{
 "cells": [
  {
   "cell_type": "code",
   "source": [
    "import torch \n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline "
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "2024-08-20 03:12:24.098444: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n2024-08-20 03:12:24.098474: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
    }
   ],
   "execution_count": 1,
   "metadata": {
    "gather": {
     "logged": 1724123553820
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "torch.random.manual_seed(0) \n",
    "model = AutoModelForCausalLM.from_pretrained( \n",
    "    \"../phi-3-instruct\",  \n",
    "    device_map=\"cuda\",  \n",
    "    torch_dtype=\"auto\",  \n",
    "    trust_remote_code=True,  \n",
    ") "
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c29888138a4f4714b7f65112733da2d2"
      }
     },
     "metadata": {}
    }
   ],
   "execution_count": 2,
   "metadata": {
    "jupyter": {
     "source_hidden": false,
     "outputs_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
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    "gather": {
     "logged": 1724123630099
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   }
  },
  {
   "cell_type": "code",
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"../phi-3-instruct\") "
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565 - if you loaded a llama tokenizer from a GGUF file you can ignore this message.\n"
    }
   ],
   "execution_count": 3,
   "metadata": {
    "jupyter": {
     "source_hidden": false,
     "outputs_hidden": false
    },
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      "deleting": false
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    },
    "gather": {
     "logged": 1724123630234
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   }
  },
  {
   "cell_type": "code",
   "source": [
    "# messages = [ \n",
    "#     {\"role\": \"system\", \"content\": \"你是我的人工智能助手，协助我用中文解答问题\"}, \n",
    "#     {\"role\": \"user\", \"content\": \"你能否介绍一下如何提升学习效率吗?\"}, \n",
    "# ] \n",
    "\n",
    "# <|system|>\n",
    "# You are a helpful assistant.<|end|>\n",
    "# <|user|>\n",
    "# Question?<|end|>\n",
    "# <|assistant|>\n",
    "\n",
    "messages = \"<|system|>\\n 你是我的人工智能助手，协助我用中文解答问题.\\n<|end|><|user|>\\n 你知道长沙吗？? \\n<|end|><|assistant|>\"\n"
   ],
   "outputs": [],
   "execution_count": 9,
   "metadata": {
    "jupyter": {
     "source_hidden": false,
     "outputs_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    },
    "gather": {
     "logged": 1724123692174
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "pipe = pipeline( \n",
    "    \"text-generation\", \n",
    "    model=model, \n",
    "    tokenizer=tokenizer, \n",
    ") "
   ],
   "outputs": [],
   "execution_count": 10,
   "metadata": {
    "jupyter": {
     "source_hidden": false,
     "outputs_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    },
    "gather": {
     "logged": 1724123693760
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "generation_args = { \n",
    "    \"max_new_tokens\": 1024, \n",
    "    \"return_full_text\": False, \n",
    "    \"temperature\": 0.3, \n",
    "    \"do_sample\": False, \n",
    "} "
   ],
   "outputs": [],
   "execution_count": 11,
   "metadata": {
    "jupyter": {
     "source_hidden": false,
     "outputs_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    },
    "gather": {
     "logged": 1724123695747
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "output = pipe(messages, **generation_args) "
   ],
   "outputs": [],
   "execution_count": 12,
   "metadata": {
    "jupyter": {
     "source_hidden": false,
     "outputs_hidden": false
    },
    "nteract": {
     "transient": {
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    },
    "gather": {
     "logged": 1724123704229
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "output[0]['generated_text']"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 8,
     "data": {
      "text/plain": "' 是的，我知道长沙。长沙是中国南部的一座大城市，位于湖南省中部，是中国的国际性大都市。它拥有丰富的历史和文化，曾是明朝的都城。长沙以其繁华的商业、独特的自然风光和丰富的历史遗迹而闻名，如橘子洲、岳麓山和长沙博物院。此外，长沙也是中国重要的科技和教育中心，包括中国工程院和中国科学院长沙分院。'"
     },
     "metadata": {}
    }
   ],
   "execution_count": 8,
   "metadata": {
    "jupyter": {
     "source_hidden": false,
     "outputs_hidden": false
    },
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    },
    "gather": {
     "logged": 1724123639263
    }
   }
  },
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   "metadata": {},
   "source": [
    "\n---\n\n**免责声明**：  \n本文件由 AI 翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻译。我们力求准确，但请注意，自动翻译可能包含错误或不准确之处。原始语言版本应被视为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而产生的任何误解或曲解，我们概不负责。\n"
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