dora/node-hub/dora-llama-cpp-python

• dora-llama-cpp-python
• • Features
  • Getting started
  • • Installation
  • Usage
  • • Configuration Options
  • Example: Speech Assistant Pipeline
  • • Running the Example
  • Contribution Guide
  • License

dora-llama-cpp-python

A Dora node that provides access to LLaMA models using llama-cpp-python for efficient CPU/GPU inference.

Features

• GPU acceleration support (CUDA on Linux, Metal on macOS)
• Easy integration with speech-to-text and text-to-speech pipelines
• Configurable system prompts and activation words
• Lightweight CPU inference with GGUF models
• Thread-level CPU optimization
• Adjustable context window size

Getting started

Installation

uv venv -p 3.11 --seed
uv pip install -e .

Usage

The node can be configured in your dataflow YAML file:

# Using a HuggingFace model
- id: dora-llama-cpp-python
  build: pip install -e path/to/dora-llama-cpp-python
  path: dora-llama-cpp-python
  inputs:
    text: source_node/text  # Input text to generate response for
  outputs:
    - text  # Generated response text
  env:
    MODEL_NAME_OR_PATH: "TheBloke/Llama-2-7B-Chat-GGUF"
    MODEL_FILE_PATTERN: "*Q4_K_M.gguf"
    SYSTEM_PROMPT: "You're a very succinct AI assistant with short answers."
    ACTIVATION_WORDS: "what how who where you"
    MAX_TOKENS: "512"
    N_GPU_LAYERS: "35"     # Enable GPU acceleration
    N_THREADS: "4"         # CPU threads
    CONTEXT_SIZE: "4096"   # Maximum context window

Configuration Options

• MODEL_NAME_OR_PATH: Path to local model file or HuggingFace repo id (default: "TheBloke/Llama-2-7B-Chat-GGUF")
• MODEL_FILE_PATTERN: Pattern to match model file when downloading from HF (default: "*Q4_K_M.gguf")
• SYSTEM_PROMPT: Customize the AI assistant's personality/behavior
• ACTIVATION_WORDS: Space-separated list of words that trigger model response
• MAX_TOKENS: Maximum number of tokens to generate (default: 512)
• N_GPU_LAYERS: Number of layers to offload to GPU (default: 0, set to 35 for GPU acceleration)
• N_THREADS: Number of CPU threads to use (default: 4)
• CONTEXT_SIZE: Maximum context window size (default: 4096)

Example: Speech Assistant Pipeline

This example shows how to create a conversational AI pipeline that:

1. Captures audio from microphone
2. Converts speech to text
3. Generates AI responses
4. Converts responses back to speech

nodes:
  - id: dora-microphone
    build: pip install dora-microphone
    path: dora-microphone
    inputs:
      tick: dora/timer/millis/2000
    outputs:
      - audio

  - id: dora-vad
    build: pip install dora-vad
    path: dora-vad
    inputs:
      audio: dora-microphone/audio
    outputs:
      - audio
      - timestamp_start

  - id: dora-whisper
    build: pip install dora-distil-whisper
    path: dora-distil-whisper
    inputs:
      input: dora-vad/audio
    outputs:
      - text

  - id: dora-llama-cpp-python
    build: pip install -e .
    path: dora-llama-cpp-python
    inputs:
      text: dora-whisper/text
    outputs:
      - text
    env:
      MODEL_NAME: "TheBloke/Llama-2-7B-Chat-GGUF"
      MODEL_FILE_PATTERN: "*Q4_K_M.gguf"
      SYSTEM_PROMPT: "You're a helpful assistant."
      ACTIVATION_WORDS: "hey help what how"
      MAX_TOKENS: "512"
      N_GPU_LAYERS: "35"
      N_THREADS: "4"
      CONTEXT_SIZE: "4096"

  - id: dora-tts
    build: pip install dora-kokoro-tts
    path: dora-kokoro-tts
    inputs:
      text: dora-llama-cpp-python/text
    outputs:
      - audio

Running the Example

dora build example.yml
dora run example.yml

Contribution Guide

• Format with ruff:

uv pip install ruff
uv run ruff check . --fix

• Lint with ruff:

uv run ruff check .

• Test with pytest

uv pip install pytest
uv run pytest . # Test

License

dora-llama-cpp-python is released under the MIT License