> ## Documentation Index
> Fetch the complete documentation index at: https://miles.radixark.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# GLM

> Miles recipes for the GLM4.5, GLM4.7 Flash, GLM5, and GLM5.2 families, plus the KDA + DSA hybrid GLM-5.3-Flash.

Miles ships RL recipes for every GLM generation currently in production: the GLM4.5 MoE at 106 B-A12B and 355 B-A32B, the compact GLM4.7 Flash with 64 routed experts, the 744 B-A40B GLM5 and GLM5.2 flagships, and GLM-5.3-Flash, a KDA + DSA hybrid on a different architecture entirely.

## Variants

| Family        | Class                   | Sizes                          | Recipe                                   |
| ------------- | ----------------------- | ------------------------------ | ---------------------------------------- |
| GLM4.5        | MoE                     | 12 B / 106 B · 32 B / 355 B    | [glm4-5](/docs/models/glm/glm4-5)             |
| GLM4.7 Flash  | MoE (64 experts, top-4) | Compact                        | [glm4-7-flash](/docs/models/glm/glm4-7-flash) |
| GLM5          | MoE                     | 40 B / 744 B                   | [glm5](/docs/models/glm/glm5)                 |
| GLM5.2        | MoE                     | 40 B / 744 B                   | [glm5-2](/docs/models/glm/glm5-2)             |
| GLM-5.3-Flash | MoE (KDA + DSA hybrid)  | 45 layers · 288 experts, top-8 | [glm5-3-flash](/docs/models/glm/glm5-3-flash) |

## Fastest path to train

GLM4.7 Flash on a single 8× H100 node — the smallest GLM recipe:

```bash theme={null}
python scripts/run_glm47_flash.py
```

See the [GLM4.7 Flash](/docs/models/glm/glm4-7-flash) page for weight conversion and the full walkthrough.

## Which variant do I pick?

* **Single-node GLM first try** → GLM4.7 Flash ([glm4-7-flash](/docs/models/glm/glm4-7-flash)).
* **MoE on a budget** → GLM4.5-106B-A12B ([glm4-5](/docs/models/glm/glm4-5)).
* **Full MoE scale (multi-node)** → GLM4.5-355B-A32B ([glm4-5](/docs/models/glm/glm4-5)).
* **Compact MoE for routing experiments (R3)** → GLM4.7 Flash ([glm4-7-flash](/docs/models/glm/glm4-7-flash)).
* **Frontier scale (744 B)** → GLM5.2 ([glm5-2](/docs/models/glm/glm5-2)); GLM5/GLM5.1 ([glm5](/docs/models/glm/glm5)) for the previous generation.
* **Hybrid linear + sparse attention with mHC** → GLM-5.3-Flash ([glm5-3-flash](/docs/models/glm/glm5-3-flash)).