Instructions to use MoYoYoTech/Translator 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 MoYoYoTech/Translator 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 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 serve -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 serve -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 serve -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
- Atomic Chat new
- OpenClaw new
How to use MoYoYoTech/Translator with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf MoYoYoTech/Translator:Q5_0
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "MoYoYoTech/Translator:Q5_0" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- 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
File size: 2,735 Bytes
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license: mit
---
## 环境安装
### 系统环境
> 1. macOS 14.5 或更高版本m系列芯片(如M1、M2等)。
> 2. 确保安装了 Xcode 和命令行工具:
```bash
# 安装Xcode命令行工具
xcode-select --install
# 或者者安装Xcode
# 打开App Store,搜索Xcode并安装。
# 安装完成后,打开Xcode并同意许可协议。
```
> 3. 安装portaudio, cmake 环境
```bash
brew install portaudio cmake
```
### Python 基本环境
> 1. 创建一个新的 Python 虚拟环境:
```bash
conda create -n translator python=3.11.9
# 如果没有安装 conda,请先安装 conda 或 Miniconda。
# 参考 [Miniconda 安装指南](https://docs.conda.io/en/latest/miniconda.html)。
```
> 2. 激活虚拟环境:
```bash
conda activate translator
```
> 3. 克隆仓库:
```bash
# 如果没有安装git lfs,请先安装git lfs。
# 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。
git lfs install
# repo中包含了模型文件,clone时间可能会比较长。
git clone https://huggingface.co/MoYoYoTech/Translator.git
# 进入项目目录
cd Translator
```
> 4. 使用以下命令安装所需的 Python 库:
```bash
pip install -r requirements.txt
```
### WhisperCPP 安装
> 1. 克隆 WhisperCPP 仓库并初始化子模块:
```bash
git clone --recurse-submodules https://github.com/absadiki/pywhispercpp.git && cd pywhispercpp/whisper.cpp && git checkout 170b2faf75c2f6173ef947e6ef346961f3368e1b && cd ../..
```
> 2. 切换到特定的提交版本:
```bash
cd pywhispercpp && git checkout d43237bd75076615349004270a721e3ebe1deabb
```
> 3. 安装 WhisperCPP,确保启用 CoreML 支持:
```bash
WHISPER_COREML=1 python setup.py install && cd ..
```
### Llama-cpp-python 安装
> 1. 克隆 Llama-cpp-python 仓库并初始化子模块:
```bash
git clone --recurse-submodules https://github.com/abetlen/llama-cpp-python.git
```
> 2. 切换到特定的提交版本:
```bash
cd llama-cpp-python && git checkout 99f2ebfde18912adeb7f714b49c1ddb624df3087 && cd vendor/llama.cpp && git checkout 80f19b41869728eeb6a26569957b92a773a2b2c6 && cd ../..
```
> 3. 使用以下命令安装 Llama-cpp-python,确保启用 Metal 支持:
```bash
CMAKE_ARGS="-DGGML_METAL=on" pip install . && cd ..
```
## 运行
> 1. 运行命令 `python main.py` 启动应用程序。
> 2. 打开浏览器并访问 `http://localhost:9191/` 以使用该应用。
> 3. 推荐是用 Chrome 浏览器,Safari/Firefox 可能会出现一些问题。
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