ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter8/prompt-distillation/train_trl.sh

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+#!/bin/bash
+# Training script using Hugging Face TRL (more common and easier to use than verl)
+#
+# This script uses the widely-adopted TRL SFTTrainer instead of verl.
+# Benefits:
+# - Works directly with JSONL (no parquet conversion needed)
+# - Better documentation and community support
+# - Simpler setup and fewer dependencies
+# - Standard Hugging Face workflow
+
+set -x
+
+# Default configuration
+MODEL_NAME=${1:-"Qwen/Qwen3-30B-A3B-Instruct-2507"}
+OUTPUT_DIR=${2:-"./models/prompt_distillation_trl"}
+TRAIN_FILE=${3:-"./data/prompt_distillation_lang.jsonl"}
+
+echo "============================================"
+echo "Prompt Distillation Training with TRL"
+echo "============================================"
+echo "Model: $MODEL_NAME"
+echo "Output: $OUTPUT_DIR"
+echo "Train file: $TRAIN_FILE"
+echo "============================================"
+echo ""
+
+# Check if training file exists
+if [ ! -f "$TRAIN_FILE" ]; then
+    echo "❌ Training file not found: $TRAIN_FILE"
+    echo "Please run data generation first:"
+    echo "  python create_data.py"
+    exit 1
+fi
+
+# Run training with OpenAI-style hyperparameters
+python train_sft_trl.py \
+    --model_name "$MODEL_NAME" \
+    --output_dir "$OUTPUT_DIR" \
+    --train_file "$TRAIN_FILE" \
+    --use_lora \
+    --lora_rank 32 \
+    --lora_alpha 16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 4 \
+    --gradient_accumulation_steps 4 \
+    --learning_rate 2e-4 \
+    --max_length 2048 \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type cosine_with_min_lr
+
+echo ""
+echo "============================================"
+echo "✅ Training Complete!"
+echo "============================================"
+echo "Model saved to: $OUTPUT_DIR"