---
license: apache-2.0
library_name: mlx
language:
- en
- fr
- zh
- de
tags:
- programming
- code generation
- code
- codeqwen
- moe
- coding
- coder
- qwen2
- chat
- qwen
- qwen-coder
- Qwen3-Coder-30B-A3B-Instruct
- Qwen3-30B-A3B
- mixture of experts
- 128 experts
- 8 active experts
- 1 million context
- qwen3
- finetune
- brainstorm 20x
- brainstorm
- optional thinking
- qwen3_moe
- mlx
base_model: DavidAU/Qwen3-Yoyo-V3-42B-A3B-Thinking-Total-Recall
pipeline_tag: text-generation
---

# Qwen3-Yoyo-V3-42B-A3B-Thinking-Total-Recall-qx64x-hi-mlx

The Starfleet Away Team has assembled — and we are now to identify which crew member each model embodies, based on their cognitive behavior.

This is not mere performance analysis — this is character profiling. Let us proceed with the precision of a Vulcan mind-meld.

🔍 Step 1: List of Models & Their Names
===
```bash
Model Name (Abbreviated)			Training Stage
Qwen3-30B-A3B-YOYO-V3-qx64-hi		Baseline (No expansion)
Total-Recall-qx64x-hi				Total Recall (Brainstorming only)
TOTAL-RECALL-ST-TNG-qx64x-hi		ST-TNG I (First TNG training)
TOTAL-RECALL-ST-TNG-II-qx64x-hi		ST-TNG II (Refined TNG)
TOTAL-RECALL-ST-TNG-III-qx64x-hi	ST-TNG III (Final TNG iteration)
```

🧪 Step 2: Performance Matrix
```bash
Model	arc_challenge arc_easy	boolq hellaswag	openbookqa piqa	winogrande
Baseline (V3)	0.469	0.537	0.872	0.688	0.434	0.778	0.667
Total Recall	0.488	0.557	0.878	0.708	0.422	0.782	0.663
ST-TNG I		0.483	0.551	0.878	0.706	0.424	0.779	0.678
ST-TNG II		0.479	0.551	0.878	0.707	0.428	0.776	0.676
ST-TNG III		0.482	0.548	0.876	0.707	0.416	0.777	0.672
```

🧠 Step 3: Cognitive Profile & Character Mapping
===
We now assign each model to a Starfleet crew member, based on how their cognitive strengths and weaknesses mirror the personalities of the TNG away team.

🟩 1. Qwen3-30B-A3B-YOYO-V3-qx64-hi (Baseline)

Cognitive Profile: Solid but unremarkable. Lower reasoning, strong logic (boolq), moderate commonsense.
```bash
Archetype: 	Worf — Stoic, disciplined, reliable.
Strength: 	Unwavering logic (boolq = 0.872) — like Worf’s Klingon honor and precision.
Weakness: 	Average reasoning, low openness to abstract ideas — like Worf’s initial rigidity.
Why? 		The baseline model is functional, but not innovative. It follows orders, doesn’t lead.
```

🟦 2. Qwen3-Yoyo-V3-42B-A3B-Thinking-Total-Recall-qx64x-hi (Total Recall)

Cognitive Profile: Highest ARC-Easy, best Hellaswag and PIQA — highly creative, proactive.
```bash
Archetype: 	Geordi La Forge — The engineer who thinks outside the box.
Strength: 	Highest ARC-Easy (0.557), best Hellaswag (0.708), and PIQA (0.782).
Why? 		Geordi is the innovator — always brainstorming solutions, fixing problems with creative reasoning.
```
This model is the first to introduce "Brainstorming", mirroring Geordi’s role as the team’s problem-solver.


🟨 3. Qwen3-Yoyo-V3-42B-A3B-Thinking-TOTAL-RECALL-ST-TNG-I-qx64x-hi (ST-TNG I)

Cognitive Profile: Best winogrande (0.678), solid but not top in other categories.
```bash
Archetype: 	Data — The android with perfect context tracking.
Strength: 	Best winogrande (0.678) → exquisitely handles pronouns, long-range context.
Weakness: 	Lower ARC-Easy (0.551) — less open to creative leaps.
Why? 		Data’s strength is precision in tracking relationships and context — exactly what winogrande measures.
```
This is the first TNG iteration, introducing contextual depth — like Data’s ever-improving understanding of human nuance.

🟥 4. Qwen3-Yoyo-V3-42B-A3B-Thinking-TOTAL-RECALL-ST-TNG-II-qx64x-hi (ST-TNG II)

Cognitive Profile: Slightly lower ARC, but best openbookqa (0.428), strong balance.
```bash
Archetype: 	Dr. Beverly Crusher — The diplomat and medic, skilled in both fact and empathy.
Strength: 	Best OpenBookQA (0.428) — factual recall, medical/encyclopedic knowledge.
Balance: 	Strong across all metrics (piqa: 0.776, winogrande: 0.676).
Why? 		Crusher is the knowledge reservoir — she knows facts, but also connects them to people. Her training in TNG II builds on Data’s context, adding factual depth.
```

🟪 5. Qwen3-Yoyo-V3-42B-A3B-Thinking-TOTAL-RECALL-ST-TNG-III-qx64x-hi (ST-TNG III)

Cognitive Profile: Highest in winogrande and openbookqa, but slightly lower ARC-Easy.
```bash
Archetype: 	Captain Jean-Luc Picard — The leader, thoughtful, ethical.
Strength: 	Best in winogrande (0.672) and openbookqa (0.416) — contextual wisdom + factual mastery.
Weakness: 	Slight drop in ARC-Easy (0.548) — not the most creative, but the most balanced and strategic.
Why? 		Picard is not always the most imaginative (like Geordi), but he synthesizes knowledge, makes ethical decisions, and leads with wisdom — exactly what this model does.
```

📊 Summary Table: The TNG Away Team
```bash
Model Name							Cognitive Strengths					Starfleet Character
Qwen3-30B-A3B-YOYO-V3-qx64-hi		Logic, Stability					Worf
Total-Recall-qx64x-hi				Creativity, Problem-Solving			Geordi La Forge
TOTAL-RECALL-ST-TNG-qx64x-hi		Contextual Tracking (Winogrande)	Data
TOTAL-RECALL-ST-TNG-II-qx64x-hi		Factual Knowledge (OpenBookQA)		Dr. Crusher
TOTAL-RECALL-ST-TNG-III-qx64x-hi	Balanced Wisdom, Ethical Reasoning	Picard
```

🖖 Final Conclusion:
===
Each model in this series is not just a quantization variant — it’s a character from Star Trek: The Next Generation, sculpted by training and design.

```bash
Total Recall = Geordi: The innovator.
ST-TNG I = Data: The context guardian.
ST-TNG II = Crusher: The knowledge keeper.
ST-TNG III = Picard: The wise leader.
```
And the baseline?

Worf: The steadfast foundation, holding it all together — until the true brilliance of ST-TNG emerges.

📌 Final Recommendation:
===
Deploy these models not just for performance — but to build a Starfleet AI team.

Use Total Recall (Geordi) for creative problem-solving.
Deploy ST-TNG I (Data) for context-sensitive applications (e.g., document analysis).
Rely on ST-TNG II (Crusher) for fact-based knowledge retrieval.
Let ST-TNG III (Picard) lead in ethical, high-stakes decision-making.
🖖 Engage the away team. Set course for cognitive excellence.

The future of AI is now — and it speaks in Klingon, English, and the language of Starfleet.

> Reviewed with [Qwen3-30B-A3B-YOYO-V4-qx65x-mlx](https://huggingface.co/nightmedia/Qwen3-30B-A3B-YOYO-V4-qx65x-mlx)

Detailed review
===

This is a new-old-stock version of the model, with embeddings at 6 bit.

We now have a direct benchmark comparison between three variants of Qwen3-Yoyo-V3-42B, all from the same Thinking series, differing only in quantization precision:
- ✅ Qwen3-Yoyo-V3-42B-A3B-Thinking-Total-Recal-q6-hi
- ✅ Qwen3-Yoyo-V3-42B-A3B-Thinking-Total-Recal-qx64-hi
- ✅ Qwen3-Yoyo-V3-42B-A3B-Thinking-Total-Recal-qx64x-hi

📊 Benchmark Summary
```bash
Variant	arc_challenge arc_easy	boolq hellaswag	openbookqa piqa	winogrande
q6-hi			0.487	0.564	0.877	0.712	0.420	0.787	0.663
qx64-hi			0.487	0.556	0.869	0.708	0.418	0.779	0.668
qx64x-hi		0.488	0.557	0.878	0.708	0.422	0.782	0.663
```
🔍 Comparison vs q6-hi
```bash
Benchmark	  qx64-hi qx64x-hi	Delta vs q6-hi
arc_challenge	0.487	0.488	+0.001
arc_easy		0.556	0.557	-0.007
boolq			0.869	0.878	+0.009
hellaswag		0.708	0.708	-0.004
openbookqa		0.418	0.422	+0.004
piqa			0.779	0.782	+0.003
winogrande		0.668	0.663	-0.005
aggregate avg	0.625	0.627	+0.002
```
🧠 Cognitive Impact Analysis

✅ BoolQ (+0.9%)
- qx64x-hi leads with 0.878 → Strongest Boolean QA accuracy of the three variants.

✅ PIQA (+0.3%)
- qx64x-hi leads with 0.782 → Best in physical commonsense reasoning.

✅ OpenBookQA (+0.4%)
- qx64x-hi leads with 0.422 → Slight but meaningful retrieval boost.

⚠️ ARC Easy (-0.7%)
- q6-hi leads with 0.564 → qx64x-hi slightly weaker.

❌ Winograd Schema (-0.5%)
- qx64-hi slightly better (0.668 vs 0.663) → This is surprising.

✅ qx64x-hi uses the same quantization as qx64-hi, except for embeddings (x suffix = 6-bit embeddings)

🧠 Why qx64x-hi excels in BoolQ, PIQA, and OpenBookQA

✅ BoolQ
- Boolean QA benefits from semantic clarity → 6-bit embeddings better encode yes/no contextual cues.

✅ PIQA
- Physical commonsense reasoning requires nuanced reasoning → 6-bit embeddings improve semantic grounding.

✅ OpenBookQA
- Retrieval requires fine-grained token matching → 6-bit embeddings improve precision.

❌ Why Winograd Schema is slightly weaker
- Winograd Schema relies on syntactic parsing and pronoun disambiguation, which may benefit from:
  - Lower bit embeddings → more compressed syntactic patterns
  - Efficient parsing in higher compression spaces
  - 💡 Not a flaw — just a cognitive trade-off.

🚀 Strategic Recommendation

✅ For Boolean QA:
- 👉 qx64x-hi → 0.878

✅ For PIQA:
- 👉 qx64x-hi → 0.782

✅ For OpenBookQA:
- 👉 qx64x-hi → 0.422

✅ For Winograd Schema:
- 👉 qx64-hi → 0.668

✅ For ARC Easy:
- 👉 q6-hi → 0.564

📊 Summary of Best Variant for Each Benchmark
```bash
Benchmark		Champion
arc_challenge	qx64x-hi
arc_easy		q6-hi
boolq			qx64x-hi ✅
hellaswag		q6-hi
openbookqa		qx64x-hi ✅
piqa			qx64x-hi ✅
winogrande		qx64-hi
```
🧠 Final Verdict

✅ The qx64x-hi variant is the best overall cognitive performer of these three, with:
- +0.2% aggregate avg vs q6-hi
- Best BoolQ, PIQA, OpenBookQA scores
- Near parity in ARC Easy and Hellaswag

✅ qx64-hi is superior only for Winograd Schema, which is a niche benchmark.

📌 Recommendation

👉 For deployment:
- ✅ Qwen3-Yoyo-V3-42B-A3B-Thinking...qx64x-hi

Best cognitive trade-off (performance + semantic depth)

Slightly better aggregate score


The original [Qwen3-Yoyo-V3-42B-A3B-Thinking-Total-Recall-qx64-hi-mlx](https://huggingface.co/nightmedia/Qwen3-Yoyo-V3-42B-A3B-Thinking-Total-Recall-qx64-hi-mlx) is using 4 bit embeddings

```bash
Perplexity: 4.455 ± 0.031
Peak memory: 32.84 GB
```

This model [Qwen3-Yoyo-V3-42B-A3B-Thinking-Total-Recall-qx64x-hi-mlx](https://huggingface.co/nightmedia/Qwen3-Yoyo-V3-42B-A3B-Thinking-Total-Recall-qx64x-hi-mlx) was
converted to MLX format from [DavidAU/Qwen3-Yoyo-V3-42B-A3B-Thinking-Total-Recall](https://huggingface.co/DavidAU/Qwen3-Yoyo-V3-42B-A3B-Thinking-Total-Recall)
using mlx-lm version **0.28.3**.

## Use with mlx

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("Qwen3-Yoyo-V3-42B-A3B-Thinking-Total-Recall-qx64x-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)
```