---
license: mit
pipeline_tag: text-generation
base_model_relation: quantized
library_name: gguf
tags:
- gguf
- qwen3.6
- reap
---

> [!TIP]
> **[Support this work →](https://donate.sybilsolutions.ai)** · [X](https://x.com/0xsero) · [GitHub](https://github.com/0xsero) · [REAP paper](https://arxiv.org/abs/2510.13999) · [Cerebras REAP](https://huggingface.co/collections/cerebras/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](https://huggingface.co/0xSero/Qwen3.6-28B) |
| `Qwen3.6-28B-GGUF` **(this)** | GGUF | [link](https://huggingface.co/0xSero/Qwen3.6-28B-GGUF) |
| `Qwen3.6-35B-GGUF` | GGUF | [link](https://huggingface.co/0xSero/Qwen3.6-35B-GGUF) |

## License & citation
License inherited from the base model.

```bibtex
@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**.