Instructions to use MoYoYoTech/Translator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
- 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
- Pi
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
Xin Zhang commited on
Commit ·
d30439a
1
Parent(s): f5bdb50
[fix]: update installation.
Browse files
README.md
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```bash
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brew install portaudio cmake
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```
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### Python 基本环境
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```bash
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```
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### WhisperCPP 安装
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> 1. 克隆 WhisperCPP 仓库并初始化子模块:
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```bash
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git clone --recurse-submodules https://github.com/absadiki/pywhispercpp.git && cd pywhispercpp/whisper.cpp && git checkout 170b2faf75c2f6173ef947e6ef346961f3368e1b && cd ../..
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```
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> 2. 切换到特定的提交版本:
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```bash
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cd pywhispercpp && git checkout d43237bd75076615349004270a721e3ebe1deabb
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```
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> 3. 安装 WhisperCPP,确保启用 CoreML 支持:
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```bash
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WHISPER_COREML=1 python setup.py install && cd ..
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```
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### Llama-cpp-python 安装
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> 1. 克隆 Llama-cpp-python 仓库并初始化子模块:
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```bash
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git clone --recurse-submodules https://github.com/abetlen/llama-cpp-python.git
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```
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> 2. 切换到特定的提交版本:
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```bash
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cd llama-cpp-python && git checkout 99f2ebfde18912adeb7f714b49c1ddb624df3087 && cd vendor/llama.cpp && git checkout 80f19b41869728eeb6a26569957b92a773a2b2c6 && cd ../..
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```
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> 3. 使用以下命令安装 Llama-cpp-python,确保启用 Metal 支持:
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```bash
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CMAKE_ARGS="-DGGML_METAL=on" pip install . && cd ..
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```
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## 运行
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> 1. 运行命令 `python main.py` 启动应用程序。
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> 2. 打开浏览器并访问 `http://localhost:9191/` 以使用该应用。
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```bash
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brew install portaudio cmake
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```
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### Python 基本环境
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> 1. 创建一个新的 Python 虚拟环境:
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```bash
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conda create -n translator python=3.11.9
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# 如果没有安装 conda,请先安装 conda 或 Miniconda。
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# 参考 [Miniconda 安装指南](https://docs.conda.io/en/latest/miniconda.html)。
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```
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> 2. 激活虚拟环境:
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```bash
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conda activate translator
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```
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> 3. 克隆仓库:
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```bash
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# 如果没有安装git lfs,请先安装git lfs。
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# macos系统可以使用brew安装git lfs。用git lfs version命令检查是否安装成功。
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git lfs install
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# repo中包含了模型文件,clone时间可能会比较长。
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git clone https://huggingface.co/MoYoYoTech/Translator.git
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# 进入项目目录
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cd Translator
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```
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> 4. 使用以下命令安装所需的 Python 库:
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```bash
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pip install -r requirements.txt
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```
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### WhisperCPP 安装
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> 1. 克隆 WhisperCPP 仓库并初始化子模块:
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```bash
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git clone --recurse-submodules https://github.com/absadiki/pywhispercpp.git && cd pywhispercpp/whisper.cpp && git checkout 170b2faf75c2f6173ef947e6ef346961f3368e1b && cd ../..
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```
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> 2. 切换到特定的提交版本:
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```bash
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cd pywhispercpp && git checkout d43237bd75076615349004270a721e3ebe1deabb
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```
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> 3. 安装 WhisperCPP,确保启用 CoreML 支持:
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```bash
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WHISPER_COREML=1 python setup.py install && cd ..
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```
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### Llama-cpp-python 安装
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> 1. 克隆 Llama-cpp-python 仓库并初始化子模块:
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```bash
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git clone --recurse-submodules https://github.com/abetlen/llama-cpp-python.git
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```
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> 2. 切换到特定的提交版本:
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```bash
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cd llama-cpp-python && git checkout 99f2ebfde18912adeb7f714b49c1ddb624df3087 && cd vendor/llama.cpp && git checkout 80f19b41869728eeb6a26569957b92a773a2b2c6 && cd ../..
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```
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> 3. 使用以下命令安装 Llama-cpp-python,确保启用 Metal 支持:
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```bash
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CMAKE_ARGS="-DGGML_METAL=on" pip install . && cd ..
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```
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## 运行
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> 1. 运行命令 `python main.py` 启动应用程序。
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> 2. 打开浏览器并访问 `http://localhost:9191/` 以使用该应用。
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