Gemma-4-26B-A4B-it-RAZOR-20B-A4B-E96of128
Gemma 4 26B-A4B Instruct with a quarter of its routed experts removed by RAZOR, a training-free expert pruning method. Every MoE layer keeps 96 of its original 128 routed experts. No gradient updates or recovery training were applied: the retained weights are the base model's own weights.
| Base | This model | |
|---|---|---|
| Routed experts per MoE layer | 128 | 96 |
| Total parameters | 26B | 20.1B |
| Active parameters per token | ~4B | ~4B |
| Active experts per token (top-k) | 8 | 8 (unchanged) |
| MoE decoder layers | 30 | 30 (unchanged) |
| Vision tower | 0.57B | 0.57B (unchanged) |
Pruning touches only the routed expert pool of the language model. The vision tower, attention, the embedding, the LM head and the router's remaining rows are untouched, so the model remains multimodal and the compute per token is essentially unchanged while expert storage shrinks.
The other budget is Gemma-4-26B-A4B-it-RAZOR-14B-A4B-E64of128.
Usage
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.
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.
How the experts were selected
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 $w_j$, let $c=\sum_{j\in S} w_j f_j$ be the routed mixture and $r_j=f_j-c$ the consensus residual of expert $j$. Deleting a selected expert $i$ makes the router promote its highest-ranked unselected expert $r$, whose pseudo-weight $w_r$ is its score divided by the original selected score sum. With $\lambda$ the routed-output scale, the exact local output change is
$$ \delta_i=\lambda\lVert c-\tilde c^{-i}\rVert_2 =\lambda,\frac{\lVert w_i r_i - w_r r_r\rVert_2}{1-w_i+w_r}. $$
Scores 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.
Reproducing this checkpoint
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.
pip install razor-moe
razor saliency --model <path-to-gemma-4-26B-A4B-it> \
--data RazorCal.json --max-len 32768 --out out/sal
razor prune --model <path-to-gemma-4-26B-A4B-it> --saliency out/sal \
--method razor --ratio 0.25 --out out/pruned
kept_expert_indices.json is the keep-set manifest this checkpoint was built
from, in the format razor verify expects:
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.
Limitations
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.
License and attribution
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.
Citation
@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},
}