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
| from fastapi import FastAPI, WebSocket, WebSocketDisconnect | |
| from transcribe.serve import WhisperTranscriptionService | |
| from uuid import uuid1 | |
| from logging import getLogger | |
| import numpy as np | |
| from transcribe.processing import ProcessingPipes | |
| from contextlib import asynccontextmanager | |
| from multiprocessing import Process, freeze_support | |
| from fastapi.staticfiles import StaticFiles | |
| from fastapi.responses import RedirectResponse | |
| import os | |
| from transcribe.utils import pcm_bytes_to_np_array | |
| from config import BASE_DIR | |
| logger = getLogger(__name__) | |
| async def get_audio_from_websocket(websocket)->np.array: | |
| """ | |
| Receives audio buffer from websocket and creates a numpy array out of it. | |
| Args: | |
| websocket: The websocket to receive audio from. | |
| Returns: | |
| A numpy array containing the audio. | |
| """ | |
| frame_data = await websocket.receive_bytes() | |
| if frame_data == b"END_OF_AUDIO": | |
| return False | |
| return pcm_bytes_to_np_array(frame_data) | |
| async def lifespan(app:FastAPI): | |
| global pipe | |
| pipe = ProcessingPipes() | |
| pipe.wait_ready() | |
| logger.info("Pipeline is ready.") | |
| yield | |
| FRONTEND_DIR = os.path.join(BASE_DIR, "web") | |
| app = FastAPI(lifespan=lifespan) | |
| app.mount("/app", StaticFiles(directory=FRONTEND_DIR, html=True), name="web") | |
| pipe = None | |
| async def root(): | |
| return RedirectResponse(url="/app/") | |
| async def translate(websocket: WebSocket): | |
| query_parameters_dict = websocket.query_params | |
| from_lang, to_lang = query_parameters_dict.get('from'), query_parameters_dict.get('to') | |
| client = WhisperTranscriptionService( | |
| websocket, | |
| pipe, | |
| language=from_lang, | |
| dst_lang=to_lang, | |
| client_uid=f"{uuid1()}", | |
| ) | |
| if from_lang and to_lang and client: | |
| logger.info(f"Source lange: {from_lang} -> Dst lange: {to_lang}") | |
| await websocket.accept() | |
| try: | |
| while True: | |
| frame_data = await get_audio_from_websocket(websocket) | |
| client.add_frames(frame_data) | |
| except WebSocketDisconnect: | |
| return | |
| if __name__ == '__main__': | |
| freeze_support() | |
| import uvicorn | |
| uvicorn.run(app, host="0.0.0.0", port=9191) | |