chatglm2-6b

Maintainer: THUDM

Total Score

2.0K

Last updated 5/28/2024

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

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 several new features. Compared to the previous version, ChatGLM2-6B has stronger performance, longer context, and more efficient inference.

The model was developed by THUDM, a leading AI research institute in China. It is based on the GLM architecture and has undergone extensive pre-training and fine-tuning to achieve high performance across a variety of benchmarks.

Model inputs and outputs

Inputs

  • Text prompts for the model to generate a response

Outputs

  • Generated text responses to the input prompt

Capabilities

ChatGLM2-6B demonstrates significant improvements over its predecessor. It has achieved substantial gains on datasets like MMLU (+23%), CEval (+33%), GSM8K (+571%), and BBH (+60%), showing strong competitiveness among models of the same size. The model also uses FlashAttention to extend the context length from 2K to 32K, and Multi-Query Attention to enable more efficient inference with lower GPU memory usage.

What can I use it for?

ChatGLM2-6B is well-suited for a variety of natural language processing tasks, particularly open-ended conversations and question-answering. The model's bilingual (Chinese-English) capabilities make it useful for cross-cultural communication and language understanding. Developers can use ChatGLM2-6B to build chatbots, virtual assistants, and other interactive applications that require fluent dialogue.

Things to try

One interesting aspect of ChatGLM2-6B is its ability to maintain coherent and contextual conversations over longer sequences. You can experiment with providing the model with multi-turn dialogue histories and observe how it maintains the flow and consistency of the conversation. Additionally, you can explore the model's capabilities in tasks like summarization, translation, and open-ended question answering to see how it performs across a range of natural language understanding and generation challenges.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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