Quantization-aware-distillation checkpoint of Qwen3-0.6B at 1.88-bit weights, produced with EdgeRazor.

This is the starting point for the on-policy-distillation recovery pipeline in MingZwhy/QAOPD, not a finished model.

The tensors in this repo are bf16 and NOT quantized. QAT keeps high-precision master weights and applies fake-quant only inside the forward pass; what gets saved is the master copy.

config.json here is a plain Qwen3ForCausalLM with no auto_map and no edgerazor_config, so nothing in the file tells the loader to quantize. To use it as a 1.88-bit model you must supply both the EdgeRazor code and the exact quantization config it was trained under.

Want a model you can just load and evaluate? Use Qwen3-0.6B-W2.79A8KV16-OPD-3phase (the W2.79 sibling) instead — quantization is baked in there.

This form is exactly what you want for further QAT/OPD, because the optimizer needs the continuous master weights. The training scripts pass the quantization config themselves:

EdgeRazor QAT distillation on general instruction data (ii/tulu/am/task mixtures with teacher-regenerated assistant turns), step 9000. Full procedure: docs/EDGERAZOR_QAD.md.

The 3-phase recovery pipeline has been validated on the W2.79 sibling checkpoint; W1.88 is the next planned run and has no recovery numbers yet. Expect larger degradation at this bit-width. See docs/ROADMAP.md.

Do not assume the W2.79 recipe transfers unchanged — the project's own rule is that every model/bit-width combination needs its checkpoint sweep redone.

Apache-2.0, inherited from Qwen3-0.6B.