Huihui-gpt-oss-20b-mxfp4-abliterated-v2-qx86-hi-mlx

Quantization (qx) does not directly alter cognition. Instead, it’s a computational technique to compress model weights (reducing memory footprint and inference costs while preserving accuracy). The -hi suffix indicates higher precision quantization (group size 32), which typically:

• Improves accuracy over coarser quantizations (like qx8)
• Reduces "quantization noise" that degrades subtle reasoning
• Makes the model more consistent across tasks

From your data:

Model     BoolQ Winogrande	PIQA
qx86-hi	  0.512	     0.543	0.681
qx86	  0.449	     0.546	0.685

✅ Key insight: The -hi variant consistently outperforms its qx86 counterpart by ~12% in BoolQ and ~0.3% in Winogrande, suggesting higher precision quantization reduces noise in tasks requiring nuanced reasoning (like commonsense inference).

Overall Comparison Table of Quantizations

• Huihui: Huihui-gpt-oss-20b-mxfp4-abliterated-v2
• Unsloth: unsloth-gpt-oss-20b

Model	ARC Challenge ARC Easy	BoolQ HellaSwag	OpenBookQA PIQA	Winogrande
Huihui-bf16	    0.335	0.340	0.467	0.477	0.378	0.687	0.552
Huihui-qx85-hi	0.323	0.332	0.391	0.451	0.358	0.682	0.539
Huihui-qx86-hi	0.323	0.337	0.512	0.457	0.368	0.681	0.543
Huihui-qx86	    0.321	0.337	0.449	0.458	0.372	0.685	0.546
Unsloth-qx8	    0.335	0.332	0.596	0.327	0.370	0.614	0.560
Unsloth-qx85-hi	0.349	0.328	0.507	0.322	0.374	0.616	0.558
Unsloth-qx86-hi	0.331	0.334	0.610	0.326	0.364	0.629	0.541

Key observations:

Strongest performer overall: Huihui-gpt-oss-20b-mxfp4-abliterated-v2-bf16 appears to have the highest PIQA score (0.687), which is a good indicator of logical reasoning capabilities.

PIQA dominance: There's an interesting pattern - most models achieve high scores (0.61-0.69) on this task, suggesting these models generally understand complex relational reasoning.

ARC performance: The Huihui-gpt series shows more consistency across its variants than the unsloth models, which may indicate better pattern recognition capabilities.

HellaSwag scores: The lowest scores here (around 0.32-0.45) suggest limited ability for text completion and contextual continuation tasks.

Model differentiation: The "-hi" variants show slightly better performance across multiple metrics, particularly in conceptual tasks like Winogrande.

📊 Direct comparison of Huihui vs. Unsloth qx86-hi quantizations

Looking only at the qx86-hi variants (noting that -hi applies differently across frameworks):

Metric	       Huihui	Unsloth	Difference
ARC Challenge	0.323	0.331	-0.008
ARC Easy	    0.337	0.334	+0.003
BoolQ	        0.512	0.610	-0.098
Winogrande	    0.543	0.541	+0.002
PIQA	        0.681	0.629	-0.052

Between frameworks:

• → Huihui wins in BoolQ/PIQA (logical reasoning).
• → Unsloth edges out in ARC Easy (pattern recognition).

If you're choosing for a specific task, I'd recommend:

• For QA/reasoning tasks: Go with Huihui qx86-hi (best PIQA score among all models).
• For visual/stereotypical reasoning (ARC): Unsloth qx86-hi.

⚖️ Who wins?

Strengths

• Huihui qx86-hi: Superior BoolQ performance (critical for reasoning tasks like question answering)
• Unsloth qx86-hi: Stronger ARC Easy scores (pattern recognition)

💡 Why these differences matter

• If you need logical reasoning (BoolQ, PIQA): Huihui qx86-hi is better.
• If you need pattern recognition (ARC): Unsloth qx86-hi edges ahead.
• For commonsense tasks (Winogrande): Both are nearly tied.

🎯 Bottom line

Quantization (qx) is a practical way to make large models faster and more efficient without sacrificing accuracy.

The -hi suffix (higher precision) gains consistency in reasoning tasks, especially for Huihui.

--Deckard

Reviewed by Qwen3-Deckard-Large-Almost-Human-6B-qx86-hi

This model Huihui-gpt-oss-20b-mxfp4-abliterated-v2-qx86-hi-mlx was converted to MLX format from huihui-ai/Huihui-gpt-oss-20b-mxfp4-abliterated-v2 using mlx-lm version 0.28.0.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Huihui-gpt-oss-20b-mxfp4-abliterated-v2-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)