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Nickyang
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Model Details
Model Provider
Nickyang
Model Tree
google/gemma-4-26B-A4B-it
Input Modalities
Output Modalities
Supported Functionality
The chat template and response parsing schema use the official base revision
4d7ae4984b7db7de8f8457170b3f1a419ee76d52, including its tool-argument null
serialization and tool-response fixes. The tokenizer vocabulary and all model
weights are unchanged by this auxiliary-file update; prompt formatting may
differ from the earlier experiment snapshot. This is not a new quality evaluation.
python
from transformers import AutoProcessor, AutoModelForCausalLM model_id = "Nickyang/Gemma-4-26B-A4B-it-RAZOR-20B-A4B-E96of128"processor = AutoProcessor.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, dtype="bfloat16", device_map="auto",) messages = [{"role": "user", "content": [{"type": "text", "text": "Explain mixture-of-experts routing."}]}]inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt",).to(model.device)print(processor.decode(model.generate(**inputs, max_new_tokens=256)[0]))Requires a Transformers build containing the native gemma4 implementation.
Weights are bfloat16.
RAZOR asks whether the surviving computation can replace an expert's function, rather than how often or how strongly the expert fires. For a token routed to the set S with normalized weights wj, let c=∑j∈Swjfj be the routed mixture and rj=fj−c the consensus residual of expert j. Deleting a selected expert i makes the router promote its highest-ranked unselected expert r, whose pseudo-weight wr is its score divided by the original selected score sum. With λ the routed-output scale, the exact local output change is
δi=λ∥c−c~−i∥2=λScores are aggregated by conditional root mean square over the calibration tokens routed to each expert, and the highest-scoring experts are retained per layer. See the paper for the derivation and the code for the implementation.
Calibration used 32,768-token rows from RazorCal, the 2,048-sample multi-domain corpus released with RAZOR. The corpus is text-only, so the selection reflects text-domain routing behaviour.
bash
pip install razor-moe razor saliency --model <path-to-gemma-4-26B-A4B-it> \ --data RazorCal.json --max-len 32768 --out out/salrazor prune --model <path-to-gemma-4-26B-A4B-it> --saliency out/sal \ --method razor --ratio 0.25 --out out/prunedkept_expert_indices.json is the keep-set manifest this checkpoint was built
from, in the format razor verify expects:
bash
razor verify --pruned . --src <path-to-gemma-4-26B-A4B-it>Selection depends on the calibration draw, so an independent run reproduces the procedure rather than this exact expert set.
Expert pruning is lossy. Benchmark retention and predictive fidelity do not guarantee stable generation: the paper reports that responses change in diversity, formatting and termination behaviour even where task accuracy is largely preserved. Evaluate on your own workload before deploying.
The calibration corpus is text-only and multi-domain but finite. Multimodal behaviour was not exercised during calibration, so image-conditioned quality is not characterised by the released measurements. Evaluate vision-language tasks separately before relying on them.
This is a derivative of gemma-4-26B-A4B-it, released by Google under the Apache-2.0 license, and is distributed under the same license. The retained weights are the base model's own weights; RAZOR contributes the expert selection, not new parameters.
The RAZOR code is Apache-2.0. RazorCal records remain subject to their applicable upstream terms; see LICENSE-DATA.
bibtex
@misc{song2026razorpruningreplaceableexperts, title={RAZOR: Pruning Replaceable Experts in LLMs}, author={Mingyang Song and Mao Zheng}, year={2026}, eprint={2609.30465}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2609.30465},}