Text-to-Speech
Transformers
rotorquant
kv-cache-quantization
voxtral
mistral
tts
voice-cloning
zero-shot
quantized
Instructions to use majentik/Voxtral-4B-TTS-2603-RotorQuant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use majentik/Voxtral-4B-TTS-2603-RotorQuant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="majentik/Voxtral-4B-TTS-2603-RotorQuant")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("majentik/Voxtral-4B-TTS-2603-RotorQuant", dtype="auto") - Notebooks
- Google Colab
- Kaggle
chore(card): enrich YAML frontmatter (pipeline_tag, language, library_name, inference)
Browse files
README.md
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license: apache-2.0
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base_model: mistralai/Voxtral-4B-TTS-2603
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tags:
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library_name: transformers
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pipeline_tag: text-to-speech
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---
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# Voxtral-4B-TTS-2603-RotorQuant
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license: apache-2.0
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base_model: mistralai/Voxtral-4B-TTS-2603
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tags:
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- rotorquant
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- kv-cache-quantization
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- voxtral
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- mistral
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- tts
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- voice-cloning
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- zero-shot
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- quantized
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library_name: transformers
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pipeline_tag: text-to-speech
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language:
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- en
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inference: false
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---
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# Voxtral-4B-TTS-2603-RotorQuant
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