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GLM-2|6|10|13|70B

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Org profile for THUDM on Hugging Face, the AI community building the future.

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OSS

GLM-2|6|10|13|70B

Added 1 June 2026

Overview

THUDM is the Hugging Face organization for Tsinghua University's research group, hosting open-source GLM series models (2B, 6B, 10B, 13B, 70B). These are transformer-based language models for text generation and understanding, available for download and fine-tuning.

Best for

Best for
Developers and researchers who need open-source, customizable Chinese-English LLMs for fine-tuning or deployment.

Use cases

  • Fine-tuning GLM models for domain-specific text tasks
  • Deploying open-source LLMs for inference in production
  • Benchmarking or comparing GLM variants against other models

Notes

THUDM is the Hugging Face organization for Tsinghua University’s research group, hosting open-source GLM series models (2B, 6B, 10B, 13B, 70B). These are transformer-based language models for text generation and understanding, available for download and fine-tuning.

Use cases

  • Fine-tuning GLM models for domain-specific text tasks
  • Deploying open-source LLMs for inference in production
  • Benchmarking or comparing GLM variants against other models

Pros

  • Multiple model sizes from 2B to 70B fit different compute budgets
  • Open-source weights allow full customization and local deployment
  • Backed by academic research from Tsinghua University

Cons

  • Community-maintained with no official support or SLAs
  • Documentation and examples may be less extensive than commercial models
  • Larger models require significant GPU memory and compute

Indexed from awesome-llm and enriched against its public facts.

Pros

  • Multiple model sizes from 2B to 70B fit different compute budgets
  • Open-source weights allow full customization and local deployment
  • Backed by academic research from Tsinghua University

Cons

  • Community-maintained with no official support or SLAs
  • Documentation and examples may be less extensive than commercial models
  • Larger models require significant GPU memory and compute