Instructions to use MoYoYoTech/Translator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MoYoYoTech/Translator with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MoYoYoTech/Translator",
	filename="moyoyo_asr_models/qwen2.5-1.5b-instruct-q5_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MoYoYoTech/Translator with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MoYoYoTech/Translator:Q5_0
# Run inference directly in the terminal:
llama-cli -hf MoYoYoTech/Translator:Q5_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MoYoYoTech/Translator:Q5_0
# Run inference directly in the terminal:
llama-cli -hf MoYoYoTech/Translator:Q5_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 MoYoYoTech/Translator:Q5_0
# Run inference directly in the terminal:
./llama-cli -hf MoYoYoTech/Translator:Q5_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 MoYoYoTech/Translator:Q5_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MoYoYoTech/Translator:Q5_0

Use Docker

docker model run hf.co/MoYoYoTech/Translator:Q5_0

LM Studio
Jan
Ollama
How to use MoYoYoTech/Translator with Ollama:
```
ollama run hf.co/MoYoYoTech/Translator:Q5_0
```

Unsloth Studio

How to use MoYoYoTech/Translator 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 MoYoYoTech/Translator 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 MoYoYoTech/Translator to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MoYoYoTech/Translator to start chatting

How to use MoYoYoTech/Translator with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf MoYoYoTech/Translator:Q5_0

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": "MoYoYoTech/Translator:Q5_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use MoYoYoTech/Translator with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf MoYoYoTech/Translator:Q5_0

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 MoYoYoTech/Translator:Q5_0

Run Hermes

hermes

Docker Model Runner
How to use MoYoYoTech/Translator with Docker Model Runner:
```
docker model run hf.co/MoYoYoTech/Translator:Q5_0
```

Lemonade

How to use MoYoYoTech/Translator with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MoYoYoTech/Translator:Q5_0

Run and chat with the model

lemonade run user.Translator-Q5_0

List all available models

lemonade list

daihui.zhang commited on Apr 9, 2025

Commit

c7d7f25

1 Parent(s): 9e66f7d

update

Browse files

Files changed (2) hide show

README.md +2 -3
transcribe/whisper_llm_serve.py +4 -1

README.md CHANGED Viewed

@@ -13,12 +13,11 @@ license: mit
 ## whispercpp安装
 >  git clone --recurse-submodules https://github.com/absadiki/pywhispercpp.git
 >  git checkout d43237bd75076615349004270a721e3ebe1deabb
->  WHISPER_COREML=1 pip setup.py install
 ## Llama-cpp-python
 > git clone --recurse-submodules https://github.com/abetlen/llama-cpp-python.git
-> cd llama-cpp-python && git checkout  0580cf273debf4a7f2efcdfd5ef092ff5cedf9b0
-> cd llama-cpp-python/vendor/llama.cpp &7 git checkout ecebbd292d741ac084cf248146b2cfb17002aa1d
 > 安装命令： CMAKE_ARGS="-DGGML_METAL=on"  pip install -e .

 ## whispercpp安装
 >  git clone --recurse-submodules https://github.com/absadiki/pywhispercpp.git
 >  git checkout d43237bd75076615349004270a721e3ebe1deabb
+>  WHISPER_COREML=1 python setup.py install
 ## Llama-cpp-python
 > git clone --recurse-submodules https://github.com/abetlen/llama-cpp-python.git
+> cd llama-cpp-python && git checkout  0580cf273debf4a7f2efcdfd5ef092ff5cedf9b0 && cd vendor/llama.cpp && git checkout ecebbd292d741ac084cf248146b2cfb17002aa1d
 > 安装命令： CMAKE_ARGS="-DGGML_METAL=on"  pip install -e .

transcribe/whisper_llm_serve.py CHANGED Viewed

@@ -135,11 +135,14 @@ class PyWhiperCppServe(ServeClientBase):
                 logger.info("Exiting speech to text thread")
                 break
-            if self.frames_np is None or self.frames_np.shape[0] < self.sample_rate * 2:
                 time.sleep(0.02)  # wait for any audio to arrive
                 continue
             audio_buffer = self.get_audio_chunk_for_processing()
             # c+= 1
             # name = f"dev-{c}.wav"
             # save_to_wave(name, audio_buffer)

                 logger.info("Exiting speech to text thread")
                 break
+            if self.frames_np is None:
                 time.sleep(0.02)  # wait for any audio to arrive
                 continue
             audio_buffer = self.get_audio_chunk_for_processing()
+            if audio_buffer.shape[0] < self.sample_rate * 2:
+                time.sleep(0.02)
+                continue
             # c+= 1
             # name = f"dev-{c}.wav"
             # save_to_wave(name, audio_buffer)