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Mistral Large Instruct 2411
Overview :
Mistral-Large-Instruct-2411 is a large language model provided by Mistral AI, featuring 123 billion parameters with cutting-edge abilities in reasoning, knowledge, and coding. It supports multiple languages and has been trained in over 80 programming languages, including but not limited to Python, Java, C, C++, and more. With a focus on agent-based interactions, it offers native function calls and JSON output capabilities, making it an ideal choice for research and development.
Target Users :
The target audience includes researchers, developers, and data scientists who need a large language model capable of handling complex tasks, providing advanced reasoning, and coding support. Mistral-Large-Instruct-2411's multilingual capabilities and training in programming languages make it an ideal tool for developers worldwide.
Use Cases
Researchers utilize Mistral-Large-Instruct-2411 to process and analyze large-scale multilingual datasets.
Developers leverage its programming capabilities to create and optimize software applications.
Data scientists harness its reasoning abilities to build predictive models and conduct data analysis.
Features
Supports dozens of languages, including English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch, and Polish.
Trained on over 80 programming languages, including Python, Java, C, C++, JavaScript, Bash, as well as specific languages such as Swift and Fortran.
Agent-centered with native function call and JSON output capabilities.
Possesses state-of-the-art mathematical and reasoning abilities.
Adheres to the Mistral research license, allowing for non-commercial use and modification.
Features a large context window of 128k.
Ensures strong adherence for RAG and large context applications.
Supports system prompts that maintain strong reliability.
How to Use
1. Install the vLLM and mistral_common libraries, ensuring the versions meet the requirements.
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2. Start the model service using the vLLM library, configuring the necessary parameters.
3. Write system prompts and user messages to construct the request data.
4. Send the data to the model service endpoint via HTTP requests.
5. Process the response returned by the model to obtain the desired output.
6. Deploy the model to a server or client environment as needed.
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