Instructions to use Tinysoft/Cosyvoice2-0.5B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tinysoft/Cosyvoice2-0.5B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Tinysoft/Cosyvoice2-0.5B-GGUF",
	filename="cosyvoice.gguf",
)

llm.create_chat_completion(
	messages = "\"The answer to the universe is 42\""
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Tinysoft/Cosyvoice2-0.5B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Tinysoft/Cosyvoice2-0.5B-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf Tinysoft/Cosyvoice2-0.5B-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Tinysoft/Cosyvoice2-0.5B-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf Tinysoft/Cosyvoice2-0.5B-GGUF:F16

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 Tinysoft/Cosyvoice2-0.5B-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf Tinysoft/Cosyvoice2-0.5B-GGUF:F16

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 Tinysoft/Cosyvoice2-0.5B-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Tinysoft/Cosyvoice2-0.5B-GGUF:F16

Use Docker

docker model run hf.co/Tinysoft/Cosyvoice2-0.5B-GGUF:F16

LM Studio
Jan
Ollama
How to use Tinysoft/Cosyvoice2-0.5B-GGUF with Ollama:
```
ollama run hf.co/Tinysoft/Cosyvoice2-0.5B-GGUF:F16
```

Unsloth Studio new

How to use Tinysoft/Cosyvoice2-0.5B-GGUF 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 Tinysoft/Cosyvoice2-0.5B-GGUF 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 Tinysoft/Cosyvoice2-0.5B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Tinysoft/Cosyvoice2-0.5B-GGUF to start chatting

Docker Model Runner
How to use Tinysoft/Cosyvoice2-0.5B-GGUF with Docker Model Runner:
```
docker model run hf.co/Tinysoft/Cosyvoice2-0.5B-GGUF:F16
```

Lemonade

How to use Tinysoft/Cosyvoice2-0.5B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Tinysoft/Cosyvoice2-0.5B-GGUF:F16

Run and chat with the model

lemonade run user.Cosyvoice2-0.5B-GGUF-F16

List all available models

lemonade list

Tinysoft commited on Jul 8, 2025

Commit

b5feafe

verified ·

1 Parent(s): 6b23fc4

Update README.md

Browse files

Files changed (1) hide show

README.md +7 -6

README.md CHANGED Viewed

@@ -1,10 +1,11 @@
-This only works with the token ID directly. The tokenizer is completely busted.
-CosyVoice also has a rich pre- and post- processing on top of the LLM step, so you can't do TTS out of the box with llamacpp.
-Nevertheless, the LLM step is the slowest, and switching from pytorch to llamacpp yields 10x perf gain.
 ---
 license: apache-2.0
 base_model:
 - FunAudioLLM/CosyVoice2-0.5B
----

 ---
+pipeline_tag: text-to-speech
 license: apache-2.0
 base_model:
 - FunAudioLLM/CosyVoice2-0.5B
+---
+This only works with the token ID directly. The tokenizer is completely busted.
+CosyVoice also has a rich pre- and post- processing on top of the LLM step, so you can't do TTS out of the box with llamacpp.
+Nevertheless, the LLM step is the slowest, and switching from pytorch to llamacpp yields 10x perf gain.