{"v":1,"id":"model:hf:unsloth/GLM-5.3-Flash-GGUF","slug":"model-unsloth-glm-5-3-flash-gguf","kind":"model","category":"llm","title":"GLM-5.3-Flash-GGUF","summary":"Unsloth Dynamic 3.0 achieves superior accuracy & outperforms other leading quants. To run, please use our llama.cpp PR or use the Unsloth Desktop app. You can now run GLM-5.3-Flash in our Unsloth Des…","source":{"provider":"hf","ref":"unsloth/GLM-5.3-Flash-GGUF","url":"https://huggingface.co/unsloth/GLM-5.3-Flash-GGUF","rev":"621d456e93e926e4b52f85cff5f634358c1828f9","fetchedAt":"2026-10-02T20:52:22.283Z","etag":"W/\"85bb-FeDecc41pigAjyrvbfi0jI4nJEM\""},"author":{"name":"unsloth","url":"https://huggingface.co/unsloth"},"license":{"spdx":"mit","raw":"mit","open":true},"metrics":{"downloads":1012847,"downloadsWeek":327838,"likes":443,"stars":130156,"openIssues":2528,"lastRelease":{"tag":"v0.5.0","at":"2026-09-23T20:50:06Z"},"pushedAt":"2026-10-02T20:17:08Z","takenAt":"2026-10-02T20:52:22.283Z"},"tags":["gguf","unsloth","glm5_next","text-generation","en","zh","endpoints_compatible","conversational"],"pipeline":"text-generation","links":{"github":"ggml-org/llama.cpp","npm":"node-llama-cpp"},"updatedAt":"2026-09-06T06:55:24.000Z","collectedAt":"2026-10-02T20:52:22.283Z","review":{"numbers":["1,012,847 downloads on Hugging Face","443 likes","license mit","0.0 GB for Q8_0/GLM-5.3-Flash-Q8_0-00001-of-00008.gguf","130,156 stars on ggml-org/llama.cpp","2,528 open issues and PRs","last release v0.5.0 on 2026-09-23","327,838 npm downloads a week for node-llama-cpp","latest node-llama-cpp@3.22.1"],"log":null},"trust":"unlabeled","health":{"status":"alive","checkedAt":"2026-10-02T20:52:22.283Z","http":200},"description":"# Read our How to [Run GLM-5.3-Flash Guide!](https://unsloth.ai/docs/models/glm-5.3-flash)\n\n  \n    Unsloth Dynamic 3.0 achieves superior accuracy & outperforms other leading quants.\n  \n  \n    \n      \n    \n    \n      \n    \n    \n      \n    \n  \n      \n    To run, please use our llama.cpp PR or use the Unsloth Desktop app.\n    You can now run GLM-5.3-Flash in our Unsloth Desktop UI.\n See below for 1-bit GLM-5.3-Flash (Low) run inside of Unsloth Desktop:\n\n---\n\n# GLM-5.3-Flash\n\n    👋 Join our WeChat or Discord community.\n    \n    📖 Check out the GLM-5.3-Flash blog and GLM-5 Technical report.\n    \n    📍 Use GLM-5.3-Flash API services on Z.ai API Platform. \n\n## Introduction\n\nWe introduce GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks.\n\nGLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear attention, sharply reducing long-context serving costs while preserving precise long-context capabilities. The model also adopts Manifold-Constrained Hyper-Connections (mHC) to further improve scaling efficiency. Together with our latest 30T-token multimodal pre-training corpus, these changes enable GLM-5.3-Flash to deliver more intelligence with less compute.\n\n## Footnotes\n\n* **HLE w/ tools (full set)**: We use sampling parameters of `temperature=1.0` and `top_p=0.95` for evaluation, with a maximum generation length of `163,840` tokens. The evaluation is conducted with a maximum context length of `300,000` tokens, using a context management strategy. We use GPT-5.6-luna (medium) as the judge model.\n* **NL2…\n\nSource: 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