Add Voxtral-Mini-4B-Realtime model support via vLLM backend

gallery/index.yaml

@@ -478,34 +478,22 @@
       model: nvidia/parakeet-tdt-0.6b-v3
 - name: voxtral-mini-4b-realtime
   license: apache-2.0
-  url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
+  url: "github:mudler/LocalAI/gallery/voxtral-mini-4b-realtime.yaml@master"
   description: |
-    Voxtral Mini 4B Realtime is a speech-to-text model from Mistral AI. It is a 4B parameter model optimized for fast, accurate audio transcription with low latency, making it ideal for real-time applications. The model uses the Voxtral architecture for efficient audio processing.
+    Voxtral Mini 4B Realtime is a multilingual, realtime speech-transcription model from Mistral AI.
+    It achieves accuracy comparable to offline systems with a delay of <500ms and supports 13 languages.
+    This model is designed for real-time automatic speech recognition (ASR) with streaming capabilities
+    and benefits from vLLM's Realtime API for low-latency transcription workflows.
   urls:
     - https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602
-    - https://github.com/antirez/voxtral.c
   tags:
     - stt
     - speech-to-text
     - audio-transcription
+    - vllm
     - cpu
     - metal
     - mistral
-  overrides:
-    backend: voxtral
-    known_usecases:
-      - transcript
-    parameters:
-      model: voxtral-model
-  files:
-    - filename: voxtral-model/consolidated.safetensors
-      uri: https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602/resolve/main/consolidated.safetensors
-      sha256: 263f178fe752c90a2ae58f037a95ed092db8b14768b0978b8c48f66979c8345d
-    - filename: voxtral-model/params.json
-      uri: https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602/resolve/main/params.json
-    - filename: voxtral-model/tekken.json
-      uri: https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602/resolve/main/tekken.json
-      sha256: 8434af1d39eba99f0ef46cf1450bf1a63fa941a26933a1ef5dbbf4adf0d00e44
 - name: moonshine-tiny
   license: apache-2.0
   size: "108MB"
@@ -723,7 +711,7 @@
       - "offload_to_cpu:false"
       - "offload_dit_to_cpu:false"
       - "init_lm:true"
       - "lm_model_path:acestep-5Hz-lm-0.6B" # or acestep-5Hz-lm-4B
       - "lm_backend:pt"
       - "temperature:0.85"
       - "top_p:0.9"
@@ -956,7 +944,7 @@
     known_usecases:
       - tts
     tts:
       voice: Aiden # Available speakers: Vivian, Serena, Uncle_Fu, Dylan, Eric, Ryan, Aiden, Ono_Anna, Sohee
     parameters:
       model: Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice
 - !!merge <<: *qwen-tts
@@ -968,7 +956,7 @@
     known_usecases:
       - tts
     tts:
       voice: Aiden # Available speakers: Vivian, Serena, Uncle_Fu, Dylan, Eric, Ryan, Aiden, Ono_Anna, Sohee
     parameters:
       model: Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice
 - &qwen-asr
@@ -5013,7 +5001,7 @@
     - gemma3
     - gemma-3
   overrides:
     #mmproj: gemma-3-27b-it-mmproj-f16.gguf
     parameters:
       model: gemma-3-27b-it-Q4_K_M.gguf
   files:
@@ -5031,7 +5019,7 @@
   description: |
     google/gemma-3-12b-it is an open-source, state-of-the-art, lightweight, multimodal model built from the same research and technology used to create the Gemini models. It is capable of handling text and image input and generating text output. It has a large context window of 128K tokens and supports over 140 languages. The 12B variant has been fine-tuned using the instruction-tuning approach. Gemma 3 models are suitable for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes them deployable in environments with limited resources such as laptops, desktops, or your own cloud infrastructure.
   overrides:
     #mmproj: gemma-3-12b-it-mmproj-f16.gguf
     parameters:
       model: gemma-3-12b-it-Q4_K_M.gguf
   files:
@@ -5049,7 +5037,7 @@
   description: |
     Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. Gemma-3-4b-it is a 4 billion parameter model.
   overrides:
     #mmproj: gemma-3-4b-it-mmproj-f16.gguf
     parameters:
       model: gemma-3-4b-it-Q4_K_M.gguf
   files:
@@ -8267,7 +8255,7 @@
       sha256: 2756551de7d8ff7093c2c5eec1cd00f1868bc128433af53f5a8d434091d4eb5a
       uri: huggingface://Triangle104/Nano_Imp_1B-Q8_0-GGUF/nano_imp_1b-q8_0.gguf
 - &smollm
   url: "github:mudler/LocalAI/gallery/chatml.yaml@master" ## SmolLM
   name: "smollm-1.7b-instruct"
   icon: https://huggingface.co/datasets/HuggingFaceTB/images/resolve/main/banner_smol.png
   tags:
@@ -8311,7 +8299,7 @@
       sha256: decd2598bc2c8ed08c19adc3c8fdd461ee19ed5708679d1c54ef54a5a30d4f33
       uri: huggingface://HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF/smollm2-1.7b-instruct-q4_k_m.gguf
 - &llama31
   url: "github:mudler/LocalAI/gallery/llama3.1-instruct.yaml@master" ## LLama3.1
   icon: https://avatars.githubusercontent.com/u/153379578
   name: "meta-llama-3.1-8b-instruct"
   license: llama3.1

gallery/voxtral-mini-4b-realtime.yaml

@@ -0,0 +1,27 @@
+---
+name: "voxtral-mini-4b-realtime"
+
+description: |
+  Voxtral Mini 4B Realtime is a multilingual, realtime speech-transcription model from Mistral AI.
+  It achieves accuracy comparable to offline systems with a delay of <500ms and supports 13 languages.
+  This model is designed for real-time automatic speech recognition (ASR) with streaming capabilities
+  and benefits from vLLM's Realtime API for low-latency transcription workflows.
+
+config_file: |
+  name: voxtral-mini-4b-realtime
+  description: Voxtral Mini 4B Realtime - Real-time ASR model via vLLM
+  backend: vllm
+  parameters:
+    model: mistralai/Voxtral-Mini-4B-Realtime-2602
+  known_usecases:
+    - transcript
+  template:
+    use_tokenizer_template: true
+  prediction:
+    max_tokens: 45000
+  backend_options:
+    vllm:
+      # Recommended settings for Voxtral Realtime
+      # --max-model-len: 131072 (default, supports ~3h of transcription)
+      # Temperature should be set to 0.0 for ASR
+      compilation_config: '{"cudagraph_mode": "PIECEWISE"}'