
|
|
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}) |
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|
|
outputs = llm.chat(messages, sampling_params=sampling_params) |
|
|
|
print(outputs[0].outputs[0].text) |
|
``` |
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|
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## The Mistral AI Team |
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|
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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 |
