---
title: Qwen3.6-28B-REAP20-A3B-GGUF
canonical_url: "https://www.modelscope.ai/models/barozp/Qwen3.6-28B-REAP20-A3B-GGUF"
md_url: "https://www.modelscope.ai/models/barozp/Qwen3.6-28B-REAP20-A3B-GGUF.md"
repository: barozp/Qwen3.6-28B-REAP20-A3B-GGUF
last_updated: 2026-06-10
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
base_model:
  - 0xSero/Qwen3.6-28B-REAP20-A3B
base_model_relation: quantized
library_name:
  - gguf
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 69
stars: 0
tags:
  - gguf
  - qwen3
  - text-generation
  - moe
  - pruning
  - reap
  - cerebras
  - expert-pruning
  - conversational
  - llama-cpp
  - quantized
---

# Qwen3.6-28B-REAP20-A3B-GGUF

> Qwen3.6-28B-REAP20-A3B-GGUF - An open-source model by barozp on ModelScope. Qwen3.6-28B-REAP20-A3B — GGUF Quantizations GGUF quantizations of 0xSero/Qwen3.6-28B-REAP20-A3B, a 20% expert-pruned variant of Qwen/Qwen3.6-35B-A3B using the REAP (Router-weighted…

barozp/Qwen3.6-28B-REAP20-A3B-GGUF is a text-generation model on ModelScope. licensed under apache-2.0. derived from 0xSero/Qwen3.6-28B-REAP20-A3B.

- **Repository**: barozp/Qwen3.6-28B-REAP20-A3B-GGUF
- **License**: apache-2.0
- **Tasks**: text-generation
- **Base model**: 0xSero/Qwen3.6-28B-REAP20-A3B
- **Tags**: gguf, qwen3, text-generation, moe, pruning, reap, cerebras, expert-pruning, conversational, llama-cpp, quantized
- **Downloads**: 69
- **Stars**: 0
- **Last updated**: 2026-06-10

Source: https://www.modelscope.ai/models/barozp/Qwen3.6-28B-REAP20-A3B-GGUF

---

# Qwen3.6-28B-REAP20-A3B — GGUF Quantizations
GGUF quantizations of [0xSero/Qwen3.6-28B-REAP20-A3B](https://huggingface.co/0xSero/Qwen3.6-28B-REAP20-A3B), a 20% expert-pruned variant of [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) using the REAP (Router-weighted Expert Activation Pruning) method.
## Available Files
| File | Quant | Size | BPW | Description |
| --- | --- | --- | --- | --- |
| `Qwen3.6-28B-REAP20-A3B-BF16.gguf` | BF16 | ~56.5 GB | 16.0 | Full precision, for re-quantization |
| `Qwen3.6-28B-REAP20-A3B-Q8_0.gguf` | Q8_0 | ~30 GB | 8.0 | Near-lossless, large file |
| `Qwen3.6-28B-REAP20-A3B-Q6_K.gguf` | Q6_K | ~23 GB | 6.56 | Near-lossless, recommended for high quality |
| `Qwen3.6-28B-REAP20-A3B-Q5_K_M.gguf` | Q5_K_M | ~20 GB | 5.68 | High quality, larger size |
| `Qwen3.6-28B-REAP20-A3B-Q5_K_S.gguf` | Q5_K_S | ~19 GB | 5.52 | High quality, slightly smaller |
| `Qwen3.6-28B-REAP20-A3B-Q4_K_M.gguf` | Q4_K_M | ~17 GB | 4.89 | **Recommended** — best quality/size balance |
| `Qwen3.6-28B-REAP20-A3B-Q4_K_S.gguf` | Q4_K_S | ~16 GB | 4.63 | 4-bit small |
| `Qwen3.6-28B-REAP20-A3B-Q3_K_L.gguf` | Q3_K_L | ~15 GB | 4.27 | 3-bit large |
| `Qwen3.6-28B-REAP20-A3B-Q3_K_M.gguf` | Q3_K_M | ~14 GB | 3.91 | 3-bit medium |
| `Qwen3.6-28B-REAP20-A3B-Q3_K_S.gguf` | Q3_K_S | ~13 GB | 3.66 | 3-bit small |
| `Qwen3.6-28B-REAP20-A3B-IQ3_XXS.gguf` | IQ3_XXS | ~12 GB | 3.06 | Ultra-small, imatrix-based |
| `Qwen3.6-28B-REAP20-A3B-Q2_K.gguf` | Q2_K | ~11 GB | 2.96 | Smallest size, lowest quality |
## Model Details
| Property | Value |
| --- | --- |
| Architecture | Qwen3.6 MoE (hybrid Gated DeltaNet + MoE) |
| Parameters | ~28B total / ~3B active per token |
| Experts | 205 total / 8 active per token (pruned from 256) |
| Context Length | 262,144 tokens |
| Original dtype | BF16 |
| Quantization source | BF16 GGUF from [0xSero/Qwen3.6-28B-REAP20-A3B-GGUF](https://huggingface.co/0xSero/Qwen3.6-28B-REAP20-A3B-GGUF) |
| Quantization tool | llama.cpp |
| imatrix | Used for IQ3_XXS (from source repo) |
| License | Apache 2.0 |
## Quantization Process
```bash
# 1. Download BF16 GGUF from source
huggingface-cli download 0xSero/Qwen3.6-28B-REAP20-A3B-GGUF \
  --include "model.bf16.gguf" --local-dir ./

# 2. Download imatrix (for IQ quants)
huggingface-cli download 0xSero/Qwen3.6-28B-REAP20-A3B-GGUF \
  --include "imatrix.dat" --local-dir ./

# 3. Quantize (example: Q4_K_M)
llama-quantize model.bf16.gguf Qwen3.6-28B-REAP20-A3B-Q4_K_M.gguf Q4_K_M

# 4. Quantize with imatrix (example: IQ3_XXS)
llama-quantize --imatrix imatrix.dat model.bf16.gguf \
  Qwen3.6-28B-REAP20-A3B-IQ3_XXS.gguf IQ3_XXS
```
## Usage
### llama.cpp
```bash
llama-cli \
  -m Qwen3.6-28B-REAP20-A3B-Q4_K_M.gguf \
  -ngl 99 -c 4096 \
  -p "Your prompt here"
```
### llama-server (OpenAI-compatible API)
```bash
llama-server \
  -m Qwen3.6-28B-REAP20-A3B-Q4_K_M.gguf \
  -ngl 99 -c 4096 \
  --port 8080
```
### LM Studio / Jan / Ollama
Download the `.gguf` file and load it directly in your preferred local inference UI.
## Hardware Requirements
| Config | VRAM / RAM |
| --- | --- |
| Full GPU (Q4_K_M, recommended) | 20+ GB VRAM |
| Hybrid CPU+GPU (Q4_K_M) | 10 GB VRAM + 10 GB RAM |
| CPU only (Q4_K_M) | 24+ GB RAM |
## About the Original Model
[0xSero/Qwen3.6-28B-REAP20-A3B](https://huggingface.co/0xSero/Qwen3.6-28B-REAP20-A3B) applies REAP expert pruning ([arXiv:2510.13999](https://arxiv.org/abs/2510.13999)) to remove 20% of MoE experts (51 of 256 per layer) from Qwen3.6-35B-A3B, while preserving routing behavior via router weight renormalization. Active parameters per token remain unchanged at ~3B. The result is a ~25% smaller model with competitive generation quality across coding, reasoning, and knowledge benchmarks.
## License
Apache 2.0 — see [Qwen License](https://huggingface.co/Qwen/Qwen3.6-35B-A3B/blob/main/LICENSE).