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Table Of Contents
- Introduction
- What is Codestral ?
- Self-Correcting Code Assistants With Codestral By Mistral AI
- Summary
- References
Introduction
In this post, we discuss about Self-Correcting Code Assistants With Codestral By Mistral AI. Which is just releasd by Mistral AI.
What is Codestral ?
Codestral designs to streamline code generation tasks, serving as a powerful tool. It provides developers with a shared instruction and completion API endpoint, facilitating seamless interaction with code.
With expertise in over 80 programming languages, including Python, Java, C, C++, JavaScript, and Bash, Codestral offers comprehensive support across diverse coding environments and projects.
Codestral empowers developers to efficiently complete coding functions, write tests, and fill in partial code.
Codestral not only saves time and effort but also enhances developers’ coding proficiency while mitigating the risk of errors and bugs.
Whether working on general-purpose or specialized languages like Swift and Fortran, Codestral remains an invaluable asset for software development endeavors.
Self-Correcting Code Assistants With Codestral By Mistral AI
A model fluent in 80+ programming languages
- Codestral trains on a diverse dataset of 80+ programming languages, including the most popular ones like Python, Java, C, C++, JavaScript, and Bash.
- Codestral performs well on more specific ones like Swift and Fortran. This broad language base ensures Codestral can assist developers in various coding environments and projects.
- Codestral saves developers time and effort: it can complete coding functions, write tests, and complete any partial code using a fill-in-the-middle mechanism.
- Interacting with Codestral will help level up the developer’s coding game and reduce the risk of errors and bugs.
Code Generation Performance Of Codestral
Performance.
- As a 22B model, Codestral sets a new standard on the performance/latency space for code generation compared to previous models used for coding.
Python.
- They use four benchmarks: HumanEval pass@1, MBPP sanitised pass@1 to evaluate Codestral’s Python code generation ability, CruxEval to evaluate Python output prediction, and RepoBench EM to evaluate Codestral’s Long-Range Repository-Level Code Completion.
SQL.
- To evaluate Codestral’s performance in SQL, they used the Spider benchmark.
To get started with Codestral, follow these steps:
Download and test Codestral.
- Codestral, a 22B open-weight model, is licensed under the new Mistral AI Non-Production License, allowing us to use it for research and testing purposes.
- You can download Codestral on HuggingFace.
Talk to Codestral on le Chat
- They are exposing an instructed version of Codestral, which is accessible today through Le Chat, their free conversational interface.
- Developers can interact with Codestral naturally and intuitively to leverage the model’s capabilities.
- They see Codestral as a new stepping stone towards empowering everyone with code generation and understanding.
Use Codestral in your favourite coding and building environment.
They worked with community partners to expose Codestral to popular tools for developer productivity and AI application-making.
- Application frameworks.
- Codestral is integrated into LlamaIndex and LangChain starting today, which allows users to build agentic applications with Codestral easily
- VSCode/JetBrains integration.
- Continue.dev and Tabnine are empowering developers to use Codestral within the VSCode and JetBrains environments and now enable them to generate and chat with the code using Codestral.
- Here is how you can use the Continue.dev VSCode plugin for code generation, interactive conversation, and inline editing with Codestral, and here is how users can use the Tabnine VSCode plugin to chat with Codestral.
- For detailed information on how various integrations work with Codestral, please check their documentation for set-up instructions and examples.