mamba-codestral-7B-v0.1
Maintainer: mistralai
458
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
Mamba-Codestral-7B-v0.1 is an open code model based on the Mamba2 architecture. It performs on par with state-of-the-art Transformer-based code models, as shown in the evaluation section. You can read more about the model in the official blog post.
Similar models from the same maintainer include mamba-codestral-7B-v0.1, Codestral-22B-v0.1, Mathstral-7B-v0.1, and Mistral-7B-v0.1.
Model inputs and outputs
Mamba-Codestral-7B-v0.1 is a text-to-text model that can be used for a variety of code-related tasks. It takes text prompts as input and generates text outputs.
Inputs
- Text prompts, such as:
- Instructions for generating or modifying code
- Natural language descriptions of desired functionality
- Partially completed code snippets
Outputs
- Text completions, such as:
- Fully implemented code functions
- Explanations and documentation for code
- Refactored or optimized code
Capabilities
Mamba-Codestral-7B-v0.1 demonstrates strong performance on industry-standard benchmarks for code-related tasks, including HumanEval, MBPP, Spider, CruxE, and several domain-specific HumanEval tests. It outperforms several other open-source and commercial code models of similar size.
What can I use it for?
Mamba-Codestral-7B-v0.1 can be used for a variety of software development and code-related tasks, such as:
- Generating code snippets or functions based on natural language descriptions
- Explaining and documenting code
- Refactoring and optimizing existing code
- Performing code-related tasks like unit testing, linting, and debugging
The model's broad knowledge of programming languages and strong performance make it a useful tool for developers, engineers, and researchers working on code-intensive projects.
Things to try
Try prompting Mamba-Codestral-7B-v0.1 with natural language instructions for generating code, such as "Write a function that computes the Fibonacci sequence in Python." The model should be able to provide a complete implementation of the requested functionality.
You can also experiment with partially completed code snippets, asking the model to fill in the missing parts or refactor the code. This can be a helpful way to quickly prototype and iterate on software solutions.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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