code/FINETUNE_RECOMMENDATION.md

Fine-Tuning Model Recommendation

Current Model Performance Comparison

Metric	Llama-3	Qwen	Winner
Validity Rate	86.00%	90.00%	Qwen (+4%)
Tanimoto Similarity	0.1089	0.0634	Llama-3 (1.7x better)
Mass Error (Da)	314.28	957.96	Llama-3 (3.0x better)
Size Ratio	0.585	2.390	Llama-3 (much closer to 1.0)
Failed Predictions	14%	10%	Qwen (4% better)

Analysis

Llama-3 Advantages

• ✅ Much better structural similarity (0.109 vs 0.063) - 72% better
• ✅ Much better mass accuracy (314 Da vs 958 Da) - 3x better
• ✅ Better size prediction (0.585 vs 2.390 ratio) - closer to target
• ✅ More consistent behavior - understands molecular complexity better
• ✅ Better foundation for fine-tuning - already has good understanding

Qwen Disadvantages

• ❌ Very poor structural similarity (0.063) - worse than Llama-3
• ❌ Very large mass errors (958 Da) - 3x worse than Llama-3
• ❌ Generates molecules that are too large (2.4x target size)
• ❌ Poor understanding of molecular complexity - generates long C chains

Recommendation: Fine-tune Llama-3

Reasons:

1. Better baseline performance: Llama-3 already shows much better understanding of molecular structure
2. More room for improvement: With fine-tuning, can improve from 0.109 to potentially 0.3+ Tanimoto
3. Better mass accuracy: Already at 314 Da, fine-tuning can bring it down to <50 Da
4. Proven track record: Llama models have excellent fine-tuning support and community resources
5. Better prompt following: Llama-3 better understands complexity requirements

Fine-Tuning Strategy:

Option 1: Llama-3-8B-Instruct (Recommended)

• Model: meta-llama/Meta-Llama-3-8B-Instruct
• Method: LoRA (QLoRA with 4-bit quantization)
• Target modules: q_proj, k_proj, v_proj, o_proj (attention layers)
• Training data: Use prepare_finetune_data.py to create chat-formatted data
• Expected improvements:
  • Tanimoto: 0.109 → 0.25-0.35 (2-3x improvement)
  • Mass Error: 314 Da → 50-100 Da (3-6x improvement)
  • Validity: 86% → 95%+ (with better prompt following)
  • Size Ratio: 0.585 → 0.8-0.9 (closer to target)

Option 2: Llama-2-13B-Chat (Alternative)

• Model: meta-llama/Llama-2-13B-Chat
• Advantage: Larger model, may have better performance
• Disadvantage: Requires more GPU memory (~26GB for 4-bit)

Fine-Tuning Implementation:

# 1. Prepare fine-tuning data
python scripts/prepare_finetune_data.py \
  --train-jsonl runs/rag_molt5_train.jsonl \
  --spec-embeddings runs/spec_embeddings_train.npy \
  --faiss-index runs/index_all \
  --smiles-path data/pubchem_1k.smi \
  --mgf-path /cluster/tufts/liulab/yiwan01/SpecBridge/data/MassSpecGym_train.mgf \
  --output-jsonl runs/finetune_data.jsonl \
  --top-k 5

# 2. Fine-tune with LoRA
python scripts/train_llama_lora.py \
  --model-name meta-llama/Meta-Llama-3-8B-Instruct \
  --train-jsonl runs/finetune_data.jsonl \
  --output-dir runs/llama3_finetuned \
  --load-in-4bit \
  --use-chat-template \
  --batch-size 4 \
  --epochs 3 \
  --lr 2e-4 \
  --max-length 2048

Expected Training Time:

• Data preparation: ~10-30 minutes (depending on dataset size)
• Fine-tuning: ~2-6 hours (depending on GPU and dataset size)
• GPU requirements: ~16GB VRAM (with 4-bit quantization)

Success Metrics:

After fine-tuning, expect:

• Tanimoto Similarity: >0.25 (vs current 0.109)
• Mass Error: <100 Da (vs current 314 Da)
• Validity Rate: >95% (vs current 86%)
• Size Ratio: 0.8-0.9 (vs current 0.585)

Conclusion

Recommendation: Fine-tune Llama-3-8B-Instruct

Llama-3 shows significantly better baseline performance in structural similarity and mass accuracy, making it the better foundation for fine-tuning. With proper fine-tuning, we can expect 2-3x improvement in Tanimoto similarity and 3-6x improvement in mass accuracy.