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zmi/Qwen3-30B-A3B-RFID60-Linear-Factors
Qwen3-30B-A3B-RFID60-Linear-Factors is a text generation model from zmi. Use it when you need the model to write or continue text. It is set up for pytorch. The card lists the license as apache-2.0.
This is a factor-only research checkpoint. It stores the original rank-heterogeneous RFID representation rather than materialized dense expert weights. Unchanged parameters are inherited from Qwen/Qwen3-30B-A3B-Instru…
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Updated Aug 23, 2026
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From the Hugging Face model README
This is a factor-only research checkpoint. It stores the original
rank-heterogeneous RFID representation rather than materialized dense expert
weights. Unchanged parameters are inherited from Qwen/Qwen3-30B-A3B-Instruct-2507.
Only expert gate_proj and up_proj matrices are compressed. down_proj
and all non-expert parameters remain in the base model. The checkpoint uses
32 groups of four experts, heterogeneous group ranks, basis blending, and the
3% RFID sparse-projection residual.
Each file in factors/ contains one layer/projection family in the lossless
rfid-ragged-factors-v1 format. Zero padding and derivable rank masks from the
training checkpoints are omitted. All learned floating-point tensors remain
FP32; projection indices are losslessly stored as int32. This conversion does
not quantize or materialize the factors.
The included rfid_factor_checkpoint.py supports per-expert materialization
and direct two-GEMM execution of the factorized main path. Vanilla
Transformers and upstream vLLM do not natively execute this checkpoint.
Zero-shot evaluation uses normalized accuracy for OpenBookQA, ARC-e, HellaSwag, and PIQA, and raw accuracy for WinoGrande and MathQA.
| Variant | WikiText-2 PPL | PTB PPL | C4 PPL | Six-task average |
|---|---|---|---|---|
| Linear | 9.5000 | 16.4574 | 17.4657 | 0.6571 |
See evaluation/ for the complete lm-eval and perplexity JSON artifacts.