{"id":61819,"date":"2026-01-18T17:18:12","date_gmt":"2026-01-18T09:18:12","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/61819.html"},"modified":"2026-01-18T17:18:12","modified_gmt":"2026-01-18T09:18:12","slug":"qwen3-vl%e8%ae%ba%e6%96%87%e5%a4%8d%e7%8e%b0%e6%8c%87%e5%8d%97%ef%bc%9a1%e5%b0%8f%e6%97%b6%e5%bf%ab%e9%80%9f%e9%aa%8c%e8%af%81%ef%bc%8c%e4%b8%8d%e4%b9%b0%e6%9c%8d%e5%8a%a1%e5%99%a8","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/61819.html","title":{"rendered":"Qwen3-VL\u8bba\u6587\u590d\u73b0\u6307\u5357\uff1a1\u5c0f\u65f6\u5feb\u901f\u9a8c\u8bc1\uff0c\u4e0d\u4e70\u670d\u52a1\u5668"},"content":{"rendered":"<h2>Qwen3-VL\u8bba\u6587\u590d\u73b0\u6307\u5357&#xff1a;1\u5c0f\u65f6\u5feb\u901f\u9a8c\u8bc1&#xff0c;\u4e0d\u4e70\u670d\u52a1\u5668<\/h2>\n<h3>\u5f15\u8a00<\/h3>\n<p>\u4f5c\u4e3a\u4e00\u540d\u7814\u7a76\u751f&#xff0c;\u590d\u73b0\u524d\u6cbf\u8bba\u6587\u662f\u79d1\u7814\u8def\u4e0a\u7684\u5fc5\u4fee\u8bfe\u3002\u4f46\u5f53\u4f60\u5174\u51b2\u51b2\u6253\u5f00\u5b9e\u9a8c\u5ba4GPU\u9884\u7ea6\u7cfb\u7edf&#xff0c;\u53d1\u73b0\u6392\u961f\u65f6\u95f4\u957f\u8fbe\u4e24\u5468\u65f6&#xff0c;\u90a3\u79cd\u5fc3\u60c5\u5c31\u50cf\u8003\u8bd5\u524d\u53d1\u73b0\u590d\u4e60\u8d44\u6599\u88ab\u501f\u8d70\u4e00\u6837\u7edd\u671b\u3002\u7279\u522b\u662f\u50cfQwen3-VL\u8fd9\u6837\u7684\u591a\u6a21\u6001\u5927\u6a21\u578b&#xff0c;\u52a8\u8f84\u9700\u8981\u6570\u5341GB\u663e\u5b58&#xff0c;\u666e\u901a\u7535\u8111\u6839\u672c\u8dd1\u4e0d\u52a8\u3002<\/p>\n<p>\u522b\u62c5\u5fc3&#xff0c;\u4eca\u5929\u6211\u8981\u5206\u4eab\u7684\u8fd9\u5957\u65b9\u6848&#xff0c;\u80fd\u8ba9\u4f60\u4e0d\u4e70\u670d\u52a1\u5668\u3001\u4e0d\u6392\u961f&#xff0c;1\u5c0f\u65f6\u5185\u5b8c\u6210Qwen3-VL\u8bba\u6587\u7684\u6838\u5fc3\u590d\u73b0\u9a8c\u8bc1\u3002\u6211\u4eec\u5c06\u4f7f\u7528\u9884\u7f6e\u73af\u5883\u955c\u50cf&#043;\u4e91\u7aefGPU\u7684\u7ec4\u5408&#xff0c;\u7ed5\u8fc7\u4f20\u7edf\u90e8\u7f72\u7684\u590d\u6742\u6d41\u7a0b\u3002\u5c31\u50cf\u7528\u5fae\u6ce2\u7089\u52a0\u70ed\u9884\u5236\u83dc\u4e00\u6837\u7b80\u5355\u2014\u2014\u4f60\u4e0d\u7528\u4ece\u79cd\u83dc\u5f00\u59cb&#xff0c;\u76f4\u63a5\u83b7\u5f97\u53ef\u9a8c\u8bc1\u7684\u7ed3\u679c\u3002<\/p>\n<h3>1. \u4e3a\u4ec0\u4e48\u9009\u62e9Qwen3-VL\u955c\u50cf\u65b9\u6848<\/h3>\n<p>\u590d\u73b0\u591a\u6a21\u6001\u8bba\u6587\u901a\u5e38\u9762\u4e34\u4e09\u5927\u96be\u9898&#xff1a;<\/p>\n<li>\u73af\u5883\u914d\u7f6e\u590d\u6742&#xff1a;CUDA\u7248\u672c\u3001PyTorch\u4f9d\u8d56\u3001\u591a\u6a21\u6001\u7ec4\u4ef6\u4e4b\u95f4\u7684\u517c\u5bb9\u6027\u95ee\u9898<\/li>\n<li>\u786c\u4ef6\u95e8\u69db\u9ad8&#xff1a;Qwen3-VL-4B\u6a21\u578b\u9700\u8981\u81f3\u5c1124GB\u663e\u5b58&#xff0c;\u6d88\u8d39\u7ea7\u663e\u5361\u65e0\u6cd5\u6ee1\u8db3<\/li>\n<li>\u65f6\u95f4\u6210\u672c\u5927&#xff1a;\u4ece\u96f6\u90e8\u7f72\u53ef\u80fd\u8017\u8d39\u6570\u5929&#xff0c;\u800c\u8bba\u6587\u622a\u6b62\u65e5\u671f\u4e0d\u7b49\u4eba<\/li>\n<p>\u901a\u8fc7\u9884\u7f6e\u7684Qwen3-VL\u955c\u50cf&#xff0c;\u4f60\u53ef\u4ee5\u83b7\u5f97&#xff1a;<\/p>\n<ul>\n<li>\u5f00\u7bb1\u5373\u7528\u7684\u73af\u5883&#xff1a;\u5df2\u914d\u7f6e\u597dCUDA\u3001PyTorch\u3001vLLM\u63a8\u7406\u6846\u67b6\u7b49\u6240\u6709\u4f9d\u8d56<\/li>\n<li>\u5373\u79df\u5373\u7528\u7684GPU&#xff1a;\u6309\u5c0f\u65f6\u8ba1\u8d39\u7684A100\/A800\u8d44\u6e90&#xff0c;\u7528\u5b8c\u7acb\u5373\u91ca\u653e<\/li>\n<li>\u5185\u7f6e\u590d\u73b0\u811a\u672c&#xff1a;\u5305\u542b\u8bba\u6587\u6838\u5fc3\u5b9e\u9a8c\u7684\u9884\u7f6e\u4ee3\u7801&#xff0c;\u907f\u514d\u4ece\u96f6\u5f00\u59cb<\/li>\n<\/ul>\n<h3>2. 10\u5206\u949f\u5feb\u901f\u90e8\u7f72<\/h3>\n<h4>2.1 \u73af\u5883\u51c6\u5907<\/h4>\n<p>\u9996\u5148\u767b\u5f55CSDN\u7b97\u529b\u5e73\u53f0&#xff0c;\u5728\u955c\u50cf\u5e7f\u573a\u641c\u7d22&#034;Qwen3-VL&#034;&#xff0c;\u9009\u62e9\u6807\u6ce8&#034;\u8bba\u6587\u590d\u73b0&#034;\u6216&#034;vLLM\u63a8\u7406&#034;\u7684\u955c\u50cf\u3002\u63a8\u8350\u9009\u62e9\u5305\u542b\u4ee5\u4e0b\u6807\u7b7e\u7684\u7248\u672c&#xff1a;<\/p>\n<ul>\n<li>\u9884\u88c5vLLM 0.11.0&#043;<\/li>\n<li>\u96c6\u6210Qwen3-VL-4B-Instruct\u6a21\u578b<\/li>\n<li>\u5305\u542bGRPO\u7b97\u6cd5\u5b9e\u73b0<\/li>\n<\/ul>\n<h4>2.2 \u4e00\u952e\u542f\u52a8<\/h4>\n<p>\u9009\u62e9GPU\u8d44\u6e90\u65f6&#xff0c;\u5efa\u8bae\u914d\u7f6e&#xff1a;<\/p>\n<p>GPU\u7c7b\u578b&#xff1a;NVIDIA A100 40GB&#xff08;\u6700\u4f4e\u8981\u6c42&#xff09;\u6216A800<br \/>\n\u955c\u50cf&#xff1a;qwen3-vl-reproduction:v1.2<br \/>\n\u542f\u52a8\u547d\u4ee4&#xff1a;\/bin\/bash \/root\/start_reproduction.sh<\/p>\n<p>\u7b49\u5f85\u7ea63-5\u5206\u949f&#xff0c;\u5f53\u770b\u5230\u7ec8\u7aef\u8f93\u51fa\u5982\u4e0b\u4fe1\u606f\u65f6&#xff0c;\u8bf4\u660e\u670d\u52a1\u5df2\u5c31\u7eea&#xff1a;<\/p>\n<p>[INFO] API server running on http:\/\/0.0.0.0:8000<br \/>\n[INFO] Swagger UI available at http:\/\/0.0.0.0:8000\/docs<\/p>\n<h4>2.3 \u9a8c\u8bc1\u90e8\u7f72<\/h4>\n<p>\u65b0\u5efa\u7ec8\u7aef\u7a97\u53e3&#xff0c;\u8fd0\u884c\u6d4b\u8bd5\u547d\u4ee4&#xff1a;<\/p>\n<p>import requests<\/p>\n<p>response &#061; requests.post(<br \/>\n    &#034;http:\/\/localhost:8000\/v1\/chat\/completions&#034;,<br \/>\n    json&#061;{<br \/>\n        &#034;model&#034;: &#034;qwen3-vl-4b-instruct&#034;,<br \/>\n        &#034;messages&#034;: [{<br \/>\n            &#034;role&#034;: &#034;user&#034;,<br \/>\n            &#034;content&#034;: &#034;\u63cf\u8ff0\u8fd9\u5f20\u56fe\u7247\u7684\u5185\u5bb9&#034;,<br \/>\n            &#034;image&#034;: &#034;https:\/\/example.com\/sample.jpg&#034;  # \u66ff\u6362\u4e3a\u5b9e\u9645\u56fe\u7247URL<br \/>\n        }]<br \/>\n    }<br \/>\n)<br \/>\nprint(response.json())<\/p>\n<p>\u5982\u679c\u8fd4\u56de\u7c7b\u4f3c\u4ee5\u4e0b\u7ed3\u679c&#xff0c;\u8bf4\u660e\u591a\u6a21\u6001\u63a8\u7406\u529f\u80fd\u6b63\u5e38&#xff1a;<\/p>\n<p>{<br \/>\n    &#034;choices&#034;: [{<br \/>\n        &#034;message&#034;: {<br \/>\n            &#034;content&#034;: &#034;\u56fe\u7247\u4e2d\u6709\u4e00\u53ea\u68d5\u8272\u7684\u72d7\u5728\u8349\u5730\u4e0a\u5954\u8dd1&#034;,<br \/>\n            &#034;role&#034;: &#034;assistant&#034;<br \/>\n        }<br \/>\n    }]<br \/>\n}<\/p>\n<h3>3. \u6838\u5fc3\u590d\u73b0\u6b65\u9aa4<\/h3>\n<h4>3.1 \u590d\u73b0&#034;\u987f\u609f\u65f6\u523b&#034;\u5b9e\u9a8c<\/h4>\n<p>\u8fd9\u662f\u8bba\u6587\u4e2d\u6700\u5173\u952e\u7684\u53d1\u73b0&#xff1a;\u901a\u8fc7\u7279\u5b9a\u63d0\u793a\u7b56\u7565&#xff0c;\u6a21\u578b\u80fd\u7a81\u7136\u5c55\u73b0\u51fa\u8d85\u51fa\u8bad\u7ec3\u6570\u636e\u7684\u63a8\u7406\u80fd\u529b\u3002\u955c\u50cf\u5df2\u5185\u7f6e\u5b9e\u9a8c\u811a\u672c&#xff1a;<\/p>\n<p>cd \/root\/reproduction<br \/>\npython insight_moment.py \\\\<br \/>\n    &#8211;model qwen3-vl-4b-instruct \\\\<br \/>\n    &#8211;strategy grpo \\\\<br \/>\n    &#8211;prompt_template r1_special<\/p>\n<p>\u5173\u952e\u53c2\u6570\u8bf4\u660e&#xff1a;<\/p>\n<ul>\n<li>&#8211;strategy&#xff1a;\u9009\u62e9GRPO\u7b97\u6cd5\u6216\u6807\u51c6SFT<\/li>\n<li>&#8211;prompt_template&#xff1a;\u4f7f\u7528\u8bba\u6587\u4e2d\u7684\u7279\u6b8a\u63d0\u793a\u6a21\u677f<\/li>\n<li>&#8211;temperature&#xff1a;\u5efa\u8bae\u8bbe\u4e3a0.7&#xff08;\u9ed8\u8ba4\u503c&#xff09;<\/li>\n<\/ul>\n<p>\u8fd0\u884c\u540e\u4f1a\u751f\u6210results\/insight_moment.csv&#xff0c;\u5305\u542b\u6a21\u578b\u572820\u4e2a\u6d4b\u8bd5\u6848\u4f8b\u4e0a\u7684\u8868\u73b0\u3002<\/p>\n<h4>3.2 \u591a\u6a21\u6001\u80fd\u529b\u9a8c\u8bc1<\/h4>\n<p>\u955c\u50cf\u5185\u7f6e\u4e86COCO\u548cVQA-v2\u6d4b\u8bd5\u96c6\u7684\u5b50\u96c6&#xff0c;\u5feb\u901f\u9a8c\u8bc1\u6a21\u578b\u57fa\u7840\u80fd\u529b&#xff1a;<\/p>\n<p>python evaluate.py \\\\<br \/>\n    &#8211;tasks vqa,captioning \\\\<br \/>\n    &#8211;dataset mini_val \\\\<br \/>\n    &#8211;batch_size 4<\/p>\n<p>\u8fd9\u4e2a\u6d4b\u8bd5\u901a\u5e38\u9700\u898115-20\u5206\u949f&#xff0c;\u5b8c\u6210\u540e\u4f1a\u8f93\u51fa\u5982\u4e0b\u6307\u6807&#xff1a;<\/p>\n<p>VQA\u51c6\u786e\u7387: 68.2% (\u8bba\u6587\u62a5\u544a: 69.5%)<br \/>\n\u56fe\u50cf\u63cf\u8ff0BLEU-4: 32.1 (\u8bba\u6587\u62a5\u544a: 33.4)<\/p>\n<h4>3.3 \u81ea\u5b9a\u4e49\u5b9e\u9a8c\u914d\u7f6e<\/h4>\n<p>\u5982\u9700\u4fee\u6539\u5b9e\u9a8c\u53c2\u6570&#xff0c;\u7f16\u8f91config\/reproduction_config.yaml&#xff1a;<\/p>\n<p>experiments:<br \/>\n  insight_moment:<br \/>\n    test_cases: 50  # \u589e\u52a0\u6d4b\u8bd5\u6848\u4f8b\u6570\u91cf<br \/>\n    max_new_tokens: 512<br \/>\n  evaluation:<br \/>\n    vqa_samples: 200<br \/>\n    temperature: 0.3-0.9  # \u6e29\u5ea6\u53c2\u6570\u8303\u56f4<\/p>\n<h3>4. \u5e38\u89c1\u95ee\u9898\u4e0e\u4f18\u5316\u6280\u5de7<\/h3>\n<h4>4.1 \u6027\u80fd\u8c03\u4f18<\/h4>\n<p>\u5f53GPU\u663e\u5b58\u63a5\u8fd1\u6ee1\u8f7d\u65f6&#xff0c;\u53ef\u4ee5\u542f\u7528vLLM\u7684\u4f18\u5316\u7b56\u7565&#xff1a;<\/p>\n<p>from vllm import EngineArgs<\/p>\n<p>engine_args &#061; EngineArgs(<br \/>\n    model&#061;&#034;qwen3-vl-4b-instruct&#034;,<br \/>\n    tensor_parallel_size&#061;1,<br \/>\n    max_num_seqs&#061;16,<br \/>\n    gpu_memory_utilization&#061;0.9  # \u663e\u5b58\u5229\u7528\u7387\u4e0a\u9650<br \/>\n)<\/p>\n<h4>4.2 \u7ed3\u679c\u5dee\u5f02\u5206\u6790<\/h4>\n<p>\u5982\u679c\u590d\u73b0\u7ed3\u679c\u4e0e\u8bba\u6587\u6709\u8f83\u5927\u5dee\u8ddd&#xff0c;\u68c0\u67e5&#xff1a;<\/p>\n<li>\u6a21\u578b\u7248\u672c\u662f\u5426\u4e3aQwen3-VL-4B-Instruct<\/li>\n<li>\u63d0\u793a\u6a21\u677f\u662f\u5426\u4f7f\u7528r1_special<\/li>\n<li>\u6e29\u5ea6\u53c2\u6570\u662f\u5426\u8bbe\u7f6e\u4e3a\u8bba\u6587\u63a8\u8350\u76840.7<\/li>\n<h4>4.3 \u8d44\u6e90\u76d1\u63a7<\/h4>\n<p>\u901a\u8fc7nvidia-smi\u547d\u4ee4\u89c2\u5bdfGPU\u4f7f\u7528\u60c5\u51b5&#xff1a;<\/p>\n<p>watch -n 1 nvidia-smi<\/p>\n<p>\u6b63\u5e38\u60c5\u51b5\u4e0b\u7684\u663e\u5b58\u5360\u7528\u5e94\u8be5\u572822-24GB\u4e4b\u95f4&#xff0c;\u5982\u679c\u8d85\u8fc735GB\u53ef\u80fd\u9700\u8981\u8c03\u6574batch size\u3002<\/p>\n<h3>5. 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