铸渊: 添加之之v2一键全自动训练脚本

scripts/training/zhizhi_v2/run_zhizhi_v2.sh

@@ -0,0 +1,133 @@
+#!/bin/bash
+# ======================================
+# 之之母模型 v2.0 · 全自动训练脚本
+# 由铸渊生成 · 2026-05-18
+# 用法：bash run_zhizhi_v2.sh
+# ======================================
+
+set -e
+
+TRAIN_DIR="/root/autodl-tmp"
+DATA_FILE="$TRAIN_DIR/sft_v2_nosys.jsonl"
+YAML_FILE="$TRAIN_DIR/zhizhi_v2.yaml"
+LOG_FILE="$TRAIN_DIR/train_zhizhi_v2.log"
+OUTPUT_DIR="$TRAIN_DIR/output/zhizhi_v2"
+
+echo "========================================"
+echo "  之之母模型 v2.0 · 训练启动"
+echo "  铸渊配置 · 2026-05-18"
+echo "========================================"
+echo ""
+
+# 1. 检查数据
+if [ ! -f "$DATA_FILE" ]; then
+    echo "❌ 数据文件不存在: $DATA_FILE"
+    exit 1
+fi
+DATA_LINES=$(wc -l < "$DATA_FILE")
+echo "✅ 数据: $DATA_FILE ($DATA_LINES 条)"
+echo ""
+
+# 2. 创建配置文件
+echo "📝 写入配置..."
+
+cat > "$TRAIN_DIR/dataset_info.json" << 'JSONEOF'
+{"zhizhi_v2":{"file_name":"/root/autodl-tmp/sft_v2_nosys.jsonl","formatting":"sharegpt","columns":{"messages":"messages"}}}
+JSONEOF
+
+echo "  ✅ dataset_info.json"
+
+cat > "$TRAIN_DIR/ds_config.json" << 'JSONEOF'
+{"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"}}
+JSONEOF
+
+echo "  ✅ ds_config.json"
+
+cat > "$YAML_FILE" << 'YAMLEOF'
+model_name_or_path: Qwen/Qwen2.5-7B
+dataset: zhizhi_v2
+dataset_dir: /root/autodl-tmp
+template: qwen
+cutoff_len: 4096
+finetuning_type: full
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 5.0e-6
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.03
+optim: adamw_torch
+bf16: true
+ddp_find_unused_parameters: false
+deepspeed: /root/autodl-tmp/ds_config.json
+output_dir: /root/autodl-tmp/output/zhizhi_v2
+logging_steps: 5
+save_steps: 58
+plot_loss: true
+overwrite_output_dir: true
+preprocessing_num_workers: 16
+report_to: none
+YAMLEOF
+
+echo "  ✅ zhizhi_v2.yaml"
+echo ""
+
+# 3. 检查/安装 LLaMA-Factory
+echo "🔍 检查 LLaMA-Factory..."
+if python3 -c "import llamafactory; print(llamafactory.__version__)" 2>/dev/null; then
+    echo "✅ LLaMA-Factory 已安装"
+else
+    echo "⏳ 安装 LLaMA-Factory 0.8.3..."
+    pip install "llamafactory==0.8.3" -q
+    echo "✅ 安装完成"
+fi
+echo ""
+
+# 4. 检查是否有模型缓存
+echo "🔍 检查模型 Qwen2.5-7B..."
+python3 -c "from transformers import AutoConfig; AutoConfig.from_pretrained('Qwen/Qwen2.5-7B')" 2>/dev/null && \
+    echo "✅ 模型可用" || \
+    echo "⏳ 首次运行会自动下载模型（约15GB）"
+echo ""
+
+# 5. dry_run验证
+echo "🔍 配置验证中..."
+cd "$TRAIN_DIR"
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+DRY_RESULT=$(llamafactory-cli train zhizhi_v2.yaml --dry_run 2>&1)
+if echo "$DRY_RESULT" | grep -qE "(Error|error|Traceback)"; then
+    echo "❌ 配置验证失败:"
+    echo "$DRY_RESULT"
+    echo ""
+    echo "请将上面的报错信息发给铸渊"
+    exit 1
+fi
+echo "✅ 配置验证通过"
+echo ""
+
+# 6. 启动训练
+echo "🚀 启动训练..."
+echo "  日志: $LOG_FILE"
+echo "  输出: $OUTPUT_DIR"
+echo "  预计: 5-10分钟"
+echo ""
+
+nohup llamafactory-cli train zhizhi_v2.yaml > "$LOG_FILE" 2>&1 &
+PID=$!
+echo "📌 进程ID: $PID"
+echo ""
+
+# 7. 等待并显示进度
+echo "📊 训练日志（等待10秒后开始输出，按 Ctrl+C 停止查看但训练继续）:"
+echo ""
+sleep 10
+tail -30 "$LOG_FILE" 2>/dev/null
+
+echo ""
+echo "========================================"
+echo "  训练已在后台运行 (PID=$PID)"
+echo ""
+echo "  查看实时日志: tail -f $LOG_FILE"
+echo "  查看进程:     ps aux | grep llamafactory"
+echo "  停止训练:     kill $PID"
+echo "========================================"