mirror of https://github.com/THUDM/ChatGLM2-6B
207 lines
9.5 KiB
Plaintext
207 lines
9.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 79,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import openai"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 80,
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"metadata": {},
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"outputs": [],
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"source": [
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"openai.api_base = \"http://localhost:8000/v1\"\n",
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"openai.api_key = \"none\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 81,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_completion(prompt, model=\"chatglm2-6b-model\"):\n",
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" messages = [{\"role\": \"user\", \"content\": prompt}]\n",
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" response = openai.ChatCompletion.create(\n",
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" model=model,\n",
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" messages=messages,\n",
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" temperature=0,\n",
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" max_tokens=2048\n",
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" )\n",
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" #return response.choices[0].message[\"content\"]\n",
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" return response"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 82,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"返回内容:\n",
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"《三国演义》中的曹操是三国时期最具代表性的历史人物之一,被誉为“三国第一谋士”。他是一个极富谋略、谋断和领导力的领袖,也是一位出色的诗人。\n",
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"\n",
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"曹操在小说中被描绘成一个极具野心、聪明狡猾、冷酷无情的政治家。他为了实现自己的理想,不惜牺牲他人的利益,甚至为了达到目的,不惜杀妻灭子。这种冷酷无情,虽然有些令人震驚,但也反映了他作为一个政治家的决心和勇气。\n",
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"\n",
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"曹操在小说中也被描绘成一个出色的军事家。他指挥过许多战役,并制定了许多军事战略和计划,如“三顾茅庐”、“赤壁之战”等,都取得了重大胜利。他的军事才能和领导力,使得他成为了三国中最强大的军队的指挥官之一。\n",
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"\n",
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"曹操在小说中也被描绘成一个很有文化修养的人。他非常热爱文学,精通诗词,也善于书法。这种文化修养不仅彰显出他的品味和才华,也反映出他对于国家和民族的重视,以及他对于文化传承和发展的贡献。\n",
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"\n",
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"曹操是一个极具矛盾性和复杂性的历史人物。他的聪明、狡猾、冷酷、果断、文化和军事才能都使他成为了三国时期最杰出的政治家和军事家之一。\n"
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]
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}
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],
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"source": [
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"response=get_completion(\"三百字评价一下下三国演义中的曹操\")\n",
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"\n",
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"\n",
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"# 返回的内容\n",
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"print(f\"返回内容:\")\n",
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"print(response.choices[0].message[\"content\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 83,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"返回字数: 439\n"
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]
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}
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],
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"source": [
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"# 返回的字数\n",
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"print(\"返回字数:\", len(response.choices[0].message[\"content\"]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 84,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"第0次返回的字数: 578\n",
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"第1次返回的字数: 401\n",
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"第2次返回的字数: 599\n",
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"第3次返回的字数: 524\n",
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"第4次返回的字数: 484\n",
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"第5次返回的字数: 761\n",
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"第6次返回的字数: 625\n",
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"第7次返回的字数: 342\n",
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"第8次返回的字数: 515\n",
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"第9次返回的字数: 507\n",
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"平均每次返回的字数: 533.6\n",
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"平均每秒输出的字数：57.0\n"
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]
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}
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],
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"source": [
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"# Throughput Benchmark\n",
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"# 我的测试中，RTX3090的吞吐为每秒约57字\n",
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"\n",
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"print(\"Throughput Benchmark:\", )\n",
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"\n",
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"import time\n",
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"\n",
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"total_words = 0\n",
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"start_time = time.time()\n",
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"for i in range(10):\n",
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" response=get_completion(\"一千字评价一下下三国演义中的曹操\")\n",
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" total_words += len(response.choices[0].message[\"content\"])\n",
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" print(f\"第{i}次返回的字数:\", len(response.choices[0].message[\"content\"]))\n",
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"print(f\"平均每次返回的字数: {total_words / 10}\")\n",
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"print(f\"平均每秒输出的字数：{total_words // (time.time() - start_time)}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# openai的文档（https://platform.openai.com/docs/api-reference/completions/object）中提到，\n",
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"# openai.ChatCompletion.create返回对象中包含usage字段，该字段统计了input promot和output以及两者之和的token数量。但是实际打印中该字段已经被移除。\n",
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"\n",
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"# print(response)\n",
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"\n",
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"{\n",
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" \"model\": \"chatglm2-6b-model\",\n",
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" \"object\": \"chat.completion\",\n",
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" \"choices\": [\n",
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" {\n",
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" \"index\": 0,\n",
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" \"message\": {\n",
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" \"role\": \"assistant\",\n",
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" \"content\": \"\\u4e0b\\u4e09\\u56fd\\u6f14\\u4e49\\u4e2d\\u7684\\u66f9\\u64cd\\uff0c\\u4f5c\\u4e3a\\u4e1c\\u6c49\\u672b\\u5e74\\u7684\\u653f\\u6cbb\\u5bb6\\u548c\\u519b\\u4e8b\\u5bb6\\uff0c\\u662f\\u4e00\\u4f4d\\u6781\\u5177\\u4e89\\u8bae\\u7684\\u5386\\u53f2\\u4eba\\u7269\\u3002\\u4e00\\u65b9\\u9762\\uff0c\\u66f9\\u64cd\\u8eab\\u7ecf\\u767e\\u6218\\uff0c\\u7edf\\u4e00\\u5317\\u65b9\\uff0c\\u4e3a\\u56fd\\u5bb6\\u7acb\\u4e0b\\u4e86\\u8d6b\\u8d6b\\u6218\\u529f\\uff1b\\u53e6\\u4e00\\u65b9\\u9762\\uff0c\\u66f9\\u64cd\\u53c8\\u662f\\u4e00\\u4f4d\\u6781\\u5177\\u8c0b\\u7565\\u548c\\u51b7\\u9177\\u7684\\u7edf\\u6cbb\\u8005\\uff0c\\u7ecf\\u5e38\\u65bd\\u884c\\u6b8b\\u9177\\u7684\\u5211\\u7f5a\\uff0c\\u5bfc\\u81f4\\u6c11\\u4f17\\u5bf9\\u5176\\u6028\\u58f0\\u8f7d\\u9053\\u3002\\n\\n\\u66f9\\u64cd\\u5728\\u4e09\\u56fd\\u65f6\\u671f\\u7684\\u6218\\u4e89\\u7ecf\\u5386\\u4e2d\\uff0c\\u5c55\\u73b0\\u51fa\\u4e86\\u51fa\\u8272\\u7684\\u519b\\u4e8b\\u624d\\u80fd\\u548c\\u6307\\u6325\\u80fd\\u529b\\u3002\\u4ed6\\u7387\\u9886\\u7684\\u90e8\\u961f\\u5728\\u5b98\\u6e21\\u3001\\u8d64\\u58c1\\u7b49\\u6218\\u5f79\\u4e2d\\u53d6\\u5f97\\u91cd\\u5927\\u80dc\\u5229\\uff0c\\u5bf9\\u5f53\\u65f6\\u7684\\u653f\\u6cbb\\u5c40\\u52bf\\u4ea7\\u751f\\u4e86\\u91cd\\u5927\\u5f71\\u54cd\\u3002\\u540c\\u65f6\\uff0c\\u66f9\\u64cd\\u8fd8\\u662f\\u4e00\\u4f4d\\u51fa\\u8272\\u7684\\u653f\\u6cbb\\u5bb6\\uff0c\\u5728\\u6cbb\\u7406\\u56fd\\u5bb6\\u65b9\\u9762\\u6709\\u7740\\u72ec\\u7279\\u7684\\u89c1\\u89e3\\uff0c\\u4e3a\\u56fd\\u5bb6\\u7684\\u53d1\\u5c55\\u505a\\u51fa\\u4e86\\u91cd\\u8981\\u8d21\\u732e\\u3002\\n\\n\\u7136\\u800c\\uff0c\\u66f9\\u64cd\\u7684\\u6b8b\\u9177\\u7edf\\u6cbb\\u548c\\u8840\\u8165\\u624b\\u6bb5\\u4e5f\\u96be\\u4ee5\\u9003\\u8131\\u5386\\u53f2\\u60a0\\u60a0\\u4f17\\u53e3\\u7684\\u5ba1\\u5224\\u3002\\u5728\\u4e09\\u56fd\\u65f6\\u671f\\uff0c\\u66f9\\u64cd\\u7ecf\\u5e38\\u65bd\\u884c\\u6781\\u5176\\u6b8b\\u9177\\u7684\\u5211\\u7f5a\\uff0c\\u5982\\u65a9\\u9996\\u3001\\u7ede\\u5211\\u7b49\\uff0c\\u6765\\u5de9\\u56fa\\u81ea\\u5df1\\u7684\\u7edf\\u6cbb\\u5730\\u4f4d\\u3002\\u8fd9\\u4e9b\\u5211\\u7f5a\\u4e0d\\u4ec5\\u5bf9\\u7f6a\\u72af\\u672c\\u4eba\\u9020\\u6210\\u4e86\\u6781\\u5927\\u7684\\u75db\\u82e6\\uff0c\\u4e5f\\u5bf9\\u666e\\u901a\\u767e\\u59d3\\u5e26\\u6765\\u4e86\\u4e0d\\u53ef\\u627f\\u53d7\\u7684\\u60ca\\u6050\\u548c\\u60b2\\u4f24\\u3002\\n\\n\\u603b\\u4e4b\\uff0c\\u4e0b\\u4e09\\u56fd\\u6f14\\u4e49\\u4e2d\\u7684\\u66f9\\u64cd\\u662f\\u4e00\\u4f4d\\u6781\\u5177\\u4e89\\u8bae\\u7684\\u5386\\u53f2\\u4eba\\u7269\\u3002\\u4ed6\\u65e2\\u6709\\u51fa\\u8272\\u7684\\u519b\\u4e8b\\u624d\\u80fd\\u548c\\u653f\\u6cbb\\u667a\\u6167\\uff0c\\u53c8\\u6709\\u6b8b\\u9177\\u7edf\\u6cbb\\u548c\\u8840\\u8165\\u624b\\u6bb5\\u3002\\u5728\\u8bc4\\u4ef7\\u66f9\\u64cd\\u65f6\\uff0c\\u6211\\u4eec\\u9700\\u8981\\u5168\\u9762\\u5ba2\\u89c2\\u5730\\u770b\\u5f85\\u4ed6\\u7684\\u5386\\u53f2\\u4f5c\\u7528\\u548c\\u8d1f\\u9762\\u5f71\\u54cd\\u3002\"\n",
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" },\n",
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" \"finish_reason\": \"stop\"\n",
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" }\n",
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" ],\n",
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" \"created\": 1693113050\n",
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"}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 可参考 https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb 来计算Token数量\n",
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"# 扩展阅读：https://zhuanlan.zhihu.com/p/626593576 \n",
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"\n",
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"# To-Do:\n",
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"# 可使用tiktoken库的来计算Token数量\n",
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"\n",
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"import tiktoken\n",
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"\n",
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"def num_tokens_from_string(string: str, encoding_name: str) -> int:\n",
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" \"\"\"Returns the number of tokens in a text string.\"\"\"\n",
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" encoding = tiktoken.get_encoding(encoding_name)\n",
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" num_tokens = len(encoding.encode(string))\n",
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" return num_tokens\n",
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"\n",
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"num_tokens_from_string(\"tiktoken is great!\", \"cl100k_base\")\n",
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"\n",
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"# p50k_base和cl100k_base两种不同的encoding方式，cl100k_base的Token数量比p50k_base多\n",
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"num_tokens_from_string(response.choices[0].message[\"content\"], \"p50k_base\")\n",
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"num_tokens_from_string(response.choices[0].message[\"content\"], \"cl100k_base\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "chatGLM",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.16"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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