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Co-authored-by: pandora <[email protected]>

README.md

@@ -1,11 +1,139 @@
 ---
+language:
+- en
+- fr
+- de
+- es
+- it
+- pt
+- nl
+- hi
 license: apache-2.0
+library_name: vllm
+inference: false
+base_model:
+- mistralai/Mistral-Small-24B-Base-2501
+extra_gated_description: >-
+  If you want to learn more about how we process your personal data, please read
+  our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
+pipeline_tag: audio-text-to-text
 ---
 
-**Note**: The `params.json` will be made much more concise for the release
+# Voxtral Small 24B-2507
 
-# How to test / run
+Voxtral Small is an enhancement of [Mistral Small 3](https://huggingface.co/mistralai/Mistral-Small-24B-Base-2501), incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription and understanding.
 
+Learn more about Voxtral in our blog post [here](https://mistral.ai/news/voxtral-2507).
+
+Both Voxtral models go beyond transcription with capabilities that include:
+
+
+## Key Features
+
+Voxtral builds upon Mistral Small 3 with powerful audio understanding capabilities.
+- **Long-form context**: with a 32k token context length, Voxtral handles audios up to 30 minutes for transcription, or 40 minutes for understanding 
+- **Built-in Q&A and summarization**: Supports asking questions directly about the audio content or generating structured summaries, without the need to chain separate ASR and language models
+- **Natively multilingual**: Automatic language detection and state-of-the-art performance in the world’s most widely used languages (English, Spanish, French, Portuguese, Hindi, German, Dutch, Italian, to name a few), helping teams serve global audiences with a single system
+- **Function-calling straight from voice**: Enables direct triggering of backend functions, workflows, or API calls based on spoken user intents, turning voice interactions into actionable system commands without intermediate parsing steps.
+- **Highly capable at text**: Retains the text understanding capabilities of its language model backbone, Mistral Small 3
+
+## Benchmark Results
+
+### Audio
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64161701107962562e9b1006/FKBy7KZAoTR52A_ht-ZpL.png)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64161701107962562e9b1006/-DcHsAjwuY6kbduC5XBH2.png)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64161701107962562e9b1006/7tJgqCPczuJZ4cj2PZe59.png)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64161701107962562e9b1006/puASxtajF1lDeGYPrRK5y.png)
+
+### Text 
+
+## Usage
+
+The model can be used with the following frameworks;
+- [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended)
+
+**Note 1**: We recommend using a relatively low temperature, such as `temperature=0.15`.
+
+**Note 2**: Make sure to add a system prompt to the model to best tailor it to your needs.
+
+### vLLM (recommended)
+
+We recommend using this model with [vLLM](https://github.com/vllm-project/vllm).
+
+#### Installation
+
+Make sure to install [`vLLM >= 0.#.#`](https://github.com/vllm-project/vllm/releases/tag/v0.#.#):
+
+```
+pip install vllm --upgrade
+```
+
+Doing so should automatically install [`mistral_common >= 1.#.#`](https://github.com/mistralai/mistral-common/releases/tag/v1.#.#).
+
+To check:
+```
+python -c "import mistral_common; print(mistral_common.__version__)"
+```
+
+You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39).
+
+#### Serve
+
+We recommend that you use Voxtral-Small-24B-2507 in a server/client setting. 
+
+1. Spin up a server:
+
+```
+vllm serve mistralai/Voxtral-Small-24B-2507 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2
+```
+
+**Note:** Running Voxtral-Small-24B-2507 on GPU requires ~55 GB of GPU RAM in bf16 or fp16. 
+
+
+2. To ping the client you can use a simple Python snippet. See the following examples.
+
+
+### Audio Instruct
+
+Leverage the audio capabilities of Voxtral-Small-24B-2507 to chat.
+
+<details>
+  <summary>Python snippet</summary>
+
+```py
+TODO
+```
+</details>
+
+#### Transcription
+
+Voxtral-Small-24B-2507 has powerfull transcription capabilities! 
+
+<details>
+  <summary>Python snippet</summary>
+
+```python
+TODO
+```
+</details>
+
+#### Function calling
+
+Voxtral-Small-24B-2507 is excellent at function / tool calling tasks via vLLM. *E.g.:*
+
+<details>
+  <summary>Python snippet</summary>
+
+```py
+```
+
+</details>
+
+# ORIGINAL
 
 ```
 VLLM_USE_PRECOMPILED=1 pip install --editable .\[audio\]