REAP-pruned & quantized Qwen3.5 / 3.6 / Coder variants. • 15 items • Updated
Support this work → · X · GitHub · REAP paper · Cerebras REAP
Qwen3.6-28B-GGUF
GGUF quantization of the base model.
At a glance
| Base model | — |
| Format | GGUF |
| Total params | 28B |
| Active / token | 3B |
| Experts / layer | — |
| Layers | — |
| Hidden size | — |
| Context | — |
| On-disk size | 147 GB |
Which variant should I pick?
| Variant | Format | Link |
|---|---|---|
Qwen3.6-28B |
BF16 | link |
Qwen3.6-28B-GGUF (this) |
GGUF | link |
Qwen3.6-35B-GGUF |
GGUF | link |
License & citation
License inherited from the base model.
@misc{lasby2025reap,
title = {REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression},
author = {Mike Lasby and Ivan Lazarevich and Nish Sinnadurai and Sean Lie and Yani Ioannou and Vithursan Thangarasa},
year = {2025}, eprint = {2510.13999}, archivePrefix = {arXiv}
}
Sponsors
Made possible by NVIDIA · TNG Technology · Lambda · Prime Intellect · Hot Aisle.
- Downloads last month
- 990
GGUF
Model size
28B params
Architecture
qwen35moe
Hardware compatibility
Log In to add your hardware
4-bit
5-bit
6-bit
8-bit
16-bit