diabolic6045/ELN-Llama-1B-base

Evolution Learning Network (ELN) with QLoRA and Genetic Algorithms For LLM

Overview

This project implements an Evolution Learning Network (ELN) to fine-tune transformer-based models like LLaMA using a combination of Quantized Low-Rank Adaptation (QLoRA) and Genetic Algorithms (GA). The primary objective is to evolve a population of models across multiple generations to optimize for performance (fitness) and specialization, while maintaining diversity.

Key Features

• Efficient model fine-tuning using QLoRA with 4-bit quantization
• Evolutionary strategies with tournament selection and blended crossover
• Adaptive mutation rates based on generation progress
• Comprehensive experiment tracking with WandB
• Diversity maintenance through LoRA weight fingerprinting

Model Details

Base Model

• Name: meta-llama/Llama-3.2-1B
• Architecture: Transformer-based causal language model

Quantization Configuration

• Type: 4-bit quantization using bitsandbytes
• Parameters:
  • Compute Type: torch.float16
  • Quantization Type: "nf4" (Nonlinear)
  • Double Quantization: Enabled
  • Nested Quantization: Enabled

LoRA Configuration

• Rank (r): 8
• Alpha: 16
• Target Modules: q_proj, v_proj
• Dropout: 0.05
• Task Type: CAUSAL_LM

Training Configuration

• Optimizer: paged_adamw_8bit
• Precision: Mixed precision (fp16)
• Batch Size Range: 2-16 (genome-controlled)
• Learning Rate Range: 1e-6 to 1e-2 (genome-controlled)
• Epochs Range: 1-4 (genome-controlled)

Dataset

Source

• Name: WikiText-2 Raw
• Configuration: wikitext-2-raw-v1
• Processing:
  • Max Length: 128 tokens
  • Padding: Fixed to max length
  • Splits: train, validation (general), test (specific)

Evolution Process

Population Management

1. Initialization:

   • Population Size: 6 models
   • Initial random mutations (20% rate)
   • Randomized hyperparameter genomes

2. Selection & Evolution:

   • Tournament selection (k=3)
   • Blended crossover of LoRA weights
   • Adaptive mutation rates (decreases with generations)
   • Hyperparameter mutation with controlled ranges

Experimental Results

Evolution Progress

The evolutionary learning process was run for 8 generations with a population size of 6 models. The experiment tracked several key metrics across generations:

Evolution Metrics

Fitness Progression

• Initial Performance: Best fitness started at ~0.480 (Generation 1)
• Convergence: Gradual decline to ~0.476 by Generation 8
• Population Stability: Average fitness closely tracked best fitness after Generation 2, indicating good convergence
• Fitness Range: Maintained between 0.476-0.480 throughout evolution

Specialization Trends

• High Baseline: Started at ~0.9975 specialization
• Consistency: Fluctuated minimally between 0.9975-0.9990
• Peak Performance: Reached ~0.9991 specialization in Generation 6
• Population Average: Maintained above 0.997 throughout evolution

Comparison with Standard Training

The comparison reveals several key differences between ELN and standard training:

Fitness Metrics

• ELN: 0.4762 final fitness with stable progression
• Standard: 0.4779 final fitness with steeper learning curve
• Difference: ~0.3% performance gap, favoring standard training

Training Characteristics

• Loss Reduction:
  • Standard: Sharp initial drop followed by gradual improvement
  • ELN: More controlled, stable descent
• Specialization:
  • Standard: More variable specialization scores
  • ELN: Consistently high specialization maintenance

Key Advantages of ELN

1. More stable learning trajectory
2. Better maintenance of model diversity
3. Consistent specialization scores
4. Reduced risk of catastrophic forgetting

Hardware & Framework Requirements

Hardware

• Multi-GPU support via DistributedDataParallel
• Memory optimization through gradient accumulation
• Hardware monitoring (CPU/GPU usage)

Dependencies

• transformers
• peft
• bitsandbytes
• accelerate
• wandb
• torch >= 2.0

Usage

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("diabolic6045/ELN-Llama-1B")
model = AutoModelForCausalLM.from_pretrained("diabolic6045/ELN-Llama-1B")

Framework Versions

• PEFT 0.14.0

Future Work

• Explore larger population sizes and generations
• Implement additional mutation strategies
• Test on diverse datasets and tasks
• Investigate multi-objective optimization

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Citation

If you use this work, please cite:

@misc{eln2024,
  title={Evolution Learning Network (ELN): Combining QLoRA and Genetic Algorithms for LLM Optimization},
  year={2024},
  howpublished={\url{https://github.com/diabolic6045/ELN-llama-1B-adapter}}
}

Related Works

This project builds upon several key papers and techniques:

@article{dettmers2023qlora,
  title={QLoRA: Efficient Finetuning of Quantized LLMs},
  author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},
  journal={arXiv preprint arXiv:2305.14314},
  year={2023}
}

@article{touvron2023llama,
  title={Llama: Open and Efficient Foundation Language Models},
  author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
  journal={arXiv preprint arXiv:2302.13971},
  year={2023}
}

@article{such2017deep,
  title={Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning},
  author={Such, Felipe Petroski and Madhavan, Vashisht and Conti, Edoardo and Lehman, Joel and Stanley, Kenneth O and Clune, Jeff},
  journal={arXiv preprint arXiv:1712.06567},
  year={2017}
}

@article{real2019regularized,
  title={Regularized Evolution for Image Classifier Architecture Search},
  author={Real, Esteban and Aggarwal, Alok and Huang, Yanping and Le, Quoc V},
  journal={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  number={01},
  pages={4780--4789},
  year={2019}
}

These citations cover:

1. QLoRA quantization and fine-tuning technique
2. The base LLaMA model architecture
3. Deep neuroevolution fundamentals
4. Regularized evolution in neural networks

The implementation also draws inspiration from recent advances in evolutionary algorithms and neural architecture search.

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