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--- |
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language: |
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- en |
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- fr |
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- de |
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- es |
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- it |
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- pt |
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- zh |
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- ja |
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- ru |
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- ko |
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license: other |
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license_name: mrl |
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inference: false |
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license_link: https://mistral.ai/licenses/MRL-0.1.md |
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extra_gated_prompt: >- |
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# Mistral AI Research License |
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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/ |
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**1.1. Scope of the Agreement.** This Agreement applies to any use, modification, or Distribution of any Mistral Model by You, regardless of the source You obtained a copy of such Mistral Model. |
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**1.2. Acceptance.** By accessing, using, modifying, Distributing a Mistral Model, or by creating, using or distributing a Derivative of the Mistral Model, You agree to be bound by this Agreement. |
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**2.1. Grant of rights**. Subject to Section 3 below, Mistral AI hereby grants You a non-exclusive, royalty-free, worldwide, non-sublicensable, non-transferable, limited license to use, copy, modify, and Distribute under the conditions provided in Section 2.2 below, the Mistral Model and any Derivatives made by or for Mistral AI and to create Derivatives of the Mistral Model. |
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**2.2. Distribution of Mistral Model and Derivatives made by or for Mistral AI.** Subject to Section 3 below, You may Distribute copies of the Mistral Model and/or Derivatives made by or for Mistral AI, under the following conditions: |
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You must make available a copy of this Agreement to third-party recipients of the Mistral Models and/or Derivatives made by or for Mistral AI you Distribute, it being specified that any rights to use the Mistral Models and/or Derivatives made by or for Mistral AI shall be directly granted by Mistral AI to said third-party recipients pursuant to the Mistral AI Research License agreement executed between these parties; |
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**2.3. Distribution of Derivatives made by or for You.** Subject to Section 3 below, You may Distribute any Derivatives made by or for You under additional or different terms and conditions, provided that: |
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In any event, the use and modification of Mistral Model and/or Derivatives made by or for Mistral AI shall remain governed by the terms and conditions of this Agreement; |
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You include in any such Derivatives made by or for You prominent notices stating that You modified the concerned Mistral Model; and |
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Any terms and conditions You impose on any third-party recipients relating to Derivatives made by or for You shall neither limit such third-party recipients' use of the Mistral Model or any Derivatives made by or for Mistral AI in accordance with the Mistral AI Research License nor conflict with any of its terms and conditions. |
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**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. |
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**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, shall Mistral AI be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this Agreement or out of the use or inability to use the Mistral Models and Derivatives (including but not limited to damages for loss of data, loss of goodwill, loss of expected profit or savings, work stoppage, computer failure or malfunction, or any damage caused by malware or security breaches), even if Mistral AI has been advised of the possibility of such damages. |
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**6.1. Disclaimer.** Unless required by applicable law or prior agreed to by Mistral AI in writing, Mistral AI provides the Mistral Models and Derivatives on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. Mistral AI does not represent nor warrant that the Mistral Models and Derivatives will be error-free, meet Your or any third party's requirements, be secure or will allow You or any third party to achieve any kind of result or generate any kind of content. You are solely responsible for determining the appropriateness of using or Distributing the Mistral Models and Derivatives and assume any risks associated with Your exercise of rights under this Agreement. |
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**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. |
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**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). |
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**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. |
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**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. |
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**8.2. Competent jurisdiction.** The courts of Paris shall have exclusive jurisdiction of any dispute arising out of this Agreement. |
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**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. |
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"Agreement": means this Mistral AI Research License agreement governing the access, 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 not limited to any customized or fine-tuned version thereof), (ii) work based on the Mistral Model, or (iii) any other derivative work thereof. |
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"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. |
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"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. |
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"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. |
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"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 entity of the Mistral Model, Derivative or Output whether in return for 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 behind a software layer. |
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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. |
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"You": means the individual or entity entering into this Agreement with Mistral AI. |
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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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extra_gated_fields: |
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First Name: text |
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Last Name: text |
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Country: country |
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Affiliation: text |
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Job title: text |
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I understand that I can only use the model, any derivative versions and their outputs for non-commercial research purposes: checkbox |
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? By clicking Submit below I accept the terms of the license and acknowledge that |
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the information I provide will be collected stored processed and shared in accordance |
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with the Mistral Privacy Policy |
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geo: ip_location |
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extra_gated_description: >- |
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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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extra_gated_button_content: Submit |
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library_name: vllm |
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--- |
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# Model Card for Mistral-Large-Instruct-2411 |
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Mistral-Large-Instruct-2411 is an advanced dense Large Language Model (LLM) of 123B parameters with state-of-the-art reasoning, knowledge and coding capabilities extending [Mistral-Large-Instruct-2407](https://huggingface.co/mistralai/Mistral-Large-Instruct-2407) with better Long Context, Function Calling and System Prompt. |
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## Key features |
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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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- **Agent-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 non-commercial usages. |
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- **Large Context:** A large 128k context window. |
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- **Robust Context Adherence:** Ensures strong adherence for RAG and large context applications. |
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- **System Prompt:** Maintains strong adherence and support for more reliable system prompts. |
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### System Prompt |
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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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## 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 this model with the [vLLM library](https://github.com/vllm-project/vllm) |
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to implement production-ready inference pipelines. |
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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 |
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We recommand that you use Mistral-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/Mistral-Large-Instruct-2411 --tokenizer_mode mistral --config_format mistral --load_format mistral --tensor_parallel_size 8 |
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``` |
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**Note:** Running Mistral-Large-Instruct-2411 on GPU requires over 300 GB of GPU RAM. |
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2. To ping the client you can use a simple Python snippet. |
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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>:8000/v1/chat/completions" |
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headers = {"Content-Type": "application/json", "Authorization": "Bearer token"} |
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model = "mistralai/Mistral-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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messages = [ |
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{"role": "system", "content": SYSTEM_PROMPT + "\n\nThink step by step. You're a math genius."}, |
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{ |
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"role": "user", |
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"content": "Think of four random numbers. Then add, substract or multiply them so that the solution is 10. If it's not possible, say it." |
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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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# Sure, let's start by thinking of four random numbers. For example, let's take 3, 5, 2, and 1. |
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# |
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# Now, we need to find a combination of addition, subtraction, or multiplication that results in 10. |
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# Let's try: |
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# \[ 3 + 5 + 2 - 1 = 9 \] |
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# This doesn't work. Let's try another combination: |
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# \[ 3 \times 2 + 5 - 1 = 6 + 5 - 1 = 10 \] |
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# This works! So, with the numbers 3, 5, 2, and 1, we can achieve the result 10 by performing the operations \( 3 \times 2 + 5 - 1 \). |
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``` |
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### Offline |
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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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model_name = "mistralai/Mistral-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_name, "SYSTEM_PROMPT.txt") + "\n\nThink step by step. You're a math genius." |
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user_prompt = "Without browsing the web, how many days ago was Mistral founded?" |
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messages = [ |
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{ |
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"role": "system", |
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"content": SYSTEM_PROMPT |
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}, |
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{ |
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"role": "user", |
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"content": user_prompt |
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}, |
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] |
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# note that running this model on GPU requires over 300 GB of GPU RAM |
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llm = LLM(model=model_name, tokenizer_mode="mistral", tensor_parallel_size=8) |
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sampling_params = SamplingParams(max_tokens=512) |
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outputs = llm.chat(messages, sampling_params=sampling_params) |
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print(outputs[0].outputs[0].text) |
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# I don't have real-time web browsing capabilities or access to current data, but I can help you calculate the number of days based on the information I have. |
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# |
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#Mistral AI was founded in April 2023. To determine how many days ago that was from today's date, November 18, 2024, we need to calculate the total number of days between April 2023 and November 2024. |
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# |
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#Here's the step-by-step calculation: |
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# |
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#1. **Days from April 2023 to December 2023:** |
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# - April 2023: 30 days (April has 30 days) |
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# - May 2023: 31 days |
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# - June 2023: 30 days |
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# - July 2023: 31 days |
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# - August 2023: 31 days |
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# - September 2023: 30 days |
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# - October 2023: 31 days |
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# - November 2023: 30 days |
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# - December 2023: 31 days |
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# |
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# Total days in 2023 from April to December = 30 + 31 + 30 + 31 + 31 + 30 + 31 + 30 + 31 = 275 days |
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# |
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#2. **Days from January 2024 to November 18, 2024:** |
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# - January 2024: 31 days |
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# - February 2024: 29 days (2024 is a leap year) |
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# - March 2024: 31 days |
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# - April 2024: 30 days |
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# - May 2024: 31 days |
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# - June 2024: 30 days |
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# - July 2024: 31 days |
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# - August 2024: 31 days |
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# - September 2024: 30 days |
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# - October 2024: 31 days |
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# - November 2024 (up to the 18th): 18 days |
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# |
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# Total days in 2024 from January to November 18 = 31 + 29 + 31 + 30 + 31 + 30 + 31 + 31 + 30 + 31 + 18 = 323 days |
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# |
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#3. **Total days from April 2023 to November 18, 2024:** |
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# Total days = 275 days (2023) + 323 days (2024) = 598 days |
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# |
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#Therefore, Mistral AI was founded 598 days ago from today's date, November 18, 2024. |
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``` |
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### Improved Function Calling |
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Mistral-Large-2411 has much improved function calling capabilities that are fully supported |
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using [`mistral_common >= 1.5.0`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.0) and [`vLLM >= v0.6.4.post1`](https://github.com/vllm-project/vllm/releases/tag/v0.6.4.post1). |
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Make sure to serve the model with the following flags in vLLM: |
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``` |
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vllm serve mistralai/Pixtral-Large-Instruct-2411 --tokenizer_mode mistral --tensor-parallel-size 8 --tool-call-parser mistral --enable-auto-tool-choice |
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``` |
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<details> |
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<summary>Example</summary> |
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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>:8000/v1/chat/completions" |
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headers = {"Content-Type": "application/json", "Authorization": "Bearer token"} |
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model = "mistralai/Mistral-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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tools = [ |
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{ |
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"type": "function", |
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"function": { |
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"name": "get_current_weather", |
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"description": "Get the current weather in a given location", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"city": { |
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"type": "string", |
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"description": "The city to find the weather for, e.g. 'San Francisco'", |
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}, |
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"state": { |
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"type": "string", |
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"description": "The state abbreviation, e.g. 'CA' for California", |
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}, |
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"unit": { |
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"type": "string", |
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"description": "The unit for temperature", |
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"enum": ["celsius", "fahrenheit"], |
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}, |
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}, |
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"required": ["city", "state", "unit"], |
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}, |
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}, |
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}, |
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{ |
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"type": "function", |
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"function": { |
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"name": "rewrite", |
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"description": "Rewrite a given text for improved clarity", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"text": { |
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"type": "string", |
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"description": "The input text to rewrite", |
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} |
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}, |
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}, |
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}, |
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}, |
|
] |
|
|
|
messages = [ |
|
{"role": "system", "content": SYSTEM_PROMPT}, |
|
{ |
|
"role": "user", |
|
"content": "Could you please make the below article more concise?\n\nOpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership.", |
|
}, |
|
{ |
|
"role": "assistant", |
|
"content": "", |
|
"tool_calls": [ |
|
{ |
|
"id": "bbc5b7ede", |
|
"type": "function", |
|
"function": { |
|
"name": "rewrite", |
|
"arguments": '{"text": "OpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership."}', |
|
}, |
|
} |
|
], |
|
}, |
|
{ |
|
"role": "tool", |
|
"content": '{"action":"rewrite","outcome":"OpenAI is a FOR-profit company."}', |
|
"tool_call_id": "bbc5b7ede", |
|
"name": "rewrite", |
|
}, |
|
{ |
|
"role": "assistant", |
|
"content": "---\n\nOpenAI is a FOR-profit company.", |
|
}, |
|
{ |
|
"role": "user", |
|
"content": "Can you tell me what the temperature will be in Dallas, in Fahrenheit?", |
|
}, |
|
] |
|
|
|
data = {"model": model, "messages": messages, "tools": tools} |
|
|
|
response = requests.post(url, headers=headers, data=json.dumps(data)) |
|
print(response.json()["choices"][0]["message"]["tool_calls"]) |
|
# [{'id': '8PdihwL6d', 'type': 'function', 'function': {'name': 'get_current_weather', 'arguments': '{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}'}}] |
|
``` |
|
|
|
</details> |
|
|
|
## 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 |
