Model reference · open weights
GLM-4.7-Flash is an open-weight language model from unsloth, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
[!NOTE] Includes Unsloth chat template fixes! <br For llama.cpp, use --jinja GLM-4.7-Flash 👋 Join our <a href="https://discord.gg/QR7SARHRxK" target="blank"Discord</a community. 📖 Check out the GLM-4.7 <a href="https://z.ai/blog/glm-4.7" target="blank"technical blog</a, <a href="https://arxiv.org/abs/2508.06471" target="blank"technical report(GLM-4.5)</a. 📍 Use GLM-4.7-Flash API services on <a href="https://docs.z.ai/guides/llm/glm-4.7"Z.ai API Platform. </a 👉 One click to <a href="https://chat.z.ai"GLM-4.7</a. Introduction GLM-4.7-Flash is a 30B-A3B MoE model. As the strongest model in the 30B class, GLM-4.7-Flash offers a new option for lightweight deployment that balances performance and efficiency. Performances on Benchmarks Serve GLM-4.7-Flash Locally For local deployment, GLM-4.7-Flash supports inference frameworks including vLLM and SGLang. Comprehensive deployment instructions are available in the official Github repository. vLLM and SGLang only support GLM-4.7-Flash on their main branches. vLLM + using pip (must use pypi.org as the index url): SGLang + using pip install sglang from source, then update transformers to the latest main branch. transformers using with transformers as and then run: vLLM SGLang Citation If you find our work useful in your research, please consider citing the following paper:
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | unsloth |
|---|---|
| Type | Language models |
| Parameters (lead) | 31.2B |
| Context | 198k tokens |
| Variants | 2 |
| Runs with | transformers |
| Based on | zai-org/GLM-4.7-Flash |
| Released | 2026-01-20 |
| Popularity | 272k downloads / month |
| Likes | 699 |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
Using it via the API
Once AxForge deploys unsloth-glm-4-7-flash for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (unsloth-glm-4-7-flash below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"unsloth-glm-4-7-flash","messages":[{"role":"user","content":"Hello"}]}'
Licence
Open weights under mit — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗