{"id":85178,"date":"2026-07-27T03:48:27","date_gmt":"2026-07-26T19:48:27","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/85178.html"},"modified":"2026-07-27T03:48:27","modified_gmt":"2026-07-26T19:48:27","slug":"lfm2-5-1-2b-instruct%e7%94%9f%e4%ba%a7%e7%8e%af%e5%a2%83%e5%ba%94%e7%94%a8%ef%bc%9a%e4%bd%8e%e5%8a%9f%e8%80%97arm%e6%9c%8d%e5%8a%a1%e5%99%a8%e4%b8%8a%e7%9a%84%e7%a8%b3%e5%ae%9a%e5%af%b9%e8%af%9d","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/85178.html","title":{"rendered":"LFM2.5-1.2B-Instruct\u751f\u4ea7\u73af\u5883\u5e94\u7528\uff1a\u4f4e\u529f\u8017ARM\u670d\u52a1\u5668\u4e0a\u7684\u7a33\u5b9a\u5bf9\u8bdd\u670d\u52a1"},"content":{"rendered":"<h2>LFM2.5-1.2B-Instruct\u751f\u4ea7\u73af\u5883\u5e94\u7528&#xff1a;\u4f4e\u529f\u8017ARM\u670d\u52a1\u5668\u4e0a\u7684\u7a33\u5b9a\u5bf9\u8bdd\u670d\u52a1<\/h2>\n<h3>1. \u6a21\u578b\u6982\u8ff0\u4e0e\u6838\u5fc3\u4f18\u52bf<\/h3>\n<p>LFM2.5-1.2B-Instruct\u662f\u4e00\u4e2a\u53c2\u6570\u91cf\u4e3a1.2B\u7684\u8f7b\u91cf\u7ea7\u6307\u4ee4\u5fae\u8c03\u5927\u8bed\u8a00\u6a21\u578b&#xff0c;\u4e13\u4e3a\u8fb9\u7f18\u8bbe\u5907\u548c\u4f4e\u8d44\u6e90\u670d\u52a1\u5668\u8bbe\u8ba1\u3002\u8fd9\u4e2a\u6a21\u578b\u5728\u4fdd\u6301\u8f83\u5c0f\u4f53\u79ef\u7684\u540c\u65f6&#xff0c;\u901a\u8fc7\u7279\u6b8a\u7684\u67b6\u6784\u8bbe\u8ba1\u5b9e\u73b0\u4e86\u63a5\u8fd1\u5927\u6a21\u578b\u7684\u5bf9\u8bdd\u80fd\u529b\u3002<\/p>\n<h4>1.1 \u6a21\u578b\u6280\u672f\u7279\u70b9<\/h4>\n<ul>\n<li>\u6df7\u5408\u67b6\u6784\u8bbe\u8ba1&#xff1a;\u7ed3\u5408\u4e8610\u5c42\u53cc\u95e8\u63a7LIV\u5377\u79ef\u548c6\u5c42GQA\u6ce8\u610f\u529b\u5757<\/li>\n<li>\u591a\u8bed\u8a00\u652f\u6301&#xff1a;\u539f\u751f\u652f\u6301\u82f1\u8bed\u3001\u4e2d\u6587\u3001\u963f\u62c9\u4f2f\u8bed\u7b498\u79cd\u8bed\u8a00<\/li>\n<li>\u9ad8\u6548\u63a8\u7406&#xff1a;\u4ec5\u97002.5-3GB\u663e\u5b58\u5373\u53ef\u6d41\u7545\u8fd0\u884c<\/li>\n<li>\u957f\u4e0a\u4e0b\u6587&#xff1a;\u652f\u6301\u9ad8\u8fbe32K tokens\u7684\u4e0a\u4e0b\u6587\u957f\u5ea6<\/li>\n<\/ul>\n<h4>1.2 \u9002\u7528\u573a\u666f<\/h4>\n<ul>\n<li>\u5d4c\u5165\u5f0fAI\u52a9\u624b&#xff1a;\u6811\u8393\u6d3e\u7b49\u5f00\u53d1\u677f\u4e0a\u7684\u667a\u80fd\u4ea4\u4e92<\/li>\n<li>\u8f7b\u91cf\u5ba2\u670d\u7cfb\u7edf&#xff1a;7\u00d724\u5c0f\u65f6\u8fd0\u884c\u7684\u81ea\u52a8\u5e94\u7b54\u670d\u52a1<\/li>\n<li>\u8fb9\u7f18\u8ba1\u7b97\u5e94\u7528&#xff1a;\u672c\u5730\u5316\u90e8\u7f72\u7684\u9690\u79c1\u4fdd\u62a4\u5bf9\u8bdd\u7cfb\u7edf<\/li>\n<li>\u6559\u80b2\u8bbe\u5907&#xff1a;\u79bb\u7ebf\u73af\u5883\u4e0b\u7684\u5b66\u4e60\u8f85\u52a9\u5de5\u5177<\/li>\n<\/ul>\n<h3>2. \u5feb\u901f\u90e8\u7f72\u6307\u5357<\/h3>\n<h4>2.1 \u73af\u5883\u51c6\u5907<\/h4>\n<p>\u786e\u4fdd\u60a8\u7684ARM\u670d\u52a1\u5668\u6ee1\u8db3\u4ee5\u4e0b\u8981\u6c42&#xff1a;<\/p>\n<ul>\n<li>Ubuntu 20.04\/22.04 LTS<\/li>\n<li>Python 3.8&#043;<\/li>\n<li>CUDA 11.7&#043;&#xff08;\u5982\u9700GPU\u52a0\u901f&#xff09;<\/li>\n<li>\u81f3\u5c114GB\u5185\u5b58&#xff08;\u63a8\u83508GB&#xff09;<\/li>\n<\/ul>\n<h4>2.2 \u4e00\u952e\u90e8\u7f72\u6b65\u9aa4<\/h4>\n<p># \u514b\u9686\u9879\u76ee\u4ed3\u5e93<br \/>\ngit clone https:\/\/github.com\/LiquidAI\/LFM2.5-1.2B-Instruct.git<br \/>\ncd LFM2.5-1.2B-Instruct<\/p>\n<p># \u5b89\u88c5\u4f9d\u8d56<br \/>\npip install -r requirements.txt<\/p>\n<p># \u4e0b\u8f7d\u6a21\u578b&#xff08;\u7ea62.3GB&#xff09;<br \/>\nwget https:\/\/huggingface.co\/LiquidAI\/LFM2.5-1.2B-Instruct\/resolve\/main\/model.tar.gz<br \/>\ntar -xzf model.tar.gz -C \/root\/ai-models\/unsloth\/<\/p>\n<p># \u542f\u52a8\u670d\u52a1<br \/>\nsupervisorctl start lfm25-1.2b<\/p>\n<h4>2.3 \u670d\u52a1\u9a8c\u8bc1<\/h4>\n<p>\u7b49\u5f85\u7ea61-2\u5206\u949f\u6a21\u578b\u52a0\u8f7d\u5b8c\u6210\u540e&#xff0c;\u8bbf\u95ee&#xff1a;<\/p>\n<p>http:\/\/&lt;\u670d\u52a1\u5668IP&gt;:7860<\/p>\n<p>\u60a8\u5c06\u770b\u5230\u57fa\u4e8eGradio\u7684Web\u4ea4\u4e92\u754c\u9762&#xff0c;\u53ef\u4ee5\u5f00\u59cb\u6d4b\u8bd5\u5bf9\u8bdd\u529f\u80fd\u3002<\/p>\n<h3>3. \u751f\u4ea7\u73af\u5883\u914d\u7f6e\u4f18\u5316<\/h3>\n<h4>3.1 \u6027\u80fd\u8c03\u4f18\u5efa\u8bae<\/h4>\n<table>\n<tr>\u53c2\u6570\u63a8\u8350\u503c\u8bf4\u660e<\/tr>\n<tbody>\n<tr>\n<td>temperature<\/td>\n<td>0.3-0.7<\/td>\n<td>\u5e73\u8861\u521b\u610f\u4e0e\u7a33\u5b9a\u6027<\/td>\n<\/tr>\n<tr>\n<td>top_p<\/td>\n<td>0.9<\/td>\n<td>\u63d0\u9ad8\u56de\u7b54\u591a\u6837\u6027<\/td>\n<\/tr>\n<tr>\n<td>max_new_tokens<\/td>\n<td>256<\/td>\n<td>\u63a7\u5236\u54cd\u5e94\u957f\u5ea6<\/td>\n<\/tr>\n<tr>\n<td>repetition_penalty<\/td>\n<td>1.2<\/td>\n<td>\u51cf\u5c11\u91cd\u590d\u5185\u5bb9<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h4>3.2 \u8d44\u6e90\u76d1\u63a7\u65b9\u6848<\/h4>\n<p>\u5efa\u8bae\u6dfb\u52a0\u4ee5\u4e0b\u76d1\u63a7\u811a\u672c\u5230crontab&#xff08;\u6bcf\u5206\u949f\u6267\u884c&#xff09;&#xff1a;<\/p>\n<p>#!\/bin\/bash<br \/>\n# \u76d1\u63a7GPU\u4f7f\u7528\u60c5\u51b5<br \/>\nnvidia-smi &#8211;query-gpu&#061;memory.used,utilization.gpu &#8211;format&#061;csv &gt;&gt; \/var\/log\/lfm25-gpu.log<\/p>\n<p># \u76d1\u63a7\u670d\u52a1\u72b6\u6001<br \/>\nsupervisorctl status lfm25-1.2b &gt;&gt; \/var\/log\/lfm25-status.log<\/p>\n<h4>3.3 \u9ad8\u53ef\u7528\u914d\u7f6e<\/h4>\n<p>\u5bf9\u4e8e\u5173\u952e\u4e1a\u52a1\u573a\u666f&#xff0c;\u5efa\u8bae&#xff1a;<\/p>\n<li>\u4f7f\u7528Keepalived\u5b9e\u73b0VIP\u6f02\u79fb<\/li>\n<li>\u914d\u7f6eSupervisor\u81ea\u52a8\u91cd\u542f\u7b56\u7565<\/li>\n<li>\u8bbe\u7f6e\u65e5\u5fd7\u8f6e\u8f6c\u9632\u6b62\u78c1\u76d8\u5199\u6ee1<\/li>\n<p># \u793a\u4f8b\u65e5\u5fd7\u8f6e\u8f6c\u914d\u7f6e&#xff08;\/etc\/logrotate.d\/lfm25&#xff09;<br \/>\n\/root\/LFM2.5-1.2B-Instruct\/logs\/*.log {<br \/>\n    daily<br \/>\n    rotate 7<br \/>\n    compress<br \/>\n    missingok<br \/>\n    notifempty<br \/>\n}<\/p>\n<h3>4. \u5b9e\u9645\u5e94\u7528\u6848\u4f8b<\/h3>\n<h4>4.1 \u667a\u80fd\u5ba2\u670d\u7cfb\u7edf\u96c6\u6210<\/h4>\n<p>\u901a\u8fc7\u7b80\u5355\u7684API\u5c01\u88c5&#xff0c;\u53ef\u4ee5\u5c06\u6a21\u578b\u96c6\u6210\u5230\u73b0\u6709\u5ba2\u670d\u7cfb\u7edf&#xff1a;<\/p>\n<p>import requests<\/p>\n<p>def get_ai_response(prompt):<br \/>\n    url &#061; &#034;http:\/\/localhost:7860\/api\/predict&#034;<br \/>\n    data &#061; {<br \/>\n        &#034;inputs&#034;: prompt,<br \/>\n        &#034;parameters&#034;: {<br \/>\n            &#034;temperature&#034;: 0.5,<br \/>\n            &#034;max_new_tokens&#034;: 128<br \/>\n        }<br \/>\n    }<br \/>\n    response &#061; requests.post(url, json&#061;data)<br \/>\n    return response.json()[&#034;generated_text&#034;]<\/p>\n<h4>4.2 \u591a\u8bed\u8a00\u6df7\u5408\u5bf9\u8bdd<\/h4>\n<p>\u6a21\u578b\u539f\u751f\u652f\u6301\u8bed\u8a00\u81ea\u52a8\u68c0\u6d4b\u548c\u5207\u6362&#xff1a;<\/p>\n<p>\u7528\u6237: \u4eca\u5929\u5929\u6c14\u600e\u4e48\u6837&#xff1f;<br \/>\nAI: \u4eca\u5929\u5929\u6c14\u6674\u6717&#xff0c;\u9002\u5408\u5916\u51fa\u3002<\/p>\n<p>User: How about tomorrow?<br \/>\nAI: The forecast predicts light rain tomorrow.<\/p>\n<h4>4.3 \u5782\u76f4\u9886\u57df\u5fae\u8c03\u65b9\u6848<\/h4>\n<p>\u9488\u5bf9\u7279\u5b9a\u9886\u57df&#xff08;\u5982\u533b\u7597\u3001\u6cd5\u5f8b&#xff09;&#xff0c;\u53ef\u4ee5\u4f7f\u7528LoRA\u8fdb\u884c\u9ad8\u6548\u5fae\u8c03&#xff1a;<\/p>\n<p>from peft import LoraConfig, get_peft_model<\/p>\n<p>lora_config &#061; LoraConfig(<br \/>\n    r&#061;8,<br \/>\n    lora_alpha&#061;16,<br \/>\n    target_modules&#061;[&#034;query_key_value&#034;],<br \/>\n    lora_dropout&#061;0.05,<br \/>\n    bias&#061;&#034;none&#034;<br \/>\n)<br \/>\nmodel &#061; get_peft_model(model, lora_config)<\/p>\n<h3>5. \u8fd0\u7ef4\u4e0e\u6545\u969c\u5904\u7406<\/h3>\n<h4>5.1 \u5e38\u89c1\u95ee\u9898\u89e3\u51b3\u65b9\u6848<\/h4>\n<table>\n<tr>\u95ee\u9898\u73b0\u8c61\u6392\u67e5\u6b65\u9aa4\u89e3\u51b3\u65b9\u6cd5<\/tr>\n<tbody>\n<tr>\n<td>WebUI\u65e0\u6cd5\u8bbf\u95ee<\/td>\n<td>\u68c0\u67e57860\u7aef\u53e3\u662f\u5426\u76d1\u542c<\/td>\n<td>\u91cd\u542f\u670d\u52a1\u6216\u68c0\u67e5\u9632\u706b\u5899<\/td>\n<\/tr>\n<tr>\n<td>\u54cd\u5e94\u901f\u5ea6\u6162<\/td>\n<td>\u67e5\u770bGPU\u5229\u7528\u7387<\/td>\n<td>\u964d\u4f4emax_new_tokens\u503c<\/td>\n<\/tr>\n<tr>\n<td>\u5185\u5bb9\u91cd\u590d<\/td>\n<td>\u8c03\u6574repetition_penalty<\/td>\n<td>\u8bbe\u4e3a1.1-1.3<\/td>\n<\/tr>\n<tr>\n<td>\u663e\u5b58\u4e0d\u8db3<\/td>\n<td>\u68c0\u67e5nvidia-smi\u8f93\u51fa<\/td>\n<td>\u542f\u7528CPU\u5378\u8f7d\u6216\u51cf\u5c11\u5e76\u53d1<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h4>5.2 \u65e5\u5fd7\u5206\u6790\u6280\u5de7<\/h4>\n<p>\u5173\u952e\u65e5\u5fd7\u4fe1\u606f\u89e3\u8bfb&#xff1a;<\/p>\n<ul>\n<li>Loading checkpoint shards&#xff1a;\u6a21\u578b\u52a0\u8f7d\u8fdb\u5ea6<\/li>\n<li>Allocated CUDA memory&#xff1a;\u663e\u5b58\u5206\u914d\u60c5\u51b5<\/li>\n<li>Generated sequence length&#xff1a;\u5b9e\u9645\u751f\u6210token\u6570<\/li>\n<\/ul>\n<h4>5.3 \u5907\u4efd\u4e0e\u6062\u590d\u7b56\u7565<\/h4>\n<p>\u5efa\u8bae\u7684\u5907\u4efd\u65b9\u6848&#xff1a;<\/p>\n<li>\u5b9a\u671f\u5907\u4efd\u6a21\u578b\u6743\u91cd<\/li>\n<li>\u4f7f\u7528rsync\u540c\u6b65\u914d\u7f6e\u6587\u4ef6<\/li>\n<li>\u4fdd\u5b58Supervisor\u914d\u7f6e\u526f\u672c<\/li>\n<p># \u793a\u4f8b\u5907\u4efd\u547d\u4ee4<br \/>\ntar -czvf lfm25-backup-$(date &#043;%F).tar.gz \\\\<br \/>\n    \/root\/ai-models\/unsloth\/LFM2___5-1___2B-Instruct \\\\<br \/>\n    \/root\/LFM2.5-1.2B-Instruct\/webui.py \\\\<br \/>\n    \/etc\/supervisor\/conf.d\/lfm25.conf<\/p>\n<h3>6. \u603b\u7ed3\u4e0e\u5c55\u671b<\/h3>\n<p>LFM2.5-1.2B-Instruct\u4e3a\u8fb9\u7f18\u8ba1\u7b97\u573a\u666f\u63d0\u4f9b\u4e86\u7406\u60f3\u7684\u5bf9\u8bddAI\u89e3\u51b3\u65b9\u6848\u3002\u901a\u8fc7\u672c\u6587\u4ecb\u7ecd\u7684\u90e8\u7f72\u65b9\u6848\u548c\u4f18\u5316\u6280\u5de7&#xff0c;\u60a8\u53ef\u4ee5\u5728\u4f4e\u529f\u8017ARM\u670d\u52a1\u5668\u4e0a\u6784\u5efa\u7a33\u5b9a\u53ef\u9760\u7684\u5bf9\u8bdd\u670d\u52a1\u3002<\/p>\n<p>\u672a\u6765\u53ef\u63a2\u7d22\u7684\u65b9\u5411\u5305\u62ec&#xff1a;<\/p>\n<ul>\n<li>\u7ed3\u5408RAG\u5b9e\u73b0\u77e5\u8bc6\u589e\u5f3a<\/li>\n<li>\u5f00\u53d1\u79fb\u52a8\u7aef\u8f7b\u91cf\u5316\u5e94\u7528<\/li>\n<li>\u6784\u5efa\u591a\u6a21\u578b\u534f\u540c\u7cfb\u7edf<\/li>\n<\/ul>\n<p>\u83b7\u53d6\u66f4\u591aAI\u955c\u50cf<\/p>\n<p>\u60f3\u63a2\u7d22\u66f4\u591aAI\u955c\u50cf\u548c\u5e94\u7528\u573a\u666f&#xff1f;\u8bbf\u95ee CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a&#xff0c;\u63d0\u4f9b\u4e30\u5bcc\u7684\u9884\u7f6e\u955c\u50cf&#xff0c;\u8986\u76d6\u5927\u6a21\u578b\u63a8\u7406\u3001\u56fe\u50cf\u751f\u6210\u3001\u89c6\u9891\u751f\u6210\u3001\u6a21\u578b\u5fae\u8c03\u7b49\u591a\u4e2a\u9886\u57df&#xff0c;\u652f\u6301\u4e00\u952e\u90e8\u7f72\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>LFM2.5-1.2B-Instruct\u751f\u4ea7\u73af\u5883\u5e94\u7528&#xff1a;\u4f4e\u529f\u8017ARM\u670d\u52a1\u5668\u4e0a\u7684\u7a33\u5b9a\u5bf9\u8bdd\u670d\u52a1<br \/>\n1. \u6a21\u578b\u6982\u8ff0\u4e0e\u6838\u5fc3\u4f18\u52bf<br 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