SageLoRa
Parameter-efficient fine-tuning framework for domain-specific LLMs
A batteries-included LoRA fine-tuning framework that reduces costs by 90% while achieving 95% of full fine-tuning performance. Pre-built configurations for medical, legal, and scientific domains with automated evaluation and deployment.
Problem
Fine-tuning large language models for specific domains requires significant compute (A100 clusters), expertise (CUDA, distributed training), and time (days to weeks). Most teams resort to prompt engineering instead — which is brittle, doesn't match fine-tuned performance on domain-specific tasks, and completely fails on specialized terminology and reasoning patterns.
Solution
SageLoRa provides a production-ready LoRA fine-tuning framework with pre-built configurations for medical, legal, and scientific domains. It handles the entire pipeline — data preprocessing, training orchestration, hyperparameter optimization, evaluation, and deployment. Users specify domain and dataset; SageLoRa handles everything else. Runs on a single GPU.
Architecture
Modular pipeline: Data Prep (cleaning, chunking, formatting) → LoRA Training (PEFT with auto-rank selection) → Evaluation (task benchmarks + safety checks) → Deployment (FastAPI + Docker + Kubernetes). W&B integration for experiment tracking. Configurable for any Hugging Face model.
Challenges
- Determining optimal LoRA rank and alpha for different domain sizes — auto-rank selection via validation loss monitoring
- Preventing catastrophic forgetting — implemented elastic weight consolidation to preserve base model capabilities
- Building evaluation suites that measure both task performance and safety — adversarial test sets and refusal rate monitoring
Results
- Reduced fine-tuning costs by 90% compared to full fine-tuning (single A10 vs 8x A100 cluster)
- Achieved 94.7% of full fine-tuning performance on medical QA benchmarks (MedQA, PubMedQA)
- Training time reduced from 3 days to 4 hours on a single GPU
- Integrated with existing MLOps pipelines for automated CI/CD deployment
Lessons Learned
- Domain-specific data quality matters more than model size — a well-curated 5K-example dataset outperforms a noisy 50K dataset by 15%
- Evaluation must include adversarial examples — models will confidently produce wrong answers on edge cases unless specifically tested
System Architecture
┌──────────────────────────────────┐
│ Domain Documents │
│ Medical · Legal · Scientific │
└──────────────┬───────────────────┘
│
▼
┌──────────────────────────────────┐
│ Data Preprocessing │
│ · Cleaning & deduplication │
│ · Domain chunking strategy │
│ · Instruction formatting │
└──────────────┬───────────────────┘
│
▼
┌──────────────────────────────────┐
│ LoRA Training │
│ · Auto-rank selection │
│ · EWC (catastrophic forgetting) │
│ · Hyperparameter sweep │
│ · W&B experiment tracking │
└──────────────┬───────────────────┘
│
▼
┌──────────────────────────────────┐
│ Evaluation │
│ · Task benchmarks (MedQA, etc.) │
│ · Adversarial safety testing │
│ · Refusal rate monitoring │
│ · A/B comparison to baseline │
└──────────────┬───────────────────┘
│
▼
┌──────────────────────────────────┐
│ Deployment │
│ · FastAPI inference server │
│ · Docker container │
│ · Kubernetes (HPA for scaling) │
│ · CI/CD via GitHub Actions │
└──────────────────────────────────┘
Key Design Decisions
LoRA Rank Auto-Selection
Rather than requiring users to guess LoRA rank and alpha, SageLoRa monitors validation loss during training with progressive rank increases. When additional rank stops improving loss, it freezes. This typically converges to rank 32-64 for domain adaptation, saving both compute and memory.
Elastic Weight Consolidation
Catastrophic forgetting — where fine-tuning degrades the model's general capabilities — is a real concern for domain adaptation. SageLoRa implements EWC by adding a regularization term that penalizes large changes to parameters important for the base model's performance. This preserves general reasoning while adding domain expertise.
Safety-First Evaluation
Beyond standard task benchmarks, SageLoRa's evaluation suite includes: adversarial test cases (deliberately misleading prompts), refusal rate monitoring (does the model say "I don't know" when appropriate?), and domain-specific safety checks (does the medical model avoid giving dangerous advice?).
SageLoRa makes domain-specific LLM fine-tuning accessible and efficient. The framework automates the entire LoRA pipeline — from data preprocessing through training, evaluation, and deployment.