Mamba Codestral 7B v0.1 EU AI Act Compliance Profile

Model Card for Mamba-Codestral-7B-v0.1

Codestral Mamba is an open code model based on the Mamba2 architecture. It performs on par with state-of-the-art Transformer-based code models.
You can read more in the official blog post.

Installation

It is recommended to use mistralai/Mamba-Codestral-7B-v0.1 with mistral-inference

or directly with the original mamba package:

Download

from huggingface_hub import snapshot_download
from pathlib import Path

mistral_models_path = Path.home().joinpath('mistral_models', 'Mamba-Codestral-7B-v0.1')
mistral_models_path.mkdir(parents=True, exist_ok=True)

snapshot_download(repo_id="mistralai/Mamba-Codestral-7B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)

Chat

After installing mistral_inference, a mistral-demo CLI command should be available in your environment.

Evaluation

We evaluate Codestral Mamba, Codestral and open-weight models of similar size on industry-standard benchmarks. | Benchmarks | HumanEval | MBPP | Spider | CruxE | HumanEval C++ | HumanEvalJava |HumanEvalJS |HumanEval Bash | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | CodeGemma 1.1 7B | 61.0% | 67.7% | 46.3% | 50.4% | 49.1% | 41.8% | 52.2% | 9.4% | | CodeLlama 7B | 31.1% | 48.2% | 29.3% | 50.1% | 31.7% | 29.7% | 31.7% | 11.4% | | DeepSeek v1.5 7B | 65.9% | 70.8% | 61.2% | 55.5% | 59.0% | 62.7% | 60.9% | 33.5% | | Codestral Mamba (7B) | 75.0% | 68.5% | 58.8% | 57.8% | 59.8% | 57.0% | 61.5% | 31.1% | | | Codestral (22B) | 81.1%% | 78.2%%

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