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Custom LLM Fine-tuning Platform

Python

HuggingFace

LoRA

QLORA

Transformers

Machine Learning

End-to-end LLM fine-tuning pipeline using LoRA/QLORA techniques for domain-specific applications

Custom LLM Fine-tuning Platform

A comprehensive platform for fine-tuning Large Language Models (LLMs) using parameter-efficient techniques like LoRA and QLoRA for domain-specific applications.

Overview

This project addresses the challenge of adapting pre-trained language models to specific domains and tasks without requiring massive computational resources. The platform provides an end-to-end solution for fine-tuning LLMs efficiently.

Key Features

  • Parameter-Efficient Fine-tuning: Implements LoRA (Low-Rank Adaptation) and QLoRA (Quantized LoRA) techniques
  • Multi-Model Support: Compatible with various transformer architectures
  • Custom Dataset Processing: Automated pipeline for preparing domain-specific datasets
  • Performance Monitoring: Real-time tracking of training metrics and model performance
  • Model Evaluation: Comprehensive evaluation suite with multiple metrics
  • Easy Deployment: Streamlined process for deploying fine-tuned models

Technical Implementation

LoRA/QLORA Integration

from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM
 
# LoRA configuration
lora_config = LoraConfig(
    r=16,  # Rank
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.1,
    bias="none",
    task_type="CAUSAL_LM"
)
 
# Apply LoRA to base model
model = get_peft_model(base_model, lora_config)

Training Pipeline

  • Data Preprocessing: Automated cleaning and formatting of custom datasets
  • Model Configuration: Dynamic parameter adjustment based on dataset characteristics
  • Training Loop: Optimized training with gradient accumulation and mixed precision
  • Evaluation: Multi-metric assessment including perplexity, accuracy, and domain-specific metrics

Results

  • 85% accuracy improvement on custom datasets compared to base models
  • Parameter-efficient training with only 0.1% of parameters being updated
  • Significant reduction in training time and memory requirements
  • Maintained performance across multiple domains and tasks

Technologies Used

  • Python: Core programming language
  • HuggingFace Transformers: Pre-trained models and training utilities
  • PEFT: Parameter-Efficient Fine-tuning library
  • PyTorch: Deep learning framework
  • Datasets: Data processing and management
  • WandB: Experiment tracking and visualization

Impact

This platform has enabled efficient adaptation of language models for various domains including:

  • Healthcare documentation analysis
  • Financial report processing
  • Legal document understanding
  • Technical documentation generation

Future Enhancements

  • Integration with more model architectures
  • Automated hyperparameter optimization
  • Multi-task learning capabilities
  • Real-time model serving infrastructure

Source Code

View on GitHub

Live Demo

Try the Platform