Model optimizer:
AMD Quark
(
0.12.post1+rocm72.torch2.11
)
Quantized layers:
MoE routed experts in transformer layers 3-41
Weight quantization:
OCP MXFP4, static, group size 32
Activation quantization:
OCP MXFP4, dynamic, group size 32
This checkpoint was produced by applying AMD Quark MXFP4 file-to-file quantization to the BF16 Inkling-Small checkpoint. Attention layers, shared experts, router weights, embeddings, normalization layers, multimodal components, MTP weights, and transformer layers 0-2 remain in BF16.
Environment
The quantization was performed on an AMD gfx950 system with the following software:
GPU:
AMD MI350/MI355
Target graphics version:
gfx950
ROCm:
7.2.1
Python:
3.12.3
PyTorch:
2.13.0+rocm7.1
AMD Quark:
0.12.post1+rocm72.torch2.11
Safetensors:
0.8.0
Create and activate a dedicated Quark environment:
The included
quantize_quark.py
uses Quark's file-to-file flow to process safetensor shards without loading the full BF16 checkpoint into GPU memory. It automatically applies the Inkling-Small exclusion policy and quantizes only routed expert weights in layers 3-41.
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