清理之之越界文件

scripts/training/zhizhi_v2/run_zhizhi_v2.sh

@@ -1,133 +1 @@
-#!/bin/bash
+# 此文件已迁移至 zhizhi 仓库
-# ======================================
-# 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 "========================================"