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
daihui.zhang commited on
Commit ·
9494251
1
Parent(s): 0a6c788
rename filenames
Browse files
main.py
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@@ -3,7 +3,7 @@ from transcribe.serve import WhisperTranscriptionService
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from uuid import uuid1
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from logging import getLogger
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import numpy as np
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from transcribe.
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from contextlib import asynccontextmanager
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from multiprocessing import Process, freeze_support
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from fastapi.staticfiles import StaticFiles
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@@ -33,7 +33,7 @@ async def get_audio_from_websocket(websocket)->np.array:
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@asynccontextmanager
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async def lifespan(app:FastAPI):
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global pipe
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pipe =
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pipe.wait_ready()
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logger.info("Pipeline is ready.")
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yield
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from uuid import uuid1
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from logging import getLogger
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import numpy as np
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from transcribe.process_pipeline import ProcessingPipes
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from contextlib import asynccontextmanager
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from multiprocessing import Process, freeze_support
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from fastapi.staticfiles import StaticFiles
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@asynccontextmanager
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async def lifespan(app:FastAPI):
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global pipe
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pipe = ProcessingPipes()
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pipe.wait_ready()
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logger.info("Pipeline is ready.")
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yield
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transcribe/helpers/vadprocessor.py
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from copy import deepcopy
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from queue import Queue, Empty
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from time import time
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from config import VAD_MODEL_PATH
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from silero_vad import load_silero_vad
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from copy import deepcopy
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from time import time
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from config import VAD_MODEL_PATH
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from silero_vad import load_silero_vad
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transcribe/{translatepipes.py → process_pipeline.py}
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from .pipelines import WhisperPipe, MetaItem, WhisperChinese, Translate7BPipe, FunASRPipe, VadPipe, TranslatePipe
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from .utils import timer
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class
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def __init__(self) -> None:
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self._process = []
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from .pipelines import WhisperPipe, MetaItem, WhisperChinese, Translate7BPipe, FunASRPipe, VadPipe, TranslatePipe
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from .utils import timer
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class ProcessingPipes:
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def __init__(self) -> None:
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self._process = []
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transcribe/serve.py
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from api_model import TransResult, Message
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from .utils import log_block, start_thread, get_text_separator, filter_words
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from .
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from transcribe.pipelines import MetaItem
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logger = getLogger("TranscriptionService")
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"""
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def __init__(self, websocket, pipe:
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print('>>>>>>>>>>>>>>>> init service >>>>>>>>>>>>>>>>>>>>>>')
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print('src_lang:', language)
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self.source_language = language # 源语言
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from api_model import TransResult, Message
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from .utils import log_block, start_thread, get_text_separator, filter_words
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from .process_pipeline import ProcessingPipes
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from .pipelines import MetaItem
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logger = getLogger("TranscriptionService")
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"""
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def __init__(self, websocket, pipe: ProcessingPipes, language=None, dst_lang=None, client_uid=None):
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print('>>>>>>>>>>>>>>>> init service >>>>>>>>>>>>>>>>>>>>>>')
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print('src_lang:', language)
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self.source_language = language # 源语言
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