Self-adaptation framework for real-time LLM adaptation
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This project introduces Transformer², a self-adaptation framework for Large Language Models (LLMs) designed to address the limitations of traditional, static fine-tuning. It enables LLMs to adapt to unseen tasks in real-time, offering a more dynamic and efficient approach for researchers and developers working with diverse NLP applications.
How It Works
Transformer² adapts LLMs by selectively adjusting singular components of their weight matrices, a novel approach that reduces computational overhead compared to full fine-tuning. During inference, a two-pass mechanism is employed: a dispatch system first identifies task properties, and then task-specific "expert" vectors, trained via reinforcement learning, are dynamically mixed to achieve targeted behavior for incoming prompts. This method allows for efficient, real-time adaptation without retraining the entire model.
Quick Start & Requirements
python=3.11
), activate it, and install dependencies via pip install -r requirements.txt
. The task evaluator requires an additional pip install -e .
from within the evaluation/fishfarm
directory.scripts/train_task_expert.sh
) and evaluation (scripts/eval_prompt_based.sh
, scripts/eval_few_shot.sh
) are provided. Specific model and task configurations are set via script arguments.Highlighted Details
Maintenance & Community
The project is associated with SakanaAI. Further community engagement channels (e.g., Discord, Slack) or roadmap details are not explicitly provided in the README.
Licensing & Compatibility
The project's license is not specified in the README. Compatibility for commercial use or closed-source linking is therefore undetermined.
Limitations & Caveats
The README does not specify the license, which may impact commercial adoption. While the framework aims for real-time adaptation, the actual performance and resource requirements for different tasks are not detailed. The project appears to be research-oriented, and production-readiness is not explicitly stated.
6 months ago
1 week