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
david commited on
Commit ·
0d81579
1
Parent(s): 3556e74
add string replace
Browse files- config.py +3 -1
- transcribe/pipelines/pipe_vad.py +2 -2
- transcribe/utils.py +2 -14
config.py
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import pathlib
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import re
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import logging
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DEBUG = False
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LOG_LEVEL = logging.DEBUG if DEBUG else logging.INFO
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@@ -73,4 +75,4 @@ LLM_SYS_PROMPT_EN = """
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"""
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hotwords_file = MODEL_DIR / 'hotwords.txt'
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hotwords_json = MODEL_DIR / 'hotwords.json'
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import pathlib
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import re
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import logging
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import json
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DEBUG = False
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LOG_LEVEL = logging.DEBUG if DEBUG else logging.INFO
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"""
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hotwords_file = MODEL_DIR / 'hotwords.txt'
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hotwords_json = json.loads((MODEL_DIR / 'hotwords.json').read_text())
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transcribe/pipelines/pipe_vad.py
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@@ -28,10 +28,10 @@ class VadPipe(BasePipe):
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def init(cls):
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if cls.vac is None:
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cls.vac = FixedVADIterator(
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threshold=0.
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sampling_rate=cls.sample_rate,
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# speech_pad_ms=10
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min_silence_duration_ms =
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# speech_pad_ms = 30,
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)
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cls.vac.reset_states()
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def init(cls):
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if cls.vac is None:
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cls.vac = FixedVADIterator(
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threshold=0.6,
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sampling_rate=cls.sample_rate,
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# speech_pad_ms=10
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min_silence_duration_ms = 80,
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# speech_pad_ms = 30,
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)
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cls.vac.reset_states()
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transcribe/utils.py
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@@ -67,25 +67,13 @@ def replace_hotwords(text: str) -> str:
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Returns:
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The string with hotwords replaced.
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"""
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# Using the provided absolute path for simplicity in this example
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hotwords_path = config.hotwords_file.as_posix()
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try:
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with open(hotwords_path, 'r', encoding='utf-8') as f:
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hotwords = json.load(f)
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except FileNotFoundError:
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print(f"Error: hotwords.json not found at {hotwords_path}")
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return text
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except json.JSONDecodeError:
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print(f"Error: Could not decode JSON from {hotwords_path}")
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return text
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processed_text = text
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# Iterate through the hotwords dictionary
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for key, value in
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# Replace all occurrences of the key with the value in the text
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processed_text = processed_text.replace(key, value)
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return processed_text
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Returns:
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The string with hotwords replaced.
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"""
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processed_text = text
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# Iterate through the hotwords dictionary
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for key, value in config.hotwords_json.items():
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# Replace all occurrences of the key with the value in the text
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processed_text = processed_text.replace(key, value)
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logging.debug(f"Replace string: {text} => {processed_text}")
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return processed_text
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