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1Gemma-3-27b-it-Gemini-Deep-Reasoning
2q8 0.590,0.742,0.883,0.781,0.458,0.822,0.7511Qwen3-30B-A3B-Element7-1M
2qx86-hi 0.578,0.750,0.883,0.742,0.478,0.804,0.684
3
4Qwen3-30B-A3B-Element6-1M
5qx86-hi 0.568,0.737,0.880,0.760,0.450,0.803,0.714
6
7Qwen3-42B-A3B-Architect
8qx86-hi 0.563,0.719,0.881,0.761,0.454,0.805,0.703
9
10Qwen3-32B-Element5-Heretic
11qx86-hi 0.483,0.596,0.738,0.754,0.394,0.802,0.710
12
13Qwen3-32B-Engineer4
14qx86-hi 0.516,0.661,0.829,0.753,0.386,0.798,0.717
15
16Qwen3-4B-Agent-Claude
17qx86-hi 0.572,0.763,0.861,0.708,0.414,0.773,0.676
18
19Qwen3-4B-Engineer3x-F32
20qx86-hi 0.613,0.842,0.855,0.748,0.428,0.781,0.709
21
22Qwen3-4B-Engineer3x2
23qx86-hi 0.619,0.829,0.850,0.747,0.422,0.776,0.6901q8 10.968 ± 0.104
2mxfp4 12.381 ± 0.119pip install mlx-lm1from mlx_lm import load, generate
2
3model, tokenizer = load("Gemma-3-27b-it-Gemini-Deep-Reasoning-q8-mlx")
4
5prompt = "hello"
6
7if tokenizer.chat_template is not None:
8 messages = [{"role": "user", "content": prompt}]
9 prompt = tokenizer.apply_chat_template(
10 messages, add_generation_prompt=True, return_dict=False,
11 )
12
13response = generate(model, tokenizer, prompt=prompt, verbose=True)