REPLACE_ME: Custom LLM Fine-tuning for Domain Expertise
REPLACE_ME: Fine-tuned large language models for specialized domain applications with improved accuracy and reduced hallucinations.
Achieved 40% improvement in domain-specific accuracy by fine-tuning LLaMA-2 7B model using LoRA techniques, reducing inference costs by 60% while maintaining performance.
The Problem
REPLACE_ME: The client needed a language model that could understand and generate content specific to their industry domain. Off-the-shelf models were producing generic responses with frequent hallucinations and lacked the specialized knowledge required for their use case.
Approach
- 1
Conducted comprehensive analysis of domain-specific requirements and data patterns
- 2
Curated and preprocessed a high-quality dataset of 50K+ domain-specific examples
- 3
Implemented LoRA (Low-Rank Adaptation) fine-tuning to efficiently adapt LLaMA-2 7B
- 4
Developed custom evaluation metrics for domain-specific performance assessment
- 5
Optimized inference pipeline for production deployment with cost constraints
Solution
REPLACE_ME: Built a comprehensive fine-tuning pipeline using PyTorch and Hugging Face Transformers. Implemented LoRA adapters to efficiently fine-tune the model while preserving general capabilities. Created automated evaluation framework with domain-specific benchmarks.
Technologies Used
Results & Impact
40% improvement in domain-specific accuracy compared to base model
60% reduction in inference costs through efficient LoRA implementation
95% reduction in hallucinations for domain-specific queries
Successfully deployed to production serving 10K+ daily requests