Domain-Specific Fine-tuning: Adapting to Your Industry¶

Overview¶

Domain-Specific Fine-tuning tailors general models to specialized domains (medical, legal, finance, code) where domain knowledge is crucial.

Goal: Model learns domain-specific terminology, patterns, and knowledge
Approaches: Continued pretraining, SFT on domain data, domain adaptation
Quality: 15-30% improvement typical
Cost: Moderate ($500-5K)

Domain Fine-tuning Strategies¶

Strategy 1: Continued Pretraining¶

Continue pretraining on domain text (no labels needed)

Data: Raw domain text (books, papers, documentation)
  - Medical: Papers, textbooks, clinical notes
  - Legal: Case law, contracts, regulations
  - Finance: News, earnings reports, research
  - Code: GitHub repositories, documentation

Benefit:
  - Model learns domain vocabulary
  - Learns domain patterns
  - No annotation needed (unsupervised)
  - Fast and cheap

Example:
```python
# Continue pretraining on domain corpus
from transformers import TextDataset, DataCollatorForLanguageModeling, Trainer

dataset = TextDataset(
    tokenizer=tokenizer,
    file_path="medical_texts.txt",
    block_size=512
)

data_collator = DataCollatorForLanguageModeling(
    tokenizer=tokenizer,
    mlm=False
)

trainer = Trainer(
    model=model,
    args=training_args,
    data_collate_fn=data_collator,
    train_dataset=dataset,
)

trainer.train()  # Continue pretraining

Pros: ✅ No annotation needed ✅ Fast (only few hours) ✅ Learn domain vocab + patterns ✅ Cheap ($50-200)

Cons: ❌ Generic knowledge gain ❌ Not task-specific ❌ May pick up noise from domain

### Strategy 2: Domain-Specific SFT

SFT on domain (instruction, output) pairs

Data: Annotated domain tasks - Medical: Diagnosis given symptoms - Legal: Legal advice for scenarios - Finance: Portfolio recommendations - Code: Code generation/review

Example (Medical): { "instruction": "What are the symptoms of Type 2 Diabetes?", "output": "Type 2 Diabetes symptoms include: increased thirst, frequent urination, fatigue, blurred vision, slow-healing sores..." }

Example (Legal): { "instruction": "What rights does a tenant have for repairs?", "output": "Under most housing codes, landlords must maintain habitable conditions including: working plumbing, heat, structural integrity..." }

Pros: ✅ Task-specific knowledge ✅ Better instruction following in domain ✅ Targeted quality improvement

Cons: ❌ Requires annotation ❌ More expensive ($1K-5K for 1K-10K examples)

### Strategy 3: Hybrid (Continued PT + Domain SFT)

Best approach combines both:

Phase 1: Continued pretraining (optional, 1-2 hours) - On raw domain text - Model learns domain basics - Fast, cheap

Phase 2: Domain-specific SFT (3-4 hours) - On annotated examples - Model learns to follow domain instructions - More expensive but targeted

Result: - Knowledge + skills - 20-30% quality improvement - Total cost: $200-1000

---

## Domain-Specific Considerations

### Medical Domain

Challenges: - Needs high accuracy (safety critical) - Complex terminology - Privacy concerns (HIPAA, etc.) - Rapid updates (new treatments)

Approaches: - SFT on medical literature + annotated QA - Safety filtering (don't give medical advice) - Regular updates (quarterly retraining) - Human review of important outputs

Quality targets: - Factuality: >95% (very high) - Adherence to guidelines: >90% - Avoiding harmful advice: >99% - Disclaimer usage: 100%

Data sources: - PubMed (medical literature) - UpToDate (clinical guidelines) - Annotated medical QA datasets - Institutional medical texts

### Legal Domain

Challenges: - High accuracy needed (legal consequences) - Complex, verbose language - Jurisdiction-specific (US, UK, EU different) - Frequent changes (new laws)

Approaches: - SFT on case law + legal documents - Jurisdiction-specific fine-tuning - Disclaimer about not being legal advice - Regular updates (laws change)

Quality targets: - Legal accuracy: >90% - Relevant to jurisdiction: 100% - Disclaimer clarity: 100% - Ethical compliance: 100%

Data sources: - Case law databases - Legal contracts - Statutes and regulations - Legal textbooks - Annotated legal QA

### Code Domain

Challenges: - Highly technical - Language-specific (Python vs Java) - Security concerns (code quality) - Rapidly evolving (new frameworks)

Approaches: - SFT on code + code explanation - Language-specific fine-tuning - Safety filtering (avoid bad practices) - Update frequently (new frameworks)

Quality targets: - Code correctness: >85% - Code safety: >95% - Style adherence: >90% - Explanation clarity: >80%

Data sources: - GitHub code (filtered for quality) - Stack Overflow (Q&A) - API documentation - Tutorials and guides - Annotated code datasets

### Finance Domain

Challenges: - High accuracy (financial consequences) - Complex terminology - Real-time data needed - Regulatory requirements

Approaches: - SFT on financial documents - Real-time data integration (stock prices, etc.) - Compliance filtering - Human review for major decisions

Quality targets: - Factual accuracy: >95% - Compliance adherence: 100% - Risk awareness: >95% - Disclosure completeness: 100%

Data sources: - Financial news/reports - Earnings call transcripts - Research reports - Regulatory filings (SEC, etc.) - Annotated financial QA

---

## Domain Adaptation Techniques

### Adapter Methods

Use adapters for domain adaptation (most efficient):

Without adapters: - Fine-tune full model: $1000+ (expensive) - Update all 7B parameters

With adapters (LoRA): - Add small domain-specific adapters: $50 - Update only 0.06% parameters - 20x cheaper!

Implementation:

from peft import LoraConfig, get_peft_model

# Add domain-specific LoRA
lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    target_modules=["q_proj", "v_proj"],
)

domain_model = get_peft_model(base_model, lora_config)

# Fine-tune on domain data
trainer = Trainer(model=domain_model, ...)
trainer.train()

# Save domain adapter (~50MB)
domain_model.save_pretrained("domain_adapter")

# Use domain adapter at inference:
# Load base model + load domain adapter

Advantage: - Cheap ($50 per domain) - Fast (hours per domain) - Multiple domain adapters possible

### Multi-Domain Training

Train single model on multiple domains

Approach: - Domain 1 (Medical): 20% of training data - Domain 2 (Legal): 20% of training data - Domain 3 (Finance): 20% of training data - Domain 4 (Code): 20% of training data - General: 20% of training data

Result: - Model learns multiple domains - Better at multi-domain tasks - Single model for multiple purposes - Quality: Slightly lower per-domain but good overall

Use case: - General assistant with domain competencies

---

## Domain Fine-tuning Evaluation

### Domain-Specific Metrics

Beyond standard metrics, measure domain expertise:

Medical: - Clinical accuracy (human MD review) - Guideline adherence - Safety compliance - Terminology usage

Legal: - Legal accuracy - Jurisdiction correctness - Citation accuracy - Ethical compliance

Code: - Code correctness (runs, produces right output) - Code efficiency (time/space complexity) - Style adherence - Security (no vulnerabilities)

Finance: - Calculation accuracy - Market knowledge recency - Risk assessment correctness - Regulatory compliance

Evaluation approach: - Expert review: 5-10% of outputs reviewed by domain expert - Automated tests: Where possible (code correctness, calculations) - Comparative: Compare to domain benchmarks/baselines - Regular: Monitor degradation over time

---

## Best Practices for Domain Fine-tuning

### Do's

✅ Start with high-quality domain data ✅ Include domain terminology in training ✅ Validate on domain experts' evaluation ✅ Update regularly as domain evolves ✅ Version both model and domain data ✅ Test for domain-specific edge cases ✅ Document domain-specific behavior ✅ Monitor for domain drift

### Don'ts

❌ Don't mix domains without care (can hurt) ❌ Don't ignore domain-specific terminology ❌ Don't skip expert review ❌ Don't assume generic model works in domain ❌ Don't use outdated data ❌ Don't ignore safety considerations ❌ Don't forget to validate changes

---

## Cost & Timeline

### Typical Project

Project: Fine-tune for medical domain

Timeline: Week 1: Data collection and preparation - Gather medical texts - Annotate QA examples (or use existing) - Quality check

Week 2: Model training - Continued pretraining (1-2 hours) - Domain-specific SFT (3-4 hours) - Evaluation and iteration

Week 3: Deployment and monitoring - Package model - Deploy to production - Monitor performance

Cost breakdown: - Data collection: $500-2000 (annotators or buying datasets) - Model training: $200-500 (GPU compute) - Evaluation: $500-1000 (expert review) - Total: $1200-3500 - Payback: Depends on use (QA, summarization, etc.)

Break-even analysis: - Fine-tuned model: $3000 upfront - Generic model: ~$0.01 per query accuracy loss (~20%) - If 1M queries/year @ 10% higher accuracy - ROI: ~100% (worth it!) ```

Key Takeaways¶

🎯 Domain knowledge critical: 15-30% quality improvement
📚 Continued PT: Learn domain vocab (cheap, unsupervised)
🏥 Domain SFT: Learn domain tasks (more expensive, supervised)
⚙️ Adapters: Multiple domain experts with one base model
🔄 Regular updates: Domains evolve, retrain periodically

Fine Tuning Fundamentals - Overview
Instruction Tuning & Sft - Foundation
Fine Tuning Best Practices - General tips apply

Domain-Specific Fine-tuning: Adapting to Your Industry¶

Overview¶

Domain Fine-tuning Strategies¶

Strategy 1: Continued Pretraining¶

Key Takeaways¶

Related Notes in Finetuning Subdirectory¶