glm-10b-chinese

Maintainer: THUDM

Total Score

121

Last updated 5/28/2024

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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

glm-10b-chinese is a large language model developed by THUDM (Tsinghua University Intelligent Media Lab). It is part of the General Language Model (GLM) framework, which is pretrained with an autoregressive blank-filling objective for natural language understanding and generation tasks. The model has 48 transformer layers with a hidden size of 4096 and 64 attention heads, and was trained on the WuDaoCorpora dataset.

Similar models in the GLM and ChatGLM series include chatglm-6b, chatglm-6b-int4, chatglm3-6b, and chatglm2-6b-int4. These models share a similar architecture and training approach, but have been optimized for different capabilities and deployment scenarios.

Model inputs and outputs

Inputs

  • [MASK]: Used for short blank filling tasks
  • [sMASK]: Used for sentence filling
  • [gMASK]: Used for left-to-right generation

Outputs

  • Filled-in text based on the input mask tokens, generated in an autoregressive manner.

Capabilities

glm-10b-chinese can be used for a variety of natural language understanding and generation tasks, such as question answering, text summarization, and language modeling. The model's autoregressive blank-filling training approach allows it to generate coherent and contextual text outputs.

What can I use it for?

You can use glm-10b-chinese for any text-to-text task, such as:

  • Question answering: Given a question, the model can generate a relevant answer.
  • Text summarization: The model can summarize long-form text into a concise, informative summary.
  • Language modeling: The model can be used to generate human-like text continuations, which can be useful for creative writing or chatbots.

To get started, you can follow the example code provided in the maintainer's description to load and use the model.

Things to try

One interesting aspect of glm-10b-chinese is its ability to handle different types of masking tokens for various tasks. You can experiment with using the different mask tokens ([MASK], [sMASK], [gMASK]) to see how the model's outputs change for different use cases, such as short-form filling, sentence-level completion, and left-to-right generation.



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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