Instructions to use MoYoYoTech/Translator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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

Xin Zhang commited on Apr 14, 2025

Commit

4ce78ee

2 Parent(s): 5cd2109 81f1d0a

Merge branch 'dev'

Browse files

* dev:
update traffic time log

Files changed (1) hide show

transcribe/whisper_llm_serve.py +5 -2

transcribe/whisper_llm_serve.py CHANGED Viewed

@@ -225,7 +225,7 @@ class WhisperTranscriptionService(ServeClientBase):
         if not segments:
             return
         for ana_result in self._transcrible_analysis.analysis(segments, len(audio_buffer)/self.sample_rate):
             if (cut_index :=ana_result.cut_index)>0:
                 # 更新音频缓冲区，移除已处理部分
@@ -234,7 +234,7 @@ class WhisperTranscriptionService(ServeClientBase):
                 translated_context = self._translate_text(ana_result.context)
             else:
                 translated_context = self._translate_text_large(ana_result.context)
             yield TransResult(
                 seg_id=ana_result.seg_id,
                 context=ana_result.context,
@@ -243,6 +243,9 @@ class WhisperTranscriptionService(ServeClientBase):
                 tran_content=translated_context,
                 partial=ana_result.partial()
             )
     def _send_result_to_client(self, result: TransResult) -> None:

         if not segments:
             return
+        start_time = time.perf_counter()
         for ana_result in self._transcrible_analysis.analysis(segments, len(audio_buffer)/self.sample_rate):
             if (cut_index :=ana_result.cut_index)>0:
                 # 更新音频缓冲区，移除已处理部分
                 translated_context = self._translate_text(ana_result.context)
             else:
                 translated_context = self._translate_text_large(ana_result.context)
             yield TransResult(
                 seg_id=ana_result.seg_id,
                 context=ana_result.context,
                 tran_content=translated_context,
                 partial=ana_result.partial()
             )
+            current_time = time.perf_counter()
+            time_diff = current_time - start_time
+            log_block("🚦 Traffic timestamp diff: ", round(time_diff, 2), 's')
     def _send_result_to_client(self, result: TransResult) -> None: