MODEL PROFILE

GLM-5.3-Flash-NVFP4

tattrongvu · GLM-5.3-Flash-NVFP4

NVFP4 (4-bit float, group size 16) quantization of `zai-org/GLM-5.3-Flash`, covering the routed experts of **every** MoE layer — including layer 45, the multi-token-prediction (MTP) layer — in a **single, uniform** scheme. **No calibration data is used in this quantization step**, and none is needed: the scheme quantizes each group of 16 weights with a scale derived from those 16 values (`memoryless_minmax`), so there is no forward pass for calibration data to inform. The checkpoint contains no activation-scale tensors and declares `input_activations: null`. `num_hidden_layers: 45` plus `num_nextn_predict_layers: 1` puts the MTP layer at index **45**, and it is not a small head: 288 routed experts, ~7.25 B parameters, its own DSA attention and `eh_proj`/`enorm`/`hnorm` plumbing. Keeping it in the same scheme as every other expert layer means the checkpoint has one expert layout throughout — 37,152 packed expert tensors (43 × 288 × 3).

Visual QAlicense:mit
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/10/30 18:40:41
OVERVIEW

Model overview

NVFP4 (4-bit float, group size 16) quantization of `zai-org/GLM-5.3-Flash`, covering the routed experts of **every** MoE layer — including layer 45, the multi-token-prediction (MTP) layer — in a **single, uniform** scheme. **No calibration data is used in this quantization step**, and none is needed: the scheme quantizes each group of 16 weights with a scale derived from those 16 values (`memoryless_minmax`), so there is no forward pass for calibration data to inform. The checkpoint contains no activation-scale tensors and declares `input_activations: null`. `num_hidden_layers: 45` plus `num_nextn_predict_layers: 1` puts the MTP layer at index **45**, and it is not a small head: 288 routed experts, ~7.25 B parameters, its own DSA attention and `eh_proj`/`enorm`/`hnorm` plumbing. Keeping it in the same scheme as every other expert layer means the checkpoint has one expert layout throughout — 37,152 packed expert tensors (43 × 288 × 3).

Key features

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Sources & traceability

许可证:mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。