--- library_name: mlx license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-Coder-Next/blob/main/LICENSE pipeline_tag: text-generation tags: - mlx base_model: Qwen/Qwen3-Coder-Next --- # Qwen3-Coder-Next-mxfp4-mlx The Qwen3-Coder-Next outperforms the previous Next models with ease. The mxfp4 is head and shoulders above the old Next q8, establishing itself as the highest performing quant so far. Brainwaves ``` arc arc/e boolq hswag obkqa piqa wino qx86n-hi 0.518,0.710,0.882,0.626,0.416,0.745,0.601 qx86n 0.515,0.712,0.881,0.627,0.414,0.744,0.590 mxfp8 0.514,0.709,0.884,0.639,0.420,0.748,0.611 mxfp4 0.528,0.713,0.880,0.630,0.428,0.744,0.619 qx64n-hi 0.527,0.707,0.880,0.631,0.426,0.744,0.580 qx64n 0.511,0.703,0.881,0.631,0.420,0.746,0.598 qx53n 0.520,0.714,0.872,0.630,0.438,0.744,0.599 Qwen3-Next-80B-A3B-Instruct q8 0.402,0.494,0.896,0.540,0.420,0.754,0.554 Qwen3-Next-80B-A3B-Thinking q8 0.409,0.459,0.648,0.655,0.376,0.783,0.692 Size Perplexity qx86n-hi 82G 4.484 ± 0.033 qx86n 73G 4.487 ± 0.033 mxfp8 82G 4.537 ± 0.033 mxfp4 42G 4.676 ± 0.035 qx64n-hi 54G 4.528 ± 0.033 qx64n 53G 4.525 ± 0.033 qx53n 43G 4.750 ± 0.036 ``` The Deckard(qx) formula for Next was used without changes from the previous Next series. There are very few benefits from using group size 32 in high quants for Coder-Next, as it did not bring much to qx86n-hi(not being uploaded for space constraints). At low quants however, the mxfp4 and qx64n-hi show highest combined arc, openbookqa, hellaswag and winogrande, even compared to larger quants. The qx53n still works, has a bit better openbooka than the mxfp4, sufficient to matter in some use cases. The mxfp8 seems unbeatable in speed, and its metrics are excellent, showing highest logic, hellaswag, and piqa. --- # Abliterated and REAP models I benchmarked the mxfp4/mxfp8 for being the most stable quants with very little loss from full precision. ```brainwave Qwen3-Coder-Next mxfp8 0.514,0.709,0.884,0.639,0.420,0.748,0.611 Huihui-Qwen3-Coder-Next-abliterated mxfp8 0.488,0.681,0.871,0.628,0.404,0.753,0.581 lovedheart/Qwen3-Coder-Next-REAP-40B-A3B mxfp8 0.390,0.508,0.610,0.532,0.354,0.665,0.577 Perplexity Huihui mxfp8 4.817 ± 0.036 Huihui mxfp4 4.946 ± 0.037 REAP-40B mxfp8 11.127 ± 0.103 REAP-40B mxfp4 11.479 ± 0.107 REAP-48B mxfp8 9.489 ± 0.085 REAP-48B mxfp4 9.676 ± 0.087 ``` The REAP models seem much more cheerful than the original, but lose a lot of arc and boolq that shows as heavy hallucinations in the output. --- # Nightmedia models Here are some Brainwaves for qx86-hi for Nightmedia 30B-A3B elements, to give an idea how much better Next could be These tests have nothing to do with what the model knows, but how well it thinks with what it knows. ```brainwave arc arc/e boolq hswag obkqa piqa wino Element4 0.514,0.617,0.846,0.769,0.442,0.801,0.731 Element5 0.560,0.709,0.883,0.756,0.448,0.807,0.713 Element6 0.568,0.737,0.880,0.760,0.450,0.803,0.714 Element7 0.578,0.750,0.883,0.742,0.478,0.804,0.684 ``` So cognitively, the Next has risen up to Element5 level or so. And then there is the curious case of the [Qwen3-4B-Engineer3x-qx86-hi-mlx](https://huggingface.co/nightmedia/Qwen3-4B-Engineer3x-qx86-hi-mlx) ```brainwave arc arc/e boolq hswag obkqa piqa wino qx86-hi 0.615,0.835,0.852,0.745,0.420,0.780,0.704 ``` We have other models in this range :) -G This model [Qwen3-Coder-Next-mxfp4-mlx](https://huggingface.co/nightmedia/Qwen3-Coder-Next-mxfp4-mlx) was converted to MLX format from [Qwen/Qwen3-Coder-Next](https://huggingface.co/Qwen/Qwen3-Coder-Next) using mlx-lm version **0.30.6**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("Qwen3-Coder-Next-mxfp4-mlx") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_dict=False, ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```