{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# ChatGLM\n", "\n", "[ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B) is an open bilingual language model based on General Language Model (GLM) framework, with 6.2 billion parameters. With the quantization technique, users can deploy locally on consumer-grade graphics cards (only 6GB of GPU memory is required at the INT4 quantization level). \n", "\n", "[ChatGLM2-6B](https://github.com/THUDM/ChatGLM2-6B) is the second-generation version of the open-source bilingual (Chinese-English) chat model ChatGLM-6B. It retains the smooth conversation flow and low deployment threshold of the first-generation model, while introducing the new features like better performance, longer context and more efficient inference.\n", "\n", "This example goes over how to use LangChain to interact with ChatGLM2-6B Inference for text completion.\n", "ChatGLM-6B and ChatGLM2-6B has the same api specs, so this example should work with both." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "from langchain.llms import ChatGLM\n", "from langchain import PromptTemplate, LLMChain\n", "\n", "# import os" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "template = \"\"\"{question}\"\"\"\n", "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# default endpoint_url for a local deployed ChatGLM api server\n", "endpoint_url = \"http://127.0.0.1:8000\"\n", "\n", "# direct access endpoint in a proxied environment\n", "# os.environ['NO_PROXY'] = '127.0.0.1'\n", "\n", "llm = ChatGLM(\n", " endpoint_url=endpoint_url,\n", " max_token=80000,\n", " history=[[\"我将从美国到中国来旅游,出行前希望了解中国的城市\", \"欢迎问我任何问题。\"]],\n", " top_p=0.9,\n", " model_kwargs={\"sample_model_args\": False},\n", ")" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "llm_chain = LLMChain(prompt=prompt, llm=llm)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ChatGLM payload: {'prompt': '北京和上海两座城市有什么不同?', 'temperature': 0.1, 'history': [['我将从美国到中国来旅游,出行前希望了解中国的城市', '欢迎问我任何问题。']], 'max_length': 80000, 'top_p': 0.9, 'sample_model_args': False}\n" ] }, { "data": { "text/plain": [ "'北京和上海是中国的两个首都,它们在许多方面都有所不同。\\n\\n北京是中国的政治和文化中心,拥有悠久的历史和灿烂的文化。它是中国最重要的古都之一,也是中国历史上最后一个封建王朝的都城。北京有许多著名的古迹和景点,例如紫禁城、天安门广场和长城等。\\n\\n上海是中国最现代化的城市之一,也是中国商业和金融中心。上海拥有许多国际知名的企业和金融机构,同时也有许多著名的景点和美食。上海的外滩是一个历史悠久的商业区,拥有许多欧式建筑和餐馆。\\n\\n除此之外,北京和上海在交通和人口方面也有很大差异。北京是中国的首都,人口众多,交通拥堵问题较为严重。而上海是中国的商业和金融中心,人口密度较低,交通相对较为便利。\\n\\n总的来说,北京和上海是两个拥有独特魅力和特点的城市,可以根据自己的兴趣和时间来选择前往其中一座城市旅游。'" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "question = \"北京和上海两座城市有什么不同?\"\n", "\n", "llm_chain.run(question)" ] } ], "metadata": { "kernelspec": { "display_name": "langchain-dev", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 2 }