--- library_name: transformers tags: - compression - expert-merging - moe - code license: apache-2.0 base_model: - Qwen/Qwen3-Coder-Next --- # Qwen3-Coder-Next-REAP This model is a compressed version of [Qwen/Qwen3-Coder-Next](https://huggingface.co/Qwen/Qwen3-Coder-Next). It is obtained by reducing the number of experts in each MoE layer from 512 to 384 using the REAP baseline method as described in https://bknyaz.github.io/blog/2026/moe/. **Compared to other models obtained in this collection, more coding data is used in the calibration data during pruning/merging to better preserve original's model coding abilities. Specifically, the ratio between c4, math and coding data (see https://bknyaz.github.io/blog/2026/moe/) is 0.0, 0.7, 0.3. The calibration data used here is the same as in our [Qwen3-Coder-Next-REAM](https://huggingface.co/SamsungSAILMontreal/Qwen3-Coder-Next-REAM).** The compressed model has 60B params (120GB) instead of 80B (160GB) of the original model, reducing storage and GPU memory requirements by roughly 25%. At the same time, the model retains >=96% of the original model's performance on a variety of benchmarks (see Results section below). Additional efficiency optimization (e.g., quantization) can be added similarly to the original model. See additional details at [Qwen3-30B-A3B-Instruct-2507-REAM](https://huggingface.co/SamsungSAILMontreal/Qwen3-30B-A3B-Instruct-2507-REAM). ### Results | Model | IFeval | AIME25 | GSM8K | GPQA-D | HumanEval | LiveCodeBench | AVG | |--------------------------|--------|--------|-------|--------|-----------|---------------|-------| | Qwen3-Coder-Next | 89.6 | 80.0 | 85.4 | 42.4 | 92.7 | 47.5 | 72.9 | | Qwen3-Coder-Next-REAP (this repo) | 87.6 | 70.0 | 86.8 | 35.9 | 94.5 | 48.2 | 70.5 | | [Qwen3-Coder-Next-REAM](https://huggingface.co/SamsungSAILMontreal/Qwen3-Coder-Next-REAM) | 89.3 | 80.0 | 85.3 | 40.4 | 94.5 | 48.0 | 72.9 | ## License Please refer to the license of the original model [Qwen/Qwen3-Coder-Next](https://huggingface.co/Qwen/Qwen3-Coder-Next).