---

license: apache-2.0

base_model: Qwen/Qwen3.6-35B-A3B

tags:

- moe

- expert-pruning

- code

- gguf

- llama.cpp

pipeline_tag: text-generation

---

Qwen3.6-35B-A3B coding specialist, 25% experts kept, healed (GGUF)

Companion checkpoint for [*Half the Experts, All the Code: One-Shot Domain

Pruning of Mixture-of-Experts LLMs for Coding*](https://arxiv.org/abs/2607.16721)

(arXiv:2607.16721). Pipeline and the exact keep-set JSON that produced it:

github.com/anik-jha/moep.

75% of the routed experts were removed from Qwen3.6-35B-A3B under the REAP

criterion calibrated on a coding-weighted mix, then the survivors were healed by

distilling from the base model with the expert tensors frozen (LoRA on attention,

router, and shared expert only).

Read this before using it

This is a research artifact, and the paper's headline finding is a negative one:

**at this memory budget you are better off quantizing the full model than pruning

it.** The healed checkpoint below sits at 11.2 GB and scores 0.841 on HumanEval+.

A 2-bit IQ2_M quantization of the unpruned model sits at 12.1 GB and scores

0.896. Pruning to 25% and healing does not close that gap; five separate attempts

to close it are reported in the paper and all failed.

The checkpoint is released because healing is GPU-nondeterministic and therefore

not reproducible from the keep-set JSON, unlike every other point in the study.

It is the artifact behind the paper's healing result, not a recommended

deployment. If you want something to actually run, use the 50%-keep specialist:

Qwen3.6-35B-A3B-coding-reap50-GGUF.

Evaluations

All numbers from runs/qwen36/eval.json in the pipeline repo, greedy decoding,

EvalPlus HumanEval+ and MBPP+ (pass@1). Only the Q8_0 file was functionally

evaluated.

| Checkpoint | Size | HumanEval+ | MBPP+ |

|---|---|---|---|

| Base Qwen3.6-35B-A3B, Q8_0 | 36 GB | 0.890 | 0.772 |

| Base, full model, IQ2_M (iso-memory comparator) | 12.1 GB | 0.896 | 0.730 |

| 25% keep, REAP, no healing, Q8_0 | 11.2 GB | 0.787 | 0.659 |

| 25% keep, REAP, healed, Q8_0 (this repo) | 11.2 GB | 0.841 | 0.685 |

Healing recovers 54 of the 103 HumanEval+ points lost to pruning (0.787 → 0.841

against a base of 0.890) and 26 of 113 on MBPP+. Held-out perplexity improves

from 5.45 to 4.85 on code and from 30.9 to 9.4 on general text.

The Q4_K_M file in this repo has not been functionally evaluated. It passed

an export smoke test and was benchmarked for throughput, nothing more. Do not

attribute the table above to it.

Files

| File | Size | Notes |

|---|---|---|

| qwen36-reap25-healed-Q8_0.gguf | 11.2 GB | evaluated, numbers above |

| qwen36-reap25-healed-Q4_K_M.gguf | 6.5 GB | smoke-tested only, not evaluated |

Throughput measured with llama-bench on one DGX Spark (GB10, 119 GB unified

memory): Q8_0 60.7 tok/s generation, 2780 tok/s prefill; Q4_K_M 82.0 tok/s

generation, 3081 tok/s prefill.

License

Weights are a derivative of Qwen3.6-35B-A3B and inherit its Apache-2.0 license.

The pipeline that produced them is MIT.