ollama run treyleo16/glm:5-3
Updated 2 weeks ago
2 weeks ago
1c27791118fa · 3.4kB ·
Z.ai’s flagship open-weights model — a 753B-parameter sparse Mixture-of-Experts built for coding and long-horizon agentic work.
| Model | zai-org/GLM-5.3 |
| Parameters | ~753B total, 8 of 256 experts active per token |
| Architecture | glm_moe_dsa (GlmMoeDsaForCausalLM) |
| Layers | 78 (first 3 dense, remaining 75 sparse MoE) |
| Context | 1,048,576 tokens (1M) |
| Vocabulary | 154,880 |
| Native precision | BF16, distributed FP8 (e4m3, 128×128 block) |
| Languages | English, Chinese |
| License | Custom glm-5.3 — not open source |
| Paper | arXiv:2602.15763 |
GLM-5.3 shares its base model with GLM-5.2. Every improvement comes from post-training — no new pretraining run.
Z.ai reports two headline gains:
Coding. A 50% improvement over GLM-5.2 on their in-house Code Bench, with open-source SOTA claimed on Terminal Bench 3.0 and Agents’ Last Exam.
Cyber capability. Z.ai describes this as emergent and faster-developing than they expected during post-training scaling. GLM-5.3 leads CyberGym for vulnerability discovery, with the largest gains further up the exploitation chain.
Selected results from the official model card. These are Z.ai’s own published numbers, reproduced here as claims rather than independently verified:
| Benchmark | GLM-5.3 | GLM-5.2 | Best listed competitor |
|---|---|---|---|
| Terminal Bench 3.0 | 28.3 | 4.6 | 34.6 (GPT-5.6 Sol) |
| DeepSWE v1.1 | 66.9 | 46.2 | 72.7 (GPT-5.6 Sol) |
| FrontierSWE | 78.1 | 67.5 | 88.2 (Fable 5) |
| CyberGym | 84.5 | 77.2 | — (GLM-5.3 leads) |
| AutomationBench v1.0.6 | 48.2 | 26.2 | — (GLM-5.3 leads) |
| GDPval-AA v2 | 1769 | 1508 | — (GLM-5.3 leads) |
| HLE w/ Tools | 62.5 | 54.7 | 64.5 (GPT-5.6 Sol) |
The jump from 4.6 to 28.3 on Terminal Bench 3.0 is the single most striking number in the card — a roughly 6× gain from post-training alone. Worth verifying against your own workload before relying on it.
Full table, per-benchmark harnesses, and evaluation footnotes are on the model card.
GLM-5.3 combines three efficiency techniques that matter more than the raw parameter count:
Sparse MoE. 256 routed experts plus 1 shared expert per layer, 8 routed experts active per token. Routing uses sigmoid scoring with noaux_tc top-k selection and a 2.5× routed scaling factor. The first 3 layers stay dense; layers 4–78 are sparse.
Multi-head Latent Attention (MLA). Query and key-value projections are compressed through low-rank bottlenecks — q_lora_rank 2048, kv_lora_rank 512 — which collapses KV cache size. 64 attention heads, decoupled RoPE with 192-dim NoPE and 64-dim RoPE components.
DSA sparse attention. The dsa in glm_moe_dsa. A learned indexer (32 heads, 128-dim) selects the top 2,048 positions to attend to, refreshed every 4 layers. Indexer types alternate full and shared on a 1-in-4 pattern across the stack. This is what makes a 1M-token context tractable rather than theoretical.
It also ships one multi-token-prediction layer (num_nextn_predict_layers: 1) for speculative decoding, and uses an unusually high RoPE theta of 8,000,000 to support the long context.
Be realistic about scale. At ~753B parameters this is datacenter infrastructure, not a local model. For reference, the smallest community GGUF quantization — UD-IQ1_S from unsloth/GLM-5.3-GGUF — is about 217 GB across six shards, and a 1-bit quant of a sparse MoE is heavily degraded. UD-Q4_K_XL is roughly 419 GB; BF16 is around 1.5 TB. None of that runs on consumer hardware, and no amount of quantization changes that.
GLM-5.3 is live on multiple inference providers, which is how most people should use it:
Z.ai’s API is OpenAI-compatible, so existing tooling generally works with a base-URL swap.
If you do have the hardware, Z.ai documents support for:
| Framework | Notes |
|---|---|
| SGLang | official cookbook |
| vLLM | official recipes |
| Transformers | glm_moe_dsa model docs |
| KTransformers | CPU/GPU hybrid offload |
| TokenSpeed | — |
| Unsloth | — |
Ascend NPU deployment is supported via vLLM-Ascend, xLLM, and SGLang.
Ollama is not a supported path. The glm_moe_dsa architecture has no llama.cpp implementation for the DSA indexer, and the file sizes are impractical for the registry regardless.
Two parameters are easy to get wrong:
reasoning_effort accepts low, high, or max. It defaults to max — and to any unrecognized value. Pass low or high explicitly if you want a smaller thinking budget. Keep max when reproducing benchmarks.
clear_thinking defaults to false in the chat template. For ordinary chat, pass clear_thinking=true explicitly, or reasoning traces will accumulate in context across turns.
GLM-5.3 is distributed under a custom Z.ai license (license_name: glm-5.3), not Apache-2.0 or MIT. Z.ai’s model licenses have historically required registration for commercial use and imposed downstream obligations on redistribution.
Read the license on the model repository before any commercial deployment or redistribution. Z.ai’s license text and registration forms are typically published in Chinese — budget for translation if commercial use is on the table.
Separately, note the documented cyber capabilities above. Vulnerability discovery and exploitation performance is a headline feature of this release, which makes the acceptable-use terms worth reading carefully rather than skimming.
@misc{glm5team2026glm5vibecodingagentic,
title={GLM-5: from Vibe Coding to Agentic Engineering},
author={GLM-5-Team},
year={2026},
eprint={2602.15763},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2602.15763},
}
Credit belongs to the GLM-5 Team at Z.ai.
A GGUF repackaging of zai-org/glm-edge-v-2b-gguf, Z.ai’s 2B vision-language model built for edge devices, made runnable in Ollama with the vision projector attached.
This model is not my work. All credit for the weights, training, and architecture belongs to THUDM / Z.ai. This page exists only to make their model one ollama run away.
| Model | zai-org/glm-edge-v-2b-gguf — THUDM / Z.ai |
| License | GLM-Edge License — not open source, see below |
| Packaged by | treyleo16 |
Weights are byte-identical to the upstream ggml-model-Q4_K_M.gguf and mmproj-model-f16.gguf. Nothing was retrained, merged, or altered. The only additions are an Ollama chat template and stop tokens — see Packaging notes.
ollama run treyleo16/glm:2b-v-edge
With an image:
ollama run treyleo16/glm:2b-v-edge "What is in this image?" ./photo.jpg
API:
curl http://localhost:11434/api/chat -d '{
"model": "treyleo16/glm:2b-v-edge",
"messages": [{
"role": "user",
"content": "Describe this image.",
"images": ["<base64-encoded image>"]
}]
}'
| Property | Value |
|---|---|
| Architecture | chatglm |
| Parameters | 1.6B |
| Quantization | Q4_K_M |
| Download size | ~1.9 GB (980 MB model + 933 MB projector) |
| Context length | 2,048 |
| Capabilities | completion, vision |
| Projector | CLIP, 466M params, embedding length 1152 |
2,048 tokens. That is the tightest constraint on this model by a wide margin, and images consume a large share of it before you type a word. This is a model for single-image questions and short answers: captioning, OCR-ish extraction, “what is this,” visual classification. It is not for long multi-turn conversations about images, and it is not for documents.
GLM-Edge-V was designed for phones and edge hardware, and it behaves like a 1.6B model — terse, literal, occasionally shallow. It answers visual questions directly and briefly rather than narrating. Text-only performance is adequate but unremarkable; the vision path is the reason to use it.
The source repo publishes Q4_0 (931 MB) through Q8_0 (1.7 GB) and F16 (3.2 GB). All of them need the same mmproj-model-f16.gguf for vision.
The upstream GGUF embeds its chat template in Jinja2. Ollama uses Go templates and cannot execute Jinja, so loading the file as-is produces a malformed prompt and badly degraded output — asked for the capital of France, it answered “Lille.”
This package replaces that with an equivalent Go template in GLM-Edge’s native <|user|> / <|assistant|> format, plus the matching stop tokens. Same question now returns “Paris.” If you build your own package from the upstream file, you will need to do the same.
Verified before publishing: text returns “Paris.”; a generated test image of a red circle returns “Circle, red.” — both with done_reason: stop.
This model is not Apache-2.0 or MIT. It is distributed under the GLM-Edge License, a custom license from THUDM / Z.ai with real restrictions:
Two practical notes. The authoritative license text is in Chinese, and so is the commercial registration form — budget for translation if commercial use matters to you. And the terms above are a plain-language summary, not legal advice; read the full license yourself.
The notice ships inside the model:
ollama show --license treyleo16/glm:2b-v-edge
Credit belongs to THUDM / Z.ai — they built this.