134 lines
3.9 KiB
Bash
134 lines
3.9 KiB
Bash
#!/bin/bash
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# ======================================
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# 之之母模型 v2.0 · 全自动训练脚本
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# 由铸渊生成 · 2026-05-18
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# 用法：bash run_zhizhi_v2.sh
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# ======================================
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set -e
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TRAIN_DIR="/root/autodl-tmp"
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DATA_FILE="$TRAIN_DIR/sft_v2_nosys.jsonl"
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YAML_FILE="$TRAIN_DIR/zhizhi_v2.yaml"
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LOG_FILE="$TRAIN_DIR/train_zhizhi_v2.log"
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OUTPUT_DIR="$TRAIN_DIR/output/zhizhi_v2"
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echo "========================================"
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echo " 之之母模型 v2.0 · 训练启动"
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echo " 铸渊配置 · 2026-05-18"
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echo "========================================"
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echo ""
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# 1. 检查数据
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if [ ! -f "$DATA_FILE" ]; then
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echo "❌ 数据文件不存在: $DATA_FILE"
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exit 1
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fi
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DATA_LINES=$(wc -l < "$DATA_FILE")
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echo "✅ 数据: $DATA_FILE ($DATA_LINES 条)"
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echo ""
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# 2. 创建配置文件
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echo "📝 写入配置..."
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cat > "$TRAIN_DIR/dataset_info.json" << 'JSONEOF'
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{"zhizhi_v2":{"file_name":"/root/autodl-tmp/sft_v2_nosys.jsonl","formatting":"sharegpt","columns":{"messages":"messages"}}}
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JSONEOF
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echo " ✅ dataset_info.json"
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cat > "$TRAIN_DIR/ds_config.json" << 'JSONEOF'
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{"train_batch_size":"auto","train_micro_batch_size_per_gpu":1,"gradient_accumulation_steps":8,"optimizer":{"type":"AdamW","params":{"lr":5e-6,"betas":[0.9,0.999],"eps":1e-8,"weight_decay":0.01}},"scheduler":{"type":"WarmupLR","params":{"warmup_min_lr":0,"warmup_max_lr":5e-6,"warmup_num_steps":10}},"zero_optimization":{"stage":2,"offload_optimizer":{"device":"cpu","pin_memory":true}},"gradient_clipping":1.0,"bf16":{"enabled":"auto"}}
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JSONEOF
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echo " ✅ ds_config.json"
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cat > "$YAML_FILE" << 'YAMLEOF'
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model_name_or_path: Qwen/Qwen2.5-7B
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dataset: zhizhi_v2
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dataset_dir: /root/autodl-tmp
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template: qwen
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cutoff_len: 4096
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finetuning_type: full
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per_device_train_batch_size: 1
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gradient_accumulation_steps: 8
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learning_rate: 5.0e-6
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num_train_epochs: 3.0
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lr_scheduler_type: cosine
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warmup_ratio: 0.03
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optim: adamw_torch
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bf16: true
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ddp_find_unused_parameters: false
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deepspeed: /root/autodl-tmp/ds_config.json
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output_dir: /root/autodl-tmp/output/zhizhi_v2
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logging_steps: 5
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save_steps: 58
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plot_loss: true
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overwrite_output_dir: true
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preprocessing_num_workers: 16
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report_to: none
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YAMLEOF
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echo " ✅ zhizhi_v2.yaml"
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echo ""
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# 3. 检查/安装 LLaMA-Factory
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echo "🔍 检查 LLaMA-Factory..."
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if python3 -c "import llamafactory; print(llamafactory.__version__)" 2>/dev/null; then
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echo "✅ LLaMA-Factory 已安装"
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else
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echo "⏳ 安装 LLaMA-Factory 0.8.3..."
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pip install "llamafactory==0.8.3" -q
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echo "✅ 安装完成"
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fi
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echo ""
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# 4. 检查是否有模型缓存
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echo "🔍 检查模型 Qwen2.5-7B..."
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python3 -c "from transformers import AutoConfig; AutoConfig.from_pretrained('Qwen/Qwen2.5-7B')" 2>/dev/null && \
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echo "✅ 模型可用" || \
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echo "⏳ 首次运行会自动下载模型（约15GB）"
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echo ""
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# 5. dry_run验证
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echo "🔍 配置验证中..."
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cd "$TRAIN_DIR"
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export CUDA_VISIBLE_DEVICES=0,1,2,3
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DRY_RESULT=$(llamafactory-cli train zhizhi_v2.yaml --dry_run 2>&1)
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if echo "$DRY_RESULT" | grep -qE "(Error|error|Traceback)"; then
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echo "❌ 配置验证失败:"
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echo "$DRY_RESULT"
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echo ""
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echo "请将上面的报错信息发给铸渊"
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exit 1
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fi
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echo "✅ 配置验证通过"
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echo ""
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# 6. 启动训练
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echo "🚀 启动训练..."
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echo " 日志: $LOG_FILE"
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echo " 输出: $OUTPUT_DIR"
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echo " 预计: 5-10分钟"
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echo ""
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nohup llamafactory-cli train zhizhi_v2.yaml > "$LOG_FILE" 2>&1 &
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PID=$!
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echo "📌 进程ID: $PID"
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echo ""
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# 7. 等待并显示进度
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echo "📊 训练日志（等待10秒后开始输出，按 Ctrl+C 停止查看但训练继续）:"
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echo ""
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sleep 10
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tail -30 "$LOG_FILE" 2>/dev/null
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echo ""
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echo "========================================"
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echo " 训练已在后台运行 (PID=$PID)"
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echo ""
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echo " 查看实时日志: tail -f $LOG_FILE"
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echo " 查看进程: ps aux | grep llamafactory"
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echo " 停止训练: kill $PID"
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echo "========================================"
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