GLM-130B

What is GLM-130B?

GLM-130B is a pre-trained deep 130-billion-parameter bilingual model for English-to-Chinese and Chinese-to-English bidirectional dense modeling. The pre-training method used in this model uses the General Language Model algorithm. In particular, GLM-130B should be an effective system that supports inference tasks out-of-the-box on one single-server setup, such as A100-based ones (40G*8) or V100-based ones (32G*8). It also supports INT4 quantization, reduces the hardware requirements, hence is able to run even on a server equipped with 4 * RTX 3090, 24G, with very minimal performance degradation.

Important Features & Advantages of GLM-130B

• 
  Bilingual Support:
  
  The model can support English and Chinese languages, making it a great choice for most applications in these two languages.
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  High Performance:
  
  As seen from the tables above, GLM-130B outperforms both its competition.
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  Fast Inference:
  
  GLM-130B uses the advantages of SAT and FasterTransformer to ensure that fast inference times occur with one A100 server or more.
• 
  Reproducibility:
  
  Code that is open-sourced and model checkpoints ensure that it can be brought up on more than 30 different tasks.
• 
  Cross-platform Compatibility:
  
  Works perfectly on NVIDIA, Hygon DCU, Ascend 910, and soon Sunway.

Applications and Scenarios of GLM-130B

• 
  Natural Language Processing (NLP):
  
  Being two languaged, it is very suitable for tasks like text translation, sentiment analysis, and language generation.
• 
  Research and Academic Studies:
  
  For the open-source nature of the model and high reproducibility, the GLM-130B runs well in academic research and experimental purposes.
• 
  Content Creation:
  
  The model is able to help create top-notch content, both in English and Chinese, for writers and marketers.

It can be supportive for industries such as technology, education, and media. For instance, a tech company could use the model for ways to improve AI-driven language tools, or an educational company could use it for creating applications for the purpose of language learning.

How do I use GLM-130B?

The following are your preliminary steps towards using GLM-130B as indicated:

1. Download open-source code and model checkpoints from its repository.
2. Install the required hardware with compatibility for platforms like NVIDIA, Hygon DCU, or Ascend 910.
3. Load the model and set it up through the guidelines provided for it to be optimized for your set of tasks.
4. Run inference tasks and check the model’s performance on your datasets.

Hints and Tips:

Utilize quantization to INT4 for the lowest hardware resources, with the least performance degradation. Realize the server environment with a focus on optimization of the requirements of the model for the best outcomes.

GLM-130B Working Of

The GLM model in the GLM-130B itself is propelled by an algorithm that is purposely admissible for dense bidirectional modeling. A dataset of over 400 billion text tokens is equally balanced between the Chinese and English dialects in the training data that was drawn. The huge data volume assures the model with strong bilingual support. Additionally, the basic model architecture secures the performance of real-time inference through its advanced techniques, including SAT and FasterTransformer.

GLM-130B, on the other hand, is a model that supports various hardware platforms, which are very flexible and adaptable in any server environment. Model specifications are open to the community, and thus its open-source nature allows scientists and developers to access a reproduction of results and are also able to base further development on an already existing framework.

GLM-130B Pros and Cons

Advantages:

• It is exceptionally supportive of two languages, where two languages are used – English and Chinese.
• This model has a very efflorescent result on a broad range of datasets.
• Very fast in performing inferences, adopting advanced techniques to achieve the best possible time complexity.
• Supports several hardware platforms.

Disadvantages:

• Requires massive computational resources for training, although the inference can be run on much lighter hardware.
• Need for extensive setup which can be cumbersome for users with no detailed technical background.

User Feedback

Many users said that the model worked well and is pretty good with a bilingual feature, but a trainer needs extensive computational power while training the model.

FAQs for GLM-130B

• 
  What is GLM-130B?
  
  A bilingual, bidirectional dense model with 130 billion parameters, pre-trained using the General Language Model (GLM) algorithm.
• 
  How big is the GLM-130B model?
  
  The model is trained on over 400 billion text tokens, with 200 billion each for Chinese and English text.
• 
  Can the results produced by GLM-130B be reproduced?
  
  Yes, all results on over 30 tasks can be easily reproduced using the provided open-source code and model checkpoints.
• 
  Does GLM-130B support multiple hardware platforms?
  
  GLM-130B supports not only NVIDIA but also Hygon DCU, Ascend 910, and soon Sunway platforms for training and inference.
• 
  What would be the primary contents of the GLM-130B repository?
  
  The key contents of the repository are on the evaluation of GLM-130B, specifically on fast model inference and result reproduction.