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Voxtral-Mini-4B-Realtime-2602

Voxtral Mini 4B Realtime 2602 is a compact, low-latency speech-to-text model designed for high-throughput environments. Unlike larger ASR models that struggle with inference costs, this 4B parameter version optimizes for real-time streaming and edge deployment without sacrificing significant accuracy. It is particularly suited for developers building live transcription services, voice-driven interfaces, or accessibility tools where minimal lag is critical. Integration is streamlined via standard ASR pipelines, offering a lightweight alternative for those who need a balance between performance and resource consumption. Compared to heavier models, it reduces memory overhead while maintaining the robustness required for diverse acoustic environments.

mistralaiautomatic speech recognition
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01 / MODEL CARD

Model card

Voxtral Mini 4B Realtime 2602 is a compact, low-latency speech-to-text model designed for high-throughput environments. Unlike larger ASR models that struggle with inference costs, this 4B parameter version optimizes for real-time streaming and edge deployment without sacrificing significant accuracy. It is particularly suited for developers building live transcription services, voice-driven interfaces, or accessibility tools where minimal lag is critical. Integration is streamlined via standard ASR pipelines, offering a lightweight alternative for those who need a balance between performance and resource consumption. Compared to heavier models, it reduces memory overhead while maintaining the robustness required for diverse acoustic environments.

Model typeautomatic speech recognition
Providermistralai
Licenseapache-2.0
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602
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Version informationUse the source repository for the latest version
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03 / DOWNLOAD

Download this model

We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: mistralai/Voxtral-Mini-4B-Realtime-2602
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model mistralai/Voxtral-Mini-4B-Realtime-2602
Download one file to a local directory

README.md is used as an example; replace it with another repository file when needed.

modelscope download --model mistralai/Voxtral-Mini-4B-Realtime-2602 README.md --local_dir ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('mistralai/Voxtral-Mini-4B-Realtime-2602')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/mistralai/Voxtral-Mini-4B-Realtime-2602.git
Clone without downloading LFS blobs

Fetch the repository structure first, then pull large files when needed.

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/mistralai/Voxtral-Mini-4B-Realtime-2602.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

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