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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.

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

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

1.What is ChatGLM?

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.

2.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.

3.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.

4.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.

5.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.

6.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.

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