README_TRAINING.md

Training Qwen2.5-3B-Instruct for Evaluation Agent with CoT Reasoning

This repository contains scripts and configurations for training Qwen2.5-3B-Instruct model on evaluation agent data with Chain-of-Thought (CoT) reasoning format.

Overview

The training pipeline processes evaluation results from:

• VBench: Video quality evaluation results
• T2I-CompBench: Text-to-image composition evaluation results
• Open Domain: Open-ended query evaluation results

All results are in CoT (Chain-of-Thought) reasoning format from proprietary models.

Dataset Preparation

1. Data Cleaning and Conversion

Run the data cleaning script to convert raw evaluation results into LLaMA-Factory format:

python clean_and_convert_data.py

This script:

• Processes JSON files from ea-data/agent/ subdirectories
• Converts CoT-style evaluation results into instruction-response pairs
• Outputs to LLaMA-Factory/data/evaluation_agent_cot_dataset.json
• Updates LLaMA-Factory/data/dataset_info.json with dataset metadata

Dataset Statistics

• Total training examples: ~860 (from initial processing)
• Format: Alpaca-style (instruction, input, output)

Training Configurations

1. LoRA Fine-tuning (Recommended)

Configuration: train_qwen2.5_eval_agent.yaml

Key parameters:

• Model: Qwen/Qwen2.5-3B-Instruct
• Method: LoRA (rank=16, alpha=32)
• Batch size: 2 per device × 4 gradient accumulation
• Learning rate: 5e-5 with cosine scheduler
• Epochs: 3
• Memory requirement: ~16GB VRAM

2. Full Fine-tuning

Configuration: train_qwen2.5_eval_agent_full.yaml

Key parameters:

• Model: Qwen/Qwen2.5-3B-Instruct
• Method: Full fine-tuning with DeepSpeed
• Gradient checkpointing enabled
• Memory requirement: ~32GB+ VRAM

Training Execution

Quick Start

# Make script executable
chmod +x train_qwen2.5_eval_agent.sh

# Run training
./train_qwen2.5_eval_agent.sh

Manual Training

cd LLaMA-Factory
llamafactory-cli train ../train_qwen2.5_eval_agent.yaml

Distributed Training

For multi-GPU training:

CUDA_VISIBLE_DEVICES=0,1,2,3 \
torchrun --nproc_per_node 4 \
--master_port 29500 \
src/train.py ../train_qwen2.5_eval_agent.yaml

Inference

After training, run inference with:

llamafactory-cli chat ../inference_qwen2.5_eval_agent.yaml

Or use the API:

llamafactory-cli api ../inference_qwen2.5_eval_agent.yaml

Model Merging

To merge LoRA weights with base model:

llamafactory-cli export \
    --model_name_or_path Qwen/Qwen2.5-3B-Instruct \
    --adapter_name_or_path saves/qwen2.5-3b/lora/eval_agent_cot \
    --template qwen \
    --finetuning_type lora \
    --export_dir models/qwen2.5-3b-eval-agent-merged \
    --export_size 4 \
    --export_legacy_format false

Monitoring Training

TensorBoard

tensorboard --logdir saves/qwen2.5-3b/lora/eval_agent_cot

Loss Plots

Training loss plots are automatically saved to the output directory.

Evaluation

The model will be evaluated on:

• CoT reasoning quality
• Evaluation accuracy
• Response coherence
• Format consistency

Directory Structure

evaluation_agent_dev/
├── ea-data/agent/           # Raw evaluation data
│   ├── vbench_results/
│   ├── t2i_results/
│   └── open_results/
├── LLaMA-Factory/           # Training framework
│   └── data/
│       ├── evaluation_agent_cot_dataset.json  # Processed dataset
│       └── dataset_info.json
├── clean_and_convert_data.py    # Data processing script
├── train_qwen2.5_eval_agent.yaml    # LoRA training config
├── train_qwen2.5_eval_agent_full.yaml   # Full training config
├── inference_qwen2.5_eval_agent.yaml    # Inference config
└── train_qwen2.5_eval_agent.sh      # Training script

Requirements

• Python 3.9+
• PyTorch 2.0+
• CUDA 11.6+
• LLaMA-Factory (installed)
• 16GB+ VRAM for LoRA, 32GB+ for full fine-tuning

Tips

1. Memory Management: Use gradient checkpointing and DeepSpeed for larger batch sizes
2. Learning Rate: Start with 5e-5 for LoRA, 2e-5 for full fine-tuning
3. Data Quality: Review generated dataset for quality before training
4. Checkpointing: Save checkpoints frequently (every 200 steps)
5. Mixed Precision: Use bf16 for faster training and lower memory usage

Troubleshooting

• OOM Errors: Reduce batch size or enable gradient checkpointing
• Slow Training: Enable Flash Attention 2 if available
• Poor Results: Increase training epochs or adjust learning rate
• Data Issues: Check JSON parsing in data cleaning script

Next Steps

1. Expand dataset with more evaluation examples
2. Implement custom evaluation metrics
3. Fine-tune on specific evaluation dimensions
4. Deploy model for production use

License

Follow the licenses of:

• Qwen2.5 model
• LLaMA-Factory framework
• Original evaluation datasets