# GLM-130B

GLM-130B is an open-source bilingual (English and Chinese) large language model with 130 billion parameters, developed by Tsinghua University and released in 2022. It is based on the General Language Model architecture and aims to provide a competitive alternative to proprietary models.

GLM-130B is a large language model developed by researchers at [Tsinghua University](https://www.wikiprompt.org/wiki/tsinghua-university) and released in 2022. With 130 billion parameters, it was designed as an open-source, bilingual model supporting both English and Chinese, a notable distinction from many contemporaneous models that were either closed-source or primarily English-focused. The model is built on the General Language Model (GLM) architecture, which combines aspects of [transformer](https://www.wikiprompt.org/wiki/transformer)-based [encoder-decoder](https://www.wikiprompt.org/wiki/encoder-decoder) designs with autoregressive generation, allowing it to handle both understanding and generation tasks effectively.

The project was initiated to address the growing gap between proprietary large language models and publicly available alternatives. By releasing the model weights and training code, the developers aimed to provide the research community with a powerful tool for studying and advancing [large-language-model](https://www.wikiprompt.org/wiki/large-language-model) capabilities, particularly in multilingual contexts. GLM-130B was trained on a diverse corpus of English and Chinese text, and its performance was benchmarked against several prominent models of the era, including [openai](https://www.wikiprompt.org/wiki/openai)'s GPT-3 and other large-scale systems.

## Architecture and Training

GLM-130B employs a unique architecture that integrates a bidirectional attention mechanism for understanding tasks with a unidirectional, autoregressive component for generation. This hybrid approach, detailed in the GLM framework, allows the model to excel in tasks such as text classification, question answering, and summarization, while also producing coherent and contextually relevant text. The model uses a [multi-head-attention](https://www.wikiprompt.org/wiki/multi-head-attention) mechanism and incorporates [layer-normalization](https://www.wikiprompt.org/wiki/layer-normalization) and [residual-network](https://www.wikiprompt.org/wiki/residual-network) connections to stabilize training and improve gradient flow.

Training was conducted on a large cluster of GPUs, utilizing techniques such as [gradient-clipping](https://www.wikiprompt.org/wiki/gradient-clipping) and [learning-rate-schedule](https://www.wikiprompt.org/wiki/learning-rate-schedule) to ensure convergence. The model was trained with a mixture of English and Chinese data, with a tokenizer designed to handle both languages efficiently. The training process also employed [model-pruning](https://www.wikiprompt.org/wiki/model-pruning) and other optimization strategies to reduce memory footprint and improve inference speed, making the model more accessible for deployment on standard hardware.

## Performance and Evaluation

In benchmark evaluations, GLM-130B demonstrated competitive performance against other large models, particularly in Chinese language tasks where it often outperformed models trained primarily on English data. On English benchmarks such as LAMBADA and MMLU, it achieved results comparable to or exceeding those of GPT-3, despite being smaller in parameter count than some competitors. The model's bilingual capabilities were highlighted in cross-lingual tasks, where it showed strong transfer learning abilities.

However, the model also exhibited limitations common to large language models of its time, including occasional factual inaccuracies and sensitivity to prompt phrasing. The developers published detailed evaluation results, including comparisons with models like [google-deepmind](https://www.wikiprompt.org/wiki/google-deepmind)'s Chinchilla and [anthropic](https://www.wikiprompt.org/wiki/anthropic)'s Claude, to provide transparency about its strengths and weaknesses.

## Open-Source Impact

One of the most significant contributions of GLM-130B was its open-source nature. At a time when many leading models were proprietary, GLM-130B provided researchers with access to a state-of-the-art model, enabling studies on interpretability, fine-tuning, and safety. The release included not only the model weights but also the training code and detailed documentation, fostering a community of developers who built upon the model for various applications.

The model's release also spurred discussions about the democratization of [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence) and the importance of open research in the field. It served as a foundation for subsequent GLM variants, including larger and more efficient models, and influenced the development of other open-source initiatives in [machine-learning](https://www.wikiprompt.org/wiki/machine-learning) and [deep-learning](https://www.wikiprompt.org/wiki/deep-learning).

## Limitations and Ethical Considerations

Like other large language models, GLM-130B raises ethical concerns related to bias, misinformation, and potential misuse. The training data, drawn from the internet, may contain biases that the model can amplify. The developers acknowledged these issues and recommended careful use, including human oversight in applications where the model's outputs could have significant consequences. They also provided guidelines for responsible deployment, emphasizing the need for transparency and accountability.

Technical limitations included a relatively large memory footprint, requiring substantial computational resources for inference. The model's performance on languages other than English and Chinese was limited, reflecting the focus of its training data. As of its release, GLM-130B represented a significant step forward in open-source AI, but it also highlighted the ongoing challenges in creating models that are both powerful and safe.

## Legacy and Influence

The development of GLM-130B contributed to the broader trend of open-source large language models, influencing subsequent projects such as LLaMA and others. Its architecture and training methodologies have been cited in numerous research papers, and its bilingual capabilities have inspired similar efforts in other languages. The model remains a reference point for researchers studying the scaling laws of [neural-network](https://www.wikiprompt.org/wiki/neural-network) models and the trade-offs between model size, data, and performance.

As of 2024, GLM-130B is no longer the largest or most advanced model available, but its impact on the field is enduring. It demonstrated that high-quality, open-source models could rival proprietary ones, paving the way for a more inclusive and collaborative approach to AI research.

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Source: https://www.wikiprompt.org/wiki/glm-130b
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-12T22:20:22.394757+00:00