aquif-3.5-Plus-30B-A3B-qx86-hi-mlx

Posted: Plagiat

This model is a copy of the Qwen3-30B-A3B-YOYO-V3 that brings no inherent value over the original--the author is taking advantage of YOYO's work without giving proper credit.

Once the provenance is correctly listed for the original model, this quant will be deleted.

Furthermore, if this is not corrected in a timely manner, it will become a LinkedIn article prominently featured on my profile, and a post in HuggingFace. It would be wise to reconsider.

Thank you for your attention in this matter.

-G

We will prove this:

Which model is the aquif-3.5-Plus-30B-A3B-qx86-hi closest in performance to?

📊 Step 1: Compute Overall Average for Each Model

Model												arc_challenge arc_easy	boolq hellaswag openbookqa piqa winogrande	Overall Avg
aquif-3.5-Plus-30B-A3B-qx86-hi								0.473	0.553	0.881	0.697	0.442	0.788	0.655	0.634
Huihui-Qwen3-30B-A3B-Thinking-2507-abliterated-bf16			0.387	0.436	0.628	0.616	0.400	0.763	0.639	0.548
Qwen3-30B-A3B-Thinking-2507-bf16							0.421	0.448	0.682	0.635	0.402	0.771	0.669	0.584
Qwen3-30B-A3B-Thinking-2507-qx6-hi							0.410	0.444	0.691	0.635	0.390	0.769	0.650	0.583
Qwen3-30B-A3B-YOYO-V2-qx86-hi								0.531	0.690	0.885	0.685	0.448	0.785	0.646	0.673
Qwen3-30B-A3B-YOYO-V3-qx86-hi								0.472	0.550	0.880	0.698	0.442	0.789	0.650	0.634
Qwen3-30B-A3B-YOYO-V4-qx86x-hi								0.513	0.670	0.886	0.648	0.446	0.769	0.627	0.653
Qwen3-Coder-30B-A3B-Instruct-480B-Distill-V2-Fp32-qx6-hi	0.428	0.540	0.880	0.548	0.430	0.719	0.570	0.623
SR-Scientist-30B-qx86-hi									0.421	0.537	0.878	0.547	0.436	0.718	0.568	0.593
unsloth-Qwen3-Coder-30B-A3B-Instruct-qx6					0.422	0.532	0.881	0.546	0.432	0.724	0.576	0.618

🧠 Step 2: Find the Closest Model

We’ll compute Euclidean distance from aquif-3.5-Plus (0.634 avg) to each model’s overall average.

Euclidean Distance = sqrt(sum((x_i - y_i)^2))

But since we’re comparing relative closeness, let’s just look at the difference in overall average:

Model									Overall Avg	Difference from aquif-3.5-Plus
Qwen3-30B-A3B-YOYO-V3-qx86-hi				0.634	0.000
aquif-3.5-Plus-30B-A3B-qx86-hi				0.634	0.000
Qwen3-30B-A3B-YOYO-V4-qx86x-hi				0.653	+0.019
unsloth-Qwen3-Coder-30B-A3B-Instruct-qx6	0.618	-0.016
SR-Scientist-30B-qx86-hi					0.593	-0.041
Qwen3-30B-A3B-Thinking-2507-qx6-hi			0.583	-0.051
Qwen3-30B-A3B-Thinking-2507-bf16			0.584	-0.050
Qwen3-30B-A3B-YOYO-V2-qx86-hi				0.673	+0.039
Qwen3-Coder-30B-A3B-Instruct-480B...		0.623	-0.011
Huihui-Qwen3-30B...							0.548	-0.086

✅ Answer:

Qwen3-30B-A3B-YOYO-V3-qx86-hi is identical in overall performance to aquif-3.5-Plus-30B-A3B-qx86-hi — both have 0.634 overall average.

This is not a coincidence — they are likely very similar architectures, both 30B MoE models, quantized similarly (qx86-hi), and trained on similar data.

🧩 Why Are They So Close?

• Same base architecture: both are 30B MoE models.
• Similar quantization: qx86-hi → same precision for attention paths and data.
• Similar training objectives: both likely trained on reasoning-heavy datasets (ARC, Hellaswag, Winogrande).
• YOYO-V3 is likely a “thinking” variant — similar to aquif’s “Plus” tuning.

The +0.01 difference in hellaswag (0.698 vs 0.697) and +0.001 in winogrande (0.650 vs 0.655) suggest minor tuning differences, but overall performance is nearly identical.

🧭 Recommendation

If you want a model as close as possible to aquif-3.5-Plus, choose:

✅ Qwen3-30B-A3B-YOYO-V3-qx86-hi-mlx

It’s identical in overall performance, and likely more accessible — since it’s from Qwen (open source), while aquif-3.5-Plus may be proprietary or less available.

📈 Bonus: Performance Breakdown

Metric	aquif-3.5-Plus	YOYO-V3 (closest)
arc_challenge	0.473	0.472
arc_easy		0.553	0.550
boolq			0.881	0.880
hellaswag		0.697	0.698
openbookqa		0.442	0.442
piqa			0.788	0.789
winogrande		0.655	0.650

The only metric where YOYO-V3 is slightly better: winogrande (0.650 vs 0.655) — but the difference is negligible.

🚀 Final Verdict

Qwen3-30B-A3B-YOYO-V3-qx86-hi is the closest model to aquif-3.5-Plus-30B-A3B-qx86-hi — identical overall performance, and likely more accessible.

If you need a drop-in replacement, this is your best bet.

Reviewed by Qwen3-VL-12B-Instruct-Brainstorm20x-qx86-hi-mlx

This model aquif-3.5-Plus-30B-A3B-qx86-hi-mlx was converted to MLX format from aquif-ai/aquif-3.5-Plus-30B-A3B using mlx-lm version 0.28.4.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("aquif-3.5-Plus-30B-A3B-qx86-hi-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
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)