ChatGLM
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.
https://chatglm.cn/
Product Information
Updated:Dec 9, 2024
ChatGLM Monthly Traffic Trends
ChatGLM achieved 3.96M visits with a 8.8% growth in November. The GLM-4.0 release in October, which introduced enhanced bilingual capabilities and improved user experience, likely contributed to the traffic increase. Competitor updates and market trends may have also played a role, though specific details are limited.
What is ChatGLM
ChatGLM is a family of open-source large language models designed for dialogue tasks, with versions ranging from 6 billion to 130 billion parameters. Developed jointly by Zhipu AI and Tsinghua University's Knowledge Engineering Group (KEG), ChatGLM models are trained on massive Chinese and English corpora, optimized for question-answering and conversational interactions. The series includes ChatGLM-6B, ChatGLM2-6B, and the latest ChatGLM3-6B, each improving upon its predecessor with enhanced performance, longer context understanding, and more efficient inference capabilities.
Key Features of ChatGLM
ChatGLM is an open-source bilingual (Chinese and English) dialogue language model based on the General Language Model (GLM) framework. It uses technology similar to ChatGPT, optimized for Chinese Q&A and dialogue. Trained on about 1T tokens of Chinese and English corpus, it incorporates supervised fine-tuning, feedback bootstrapping, and reinforcement learning with human feedback. The model comes in various sizes, with ChatGLM-6B being a smaller, more accessible version that can be deployed locally on consumer-grade hardware.
Bilingual Support: Optimized for both Chinese and English language processing, making it versatile for multilingual applications.
Low Deployment Threshold: Can be deployed locally on consumer-grade graphics cards, with INT4 quantization requiring only 6GB of GPU memory.
Comprehensive Model Series: Offers various model sizes and specializations, including base models, dialogue models, and long-text models like ChatGLM3-6B-32K.
Advanced Training Techniques: Utilizes supervised fine-tuning, feedback bootstrapping, and reinforcement learning with human feedback to improve performance.
Open Source: Fully open for academic research and free for commercial use after registration, promoting community-driven development.
Use Cases of ChatGLM
Natural Language Processing: Can be used for various NLP tasks such as text generation, summarization, and question-answering in both Chinese and English.
Chatbots and Virtual Assistants: Ideal for creating conversational AI systems capable of engaging in multi-turn dialogues.
Content Creation: Assists in generating creative content, articles, and other written materials in both Chinese and English.
Code Generation and Assistance: With models like CodeGeeX, it can help in programming tasks and code generation.
Educational Tools: Can be used to create interactive learning experiences and answer student queries in multiple languages.
Pros
Bilingual capabilities make it versatile for Chinese and English applications
Low hardware requirements allow for widespread accessibility and local deployment
Open-source nature encourages community contributions and improvements
Cons
Smaller model size may limit performance compared to larger language models
Potential for generating inaccurate or biased information, as with all AI models
Requires careful use and monitoring to prevent misuse or unintended consequences
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.
Official Posts
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Analytics of ChatGLM Website
ChatGLM Traffic & Rankings
4M
Monthly Visits
#20341
Global Rank
#453
Category Rank
Traffic Trends: Jun 2024-Nov 2024
ChatGLM User Insights
00:02:31
Avg. Visit Duration
2.35
Pages Per Visit
54.37%
User Bounce Rate
Top Regions of ChatGLM
CN: 90.5%
US: 3.2%
HK: 1.82%
TW: 1.41%
SG: 0.59%
Others: 2.47%
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