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 new
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 new
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
| import numpy as np | |
| import soundfile as sf | |
| import time | |
| def audio_stream_generator(audio_file_path, chunk_size=4096, simulate_realtime=True): | |
| """ | |
| 音频流生成器,从音频文件中读取数据并以流的方式输出 | |
| 参数: | |
| audio_file_path: 音频文件路径 | |
| chunk_size: 每个数据块的大小(采样点数) | |
| simulate_realtime: 是否模拟实时流处理的速度 | |
| 生成: | |
| numpy.ndarray: 每次生成一个chunk_size大小的np.float32数据块 | |
| """ | |
| # 加载音频文件 | |
| audio_data, sample_rate = sf.read(audio_file_path) | |
| # 确保音频数据是float32类型 | |
| if audio_data.dtype != np.float32: | |
| audio_data = audio_data.astype(np.float32) | |
| # 如果是立体声,转换为单声道 | |
| if len(audio_data.shape) > 1 and audio_data.shape[1] > 1: | |
| audio_data = audio_data.mean(axis=1) | |
| print(f"已加载音频文件: {audio_file_path}") | |
| print(f"采样率: {sample_rate} Hz") | |
| print(f"音频长度: {len(audio_data)/sample_rate:.2f} 秒") | |
| # 计算每个块的时长(秒) | |
| chunk_duration = chunk_size / sample_rate if simulate_realtime else 0 | |
| # 按块生成数据 | |
| audio_len = len(audio_data) | |
| for pos in range(0, audio_len, chunk_size): | |
| # 获取当前块 | |
| end_pos = min(pos + chunk_size, audio_len) | |
| chunk = audio_data[pos:end_pos] | |
| # 如果块大小不足,用0填充 | |
| if len(chunk) < chunk_size: | |
| padded_chunk = np.zeros(chunk_size, dtype=np.float32) | |
| padded_chunk[:len(chunk)] = chunk | |
| chunk = padded_chunk | |
| # 模拟实时处理的延迟 | |
| if simulate_realtime: | |
| time.sleep(chunk_duration) | |
| yield chunk | |
| print("音频流处理完成") |