Instructions to use OpenMOSS-Team/MOSS-TTS-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use OpenMOSS-Team/MOSS-TTS-GGUF 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 OpenMOSS-Team/MOSS-TTS-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf OpenMOSS-Team/MOSS-TTS-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf OpenMOSS-Team/MOSS-TTS-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf OpenMOSS-Team/MOSS-TTS-GGUF: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 OpenMOSS-Team/MOSS-TTS-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf OpenMOSS-Team/MOSS-TTS-GGUF: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 OpenMOSS-Team/MOSS-TTS-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf OpenMOSS-Team/MOSS-TTS-GGUF:Q4_K_M
Use Docker
docker model run hf.co/OpenMOSS-Team/MOSS-TTS-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use OpenMOSS-Team/MOSS-TTS-GGUF with Ollama:
ollama run hf.co/OpenMOSS-Team/MOSS-TTS-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use OpenMOSS-Team/MOSS-TTS-GGUF with Docker Model Runner:
docker model run hf.co/OpenMOSS-Team/MOSS-TTS-GGUF:Q4_K_M
- Lemonade
How to use OpenMOSS-Team/MOSS-TTS-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull OpenMOSS-Team/MOSS-TTS-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.MOSS-TTS-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
all this is rubbish
Tried on a CI CPUONLY..
downloaded hundreds of gigabytes of sh*t.
NEVER WORKED.
You must release a LEAN pipeline using llama-cpp and no pythorch hell
this is utterly useless!
Not to mention that huggingface-cli does not exist since more than a year!
also:
0. Install hf
pip install hf
1. Download GGUF weights
hf download OpenMOSS-Team/MOSS-TTS-GGUF --local-dir weights/MOSS-TTS-GGUF
2. Download ONNX audio tokenizer (companion weights)
hf download OpenMOSS-Team/MOSS-Audio-Tokenizer-ONNX --local-dir weights/MOSS-Audio-Tokenizer-ONNX
this downloads 66GB of USELESS data since the model is not even 7 gb
Hey β I hit almost exactly the same two problems (full-repo download, and
looking for a torch-free path) while getting this running myself, so
sharing what worked in case it saves the frustration:
The 66GB/hundreds-of-GB download: hf download <repo> --local-dir ...
with no filter pulls down every file in the repo β for MOSS-TTS-GGUF
that means all five quantization levels (Q4_K_M through F16, ~42GB
combined) plus every variant's embeddings/lm_heads. You only need one
GGUF file. Either:
hf download OpenMOSS-Team/MOSS-TTS-GGUF --local-dir weights/MOSS-TTS-GGUF --include "MOSS_TTS_Q5_K_M.gguf" --include "embeddings/*" --include "lm_heads/*" --include "tokenizer/*"
or, if --include misbehaves for you (it did for us in a couple of
cases), the Python API is more reliable:
from huggingface_hub import snapshot_download
snapshot_download("OpenMOSS-Team/MOSS-TTS-GGUF", local_dir="weights/MOSS-TTS-GGUF",
allow_patterns=["MOSS_TTS_Q5_K_M.gguf", "embeddings/*", "lm_heads/*", "tokenizer/*"])
Same idea for MOSS-Audio-Tokenizer-ONNX β only grab encoder.onnx* /decoder.onnx*, not every precision variant.
Also: pip install hf isn't the right package β there's an unrelatedhf on PyPI. The official way to get the hf CLI is:
pip install -U huggingface_hub
On the "lean, no-pytorch pipeline" ask β it already exists:moss_tts_delay.llama_cpp with the llama-cpp-onnx extra
(pip install -e ".[llama-cpp-onnx]") is fully torch-free at runtime β
llama.cpp for the text backbone, ONNX Runtime for the audio tokenizer.
No PyTorch involved. The one non-obvious gotcha: moss_audio_tokenizer
is a git submodule of this repo, not a separately pip-installable
package β
git submodule update --init --recursive
from the repo root, not git clone + pip install -e . of the
standalone OpenMOSS/MOSS-Audio-Tokenizer repo (that one pulls in
torch==2.7.0 as a hard dependency and still won't expose the right
import path anyway).
We wrote up the full CPU-only build (including the Windows-specific
parts, if relevant to anyone here) in more detail in
discussion #5 β
might save a few more hours for anyone stuck on the same points.