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#!/bin/bash
# Start CodeLlama fine-tuning with chat format dataset
set -e
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
cd "$SCRIPT_DIR"
# Activate virtual environment
source /venv/main/bin/activate
echo "======================================================================"
echo "🚀 Starting CodeLlama Fine-tuning with Chat Format Dataset"
echo "======================================================================"
# Configuration
BASE_MODEL="models/base-models/CodeLlama-7B-Instruct"
TRAIN_DATASET="datasets/processed/split_chat_format/train.jsonl"
VAL_DATASET="datasets/processed/split_chat_format/val.jsonl"
OUTPUT_DIR="training-outputs/codellama-fifo-v2-chat"
# Check if datasets exist
if [ ! -f "$TRAIN_DATASET" ]; then
echo "❌ Error: Training dataset not found: $TRAIN_DATASET"
exit 1
fi
if [ ! -f "$VAL_DATASET" ]; then
echo "❌ Error: Validation dataset not found: $VAL_DATASET"
exit 1
fi
echo "📊 Configuration:"
echo " Base Model: $BASE_MODEL"
echo " Train Dataset: $TRAIN_DATASET"
echo " Val Dataset: $VAL_DATASET"
echo " Output Directory: $OUTPUT_DIR"
echo ""
# Start training
# Note: val-dataset is auto-detected if val.jsonl exists in same directory as train.jsonl
python3 scripts/training/finetune_codellama.py \
--base-model "$BASE_MODEL" \
--dataset "$TRAIN_DATASET" \
--output-dir "$OUTPUT_DIR" \
--max-length 1536 \
--num-epochs 5 \
--learning-rate 2e-5 \
--batch-size 4 \
--gradient-accumulation 4 \
--lora-r 48 \
--lora-alpha 96 \
--lora-dropout 0.15 \
--resume-from-checkpoint auto
echo ""
echo "======================================================================"
echo "✅ Training started!"
echo "======================================================================"
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