Instructions to use tiny-random/voxtral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiny-random/voxtral with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tiny-random/voxtral")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("tiny-random/voxtral") model = AutoModelForMultimodalLM.from_pretrained("tiny-random/voxtral", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tiny-random/voxtral with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tiny-random/voxtral" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tiny-random/voxtral", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tiny-random/voxtral
- SGLang
How to use tiny-random/voxtral with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "tiny-random/voxtral" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tiny-random/voxtral", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "tiny-random/voxtral" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tiny-random/voxtral", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tiny-random/voxtral with Docker Model Runner:
docker model run hf.co/tiny-random/voxtral
File size: 1,380 Bytes
94efcd8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | {
"architectures": [
"VoxtralForConditionalGeneration"
],
"audio_config": {
"activation_dropout": 0.0,
"activation_function": "gelu",
"attention_dropout": 0.0,
"dropout": 0.0,
"head_dim": 32,
"hidden_size": 64,
"initializer_range": 0.02,
"intermediate_size": 256,
"layerdrop": 0.0,
"max_source_positions": 1500,
"model_type": "voxtral_encoder",
"num_attention_heads": 2,
"num_hidden_layers": 2,
"num_key_value_heads": 2,
"num_mel_bins": 128,
"scale_embedding": false,
"vocab_size": 51866
},
"audio_token_id": 24,
"hidden_size": 64,
"model_type": "voxtral",
"projector_hidden_act": "gelu",
"text_config": {
"attention_bias": false,
"attention_dropout": 0.0,
"head_dim": 32,
"hidden_act": "silu",
"hidden_size": 64,
"initializer_range": 0.02,
"intermediate_size": 128,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 2,
"num_hidden_layers": 2,
"num_key_value_heads": 1,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 100000000.0,
"sliding_window": null,
"tie_word_embeddings": true,
"use_cache": true,
"vocab_size": 131072
},
"torch_dtype": "bfloat16",
"transformers_version": "4.54.0.dev0",
"vocab_size": 131072
}
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