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Self Adaptive Llms
self-adaptive-llms
S
Self Adaptive Llms
AI ModelDevelopment & Tools#Artificial Intelligence#Large Language Models#Adaptive#Reinforcement Learning#Open SourceStandard PicksOpen Source
Overview :
SakanaAI/self-adaptive-llms is an adaptive framework called Transformer2, designed to address the challenges of traditional fine-tuning methods, which are computationally intensive and have static capabilities in handling diverse tasks. This framework adjusts large language models (LLMs) in real time during inference using a two-step mechanism: first, a scheduling system identifies task attributes; then, task-specific 'expert' vectors trained via reinforcement learning are dynamically mixed to achieve target behavior for the input prompt. Key advantages include real-time task adaptability, computational efficiency, and flexibility. Developed by the SakanaAI team, this project is open-source on GitHub, currently boasting 195 stars and 12 forks.
Target Users :
The target audience includes developers and researchers dealing with diverse tasks. They can utilize this framework to enhance the adaptability and efficiency of models, making it suitable for scenarios requiring real-time task processing and model performance optimization.
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Use Cases
Text classification and sentiment analysis in natural language processing tasks.
Real-time adjustments of the model for multi-language translation tasks based on different languages.
Adjusting response strategies in smart customer service systems based on various client inquiries.
Features
Real-time task adaptability: Quickly identifies and adapts to unseen tasks.
Two-step inference mechanism: Identifies task attributes first, then dynamically mixes expert vectors.
Reinforcement learning training: Optimizes task-specific expert vectors using reinforcement learning.
Open-source framework: Available on GitHub, facilitating use and contributions from developers.
Multi-task handling: Suitable for various tasks and application scenarios.
Efficient computation: More computationally efficient than traditional methods, saving resources.
How to Use
1. Clone the repository: git clone https://github.com/SakanaAI/self-adaptive-llms and navigate to the directory.
2. Install the dependencies: Create a conda environment and install the libraries listed in requirements.txt.
3. Install the task evaluator: Navigate to the evaluation/fishfarm directory and execute pip install -e .
4. Train the model: Run the scripts/train_task_expert.sh script to train the model.
5. Evaluate the model: Select either the prompt-based or few-shots evaluation method as needed and run the corresponding script.
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