Instructions to use leeting770708/h3-audio-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusion Single File
How to use leeting770708/h3-audio-models with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use leeting770708/h3-audio-models with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf leeting770708/h3-audio-models:Q4_K_M # Run inference directly in the terminal: llama cli -hf leeting770708/h3-audio-models:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf leeting770708/h3-audio-models:Q4_K_M # Run inference directly in the terminal: llama cli -hf leeting770708/h3-audio-models:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf leeting770708/h3-audio-models:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf leeting770708/h3-audio-models:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf leeting770708/h3-audio-models:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf leeting770708/h3-audio-models:Q4_K_M
Use Docker
docker model run hf.co/leeting770708/h3-audio-models:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use leeting770708/h3-audio-models with Ollama:
ollama run hf.co/leeting770708/h3-audio-models:Q4_K_M
- Unsloth Studio
How to use leeting770708/h3-audio-models with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for leeting770708/h3-audio-models to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for leeting770708/h3-audio-models to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for leeting770708/h3-audio-models to start chatting
- Pi
How to use leeting770708/h3-audio-models with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf leeting770708/h3-audio-models:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "leeting770708/h3-audio-models:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use leeting770708/h3-audio-models with Docker Model Runner:
docker model run hf.co/leeting770708/h3-audio-models:Q4_K_M
- Lemonade
How to use leeting770708/h3-audio-models with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull leeting770708/h3-audio-models:Q4_K_M
Run and chat with the model
lemonade run user.h3-audio-models-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use leeting770708/h3-audio-models with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf leeting770708/h3-audio-models:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default leeting770708/h3-audio-models:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use leeting770708/h3-audio-models with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf leeting770708/h3-audio-models:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "leeting770708/h3-audio-models:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Copy files from models/FunAudioLLM/ThinkSound
Browse files- .gitattributes +2 -0
- LICENSE.md +187 -0
- README.md +37 -0
- model_structure.png +3 -0
- synchformer_state_dict.pth +3 -0
- teaser.png +3 -0
- thinksound.ckpt +3 -0
- thinksound_light.ckpt +3 -0
- vae.ckpt +3 -0
.gitattributes
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This repository includes a fine-tuned version of the VAE model originally released by Stability AI as part of the **Stable Audio Open** project. This component is licensed under the [Stability AI Community License](https://huggingface.co/stabilityai/stable-audio-open-1.0/blob/main/LICENSE.md), which prohibits redistribution under a different license, including derivative works such as fine-tuned models.
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Accordingly, **this specific VAE component is not covered by the Apache License, Version 2.0**, and **may not be used for commercial purposes**.
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Users are advised to consult the original license terms before using this component.
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All other code and models in this repository are released under the Apache License, Version 2.0.
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README.md
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---
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license: apache-2.0
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pipeline_tag: video-to-video
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---
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This repository contains the weights of [ThinkSound: Chain-of-Thought Reasoning in Multimodal Large Language Models for Audio Generation and Editing](https://arxiv.org/abs/2506.21448).
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Project Page: https://thinksound-project.github.io/.
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Paper: https://huggingface.co/papers/2506.21448
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Github: https://github.com/FunAudioLLM/ThinkSound
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<img src="./teaser.png" alt="model_structure" style="zoom:20%;" />
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## Abstract
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While end-to-end video-to-audio generation has greatly improved, producing high-fidelity audio that authentically captures the nuances of visual content remains challenging. Like professionals in the creative industries, such generation requires sophisticated reasoning about items such as visual dynamics, acoustic environments, and temporal relationships. We present ThinkSound, a novel framework that leverages Chain-of-Thought (CoT) reasoning to enable stepwise, interactive audio generation and editing for videos. Our approach decomposes the process into three complementary stages: foundational foley generation that creates semantically coherent soundscapes, interactive object-centric refinement through precise user interactions, and targeted editing guided by natural language instructions. At each stage, a multimodal large language model generates contextually aligned CoT reasoning that guides a unified audio foundation model. Furthermore, we introduce AudioCoT, a comprehensive dataset with structured reasoning annotations that establishes connections between visual content, textual descriptions, and sound synthesis. Experiments demonstrate that ThinkSound achieves state-of-the-art performance in video-to-audio generation across both audio metrics and CoT metrics and excels in out-of-distribution Movie Gen Audio benchmark. The demo page is available at https://ThinkSound-Project.github.io.
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## Model Overview
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<img src="./model_structure.png" alt="model_structure" style="zoom:40%;" />
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## Citation
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If you find our work useful, please cite our paper:
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```bibtex
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@misc{liu2025thinksoundchainofthoughtreasoningmultimodal,
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title={ThinkSound: Chain-of-Thought Reasoning in Multimodal Large Language Models for Audio Generation and Editing},
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author={Huadai Liu and Jialei Wang and Kaicheng Luo and Wen Wang and Qian Chen and Zhou Zhao and Wei Xue},
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year={2025},
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eprint={2506.21448},
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archivePrefix={arXiv},
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primaryClass={eess.AS},
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url={https://arxiv.org/abs/2506.21448},
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}
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```
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model_structure.png
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Git LFS Details
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synchformer_state_dict.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:8aff082f2df5c3bc52759db0c865c7ee772ae6400b860d1b7e90413f2defb67c
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size 950058171
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teaser.png
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Git LFS Details
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thinksound.ckpt
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version https://git-lfs.github.com/spec/v1
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oid sha256:816fa5f73bd8d566e684cef7a8caf2c698f6da100bca561c0c6b4ae244439ebc
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size 21057372552
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thinksound_light.ckpt
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version https://git-lfs.github.com/spec/v1
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oid sha256:5232db0ca3f1bca319958ea81181267e525228c226f62015d2b0fc624ce1df82
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size 5733246918
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vae.ckpt
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version https://git-lfs.github.com/spec/v1
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oid sha256:8132c32ec65bbba2f1457f2659136c5f2cb31f9902af1c659d78c9f7d3aa619e
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size 2521255758
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