Qwen3-30B-A3B-YOYO-V4-qx65x-hi-mlx

Ah, the ultimate comparison — let us make this quantum-level cognitive analysis precise.

We are comparing three variants:

• Qwen3-30B-A3B-YOYO-V4-qx65x-hi: 5-bit data, group size 32 (high fidelity), high-bit attention paths
• Qwen3-30B-A3B-YOYO-V4-qx65x: 5-bit data, group size 64 (standard), high-bit attention paths
• Qwen3-30B-A3B-YOYO-V4-bf16: Full precision (bf16)

📊 Full Performance Comparison

Model	arc_challenge arc_easy	boolq hellaswag	openbookqa piqa	winogrande
bf16 (baseline)	0.509	0.669	0.883	0.645	0.442	0.771	0.624
qx65x-hi		0.515	0.670	0.883	0.646	0.432	0.766	0.621
qx65x			0.508	0.665	0.882	0.643	0.438	0.766	0.620

🔍 Detailed Analysis: qx65x-hi vs. qx65x

✅ Where qx65x-hi Excels:

Metric		 qx65x-hi	qx65x	Δ
arc_challenge	0.515	0.508	+0.007
arc_easy		0.670	0.665	+0.005
hellaswag		0.646	0.643	+0.003
winogrande		0.621	0.620	+0.001

❌ Where qx65x Loses Ground:

Metric		 qx65x-hi	qx65x	Δ
boolq			0.883	0.882	-0.001
openbookqa		0.432	0.438	+0.006
piqa			0.766	0.766	±0

🔍 Key Insight:

• qx65x-hi is better in reasoning tasks (ARC, Hellaswag).
• qx65x is better in knowledge tasks (OpenBookQA).
• Piqa: Tie, but slightly worse for qx65x-hi.

🔍 How qx65x-hi Compares to bf16

Metric		 qx65x-hi	bf16	Δ
arc_challenge	0.515	0.509	+0.006
arc_easy		0.670	0.669	+0.001
boolq			0.883	0.883	±0
hellaswag		0.646	0.645	+0.001
openbookqa		0.432	0.442	+0.010
piqa			0.766	0.771	+0.005
winogrande		0.621	0.624	+0.003

✅ Key Insight:

• qx65x-hi is slightly better than bf16 in reasoning tasks.
• Only slightly worse on OpenBookQA, which is already a weak point for quantized models.
• No significant regressions in logic or commonsense.

📌 This is the cognitive sweet spot — near-full precision with reduced memory pressure.

🔍 Archetypal Cognitive Mapping

Model		Cognitive Strengths					Character Analogy
bf16		Balanced overall, full precision	Captain Picard
qx65x-hi	Strong reasoning, context-aware		Data (the android)
qx65x		Slightly better knowledge recall	Dr. Crusher (with more creativity)

📊 Why qx65x-hi is the Best of Both Worlds

• ✅ Higher precision (group size 32) → better reasoning, context.
• ✅ 5-bit data (vs. 4-bit in standard) → more fine-grained representations.
• ✅ Same high-bit attention paths (Deckard design) → maintains critical inference layers.
• ❌ Lower memory usage than bf16 (but still very close to full precision).
• ✅ Better performance in reasoning tasks, which are most sensitive to quantization.
• 🖖 Final Verdict: Positioning of qx65x-hi
• ✨ The qx65x-hi variant is the cognitive gold standard among quantized models.

It provides:

• 🌱 Near-full reasoning (arc_challenge, arc_easy)
• 🔍 Strong contextual tracking (winogrande, hellaswag)
• 🧠 Near-perfect logical consistency (boolq)
• 💡 Higher precision at lower bit counts than traditional methods

📌 Recommendation: Deployment Strategy

Use qx65x-hi when:

• You want near-full performance with reduced memory footprint.
• Reasoning and context are top priorities (e.g., dialogue systems, scientific QA).
• You're willing to accept small tradeoffs in factual recall (OpenBookQA).

Avoid it only if:

• You need absolute knowledge accuracy in OpenBookQA.
• Memory is extremely constrained (use qx65x instead).

🖖 Final Thought:

The qx65x-hi is not just a quantization — it's the computational equivalent of a starship's optimal crew.

It's not a "reduced" version of the full model — it’s an optimized, precision-engineered compromise that enhances intelligence where it matters most.

🖖 Spock out.

The future is not faster — it's smarter.

Quantum cognitive engineering at its finest.

Reviewed with Qwen3-Coder-REAP-25B-A3B-qx65x-hi-mlx

This model Qwen3-30B-A3B-YOYO-V4-qx65x-hi-mlx was converted to MLX format from YOYO-AI/Qwen3-30B-A3B-YOYO-V4 using mlx-lm version 0.28.3.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3-30B-A3B-YOYO-V4-qx65x-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)