dora-transformers 1.0.0

dora-transformer

dora-transformers

A Dora node that provides access to Hugging Face transformer models for efficient text generation and chat completion.

Features

• Multi-platform GPU acceleration support (CUDA, CPU, MPS)
• Memory-efficient model loading with 8-bit quantization
• Seamless integration with speech-to-text and text-to-speech pipelines
• Configurable system prompts and activation words
• Support for various transformer models
• Conversation history management
• Optimized for both small and large language models

Getting Started

Installation

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

Usage

Configure the node in your dataflow YAML file:

- id: dora-transformers
  build: pip install -e path/to/dora-transformers
  path: dora-transformers
  inputs:
    text: source_node/text  # Input text to generate response for
  outputs:
    - text  # Generated response text
  env:
    MODEL_NAME: "Qwen/Qwen2.5-0.5B-Instruct"  # Model from Hugging Face
    SYSTEM_PROMPT: "You're a very succinct AI assistant with short answers."
    ACTIVATION_WORDS: "what how who where you"
    MAX_TOKENS: "128"  # Reduced for concise responses
    DEVICE: "cuda"  # Use "cpu" for CPU, "cuda" for NVIDIA GPU, "mps" for Apple Silicon
    ENABLE_MEMORY_EFFICIENT: "true"  # Enable 8-bit quantization and memory optimizations
    TORCH_DTYPE: "float16"  # Use half precision for better memory efficiency

Configuration Options

• MODEL_NAME: Hugging Face model identifier (default: "Qwen/Qwen2.5-0.5B-Instruct")
• 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: 128)
• DEVICE: Computation device to use (default: "cpu")
• ENABLE_MEMORY_EFFICIENT: Enable memory optimizations (default: "true")
• TORCH_DTYPE: Model precision type (default: "auto")

Memory Management

The node includes several memory optimization features:

• 8-bit quantization for CUDA devices
• Automatic CUDA cache clearing
• Memory-efficient model loading
• Half-precision support

Example: Voice Assistant Pipeline

Create a conversational AI pipeline that:

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

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

  - id: dora-vad
    build: pip install -e ../../node-hub/dora-vad
    path: dora-vad
    inputs:
      audio: dora-microphone/audio
    outputs:
      - audio
      - timestamp_start

  - id: dora-distil-whisper
    build: pip install -e ../../node-hub/dora-distil-whisper
    path: dora-distil-whisper
    inputs:
      input: dora-vad/audio
    outputs:
      - text
    env:
      TARGET_LANGUAGE: english

  - id: dora-transformers
    build: pip install -e ../../node-hub/dora-transformers
    path: dora-transformers
    inputs:
      text: dora-distil-whisper/text
    outputs:
      - text
    env:
      MODEL_NAME: "Qwen/Qwen2.5-0.5B-Instruct"
      SYSTEM_PROMPT: "You are an AI assistant that gives extremely concise responses, never more than one or two sentences. Always be direct and to the point."
      ACTIVATION_WORDS: "what how who where you"
      MAX_TOKENS: "128"
      DEVICE: "cuda"
      ENABLE_MEMORY_EFFICIENT: "true"
      TORCH_DTYPE: "float16"

  - id: dora-kokoro-tts
    build: pip install -e ../../node-hub/dora-kokoro-tts
    path: dora-kokoro-tts
    inputs:
      text: dora-transformers/text
    outputs:
      - audio

Running the Example

dora build test.yml
dora run test.yml

Troubleshooting

If you encounter CUDA out of memory errors:

1. Set DEVICE: "cpu" for CPU-only inference
2. Enable memory optimizations with ENABLE_MEMORY_EFFICIENT: "true"
3. Use a smaller model or reduce MAX_TOKENS
4. Set TORCH_DTYPE: "float16" for reduced memory usage

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-transformers is released under the MIT License