GLM-5.2-Vision-NVFP4

GLM-5.2-Vision (NVFP4) GLM-5.2 with sight.** A vision-language model that bolts the MoonViT vision encoder from Kimi-K2.6 onto GLM-5.2 through a trained PatchMerger projector. GLM-5.2 is a strong open reasoning model with no vision input. This checkpoint adds it, without touching a single GLM weight: the text backbone and the vision tower are both frozen and byte-identical to their upstream releases. The only newly-trained parameters are the 49.5M-parameter projector** that maps MoonViT's 1152-dim patch embeddings into GLM's 6144-dim token space. | Component | Detail | |---|---| | Text backbone | GLM-5.2 (744B total / A40B active, MoE + MLA + DSA sparse attention) — **frozen** | | Vision tower | MoonViT-3d from Kimi-K2.6, 27 layers, 1152-dim — **frozen** | | Projector | PatchMerger MLP (`pre_norm → linear_1 → GELU → linear_2`), 1152→4608→6144 — **trained** | | Text weights | NVFP4, from `nvidia/GLM-5.2-NVFP4` | | Size | ~466 GB | | Hardware | 8×B200, or 4×B200 at 256k context — **Blackwell only** | | Image tokens | up to 4096 per image (16384 MoonViT patches, 2×2 merge) | | Max context | 1048576 (1M tokens) | Quickstart SGLang needs a small out-of-tree plugin because `Glm5vForConditionalGeneration` is not yet an upstream architecture. It ships inside this repo, so there is nothing else to clone: SGLang 8×B200 — full 1M context 4×B200 — 256k context Same command with `--tp-size 4`, a higher memory fraction, and a smaller context: Query it Standard OpenAI multimodal messages deliver the image as `image_url`: GLM-5.2 is a reasoning model: with `--reasoning-parser glm45`, the chain of thought arrives in `message.reasoning_content` and the answer in `message.content`. Deploy on Baseten The repository includes ready-to-push Truss configs. The only credential you need is an API key for your own Baseten account; no Hugging Face token or pre-created Baseten secret is required. 1. Install `uv` and create a Baseten API key. 2. Export the key, download the small Truss directory