HyperPrune-Qwen3.6-27B-4to8
Qwen/Qwen3.6-27B pruned to 4:8 semi-structured sparsity with HyperPrune (Sun & Sakuma, Learning Semi-Structured Sparsity for LLMs via Shared and Context-Aware Hypernetwork, ICLR 2026, OpenReview).
This is a reproduction run produced at Elastix as part of the BLADE
sparsity-method comparison. It is plain sparse bf16/fp16 safetensors and
loads with stock transformers:
from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained("elastix-ai/HyperPrune-Qwen3.6-27B-4to8")
t = AutoTokenizer.from_pretrained("Qwen/Qwen3.6-27B")
What differs from the paper's own recipe
| paper / repo default | this checkpoint | |
|---|---|---|
| calibration corpus | allenai/c4 | DKYoon/SlimPajama-6B, validation (BLADE's corpus) |
| pruned modules | see below | see below |
Everything else — hypernet architecture, both training stages, all learning rates, step counts, temperature, prior, row selection — is HyperPrune's own shipped setting.
Configuration
{
"model": {
"name_or_path": "Qwen/Qwen3.6-27B",
"dtype": "bfloat16"
},
"data": {
"dataset_name": "elastiml:elastix-ai/elastiml-calib-qwen3.6-27b",
"num_samples": 128,
"seq_len": 2048,
"seed": 42
},
"sparsity": {
"n": 4,
"m": 8
},
"hypernet": {
"type": "mlp",
"hidden_dim": 256,
"emb_dim": 64,
"use_layer_emb": false,
"use_comp_emb": false,
"use_hessian_diag": true
},
"training": {
"sup_steps": 12000,
"sup_lr": 0.001,
"ft_lr": 0.0003,
"ft_nsamples": 4,
"rows_per_step": 400,
"cascade_inner_steps": 300,
"ft_mode": "cascade",
"tau": 0.5,
"prior_source": "sparsegpt",
"wanda_residual_alpha": 2.0,
"compensated_propagation": true,
"use_weight_compensation": true,
"train_on_compensated": true,
"fixed_rows_count": 200,
"fixed_rows_pos": "first",
"dense_layers_list": []
},
"output": {
"save_dir": "/home/ubuntu/hyperprune_work/outputs/hp-qwen36_27b-4to8",
"wanda_dir": "/home/ubuntu/hyperprune_work/outputs/hp-qwen36_27b-4to8_ref",
"preserve_wanda_dir": false
}
}
Measured
| metric | value |
|---|---|
overall decoder sparsity (check_sparsity) | 0.5005 over the pruned scope; strict 4:8 verified |
WikiText-2 PPL (HyperPrune eval_ppl.py, seqlen 2048) | 9.558 |
| WikiText-2 word PPL (lm-eval-harness, BLADE's protocol) | 11.89 |
| training wall-clock | 45.4 min |
| peak GPU during cascade FT | 14.05 GB |
| GPU | 1 x NVIDIA RTX PRO 6000 Blackwell (97 GB), CUDA 13.0, torch 2.13.0+cu130 |
Two things to know before comparing this number to the paper
1. Every decoder layer is pruned here. HyperPrune's own shipped configs set
dense_layers_list: [0, 1], leaving 2 layers fully dense and yielding ~46.9 %
sparsity rather than 50 %. This checkpoint prunes every layer, matching BLADE's
four_eight_experts spec, so it is a true 4:8 model in the modules BLADE prunes.
2. Only a few percent of this mask was chosen by the hypernet. The shipped
recipe sets fixed_rows_count: 200, so the hypernet decides the mask for the
first 200 output rows of each projection and every remaining row keeps the
SparseGPT prior's mask verbatim. This is HyperPrune's own default, kept here
deliberately because the brief was to change nothing but the calibration corpus.
The two perplexity rows are different quantities and are not comparable to each
other. The first is token-level PPL over concatenated WikiText-2 at seqlen 2048
(the Wanda/SparseGPT convention). The second is lm-evaluation-harness
word_perplexity at max_length=2048, which is BLADE's protocol — pinned
empirically by reproducing BLADE's dense LLaMA-2-7B value of 9.19 (measured
9.1915).
Provenance
Produced from HyperPrune commit 6d093d7 with a small set of documented
patches (bias-dtype autocast, calibration loader, disk-peak reduction, and —
for 4:8 checkpoints — the N:M generalization, which the reference
implementation does not ship). See the reproduction report for the full diff.