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LatestQwen · Alibaba8B
Qwen · AlibabaAny-to-Any

Qwen's Fun-Audio-Chat: An Open Speech-to-Speech LLM

The 8-billion-parameter model from Alibaba's Qwen team understands and generates spoken responses, enabling more natural audio-first applications.

Dec 23, 2025
NotableApache 2.0
Fun-Audio-Chat-8B

Alibaba's Qwen team has released Fun-Audio-Chat-8B, an 8-billion-parameter model designed for seamless speech-to-speech conversation. Released under the permissive Apache 2.0 license, the model can process spoken input and generate a spoken response, creating a more natural conversational flow than traditional text-based interfaces.

The system functions as a comprehensive audio pipeline, integrating a speech encoder, the Qwen-7B-Chat language model for reasoning, and a text-to-speech (TTS) component to vocalize the final answer. This architecture allows it to handle complex interactions entirely through audio, supporting both English and Chinese languages.

While many multimodal models can accept audio as an input, few open-source projects close the loop with integrated, high-quality speech synthesis for true conversational interaction. Fun-Audio-Chat provides a powerful, unified foundation for developers building more intuitive voice assistants, accessibility tools, and real-time interactive agents.

The model and its components are available now on Hugging Face for researchers and developers to explore. Its open license permits a wide range of academic and commercial applications, encouraging further innovation in audio-native AI.

Sources

  • FunAudioLLM/Fun-Audio-Chat-8B

    Hugging Face

    Visit
OlderMiniMax Debuts M2.1, an MoE Model Optimized with FP8MiniMax · Text / LLM · 10 months agoNewerQwen Releases Bilingual Open-Source Image ModelQwen · Alibaba · Text → Image · 10 months ago

Get the model

Hugging Face

Specs

Parameters8B
Context window262K tokens
Size18.9 GB
PrecisionBF16
ArchitectureFunAudioChatForConditionalGeneration
LicenseAPACHE-2.0
Downloads973
Likes190

Can you run it?

Runs on a laptop — about 7.9 GB at 4-bit.

  • BF16 (as published)

    24 GB GPU (RTX 3090 / 4090) · 32 GB Mac

    21.1 GB
  • 8-bit

    16 GB GPU (RTX 4080 / 5070 Ti) · 24 GB Mac

    12.2 GB
  • 4-bit

    8 GB GPU (RTX 4060) · 16 GB laptop or Mac

    7.9 GB
Your machine
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Any-to-AnyText → SpeechSpeech → Text

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