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README.md CHANGED Viewed

@@ -1,5 +1,4 @@
 ---
-library_name: vllm
 language:
 - en
 - fr
@@ -10,14 +9,13 @@ language:
 - nl
 - hi
 license: apache-2.0
 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>.
-tags:
-- vllm
 pipeline_tag: audio-text-to-text
 ---
@@ -25,7 +23,7 @@ pipeline_tag: audio-text-to-text
 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, translation and audio understanding.
-Learn more about Voxtral in our blog post [here](https://mistral.ai/news/voxtral) and our [research paper](https://arxiv.org/abs/2507.13264).
 ## Key Features
@@ -41,28 +39,29 @@ Voxtral builds upon Mistral Small 3 with powerful audio understanding capabiliti
 ### Audio
-Average word error rate (WER) over the FLEURS, Mozilla Common Voice and Multilingual LibriSpeech benchmarks:
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64161701107962562e9b1006/puASxtajF1lDeGYPrRK5y.png)
 ### Text
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/5dfcb1aada6d0311fd3d5448/uDg3hKDwJowsNuj-yyt2T.png)
 ## Usage
 The model can be used with the following frameworks;
 - [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended)
-- [`Transformers` 🤗](https://github.com/huggingface/transformers): See [here](#transformers-🤗)
-**Notes**:
 - `temperature=0.2` and `top_p=0.95` for chat completion (*e.g. Audio Understanding*) and `temperature=0.0` for transcription
 - Multiple audios per message and multiple user turns with audio are supported
-- Function calling is supported
 - System prompts are not yet supported
 ### vLLM (recommended)
@@ -70,13 +69,15 @@ We recommend using this model with [vLLM](https://github.com/vllm-project/vllm).
 #### Installation
-Make sure to install vllm >= `0.10.0`, we recommend using uv
 ```
-uv pip install -U "vllm[audio]" --system
 ```
-Doing so should automatically install [`mistral_common >= 1.8.1`](https://github.com/mistralai/mistral-common/releases/tag/v1.8.1).
 To check:
 ```
@@ -104,7 +105,7 @@ 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 --tensor-parallel-size 2 --tool-call-parser mistral --enable-auto-tool-choice
 ```
 **Note:** Running Voxtral-Small-24B-2507 on GPU requires ~55 GB of GPU RAM in bf16 or fp16.
@@ -117,12 +118,6 @@ vllm serve mistralai/Voxtral-Small-24B-2507 --tokenizer_mode mistral --config_fo
 Leverage the audio capabilities of Voxtral-Small-24B-2507 to chat.
-Make sure that your client has `mistral-common` with audio installed:
-```sh
-pip install --upgrade mistral_common\[audio\]
-```
 <details>
   <summary>Python snippet</summary>
@@ -152,7 +147,7 @@ def file_to_chunk(file: str) -> AudioChunk:
     audio = Audio.from_file(file, strict=False)
     return AudioChunk.from_audio(audio)
-text_chunk = TextChunk(text="Which speaker is more inspiring? Why? How are they different from each other? Answer in French.")
 user_msg = UserMessage(content=[file_to_chunk(obama_file), file_to_chunk(bcn_file), text_chunk]).to_openai()
 print(30 * "=" + "USER 1" + 30 * "=")
@@ -170,12 +165,11 @@ content = response.choices[0].message.content
 print(30 * "=" + "BOT 1" + 30 * "=")
 print(content)
 print("\n\n")
-# The model could give the following answer:
-# ```L'orateur le plus inspirant est le président.
-# Il est plus inspirant parce qu'il parle de ses expériences personnelles
-# et de son optimisme pour l'avenir du pays.
-# Il est différent de l'autre orateur car il ne parle pas de la météo,
-# mais plutôt de ses interactions avec les gens et de son rôle en tant que président.```
 messages = [
     user_msg,
@@ -202,12 +196,6 @@ print(content)
 Voxtral-Small-24B-2507 has powerful transcription capabilities!
-Make sure that your client has `mistral-common` with audio installed:
-```sh
-pip install --upgrade mistral_common\[audio\]
-```
 <details>
   <summary>Python snippet</summary>
@@ -240,380 +228,4 @@ req = TranscriptionRequest(model=model, audio=audio, language="en", temperature=
 response = client.audio.transcriptions.create(**req)
 print(response)
 ```
-</details>
-#### Function Calling
-Voxtral has some experimental function calling support. You can try as shown below.
-Make sure that your client has `mistral-common` with audio installed:
-```sh
-pip install --upgrade mistral_common\[audio\]
-```
-<details>
-  <summary>Python snippet</summary>
-```python
-from mistral_common.protocol.instruct.messages import AudioChunk, UserMessage, TextChunk
-from mistral_common.protocol.transcription.request import TranscriptionRequest
-from mistral_common.protocol.instruct.tool_calls import Function, Tool
-from mistral_common.audio import Audio
-from huggingface_hub import hf_hub_download
-from openai import OpenAI
-# Modify OpenAI's API key and API base to use vLLM's API server.
-openai_api_key = "EMPTY"
-openai_api_base = "http://<your-server-host>:8000/v1"
-client = OpenAI(
-    api_key=openai_api_key,
-    base_url=openai_api_base,
-)
-models = client.models.list()
-model = models.data[0].id
-tool = Tool(
-    function=Function(
-        name="get_current_weather",
-        description="Get the current weather",
-        parameters={
-            "type": "object",
-            "properties": {
-                "location": {
-                    "type": "string",
-                    "description": "The city and state, e.g. San Francisco, CA",
-                },
-                "format": {
-                    "type": "string",
-                    "enum": ["celsius", "fahrenheit"],
-                    "description": "The temperature unit to use. Infer this from the user's location.",
-                },
-            },
-            "required": ["location", "format"],
-        },
-    )
-)
-tools = [tool.to_openai()]
-weather_like = hf_hub_download("patrickvonplaten/audio_samples", "fn_calling.wav", repo_type="dataset")
-def file_to_chunk(file: str) -> AudioChunk:
-    audio = Audio.from_file(file, strict=False)
-    return AudioChunk.from_audio(audio)
-audio_chunk = file_to_chunk(weather_like)
-print(30 * "=" + "Transcription" + 30 * "=")
-req = TranscriptionRequest(model=model, audio=audio_chunk.input_audio, language="en", temperature=0.0).to_openai(exclude=("top_p", "seed"))
-response = client.audio.transcriptions.create(**req)
-print(response.text) # How is the weather in Madrid at the moment?
-print("\n")
-print(30 * "=" + "Function calling" + 30 * "=")
-audio_chunk = file_to_chunk(weather_like)
-user_msg = UserMessage(content=[audio_chunk]).to_openai()
-response = client.chat.completions.create(
-    model=model,
-    messages=[user_msg],
-    temperature=0.2,
-    top_p=0.95,
-    tools=[tool.to_openai()]
-)
-print(30 * "=" + "BOT 1" + 30 * "=")
-print(response.choices[0].message.tool_calls)
-print("\n\n")
-```
-</details>
-### Transformers 🤗
-Starting with `transformers >= 4.54.0` and above, you can run Voxtral natively!
-Install Transformers:
-```bash
-pip install -U transformers
-```
-Make sure to have `mistral-common >= 1.8.1` installed with audio dependencies:
-```bash
-pip install --upgrade "mistral-common[audio]"
-```
-#### Audio Instruct
-<details>
-  <summary>➡️ multi-audio + text instruction</summary>
-```python
-from transformers import VoxtralForConditionalGeneration, AutoProcessor
-import torch
-device = "cuda"
-repo_id = "mistralai/Voxtral-Small-24B-2507"
-processor = AutoProcessor.from_pretrained(repo_id)
-model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
-conversation = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "audio",
-                "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/mary_had_lamb.mp3",
-            },
-            {
-                "type": "audio",
-                "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
-            },
-            {"type": "text", "text": "What sport and what nursery rhyme are referenced?"},
-        ],
-    }
-]
-inputs = processor.apply_chat_template(conversation)
-inputs = inputs.to(device, dtype=torch.bfloat16)
-outputs = model.generate(**inputs, max_new_tokens=500)
-decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
-print("\nGenerated response:")
-print("=" * 80)
-print(decoded_outputs[0])
-print("=" * 80)
-```
-</details>
-<details>
-  <summary>➡️ multi-turn</summary>
-```python
-from transformers import VoxtralForConditionalGeneration, AutoProcessor
-import torch
-device = "cuda"
-repo_id = "mistralai/Voxtral-Small-24B-2507"
-processor = AutoProcessor.from_pretrained(repo_id)
-model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
-conversation = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "audio",
-                "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama.mp3",
-            },
-            {
-                "type": "audio",
-                "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3",
-            },
-            {"type": "text", "text": "Describe briefly what you can hear."},
-        ],
-    },
-    {
-        "role": "assistant",
-        "content": "The audio begins with the speaker delivering a farewell address in Chicago, reflecting on his eight years as president and expressing gratitude to the American people. The audio then transitions to a weather report, stating that it was 35 degrees in Barcelona the previous day, but the temperature would drop to minus 20 degrees the following day.",
-    },
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "audio",
-                "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
-            },
-            {"type": "text", "text": "Ok, now compare this new audio with the previous one."},
-        ],
-    },
-]
-inputs = processor.apply_chat_template(conversation)
-inputs = inputs.to(device, dtype=torch.bfloat16)
-outputs = model.generate(**inputs, max_new_tokens=500)
-decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
-print("\nGenerated response:")
-print("=" * 80)
-print(decoded_outputs[0])
-print("=" * 80)
-```
-</details>
-<details>
-  <summary>➡️ text only</summary>
-```python
-from transformers import VoxtralForConditionalGeneration, AutoProcessor
-import torch
-device = "cuda"
-repo_id = "mistralai/Voxtral-Small-24B-2507"
-processor = AutoProcessor.from_pretrained(repo_id)
-model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
-conversation = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "text",
-                "text": "Why should AI models be open-sourced?",
-            },
-        ],
-    }
-]
-inputs = processor.apply_chat_template(conversation)
-inputs = inputs.to(device, dtype=torch.bfloat16)
-outputs = model.generate(**inputs, max_new_tokens=500)
-decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
-print("\nGenerated response:")
-print("=" * 80)
-print(decoded_outputs[0])
-print("=" * 80)
-```
-</details>
-<details>
-  <summary>➡️ audio only</summary>
-```python
-from transformers import VoxtralForConditionalGeneration, AutoProcessor
-import torch
-device = "cuda"
-repo_id = "mistralai/Voxtral-Small-24B-2507"
-processor = AutoProcessor.from_pretrained(repo_id)
-model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
-conversation = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "audio",
-                "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
-            },
-        ],
-    }
-]
-inputs = processor.apply_chat_template(conversation)
-inputs = inputs.to(device, dtype=torch.bfloat16)
-outputs = model.generate(**inputs, max_new_tokens=500)
-decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
-print("\nGenerated response:")
-print("=" * 80)
-print(decoded_outputs[0])
-print("=" * 80)
-```
-</details>
-<details>
-  <summary>➡️ batched inference</summary>
-```python
-from transformers import VoxtralForConditionalGeneration, AutoProcessor
-import torch
-device = "cuda"
-repo_id = "mistralai/Voxtral-Small-24B-2507"
-processor = AutoProcessor.from_pretrained(repo_id)
-model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
-conversations = [
-    [
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "audio",
-                    "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama.mp3",
-                },
-                {
-                    "type": "audio",
-                    "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3",
-                },
-                {
-                    "type": "text",
-                    "text": "Who's speaking in the speach and what city's weather is being discussed?",
-                },
-            ],
-        }
-    ],
-    [
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "audio",
-                    "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
-                },
-                {"type": "text", "text": "What can you tell me about this audio?"},
-            ],
-        }
-    ],
-]
-inputs = processor.apply_chat_template(conversations)
-inputs = inputs.to(device, dtype=torch.bfloat16)
-outputs = model.generate(**inputs, max_new_tokens=500)
-decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
-print("\nGenerated responses:")
-print("=" * 80)
-for decoded_output in decoded_outputs:
-    print(decoded_output)
-    print("=" * 80)
-```
-</details>
-#### Transcription
-<details>
-  <summary>➡️ transcribe</summary>
-```python
-from transformers import VoxtralForConditionalGeneration, AutoProcessor
-import torch
-device = "cuda"
-repo_id = "mistralai/Voxtral-Small-24B-2507"
-processor = AutoProcessor.from_pretrained(repo_id)
-model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
-inputs = processor.apply_transcription_request(language="en", audio="https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama.mp3", model_id=repo_id)
-inputs = inputs.to(device, dtype=torch.bfloat16)
-outputs = model.generate(**inputs, max_new_tokens=500)
-decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
-print("\nGenerated responses:")
-print("=" * 80)
-for decoded_output in decoded_outputs:
-    print(decoded_output)
-    print("=" * 80)
-```
 </details>

 ---
 language:
 - en
 - fr
 - 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
 ---
 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, translation and audio understanding.
+Learn more about Voxtral in our blog post [here](https://mistral.ai/news/voxtral-2507).
 ## Key Features
 ### 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
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64161701107962562e9b1006/9i9jpA2OLwhYut_wet836.png)
 ## Usage
 The model can be used with the following frameworks;
 - [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended)
+**Recommended settings**:
 - `temperature=0.2` and `top_p=0.95` for chat completion (*e.g. Audio Understanding*) and `temperature=0.0` for transcription
 - Multiple audios per message and multiple user turns with audio are supported
 - System prompts are not yet supported
+- Function calling is not yet supported
 ### vLLM (recommended)
 #### Installation
+Make sure to install vllm from "main":
 ```
+pip install -U vllm\[audio\] \
+    --pre \
+    --extra-index-url https://wheels.vllm.ai/nightly
 ```
+Doing so should automatically install [`mistral_common >= 1.8.0`](https://github.com/mistralai/mistral-common/releases/tag/v1.8.0).
 To check:
 ```
 1. Spin up a server:
 ```
+vllm serve mistralai/Voxtral-Small-24B-2507 --tokenizer_mode mistral --config_format mistral --load_format mistral --tensor-parallel-size 2
 ```
 **Note:** Running Voxtral-Small-24B-2507 on GPU requires ~55 GB of GPU RAM in bf16 or fp16.
 Leverage the audio capabilities of Voxtral-Small-24B-2507 to chat.
 <details>
   <summary>Python snippet</summary>
     audio = Audio.from_file(file, strict=False)
     return AudioChunk.from_audio(audio)
+text_chunk = TextChunk(text="Which speaker do you prefer between the two? Why? How are they different from each other?")
 user_msg = UserMessage(content=[file_to_chunk(obama_file), file_to_chunk(bcn_file), text_chunk]).to_openai()
 print(30 * "=" + "USER 1" + 30 * "=")
 print(30 * "=" + "BOT 1" + 30 * "=")
 print(content)
 print("\n\n")
+# E.g. The speaker who delivers the farewell address is more engaging and inspiring.
+# They express gratitude and optimism, emphasizing the importance of self-government and citizenship.
+# They also share personal experiences and observations, making the speech more relatable and heartfelt.
+# In contrast, the second speaker provides factual information about the weather in Barcelona,
+# which is less engaging and lacks the emotional depth of the first speaker's address.
 messages = [
     user_msg,
 Voxtral-Small-24B-2507 has powerful transcription capabilities!
 <details>
   <summary>Python snippet</summary>
 response = client.audio.transcriptions.create(**req)
 print(response)
 ```
 </details>

config.json DELETED Viewed

@@ -1,53 +0,0 @@
-{
-  "architectures": [
-    "VoxtralForConditionalGeneration"
-  ],
-  "audio_config": {
-    "activation_dropout": 0.0,
-    "activation_function": "gelu",
-    "attention_dropout": 0.0,
-    "dropout": 0.0,
-    "head_dim": 64,
-    "hidden_size": 1280,
-    "initializer_range": 0.02,
-    "intermediate_size": 5120,
-    "layerdrop": 0.0,
-    "max_source_positions": 1500,
-    "model_type": "voxtral_encoder",
-    "num_attention_heads": 20,
-    "num_hidden_layers": 32,
-    "num_key_value_heads": 20,
-    "num_mel_bins": 128,
-    "scale_embedding": false,
-    "vocab_size": 51866
-  },
-  "audio_token_id": 24,
-  "hidden_size": 5120,
-  "model_type": "voxtral",
-  "projector_hidden_act": "gelu",
-  "text_config": {
-    "attention_bias": false,
-    "attention_dropout": 0.0,
-    "head_dim": 128,
-    "hidden_act": "silu",
-    "hidden_size": 5120,
-    "initializer_range": 0.02,
-    "intermediate_size": 32768,
-    "max_position_embeddings": 131072,
-    "mlp_bias": false,
-    "model_type": "llama",
-    "num_attention_heads": 32,
-    "num_hidden_layers": 40,
-    "num_key_value_heads": 8,
-    "pretraining_tp": 1,
-    "rms_norm_eps": 1e-05,
-    "rope_scaling": null,
-    "rope_theta": 100000000.0,
-    "sliding_window": null,
-    "use_cache": true,
-    "vocab_size": 131072
-  },
-  "torch_dtype": "bfloat16",
-  "transformers_version": "4.54.0.dev0",
-  "vocab_size": 131072
-}

generation_config.json DELETED Viewed

@@ -1,6 +0,0 @@
-{
-  "bos_token_id": 1,
-  "eos_token_id": 2,
-  "pad_token_id": 11,
-  "transformers_version": "4.54.0.dev0"
-}

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@@ -1,3 +0,0 @@
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-oid sha256:9e4c3fde4ef4a1141b4b06f2125c63bb2032f71a7985fafb4b3ae8243ca17be2
-size 4947893992

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preprocessor_config.json DELETED Viewed

@@ -1,15 +0,0 @@
-{
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-  "return_attention_mask": false,
-  "sampling_rate": 16000
-}