Model Optimizer:
AMD Quark
(0.12.post1+rocm72.torch2.11)
Quantized layers:
MoE routed experts only
Weight quantization:
OCP MXFP4, static
Activation quantization:
OCP MXFP4, dynamic
This model was built by applying AMD Quark MXFP4 quantization to the BF16 Thinking Machines Lab Inkling checkpoint. The quantization targets the MoE routed experts, while attention layers and shared experts are kept in BF16.
Environment
The quantization workflow was prepared on an AMD gfx950 system. The inspected container environment was:
The model was quantized with the Quark file-to-file flow. This avoids loading the full BF16 checkpoint into GPU memory at once, which is important for very large MoE checkpoints. Run the quantization script:
The script applies the model-specific exclusion policy automatically in file-to-file mode. The resulting checkpoint stores MXFP4 routed-expert weights and scales while preserving non-routed-expert components in BF16.
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