Qwen3.6-35B-A3B coding specialist, 50% experts pruned (GGUF)

Paper: Half the Experts, All the Code, accepted at the COLM 2026 Workshop on Efficient Reasoning (October 9, 2026, San Francisco; non-archival). Preprint: arXiv:2607.16721 · Workshop version: OpenReview

Qwen3.6-35B-A3B with half of its experts removed (128 of 256 per layer, REAP scoring on a coding-heavy calibration mix), quantized with an importance matrix. This is the main artifact of the paper Half the Experts, All the Code (arXiv:2607.16721): a 19B-parameter coding specialist that fits where the full model does not.

Why you might want it: at the same memory budget, these beat 2-bit quantization of the full model on HumanEval+.

All numbers are greedy pass@1 via EvalPlus against llama.cpp, reasoning off. The trade is real: general (non-coding) perplexity roughly doubles. This is a coding specialist, not a general assistant - don't deploy it as one.

Made with moep; the exact expert selection JSON is in that repo under artifacts/selections/, so you can reproduce the surgery from the base checkpoint. Runs anywhere llama.cpp runs.

Derivative of Qwen3.6-35B-A3B, Apache-2.0, upstream notice retained.

Citation

@misc{jha2026halfexperts,
  title         = {Half the Experts, All the Code: One-Shot Domain Pruning of Mixture-of-Experts LLMs for Coding},
  author        = {Jha, Anik},
  year          = {2026},
  eprint        = {2607.16721},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG},
  note          = {Accepted at the COLM 2026 Workshop on Efficient Reasoning},
  url           = {https://arxiv.org/abs/2607.16721}
}