\u5f15\u8a00\uff1aAI \u771f\u7684\u80fd\u201c\u8bb0\u4f4f\u201d\u6211\u5417\uff1f\n\u5f88\u591a\u73a9\u5bb6\u5728\u90e8\u7f72\u672c\u5730\u5927\u6a21\u578b\u65f6\u90fd\u4f1a\u9047\u5230\u4e00\u4e2a\u75db\u70b9\uff1a\u6a21\u578b\u4f3c\u4e4e\u6ca1\u6709\u8bb0\u5fc6\u3002<\/p>\n
\u4eca\u5929\u4f60\u6559\u5b83\u4e00\u5957\u72ec\u7279\u7684\u65b9\u6cd5\u8bba\uff0c\u660e\u5929\u5b83\u53c8\u53d8\u56de\u4e86\u90a3\u4e2a\u901a\u7528\u7684\u201c\u964c\u751f\u4eba\u201d\u3002\u867d\u7136\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u77e5\u8bc6\u5e93\uff08RAG\uff09\u8ba9\u5b83\u67e5\u9605\u8d44\u6599\uff0c\u4f46\u8fd9\u53ea\u662f\u201c\u5f00\u5377\u8003\u8bd5\u201d\uff0c\u6a21\u578b\u672c\u8eab\u5e76\u6ca1\u6709\u771f\u6b63\u5b66\u4f1a\u3002<\/p>\n
\u6709\u6ca1\u6709\u529e\u6cd5\u8ba9\u6a21\u578b\u50cf\u4eba\u7c7b\u5b66\u751f\u4e00\u6837\uff0c\u901a\u8fc7\u957f\u671f\u7684\u5b66\u4e60\uff0c\u5c06\u77e5\u8bc6\u5185\u5316\u4e3a\u81ea\u5df1\u7684\u672c\u80fd\uff08L3 \u5c42\u7ea7\u8bb0\u5fc6\uff09\uff1f<\/p>\n
\u7b54\u6848\u662f\u80af\u5b9a\u7684\u3002 \u5373\u4f7f\u4f60\u53ea\u6709\u4e00\u5f20 NVIDIA V100 16GB \u663e\u5361\uff0c\u914d\u5408 OpenClaw \u667a\u80fd\u4f53\u6846\u67b6\uff0c\u4e5f\u80fd\u901a\u8fc7\u4e00\u5957\u201c\u5468\u66f4\u8fed\u4ee3\u201d<\/strong>\u7684\u5fae\u8c03\u8ba1\u5212\uff0c\u6253\u9020\u51fa\u4e00\u4e2a\u771f\u6b63\u61c2\u4f60\u3001\u62e5\u6709\u6c38\u4e45\u8bb0\u5fc6\u7684\u4e13\u5c5e AI\u3002<\/p>\n \u4e00\u3001\u6838\u5fc3\u6982\u5ff5\uff1aAI \u8bb0\u5fc6\u7684\u4e09\u4e2a\u5c42\u7ea7\n\u5728\u5f00\u59cb\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u660e\u786e\u4ec0\u4e48\u662f\u771f\u6b63\u7684\u201c\u8bb0\u5fc6\u201d\u3002<\/p>\n \u5c42\u7ea7 \u6bd4\u55bb \u6280\u672f\u5b9e\u73b0 \u6301\u4e45\u6027 \u9002\u7528\u573a\u666f\nL1: \u4e0a\u4e0b\u6587\u8bb0\u5fc6 \u4e34\u65f6\u62b1\u4f5b\u811a Context Window \u4f1a\u8bdd\u7ed3\u675f\u5373\u6d88\u5931 \u77ed\u671f\u5bf9\u8bdd\nL2: \u5916\u90e8\u77e5\u8bc6\u5e93 \u5f00\u5377\u8003\u8bd5 RAG (\u5411\u91cf\u68c0\u7d22) \u6c38\u4e45\u5b58\u50a8\uff0c\u4f46\u9700\u68c0\u7d22 \u67e5\u8be2\u6587\u6863\u3001\u4e8b\u5b9e\nL3: \u6743\u91cd\u5185\u5316 \u771f\u6b63\u4fee\u5b8c\u8bfe\u7a0b SFT \u5fae\u8c03 (Fine-tuning) \u6c38\u4e45\u5185\u5316\u4e3a\u672c\u80fd \u601d\u7ef4\u903b\u8f91\u3001\u98ce\u683c\u3001\u6280\u80fd\n\u6211\u4eec\u7684\u76ee\u6807\uff1a \u901a\u8fc7\u6bcf\u5468\u4e00\u6b21\u7684\u5fae\u8c03\uff0c\u5c06\u4f60\u7684\u6307\u793a\u4ece L2 \u63d0\u5347\u5230 L3\uff0c\u8ba9\u6a21\u578b\u771f\u6b63\u201c\u5b66\u4f1a\u201d\u4f60\u7684\u601d\u7ef4\u3002<\/p>\n \u4e8c\u3001\u786c\u4ef6\u4e0e\u8f6f\u4ef6\u51c6\u5907\n\u672c\u65b9\u6848\u4e13\u4e3a\u4e2a\u4eba\u5f00\u53d1\u8005\u8bbe\u8ba1\uff0c\u786c\u4ef6\u95e8\u69db\u9002\u4e2d\uff0c\u7a33\u5b9a\u6027\u9ad8\u3002<\/p>\n \u663e\u5361\uff1a NVIDIA V100 16GB\uff08\u6216\u5176\u4ed6 16GB+ \u663e\u5b58\u663e\u5361\uff09\n\u5185\u5b58\uff1a 32GB \u7cfb\u7edf\u5185\u5b58\n\u6a21\u578b\u57fa\u5ea7\uff1a Qwen2.5-7B-Instruct\uff087B \u662f V100 \u5fae\u8c03\u7684\u751c\u871c\u70b9\uff09\n\u667a\u80fd\u4f53\u6846\u67b6\uff1a OpenClaw\uff08\u7528\u4e8e\u65e5\u5e38\u4ea4\u4e92\u4e0e\u6570\u636e\u6536\u96c6\uff09\n\u5fae\u8c03\u5de5\u5177\uff1a LLaMA-Factory\uff08\u652f\u6301 WebUI \u4e0e\u547d\u4ee4\u884c\uff09\n\u4e09\u3001\u201c\u8fdb\u5316\u8ba1\u5212\u201d\uff1a\u5468\u66f4\u8fed\u4ee3\u5de5\u4f5c\u6d41\n\u4e0d\u8981\u8ffd\u6c42\u201c\u804a\u7740\u5929\u5c31\u81ea\u52a8\u53d8\u5f3a\u201d\uff0c\u90a3\u662f\u4e0d\u7a33\u5b9a\u7684\u5728\u7ebf\u5b66\u4e60\u3002\u6211\u4eec\u63a8\u8350\u201c\u6536\u96c6 – \u6e05\u6d17 – \u8bad\u7ec3 – \u90e8\u7f72\u201d<\/strong>\u7684\u5468\u66f4\u5faa\u73af\u3002<\/p>\n \ud83d\udcc5 \u9636\u6bb5\u4e00\uff1a\u65e5\u5e38\u6536\u96c6\uff08\u5468\u4e00\u81f3\u5468\u4e94\uff09\n\u4efb\u52a1\uff1a\u628a OpenClaw \u53d8\u6210\u201c\u6570\u636e\u6536\u96c6\u5668\u201d<\/p>\n \u5f00\u542f\u65e5\u5fd7\u4fdd\u5b58\uff1a \u786e\u4fdd OpenClaw \u4fdd\u5b58\u6240\u6709\u5bf9\u8bdd\u5386\u53f2\u3002\n\u201c\u7ea2\u7b14\u6279\u6539\u201d\u673a\u5236\uff1a\n\u7eff\u8272\u6570\u636e\uff1a \u6a21\u578b\u56de\u7b54\u5b8c\u7f8e\uff0c\u7b26\u5408\u4f60\u7684\u903b\u8f91 -> \u4fdd\u7559\u3002\n\u7ea2\u8272\u6570\u636e\uff1a \u6a21\u578b\u56de\u7b54\u9519\u8bef\uff0c\u4f60\u8fdb\u884c\u4e86\u7ea0\u6b63 -> \u4fdd\u7559\u7ea0\u6b63\u540e\u7684\u6700\u7ec8\u7248\u672c\uff08\u8fd9\u662f\u6700\u5b9d\u8d35\u7684\u6570\u636e\uff09\u3002\n\u7070\u8272\u6570\u636e\uff1a \u7eaf\u95f2\u804a -> \u4e22\u5f03\u3002\n\u523b\u610f\u7ec3\u4e60\uff1a \u6bcf\u5468\u81f3\u5c11\u63d0\u51fa 5-10 \u4e2a\u6838\u5fc3\u95ee\u9898\uff0c\u4e13\u95e8\u6d4b\u8bd5\u4f60\u5e0c\u671b\u6a21\u578b\u638c\u63e1\u7684\u77e5\u8bc6\u70b9\u3002\n\ud83d\udcc5 \u9636\u6bb5\u4e8c\uff1a\u6570\u636e\u6e05\u6d17\uff08\u5468\u516d\uff09\n\u4efb\u52a1\uff1a\u628a\u201c\u804a\u5929\u8bb0\u5f55\u201d\u53d8\u6210\u201c\u6559\u6750\u201d<\/p>\n \u7f16\u5199\u4e00\u4e2a\u7b80\u5355\u7684 Python \u811a\u672c\uff0c\u5c06\u7b5b\u9009\u51fa\u7684\u5bf9\u8bdd\u8f6c\u6362\u4e3a JSONL \u683c\u5f0f\uff08LLaMA-Factory \u6807\u51c6\u683c\u5f0f\uff09\uff1a<\/p>\n {“messages”: [{“role”: “system”, “content”: “\u4f60\u662f\u4e00\u4e2a\u9075\u5faa [\u7528\u6237\u7406\u8bba] \u7684\u52a9\u624b\u3002”}, {“role”: “user”, “content”: “\u5982\u4f55\u7406\u89e3\u8fd9\u4e2a\u6982\u5ff5\uff1f”}, {“role”: “assistant”, “content”: “\u6839\u636e\u60a8\u7684\u7406\u8bba\uff0c\u5e94\u8be5\u8fd9\u6837\u7406\u89e3…”}]}\n\u5efa\u8bae\uff1a \u6bcf\u5468\u7cbe\u9009 50~100 \u6761 \u9ad8\u8d28\u91cf\u6570\u636e\u5373\u53ef\u3002\u5b81\u7f3a\u6bcb\u6ee5\u3002\n\u5b58\u50a8\uff1a \u5c06\u6587\u4ef6\u4fdd\u5b58\u4e3a week_01.jsonl\uff0c\u5b58\u5165\u8bad\u7ec3\u6570\u636e\u76ee\u5f55\u3002\n\ud83d\udcc5 \u9636\u6bb5\u4e09\uff1a\u6a21\u578b\u8bad\u7ec3\uff08\u5468\u65e5\uff09\n\u4efb\u52a1\uff1a\u8ba9\u6a21\u578b\u201c\u4e0a\u8bfe\u201d\uff08V100 \u5de5\u4f5c\u65f6\u6bb5\uff09<\/p>\n \u4f7f\u7528 LLaMA-Factory \u8fdb\u884c QLoRA \u5fae\u8c03\u3002\u4ee5\u4e0b\u662f\u9488\u5bf9 V100 16G \u4f18\u5316\u7684\u542f\u52a8\u547d\u4ee4\uff1a<\/p>\n llamafactory-cli train \\\n–model_name_or_path Qwen\/Qwen2.5-7B-Instruct \\\n–dataset_dir .\/data \\\n–dataset week_01 \\\n–finetuning_type lora \\\n–quantization_bit 4 \\\n–template qwen \\\n–learning_rate 1e-4 \\\n–num_train_epochs 3.0 \\\n–per_device_train_batch_size 1 \\\n–gradient_accumulation_steps 8 \\\n–lr_scheduler_type cosine \\\n–output_dir .\/saves\/qwen2.5-7b\/week_01_lora \\\n–fp16 \\\n–device_map cuda\n\u5173\u952e\u53c2\u6570\uff1a\n–quantization_bit 4\uff1a\u663e\u5b58\u8282\u7701\u5173\u952e\uff0c\u8ba9 7B \u6a21\u578b\u80fd\u5728 16G \u663e\u5b58\u4e0a\u8bad\u7ec3\u3002\n–fp16\uff1aV100 \u4e0d\u652f\u6301 BF16\uff0c\u5fc5\u987b\u6307\u5b9a FP16\u3002\n–num_train_epochs 3.0\uff1a\u5c0f\u6570\u636e\u91cf\u591a\u8dd1\u51e0\u8f6e\uff0c\u52a0\u6df1\u8bb0\u5fc6\u3002\n\u8017\u65f6\uff1a 100 \u6761\u6570\u636e\u7ea6\u9700 30 \u5206\u949f ~ 1 \u5c0f\u65f6\u3002\n\ud83d\udcc5 \u9636\u6bb5\u56db\uff1a\u5408\u5e76\u4e0e\u90e8\u7f72\uff08\u5468\u4e00\uff09\n\u4efb\u52a1\uff1a\u8ba9\u6a21\u578b\u201c\u6bd5\u4e1a\u4e0a\u5c97\u201d<\/p>\n \u8bad\u7ec3\u5f97\u5230\u7684\u662f LoRA \u6743\u91cd\uff0c\u9700\u8981\u5408\u5e76\u5230\u4e3b\u6a21\u578b\u4e2d\u65b9\u4fbf OpenClaw \u8c03\u7528\u3002<\/p>\n \u5408\u5e76\u6743\u91cd\uff1a\nllamafactory-cli export \\\n–model_name_or_path Qwen\/Qwen2.5-7B-Instruct \\\n–adapter_name_or_path .\/saves\/qwen2.5-7b\/week_01_lora \\\n–template qwen \\\n–export_dir .\/models\/qwen2.5-7b-v1.1 \\\n–export_legacy_format False\n\u63a5\u5165 OpenClaw\uff1a \u5728 OpenClaw \u914d\u7f6e\u4e2d\uff0c\u5c06\u6a21\u578b\u8def\u5f84\u6307\u5411\u65b0\u751f\u6210\u7684 qwen2.5-7b-v1.1\u3002\n\u9a8c\u8bc1\uff1a \u95ee\u51e0\u4e2a\u4e0a\u5468\u5b83\u7b54\u9519\u7684\u95ee\u9898\uff0c\u5982\u679c\u5b83\u80fd\u672c\u80fd\u5730\u7b54\u5bf9\uff0c\u8bf4\u660e\u5185\u5316\u6210\u529f\u3002\n\u56db\u3001\u907f\u5751\u6307\u5357\u4e0e\u6700\u4f73\u5b9e\u8df5\n\u4e3a\u4e86\u4fdd\u8bc1\u8ba1\u5212\u957f\u671f\u53ef\u6301\u7eed\uff0c\u8bf7\u6ce8\u610f\u4ee5\u4e0b\u51e0\u70b9\uff1a<\/p>\n \u9632\u6b62\u201c\u707e\u96be\u6027\u9057\u5fd8\u201d\n\u6a21\u578b\u5b66\u4e86\u65b0\u77e5\u8bc6\u53ef\u80fd\u4f1a\u5fd8\u6389\u65e7\u77e5\u8bc6\u3002\u5efa\u8bae\u6bcf\u9694\u4e00\u4e2a\u6708\uff0c\u5728\u8bad\u7ec3\u6570\u636e\u4e2d\u6df7\u5165 10% \u7684\u901a\u7528\u6570\u636e\uff08\u5982\u901a\u7528\u5e38\u8bc6\u95ee\u7b54\uff09\uff0c\u4fdd\u6301\u6a21\u578b\u7684\u57fa\u7840\u80fd\u529b\u3002\n\u663e\u5b58\u76d1\u63a7\n\u8bad\u7ec3\u65f6\u8fd0\u884c watch -n 1 nvidia-smi\u3002\u5982\u679c\u663e\u5b58\u7206\u6ee1\uff0c\u8bf7\u51cf\u5c0f batch_size \u6216\u589e\u52a0\u68af\u5ea6\u7d2f\u79ef\u6b65\u6570\u3002\n\u7248\u672c\u63a7\u5236\n\u4fdd\u7559\u6bcf\u4e2a\u7248\u672c\u7684\u6a21\u578b\u6587\u4ef6\u5939\uff08v1.0, v1.1, v1.2…\uff09\u3002\u5982\u679c\u65b0\u7248\u672c\u53d8\u50bb\u4e86\uff0c\u53ef\u4ee5\u968f\u65f6\u56de\u9000\u3002\n\u4e0d\u8981\u8d2a\u5927\n\u5728 V100 16G \u4e0a\uff0c7B \u6a21\u578b\u662f\u6700\u7a33\u5b9a\u7684\u9009\u62e9\u300214B \u6a21\u578b\u5fae\u8c03\u98ce\u9669\u8f83\u5927\uff0c\u5bb9\u6613\u5bfc\u81f4 OOM\uff08\u663e\u5b58\u6ea2\u51fa\uff09\u3002\n\u5907\u4efd\u6838\u5fc3\u8d44\u4ea7\n\u4f60\u7684\u6a21\u578b\u6743\u91cd\u662f\u6838\u5fc3\u8d44\u4ea7\u3002\u5b9a\u671f\u5c06 .\/models\/ \u6587\u4ef6\u5939\u5907\u4efd\u5230\u5916\u90e8\u786c\u76d8\u3002\n\u4e94\u3001\u7ed3\u8bed\uff1a\u6162\u5c31\u662f\u5feb\n\u8fd9\u4e2a\u8ba1\u5212\u7684\u6838\u5fc3\u4e0d\u5728\u4e8e\u201c\u5feb\u201d\uff0c\u800c\u5728\u4e8e\u201c\u79ef\u7d2f\u201d<\/strong>\u3002<\/p>\n 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