BioReason_new/run.sh

#!/bin/bash

# Example training scripts for Protein-LLM project

# =============================================================================
# 1. Contrastive Pre-training (Stage 1)
# Train QFormer projection layer for protein-text alignment
# =============================================================================
echo "Starting contrastive pre-training..."

python train_contrastive.py \
    --text_model_name "Qwen/Qwen3-1.7B" \
    --protein_model_name "facebook/esm2_t6_8M_UR50D" \
    --qformer_model_name "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext" \
    --dataset_name "wanglab/protein_descriptions" \
    --output_dir "./contrastive_outputs" \
    --num_epochs 10 \
    --batch_size 32 \
    --learning_rate 1e-4 \
    --temperature 0.07 \
    --freeze_protein_model \
    --freeze_text_model \
    --max_length_protein 1024 \
    --max_length_text 512 \
    --eval_dataset \
    --use_wandb \
    --wandb_project "protein-llm-contrastive" \
    --logging_steps 100 \
    --eval_steps 500 \
    --save_steps 1000

echo "Contrastive pre-training completed!"

# =============================================================================
# 2. Supervised Fine-tuning (Stage 2)
# Fine-tune the entire model on protein function prediction tasks
# =============================================================================
echo "Starting supervised fine-tuning..."

python train_protein_qwen.py \
    --model_type "protein-llm" \
    --text_model_name "Qwen/Qwen3-1.7B" \
    --protein_model_name "facebook/esm2_t6_8M_UR50D" \
    --qformer_model_name "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext" \
    --dataset_type "protein_function" \
    --protein_function_data_dir_huggingface "wanglab/protein_function" \
    --text_model_finetune True \
    --protein_model_finetune False \
    --num_query_tokens 32 \
    --seed 23 \
    --batch_size 4 \
    --max_epochs 5 \
    --learning_rate 5e-5 \
    --weight_decay 0.01 \
    --gradient_accumulation_steps 8 \
    --max_length_protein 1024 \
    --max_length_text 1024 \
    --lora_rank 32 \
    --lora_alpha 64 \
    --lora_dropout 0.05 \
    --num_gpus 1 \
    --strategy "ddp" \
    --wandb_project "esm2-qwen3-1.7b-finetune" \
    --checkpoint_dir "./checkpoints" \
    --log_dir "./logs" \
    --cache_dir "/model-weights"

echo "Supervised fine-tuning completed!"

# =============================================================================
# 3. GRPO Training (Stage 3)
# Reinforcement learning with Group Relative Policy Optimization
# =============================================================================
echo "Starting GRPO training..."

python protein_reason.py \
    --output_dir "./grpo_outputs" \
    --model_name_or_path "Qwen/Qwen3-0.6B" \
    --protein_model_name_or_path "facebook/esm2_t6_8M_UR50D" \
    --qformer_model_name_or_path "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext" \
    --dataset_name "wanglab/protein_function" \
    --sft_checkpoint "./checkpoints/best_model" \
    --per_device_train_batch_size 4 \
    --gradient_accumulation_steps 4 \
    --num_train_epochs 3 \
    --learning_rate 1e-6 \
    --beta 0.04 \
    --temperature 0.6 \
    --top_p 0.95 \
    --top_k 20 \
    --max_completion_length 800 \
    --num_generations 8 \
    --reward_funcs "xmlcount" "soft_format" "strict_format" "correctness" \
    --lora_r 32 \
    --lora_alpha 64 \
    --lora_dropout 0.05 \
    --freeze_protein_modules \
    --logging_steps 2 \
    --eval_strategy "steps" \
    --eval_steps 100 \
    --save_steps 200 \
    --report_to "wandb" \
    --log_completions

echo "GRPO training completed!"

echo "All training stages completed successfully!"