How to use from
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 netease-youdao/Confucius4-R2T2-GGUF:
# Run inference directly in the terminal:
llama cli -hf netease-youdao/Confucius4-R2T2-GGUF:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf netease-youdao/Confucius4-R2T2-GGUF:
# Run inference directly in the terminal:
llama cli -hf netease-youdao/Confucius4-R2T2-GGUF:
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 netease-youdao/Confucius4-R2T2-GGUF:
# Run inference directly in the terminal:
./llama-cli -hf netease-youdao/Confucius4-R2T2-GGUF:
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 netease-youdao/Confucius4-R2T2-GGUF:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf netease-youdao/Confucius4-R2T2-GGUF:
Use Docker
docker model run hf.co/netease-youdao/Confucius4-R2T2-GGUF:
Quick Links
Confucius4-R2T2

Confucius4-R2T2: A Low Latency and High Accuracy Real-Time Speech Recognition Model

Real Real-Time Transcription

GitHub repository      Chinese README      Model license: NetEase Model Use License Agreement      Code license: Apache 2.0      Online demo      Hugging Face model      ModelScope model      R2T2 website     

Confucius4-R2T2-GGUF

Official GGUF release from NetEase Youdao for netease-youdao/Confucius4-R2T2, a low-latency and high-accuracy true streaming Automatic Speech Recognition (ASR) model that features fine-grained and configurable decoding chunks from 80 ms to 2 s.

Files

Filename Quantization Size Notes
Confucius4-R2T2-f16.gguf f16 3.2GiB best quality
Confucius4-R2T2-Q8_0.gguf Q8_0 1.7GiB very good quality
Confucius4-R2T2-Q4_K_M.gguf Q4_K_M 1.0GiB fast, lower quality
mmproj-Confucius4-R2T2-f16.gguf mmproj-f16 0.6GiB multi-modal supplement
mmproj-Confucius4-R2T2-Q8_0.gguf mmproj-Q8_0 0.3GiB multi-modal supplement

The low-bit variants are quantized from the f16 GGUF.

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