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"
daihui.zhang commited on
Commit ·
eabbf72
1
Parent(s): 5d48b84
fix bug
Browse files
transcribe/strategy.py
CHANGED
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@@ -345,8 +345,8 @@ class TranscriptStabilityAnalyzer:
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if curr_first and prev_first:
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core = curr_first.compare(prev_first)
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-
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if core >= 0.8:
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yield from self._yield_commit_results(curr_first, curr_rest, curr_first.is_end_sentence())
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return
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if curr_first and prev_first:
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core = curr_first.compare(prev_first)
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has_punctuation = curr_first.has_punctuation()
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if core >= 0.8 and has_punctuation:
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yield from self._yield_commit_results(curr_first, curr_rest, curr_first.is_end_sentence())
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return
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transcribe/whisper_llm_serve.py
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@@ -102,7 +102,7 @@ class WhisperTranscriptionService(ServeClientBase):
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"""应用语音活动检测来优化音频缓冲区"""
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with self.lock:
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if self.frames_np is not None:
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self._c+= 1
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frame = self.frames_np.copy()
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processed_audio = self._translate_pipe.voice_detect(frame.tobytes())
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self.frames_np = np.frombuffer(processed_audio.audio, dtype=np.float32).copy()
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@@ -147,6 +147,7 @@ class WhisperTranscriptionService(ServeClientBase):
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result = self._translate_pipe.transcrible(audio_buffer.tobytes(), self.source_language)
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segments = result.segments
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logger.debug(f"📝 Transcrible: {self.text_separator.join(seg.text for seg in segments)} ")
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log_block("📝 Transcrible output", f"{self.text_separator.join(seg.text for seg in segments)}", "")
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log_block("📝 Transcrible time", f"{(time.perf_counter() - start_time):.3f}", "s")
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"""应用语音活动检测来优化音频缓冲区"""
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with self.lock:
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if self.frames_np is not None:
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# self._c+= 1
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frame = self.frames_np.copy()
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processed_audio = self._translate_pipe.voice_detect(frame.tobytes())
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self.frames_np = np.frombuffer(processed_audio.audio, dtype=np.float32).copy()
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result = self._translate_pipe.transcrible(audio_buffer.tobytes(), self.source_language)
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segments = result.segments
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logger.debug(f"📝 Transcrible Segments: {segments} ")
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logger.debug(f"📝 Transcrible: {self.text_separator.join(seg.text for seg in segments)} ")
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log_block("📝 Transcrible output", f"{self.text_separator.join(seg.text for seg in segments)}", "")
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log_block("📝 Transcrible time", f"{(time.perf_counter() - start_time):.3f}", "s")
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