README.md

---
license: apache-2.0
library_name: mlx
datasets:
- DavidAU/ST-TheNextGeneration
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
- unsloth
- mlx
base_model: DavidAU/Qwen3-Yoyo-V3-42B-A3B-Thinking-TOTAL-RECALL-ST-TNG
pipeline_tag: text-generation
---

# Qwen3-Yoyo-V3-42B-A3B-Thinking-TOTAL-RECALL-ST-TNG-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)

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

The original [Qwen3-Yoyo-V3-42B-A3B-Thinking-TOTAL-RECALL-ST-TNG-qx64-hi-mlx](https://huggingface.co/nightmedia/Qwen3-Yoyo-V3-42B-A3B-Thinking-TOTAL-RECALL-ST-TNG-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-ST-TNG-qx64x-hi-mlx](https://huggingface.co/nightmedia/Qwen3-Yoyo-V3-42B-A3B-Thinking-TOTAL-RECALL-ST-TNG-qx64x-hi-mlx) was
converted to MLX format from [DavidAU/Qwen3-Yoyo-V3-42B-A3B-Thinking-TOTAL-RECALL-ST-TNG](https://huggingface.co/DavidAU/Qwen3-Yoyo-V3-42B-A3B-Thinking-TOTAL-RECALL-ST-TNG)
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-ST-TNG-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)
```