What are the key features of ChatGLM-6B?

ChatGLM-6B has 6.2 billion parameters, supports both Chinese and English, can be deployed locally on consumer-grade GPUs, has a context length of 2048 tokens, and is trained on about 1T tokens of data with techniques like supervised fine-tuning and reinforcement learning.

Is ChatGLM open source?

Yes, ChatGLM-6B is open source. The weights are completely open for academic research, and free commercial use is allowed after completing a questionnaire.

What are the hardware requirements to run ChatGLM-6B?

With quantization techniques, ChatGLM-6B can be deployed locally on consumer-grade graphics cards. At the INT4 quantization level, only 6GB of GPU memory is required.

What is the latest version of ChatGLM?

The latest version is ChatGLM3-6B, which introduces features like stronger base model performance, support for tool invocation and code execution, and a newly designed prompt format.

Where can I access or try ChatGLM?

You can access ChatGLM through the official website https://chatglm.cn. There are also open-source versions available on GitHub for local deployment.

ChatGLM Howto

WebsiteLarge Language Models (LLMs)AI Chatbot

ChatGLM is an open-source bilingual (Chinese-English) large language model series developed by Zhipu AI and Tsinghua KEG, featuring smooth dialogue capabilities and low deployment thresholds.

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Features of ChatGLM & Use Cases

How to Use ChatGLM

Install required packages: Install the necessary Python packages by running: pip install protobuf transformers==4.30.2 cpm_kernels torch>=2.0 gradio mdtex2html sentencepiece accelerate

Import the model and tokenizer: Use the following code to import ChatGLM: from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()

Generate a response: Generate a response by calling the chat method: response, history = model.chat(tokenizer, "你好", history=[]) print(response)

Continue the conversation: To continue the conversation, pass the history to subsequent calls: response, history = model.chat(tokenizer, "晚上睡不着应该怎么办", history=history) print(response)

Use the web interface: For a more user-friendly experience, visit https://chatglm.cn to use the web interface of the larger ChatGLM model

Download mobile app: Scan the QR code on the ChatGLM website to download the mobile app for iOS or Android

ChatGLM FAQs

ChatGLM is an open bilingual language model based on the General Language Model (GLM) framework. It is trained on both Chinese and English data and optimized for question-answering and dialogue tasks.

ChatGLM Monthly Traffic Trends

ChatGLM experienced a 22.1% decline in traffic, with visits dropping by 532K. Without recent product updates or significant market activities, this decline may reflect normal market fluctuations or increased competition.

View history traffic

ChatGLM Review: Powerful Bilingual AI Language Model

How to Use ChatGLM: A Comprehensive Guide for Beginners

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