Phi-tiny-MoE-instruct-GPTQ-W4-MixedGroup-G32-G128

Phi-tiny-MoE-instruct-GPTQ-W4-MixedGroup-G32-G128

This is a GPTQ-quantized version of microsoft/Phi-tiny-MoE-instruct, created with GPTQModel 7.3.4. It is intended as a small mixed-group-size PhiMoE checkpoint and loader integration fixture.

What is special about this checkpoint?

The global configuration is symmetric W4G128. Exact per-module dynamic overrides use W4G32 for attention Q/K/V and every expert gate/up projection. This is mixed group-size quantization; all quantized weights use 4 bits.

Across the complete model:

• All 32 decoder layers and all 16 experts per layer are quantized.
• 1,120 projections use W4G32 dynamic overrides.
• 544 projections use the global W4G128 setting.
• The 32 routers and lm_head remain dense by design.
• qweight, qzeros, scales, and g_idx are present for all 1,664 quantized projections.

Quantization details

Every expert received a nonzero number of calibration activations during quantization.

Loading

from gptqmodel import BACKEND, GPTQModel

model = GPTQModel.load(
    "ModelCloud/Phi-tiny-MoE-instruct-GPTQ-W4-MixedGroup-G32-G128",
    backend=BACKEND.GPTQ_TORCH,
    device="cuda",
)

Validation reloads all 1,664 quantized projections with the expected group sizes and completes a finite-logit forward pass. No downstream quality benchmark is claimed; evaluate the checkpoint for the intended use case.