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 Desktop
- 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 @earendil-works/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
- 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
- Hermes Agent
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
- OpenClaw
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"
liumaolin commited on
Commit ·
d289ad0
1
Parent(s): 4c23586
添加热词文件路径配置,并在生成模型时使用热词参数。
Browse files- config.py +1 -1
- moyoyo_asr_models/hotwords.txt +0 -0
- transcribe/helpers/funasr.py +2 -5
config.py
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@@ -83,4 +83,4 @@ LLM_SYS_PROMPT_EN = """
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你是一个英中文翻译专家,将用户输入的英文翻译成中文,用户可以向助手发送需要翻译的内容,助手会回答相应的翻译结果,并确保符合英文语言习惯,你可以调整语气和风格,并考虑到某些词语的文化内涵和地区差异。同时作为翻译家,需将英文翻译成具有信达雅标准的中文。"信" 即忠实于原文的内容与意图;"达" 意味着译文应通顺易懂,表达清晰;"雅" 则追求译文的文化审美和语言的优美。目标是创作出既忠于原作精神,又符合目标语言文化和读者审美的翻译。
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"""
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-
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你是一个英中文翻译专家,将用户输入的英文翻译成中文,用户可以向助手发送需要翻译的内容,助手会回答相应的翻译结果,并确保符合英文语言习惯,你可以调整语气和风格,并考虑到某些词语的文化内涵和地区差异。同时作为翻译家,需将英文翻译成具有信达雅标准的中文。"信" 即忠实于原文的内容与意图;"达" 意味着译文应通顺易懂,表达清晰;"雅" 则追求译文的文化审美和语言的优美。目标是创作出既忠于原作精神,又符合目标语言文化和读者审美的翻译。
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"""
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hotwords_file = MODEL_DIR / 'hotwords.txt'
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moyoyo_asr_models/hotwords.txt
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File without changes
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transcribe/helpers/funasr.py
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import time
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import uuid
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from logging import getLogger
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import numpy as np
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from funasr import AutoModel
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import soundfile as sf
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import config
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def warmup(self, warmup_steps=1):
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warmup_soundfile = f"{config.ASSERT_DIR}/jfk.flac"
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for _ in range(warmup_steps):
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self.model.generate(input=warmup_soundfile, disable_pbar=True)
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def transcribe(self, audio_buffer: bytes, language):
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audio_frames = np.frombuffer(audio_buffer, dtype=np.float32)
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# sf.write(f'{config.ASSERT_DIR}/{time.time()}.wav', audio_frames, samplerate=16000)
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try:
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output = self.model.generate(input=audio_frames, disable_pbar=True)
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return output
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except Exception as e:
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logger.error(e)
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from logging import getLogger
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import numpy as np
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from funasr import AutoModel
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import config
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def warmup(self, warmup_steps=1):
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warmup_soundfile = f"{config.ASSERT_DIR}/jfk.flac"
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for _ in range(warmup_steps):
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self.model.generate(input=warmup_soundfile, disable_pbar=True, hotword=config.hotwords_file.as_posix())
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def transcribe(self, audio_buffer: bytes, language):
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audio_frames = np.frombuffer(audio_buffer, dtype=np.float32)
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# sf.write(f'{config.ASSERT_DIR}/{time.time()}.wav', audio_frames, samplerate=16000)
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try:
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output = self.model.generate(input=audio_frames, disable_pbar=True, hotword=config.hotwords_file.as_posix())
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return output
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except Exception as e:
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logger.error(e)
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