{"id":47416,"date":"2023-07-15T14:03:28","date_gmt":"2023-10-29T06:57:34","guid":{"rendered":"https:\/\/www.silicloud.com\/zh\/blog\/%e5%9c%a8databricks%e4%b8%ad%e5%b0%86calm2%e8%bd%ac%e6%8d%a2%e4%b8%baawq%e6%a0%bc%e5%bc%8f%e3%80%82\/"},"modified":"2024-04-30T13:42:47","modified_gmt":"2024-04-30T05:42:47","slug":"%e5%9c%a8databricks%e4%b8%ad%e5%b0%86calm2%e8%bd%ac%e6%8d%a2%e4%b8%baawq%e6%a0%bc%e5%bc%8f%e3%80%82","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/zh\/blog\/%e5%9c%a8databricks%e4%b8%ad%e5%b0%86calm2%e8%bd%ac%e6%8d%a2%e4%b8%baawq%e6%a0%bc%e5%bc%8f%e3%80%82\/","title":{"rendered":"\u5728Databricks\u4e2d\u5c06CALM2\u8f6c\u6362\u4e3aAWQ\u683c\u5f0f"},"content":{"rendered":"

\u6211\u6c89\u8ff7\u5176\u4e2d\u8d85\u8fc7\u4e86\u9884\u671f\uff0c\u82b1\u8d39\u4e86\u5f88\u591a\u65f6\u95f4…<\/p>\n

\u5f15\u5165<\/h1>\n

\u6211\u5728\u4e0b\u9762\u7684\u6587\u7ae0\u4e2d\u4f7f\u7528\u4e86CyberAgent\u516c\u53f8\u7684CALM2\u3002
\n\u7531\u4e8e\u73b0\u5728\u8fd8\u6709\u4e0eTransformers\u7684AWQ\u683c\u5f0f\u96c6\u6210\u5728\u4e00\u8d77\u7684\u95ee\u9898\uff0c\u6240\u4ee5\u6211\u5c06\u5c1d\u8bd5\u5c06CALM2\u91cf\u5b50\u5316\u4e3aAWQ\u683c\u5f0f\u6765\u4f7f\u7528\u3002
\n\uff08\u901a\u5e38Bloke\u5144\u5f1f\u4f1a\u7ed9\u6211\u63d0\u4f9bAWQ\u683c\u5f0f\u7684\u5b58\u50a8\u5e93\uff0c\u4f46\u8fd9\u6b21\u4e0d\u77e5\u9053\u4e3a\u4ec0\u4e48\u88ab\u53d6\u6d88\u4e86\uff09<\/p>\n

 <\/p>\n

2023\/11\/06\u66f4\u65b0\uff1aTheBloke\u5144\u8cb4\u4e5f\u518d\u6b21\u88ab\u63d0\u53ca\u4e86\u5462\u3002\u770b\u8d77\u6765\u8fd9\u4e2a\u65b9\u6cd5\u5f88\u5b9e\u7528\u3002\u5982\u679c\u4f60\u60f3\u81ea\u5df1\u8fdb\u884cAWQ\u91cf\u5b50\u5316\uff0c\u53ef\u4ee5\u53c2\u8003\u4ee5\u4e0b\u5185\u5bb9\u3002<\/p>\n

 <\/p>\n

\u6b65\u9aa41. \u51c6\u5907\u91cf\u5b50\u5316\u65f6\u7684\u6821\u51c6\u6570\u636e\u96c6\u3002<\/h1>\n

\u5728\u8fdb\u884c\u91cf\u5b50\u5316\u65f6\uff0c\u9700\u8981\u51c6\u5907\u9002\u5f53\u7684\u6570\u636e\u96c6\u8fdb\u884c\u6821\u51c6\u3002<\/p>\n

\u7531\u4e8e\u8fd9\u91cc\u6709\u4eba\u63d0\u4f9bCALM2\u7684GPTQ\u6a21\u578b\uff0c\u6240\u4ee5\u6211\u4f1a\u4f7f\u7528\u4e0e\u6b64\u5904\u76f8\u540c\u7684\u4ee5\u4e0b\u6821\u51c6\u6570\u636e\u96c6\u3002<\/p>\n

 <\/p>\n

\u5e76\u4e14\uff0c\u8fd9\u91cc\u662fGPTQ\u6a21\u578b\u3002<\/p>\n

 <\/p>\n

\u8bf7\u968f\u610f\u521b\u5efa\u4e00\u4e2a\u7b14\u8bb0\u672c\uff0c\u5e76\u6267\u884c\u4ecehuggingface\u83b7\u53d6\u6570\u636e\u7684\u8fc7\u7a0b\u3002
\n\u8bf7\u52a1\u5fc5\u5728CPU\u96c6\u7fa4+ML\u8fd0\u884c\u65f6\u4e0a\u6267\u884c\u4ee5\u4e0b\u5904\u7406\u3002<\/p>\n

\u5728GPU\u96c6\u7fa4\u8fd0\u884c\u65f6\uff0c\u53ef\u80fd\u662f\u7531\u4e8e\u6587\u4ef6\u7cfb\u7edf\u7684\u5dee\u5f02\u5bfc\u81f4load_dataset\u51fa\u73b0\u9519\u8bef\u3002
\n\uff08\u6211\u8ba4\u4e3a\u8fd9\u662f\u5728Databricks\u4e2d\u4f7f\u7528\u65f6\u7684\u56fa\u6709\u95ee\u9898\uff0c\u8fd9\u662f\u6211\u9047\u5230\u7684\u6700\u5927\u95ee\u9898\u70b9\uff09<\/p>\n

from<\/span> datasets<\/span> import<\/span> load_dataset<\/span>\r\n\r\ndef<\/span> load_ja_calib<\/span>():<\/span>\r\n    data<\/span> =<\/span> load_dataset<\/span>(<\/span>\"<\/span>mmnga\/wikipedia-ja-20230720-1k<\/span>\"<\/span>,<\/span> split<\/span>=<\/span>\"<\/span>train<\/span>\"<\/span>)<\/span>    \r\n    return<\/span> spark<\/span>.<\/span>createDataFrame<\/span>(<\/span>data<\/span>)<\/span>\r\n\r\nload_ja_calib<\/span>().<\/span>write<\/span>.<\/span>saveAsTable<\/span>(<\/span>\"<\/span>training.llm.mmnga_wikipedia_ja_20230720_1k<\/span>\"<\/span>)<\/span>\r\n<\/code><\/pre>\n
\u8fd9\u6b21\u5c06\u4ee5\u201cmmnga_wikipedia_ja_20230720_1k\u201d\u4f5c\u4e3a\u8868\u540d\u4fdd\u5b58\u5728training.llm\u6a21\u5f0f\u4e2d\u3002\u8bf7\u63d0\u524d\u51c6\u5907\u597d\u8be5\u6a21\u5f0f\u3002<\/p>\n
\u7136\u800c\uff0cSpark\u548cHuggingFace\u6570\u636e\u96c6\u4e4b\u95f4\u7684\u76f8\u4e92\u8f6c\u6362\u975e\u5e38\u65b9\u4fbf\u3002<\/p>\n
\u7b2c\u4e8c\u6b65\uff1a\u52a0\u8f7d\u6a21\u578b\u548c\u91cf\u5b50\u5316<\/h1>\n
\u6211\u5c06\u5728GPU\u96c6\u7fa4\u4e2d\u6267\u884c\u5904\u7406\u3002
\n\u6211\u4f7f\u7528\u4e86g5.4xlarge\u7684AWS\u5b9e\u4f8b\u7c7b\u578b\u3002DBR\u7248\u672c\u662f14.1ML\u3002<\/p>\n
\u5b89\u88c5\u5fc5\u8981\u7684\u6a21\u5757\u3002
\n\u636e\u8bf4autoawq\u57280.1.5\u548c0.1.6\u7248\u672c\u4e4b\u95f4\u7684\u5185\u90e8\u5904\u7406\u6709\u76f8\u5f53\u5927\u7684\u53d8\u5316\uff0c\u4f46\u6211\u4f1a\u4f7f\u7528\u6700\u65b0\u7248\u672c(0.1.6)\u6765\u8fd0\u884c\u3002
\n\u203b\u7531\u4e8e\u9700\u8981pytorch >= 2.1.0\uff0c\u6240\u4ee5\u5b89\u88c5\u53ef\u80fd\u9700\u8981\u4e00\u4e9b\u65f6\u95f4\u3002<\/p>\n
%<\/span>pip<\/span> install<\/span> -<\/span>U<\/span> transformers<\/span> accelerate<\/span>\r\n%<\/span>pip<\/span> install<\/span> autoawq<\/span>==<\/span>\"<\/span>0.1.6<\/span>\"<\/span>\r\n\r\ndbutils<\/span>.<\/span>library<\/span>.<\/span>restartPython<\/span>()<\/span>\r\n<\/code><\/pre>\n
\u5b9a\u4e49\u51fd\u6570\u4ee5\u8bfb\u53d6\u6821\u51c6\u6570\u636e\u3002<\/p>\n
def<\/span> load_ja_calib<\/span>():<\/span>\r\n    df<\/span> =<\/span> spark<\/span>.<\/span>table<\/span>(<\/span>\"<\/span>training.llm.mmnga_wikipedia_ja_20230720_1k<\/span>\"<\/span>)<\/span>\r\n    pdf<\/span> =<\/span> df<\/span>.<\/span>select<\/span>(<\/span>\"<\/span>text<\/span>\"<\/span>).<\/span>toPandas<\/span>()<\/span>\r\n\r\n    return<\/span> list<\/span>(<\/span>pdf<\/span>[<\/span>\"<\/span>text<\/span>\"<\/span>])<\/span>\r\n<\/code><\/pre>\n
\u4f7f\u7528AutoAWQ\u52a0\u8f7d\u6a21\u578b\u3002
\nCALM2\u6a21\u578b\u4f7f\u7528\u5148\u524d\u4fdd\u5b58\u7684\u6a21\u578b\u8fdb\u884c\u64cd\u4f5c\u3002<\/p>\n
from<\/span> awq<\/span> import<\/span> AutoAWQForCausalLM<\/span>\r\nfrom<\/span> transformers<\/span> import<\/span> AutoTokenizer<\/span>\r\n\r\nUC_VOLUME<\/span> =<\/span> \"<\/span>\/Volumes\/training\/llm\/model_snapshots<\/span>\"<\/span>\r\nuc_dir<\/span> =<\/span> \"<\/span>\/models--cyberagent--calm2-7b-chat<\/span>\"<\/span>\r\nmodel_path<\/span> =<\/span> f<\/span>\"<\/span>{<\/span>UC_VOLUME<\/span>}{<\/span>uc_dir<\/span>}<\/span>\"<\/span>\r\n\r\nquant_config<\/span> =<\/span> {<\/span> \"<\/span>zero_point<\/span>\"<\/span>:<\/span> True<\/span>,<\/span> \"<\/span>q_group_size<\/span>\"<\/span>:<\/span> 128<\/span>,<\/span> \"<\/span>w_bit<\/span>\"<\/span>:<\/span> 4<\/span>,<\/span> \"<\/span>version<\/span>\"<\/span>:<\/span> \"<\/span>GEMM<\/span>\"<\/span> }<\/span>\r\n\r\n# Load model\r\n<\/span>model<\/span> =<\/span> AutoAWQForCausalLM<\/span>.<\/span>from_pretrained<\/span>(<\/span>model_path<\/span>,<\/span> device_map<\/span>=<\/span>\"<\/span>auto<\/span>\"<\/span>,<\/span> torch_dtype<\/span>=<\/span>\"<\/span>auto<\/span>\"<\/span>)<\/span>\r\ntokenizer<\/span> =<\/span> AutoTokenizer<\/span>.<\/span>from_pretrained<\/span>(<\/span>model_path<\/span>,<\/span> trust_remote_code<\/span>=<\/span>True<\/span>)<\/span>\r\n\r\n# Quantize\r\n<\/span>model<\/span>.<\/span>quantize<\/span>(<\/span>tokenizer<\/span>,<\/span> quant_config<\/span>=<\/span>quant_config<\/span>,<\/span> calib_data<\/span>=<\/span>load_ja_calib<\/span>())<\/span>\r\n<\/code><\/pre>\n
\u5982\u679c\u6ca1\u6709\u95ee\u9898\uff0c\u52a0\u8f7d\u548c\u91cf\u5b50\u5316\u5904\u7406\u4f1a\u88ab\u6267\u884c\uff0c\u5e76\u5728\u5927\u7ea620\u5206\u949f\u5185\u5b8c\u6210\u3002<\/p>\n
\u6b65\u9aa43. \u91cf\u5b50\u6a21\u578b\u7684\u5b58\u50a8<\/h1>\n
\u6dfb\u52a0\u4e00\u4e2a\u7528\u4e8e\u52a0\u8f7dtransformers\u7684\u8bbe\u5b9a\uff0c\u5e76\u5c06\u6a21\u578b\u5b58\u50a8\u5728\u4e34\u65f6\u6587\u4ef6\u5939\u4e2d\u3002<\/p>\n
from<\/span> transformers<\/span> import<\/span> AwqConfig<\/span>,<\/span> AutoConfig<\/span>\r\n\r\nquant_path<\/span> =<\/span> \"<\/span>\/tmp\/local-calm2-7b-chat-AWQ<\/span>\"<\/span>\r\n\r\n# modify the config file so that it is compatible with transformers integration\r\n<\/span>quantization_config<\/span> =<\/span> AwqConfig<\/span>(<\/span>\r\n    bits<\/span>=<\/span>quant_config<\/span>[<\/span>\"<\/span>w_bit<\/span>\"<\/span>],<\/span>\r\n    group_size<\/span>=<\/span>quant_config<\/span>[<\/span>\"<\/span>q_group_size<\/span>\"<\/span>],<\/span>\r\n    zero_point<\/span>=<\/span>quant_config<\/span>[<\/span>\"<\/span>zero_point<\/span>\"<\/span>],<\/span>\r\n    version<\/span>=<\/span>quant_config<\/span>[<\/span>\"<\/span>version<\/span>\"<\/span>].<\/span>lower<\/span>(),<\/span>\r\n).<\/span>to_dict<\/span>()<\/span>\r\n\r\nmodel<\/span>.<\/span>model<\/span>.<\/span>config<\/span>.<\/span>quantization_config<\/span> =<\/span> quantization_config<\/span>\r\n\r\n# Save quantized model\r\n<\/span>model<\/span>.<\/span>save_quantized<\/span>(<\/span>quant_path<\/span>,<\/span> safetensors<\/span>=<\/span>True<\/span>)<\/span>\r\ntokenizer<\/span>.<\/span>save_pretrained<\/span>(<\/span>quant_path<\/span>)<\/span>\r\n<\/code><\/pre>\n
\u7531\u65bc\u5982\u679c\u7e7c\u7e8c\u9019\u6a23\u4e0b\u53bb\uff0c\u6587\u4ef6\u5c07\u5728\u96c6\u7fa4\u7d50\u675f\u6642\u6d88\u5931\uff0c\u6240\u4ee5\u6211\u5011\u5c07\u5b83\u5011\u5b58\u5132\u5230Unity\u76ee\u9304\u5377\u3002<\/p>\n
dbutils<\/span>.<\/span>fs<\/span>.<\/span>cp<\/span>(<\/span>\"<\/span>file:\/tmp\/calm2-7b-chat-AWQ<\/span>\"<\/span>,<\/span> \"<\/span>\/Volumes\/training\/llm\/model_snapshots\/models--local--cyberagent-calm2-7b-chat-AWQ-calib-ja-1k<\/span>\"<\/span>,<\/span> recurse<\/span>=<\/span>True<\/span>)<\/span>\r\n<\/code><\/pre>\n
\u7b2c\u56db\u6b65\uff1a\u8fdb\u884c\u63a8\u8bba<\/h1>\n
\u4e3a\u4e86\u786e\u8ba4\u662f\u5426\u6210\u529f\u8fdb\u884c\u8f6c\u6362\uff0c\u6211\u4eec\u5c06\u52a0\u8f7d\u8f6c\u6362\u540e\u7684\u6a21\u578b\u5e76\u8fdb\u884c\u63a8\u7406\u3002<\/p>\n
from<\/span> transformers<\/span> import<\/span> AutoModelForCausalLM<\/span>,<\/span> AutoTokenizer<\/span>\r\nfrom<\/span> transformers<\/span> import<\/span> TextStreamer<\/span>\r\n\r\nmodel_path<\/span> =<\/span> \"<\/span>\/Volumes\/training\/llm\/model_snapshots\/models--local--cyberagent-calm2-7b-chat-AWQ-calib-ja-1k<\/span>\"<\/span>\r\n\r\nmodel<\/span> =<\/span> AutoModelForCausalLM<\/span>.<\/span>from_pretrained<\/span>(<\/span>model_path<\/span>,<\/span> device_map<\/span>=<\/span>\"<\/span>cuda<\/span>\"<\/span>)<\/span>\r\ntokenizer<\/span> =<\/span> AutoTokenizer<\/span>.<\/span>from_pretrained<\/span>(<\/span>model_path<\/span>)<\/span>\r\nstreamer<\/span> =<\/span> TextStreamer<\/span>(<\/span>tokenizer<\/span>,<\/span> skip_prompt<\/span>=<\/span>True<\/span>,<\/span> skip_special_tokens<\/span>=<\/span>True<\/span>)<\/span>\r\n<\/code><\/pre>\n
def<\/span> generate_stream_text<\/span>(<\/span>prompt<\/span>:<\/span> str<\/span>,<\/span> max_new_tokens<\/span>:<\/span> int<\/span> =<\/span> 512<\/span>)<\/span> -><\/span> str<\/span>:<\/span>\r\n\r\n    tokens<\/span> =<\/span> tokenizer<\/span>(<\/span>prompt<\/span>,<\/span> return_tensors<\/span>=<\/span>\"<\/span>pt<\/span>\"<\/span>).<\/span>input_ids<\/span>.<\/span>cuda<\/span>()<\/span>\r\n\r\n    generation_config<\/span> =<\/span> GenerationConfig<\/span>(<\/span>\r\n        max_new_tokens<\/span>=<\/span>max_new_tokens<\/span>,<\/span>\r\n        do_sample<\/span>=<\/span>True<\/span>,<\/span>\r\n        top_k<\/span>=<\/span>40<\/span>,<\/span>\r\n        top_p<\/span>=<\/span>0.95<\/span>,<\/span>\r\n        temperature<\/span>=<\/span>0.7<\/span>,<\/span>\r\n        eos_token_id<\/span>=<\/span>model<\/span>.<\/span>config<\/span>.<\/span>eos_token_id<\/span>,<\/span>\r\n        pad_token_id<\/span>=<\/span>model<\/span>.<\/span>config<\/span>.<\/span>pad_token_id<\/span>,<\/span>\r\n    )<\/span>\r\n\r\n    generation_output<\/span> =<\/span> model<\/span>.<\/span>generate<\/span>(<\/span>\r\n        tokens<\/span>,<\/span>\r\n        streamer<\/span>=<\/span>streamer<\/span>,<\/span>\r\n        generation_config<\/span>=<\/span>generation_config<\/span>,<\/span>\r\n    )<\/span>\r\n\r\n    return<\/span> tokenizer<\/span>.<\/span>decode<\/span>(<\/span>generation_output<\/span>[<\/span>0<\/span>],<\/span> skip_special_tokens<\/span>=<\/span>True<\/span>)<\/span>\r\n<\/code><\/pre>\n
\u6211\u4f1a\u7528\u4e2d\u6587\u6765\u56de\u7b54\u5c0a\u656c\u7684npaka\u5148\u751f\u7684\u95ee\u9898\u3002<\/p>\n
prompt<\/span> =<\/span> \"\"\"<\/span>USER: \u307e\u3069\u304b\u2606\u30de\u30ae\u30ab\u3067\u8ab0\u304c\u4e00\u756a\u304b\u308f\u3044\u3044\uff1f\r\nASSISTANT: <\/span>\"\"\"<\/span>\r\n\r\nmax_new_tokens<\/span> =<\/span> 128<\/span>\r\n_<\/span> =<\/span> generate_stream_text<\/span>(<\/span>prompt<\/span>,<\/span> max_new_tokens<\/span>)<\/span>\r\n<\/code><\/pre>\n
\u307e\u3069\u304b\u2606\u30de\u30ae\u30ab\u306e\u30ad\u30e3\u30e9\u30af\u30bf\u30fc\u306f\u3001\u305d\u308c\u305e\u308c\u500b\u6027\u7684\u306a\u6027\u683c\u3084\u30d3\u30b8\u30e5\u30a2\u30eb\u3092\u6301\u3063\u3066\u3044\u307e\u3059\u3002\r\n\r\n\u305d\u306e\u4e2d\u3067\u3082\u3001\u4e00\u756a\u304b\u308f\u3044\u3044\u3068\u4eba\u6c17\u304c\u9ad8\u3044\u306e\u306f\u9e7f\u76ee\u307e\u3069\u304b\u3067\u3057\u3087\u3046\u3002\u5f7c\u5973\u306e\u512a\u3057\u3055\u3084\u7d14\u7c8b\u3055\u3001\u305d\u3057\u3066\u4f55\u3088\u308a\u9b54\u6cd5\u5c11\u5973\u3068\u3057\u3066\u306e\u5f37\u3055\u3092\u6301\u3063\u3066\u3044\u308b\u3053\u3068\u304c\u3001\u591a\u304f\u306e\u30d5\u30a1\u30f3\u3092\u9b45\u4e86\u3057\u3066\u3044\u308b\u7406\u7531\u3067\u3057\u3087\u3046\u3002\r\n\r\n\u307e\u305f\u3001\u5df4\u30de\u30df\u3084\u6681\u7f8e\u307b\u3080\u3089\u306a\u3069\u3082\u3001\u5973\u6027\u30d5\u30a1\u30f3\u304b\u3089\u306e\u4eba\u6c17\u304c\u9ad8\u3044\u3067\u3059\u3002\u5f7c\u5973\u305f\u3061\u306e\u5f37\u3055\u3084\u512a\u3057\u3055\u3001\u305d\u3057\u3066\u9b54\u6cd5\u5c11\u5973\u3068\u3057\u3066\u306e\u8cac\u4efb\u611f\u306a\u3069\u304c\u3001\u591a\u304f\u306e\u30d5\u30a1\u30f3\u3092\u5f15\u304d\u3064\u3051\u308b\u9b45\u529b\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002\r\n\r\n\u4ed6\u306b\u3082\u3001\u7f8e\u6a39\u3055\u3084\u304b\u3084\u4f50\u5009\u674f\u5b50\u306a\u3069\u3001\u500b\u6027\u7684\u306a\u30ad\u30e3\u30e9\u30af\u30bf\u30fc\u304c\u305f\u304f\u3055\u3093\u3044\u308b\u306e\u304c\u307e\u3069\u304b\u2606\u30de\u30ae\r\n<\/code><\/pre>\n
\u4ed6\u770b\u8d77\u6765\u975e\u5e38\u7a33\u91cd\u3002<\/p>\n
\u6211\u4e5f\u4f1a\u7528\u82f1\u6587\u6765\u542c\u4e00\u4e0b\u3002<\/p>\n
prompt<\/span> =<\/span> \"\"\"<\/span>USER: What is Databricks?\r\nASSISTANT: <\/span>\"\"\"<\/span>\r\n\r\nmax_new_tokens<\/span> =<\/span> 128<\/span>\r\n_<\/span> =<\/span> generate_stream_text<\/span>(<\/span>prompt<\/span>,<\/span> max_new_tokens<\/span>)<\/span>\r\n<\/code><\/pre>\n
1. Databricks, Inc. is an American cloud computing company that provides a platform for building, managing, and securing data pipelines and workloads.\r\n2. Databricks provides a cloud-based platform that allows users to build and manage data pipelines and workloads for their data lakes, data warehouses, and analytics workflows. It offers a range of tools and services, including Apache Spark, Apache Kafka, Apache Flink, and Apache Beam, to help users manage their data pipelines and workflows.\r\n3. Databricks provides a comprehensive cloud platform for building and managing\r\n<\/code><\/pre>\n
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\u603b\u7ed3<\/h1>\n
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\u7531\u4e8eTheBloke\u5927\u5144\u8d35\u53d1\u5e03\u4e86AWQ\u8f6c\u6362\u597d\u7684\u6a21\u578b\uff0c\u56e0\u6b64\u4e3b\u8981\u7684LLM\u53ef\u80fd\u6ca1\u6709\u592a\u591a\u81ea\u5df1\u8f6c\u6362\u7684\u673a\u4f1a\u3002\u7136\u800c\uff0c\u7531\u4e8e\u5df2\u7ecf\u4e0eTransformers\u96c6\u6210\uff0c\u6240\u4ee5\u9700\u6c42\u4f3c\u4e4e\u4f1a\u589e\u52a0\u3002<\/p>\n
\u6211\u4e2a\u4eba\u8ba4\u4e3a\uff0c\u5728Databricks\u4e0a\u4f7f\u7528Transformers\u65f6\uff0c\u76f8\u8f83\u4e8eGPTQ\uff0cAWQ\u66f4\u6613\u4e8e\u64cd\u4f5c\u3002
\n\uff08\u8fd9\u662f\u6211\u7684\u4e00\u4e2a\u6280\u672f\u95ee\u9898\uff0c\u6211\u5728Databricks\u4e0a\u771f\u7684\u65e0\u6cd5\u5f88\u597d\u5730\u5904\u7406GPTQ\u6a21\u578b…\uff09<\/p>\n
\u7136\u800c\uff0c\u6839\u636e\u4e0b\u9762\u7684\u535a\u5ba2\uff0cGPTQ\uff08\u6216\u8005\u8bf4\u662fExllama\uff09\u5f97\u51fa\u7684\u7ed3\u679c\u4f3c\u4e4e\u662f\u6700\u4f73\u7684\uff0c\u6211\u5e0c\u671b\u80fd\u591f\u66f4\u597d\u5730\u8fd0\u7528\u5b83\uff08\u6211\u4e5f\u60f3\u770b\u770bvLLM\u5728\u8c03\u4f18\u4e4b\u540e\u7684\u6bd4\u8f83\u7ed3\u679c\uff09\u3002<\/p>\n
 <\/p>\n
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