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 eugenehp/neutts:Q4_0
# Run inference directly in the terminal:
llama cli -hf eugenehp/neutts:Q4_0
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf eugenehp/neutts:Q4_0
# Run inference directly in the terminal:
llama cli -hf eugenehp/neutts:Q4_0
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 eugenehp/neutts:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf eugenehp/neutts:Q4_0
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 eugenehp/neutts:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf eugenehp/neutts:Q4_0
Use Docker
docker model run hf.co/eugenehp/neutts:Q4_0
Quick Links

NeuCodec encoder (RLX staging)

NeuCodec encoder safetensors used by RLX NeuTTS voice clone.

Field Value
Hub id eugenehp/neutts
Kind Converted / re-laid-out for RLX from an upstream checkpoint.
RLX crate rlx-neutts
Upstream https://huggingface.co/neuphonic/neucodec

Quick start

hf download eugenehp/neutts --local-dir .
export NEUTTS_ENCODER_PATH=neucodec_encoder.safetensors

File highlights

  • neutts-air-Q4_0.gguf (502.6 MiB)
  • neucodec_encoder.safetensors (210.8 MiB)
  • neucodec_encoder_config.json (301 B)

Run with RLX

Clone rlx-models, place this repo under weights/tts/neutts (or pass the path explicitly), then:

export NEUTTS_ENCODER_PATH=neucodec_encoder.safetensors

License

Apache License 2.0 โ€” see LICENSE. Inherit upstream terms when redistributing.

Original weights and authorship: https://huggingface.co/neuphonic/neucodec

Maintenance

Cards and LFS attrs are regenerated from the local weights/ tree in rlx-models via python3 scripts/prepare_weights_hf.py.

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Model size
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Architecture
qwen2
Hardware compatibility
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