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zai-org/GLM-4

GLM-4 series: Open Multilingual Multimodal Chat LMs | 开源多语言多模态对话模型 observed · 2026-08-28

github.com/zai-org/GLM-4 · Python · Apache-2.0 (permissive) observed · 2026-08-28

Health v2 · maintenance only

67/100

  • Activity 96
  • Release rhythm 35
  • Longevity 60

Flags: no_releases

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 840
  • days_rel: n/a
  • days_push: 28
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

7070 stars · 611 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Official repository for the GLM-4 series of open-weight, multilingual (primarily Chinese/English) multimodal chat language models from Z.ai (Zhipu AI), spanning base chat models like GLM-4-9B and GLM-4-32B, the GLM-Z1 reasoning variants, and GLM-4V vision-language models. It ships model weights alongside inference examples, fine-tuning scripts, and deployment guides for frameworks like transformers and vLLM.

Use cases

  • run a GLM-4 chat model locally on my own GPU
  • self-host an open-source alternative to GPT-4o
  • fine-tune a multilingual chat LLM for my domain
  • download GLM-4 or GLM-Z1 model weights from Hugging Face
  • deploy a bilingual Chinese-English dialogue model
  • use a reasoning LLM for math, code, and logic tasks
  • set up an LLM with function calling for agent workflows

When to choose

  • You want open-weights chat LLMs you can deploy on-premises or on your own hardware
  • You need strong bilingual Chinese/English dialogue, reasoning, or coding capabilities at 9B-32B scale
  • You want to fine-tune or apply reinforcement learning to a mid-size agent-capable model
  • You need vision-language (GLM-4V) or deep-thinking (GLM-Z1) variants from the same model family

When to avoid

  • You only want a hosted commercial API without managing GPUs or model weights
  • You require frontier-scale performance beyond what 32B open weights deliver
  • Your hardware cannot accommodate 9B+ parameter models even in quantized form
  • You need a fully managed, multimodal production service rather than DIY deployment

Facets

library · maturity active

llm-inference llm-training chatbot deep-learning nlp agent-framework artificial-intelligence large-language-models machine-learning deep-learning chatbots python open-weights glm-4 chatglm multilingual multimodal fine-tuning transformers vllm reasoning-model function-calling self-hosted-llm huggingface zhipu-ai natural-language-processing gpu

1 source

Member repositories

RepositoryRoleHealth v2
zai-org/GLM-4main67

For agents

markdown · JSON · MCP: product_card(name="zai-org/GLM-4")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem