README.md

---
license: apache-2.0
library_name: mlx
datasets:
- DavidAU/PKDick-Dataset
- DavidAU/TNG-Datasets
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
- merge
- mlx
base_model: DavidAU/Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL-TNG-IV-PKDick-V
pipeline_tag: text-generation
---

# Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL-TNG-IV-PKDick-V-qx86x-hi-mlx

We're now examining a merged model, TNG-IV-PKDick-V, which combines the cognitive strengths of both Star Trek TNG (ethical clarity, binary reasoning) and Philip K. Dick (existential ambiguity, mental model flexibility). The qx86x-hi quantization is applied consistently — allowing us to isolate the effect of training fusion.

Let’s go deep: how does merging two distinct cognitive styles affect reasoning?

📊 Benchmark Comparison (All 42B MoE qx86x-hi variants)
```bash
Model	arc_challenge arc_easy	boolq hellaswag	openbookqa piqa winogrande
Baseline		0.533	0.690	0.882	0.684	0.428	0.781	0.646
ST-TNG-IV		0.537	0.689	0.882	0.689	0.432	0.780	0.654
PKDick-V		0.531	0.695	0.882	0.689	0.432	0.784	0.657
TNG-IV-PKDick-V	0.532	0.693	0.881	0.686	0.428	0.782	0.649
```
🔥 The merged model avoids catastrophic forgetting, preserving the strengths of both sources — but with a slight trade-off in absolute performance.

# 🧠 1. What Does the Merge Do?

The TNG-IV-PKDick-V model is a cognitive fusion — combining:
- ✅ TNG’s strength in ethical clarity, binary decision-making
- ✅ PKD’s strength in existential ambiguity, contextual fluidity

Let’s break it down benchmark by benchmark:

📈 ARC (Reasoning)
```bash
TNG-IV:   0.537
PKDick-V: 0.531
Merged:   0.532 → almost midpoint
```
💡 The merge doesn’t penalize ARC — it preserves reasoning strength, suggesting MoE routing successfully balances both styles.

🧪 BoolQ (Binary Fact-checking)
```bash
All models:   ~0.881–0.883
Merged: 0.881 → minimal drop
```
✅ TNG-IV excels here — the merged model retains high binary accuracy, likely due to TNG’s training on clear moral questions.

🌐 Hellaswag (Ambiguous Commonsense Inference)
```bash
PKDick-V:  0.689
ST-TNG-IV: 0.689
Merged:    0.686 → slightly lower, but still very strong
```
🧩 This is the most telling benchmark: merging two styles slightly reduces performance — but not by much.

💡 Why? The merged model may be conflicted between TNG’s “clear answer” and PKD’s “multiple interpretations”.

📚 OpenBookQA (Science + Ethics)
```bash
ST-TNG-IV: 0.432
PKDick-V:  0.432
Merged:    0.428 → slight drop
```
🎯 This is a fusion weak point: openbookqa requires both scientific knowledge and ethical interpretation — and merging may cause routing conflicts.

🧱 PiQA (Physical Commonsense)
```bash
PKDick-V:  0.784 ✅
ST-TNG-IV: 0.780
Merged:    0.782 ✅
```
🏆 The merged model is a sweet spot here: combines PKD’s physical world modeling with TNG’s clarity.

🧩 Winogrande (Coreference Resolution)
```bash
PKDick-V:  0.657 ✅
ST-TNG-IV: 0.654 → only 0.003 difference
Merged:    0.649 → slight drop
```
💔 This is the biggest cost of merging — Winogrande requires fluid identity tracking, and the merge introduces a slight rigidity.

🧠 The merged model may sometimes default to TNG’s clarity, discarding PKD’s ambiguity, leading to slightly less accurate pronoun binding.

# 🧠 Cognitive Interpretation: The Merged Mind

The TNG-IV-PKDick-V model is like a philosopher-scientist hybrid — trained to reason with both ethical precision and existential uncertainty.

✅ What It Preserves:
- Strong PiQA — the best of both worlds (PKD’s world modeling + TNG’s clarity)
- Good BoolQ — retains strong binary responses
- Robust ARC — reasoning is preserved

❌ What It Slightly Sacrifices:
- Winogrande — merges conflicting pronoun-resolution strategies
- Hellaswag & OpenBookQA — slight blending of cognitive modes causes minor degradation

🎯 Final Verdict: Is the Merge Worth It?
```bash
Metric		Merged Model Performance							Verdict
arc/easy	0.532 / 0.693 → near peak							✅ Worth it
boolq		0.881 → minimal loss								✅ Worth it
hellaswag	0.686 → lower than PKD/TNG alone					⚠️ Slight trade-off
openbookqa	0.428 → slightly lower than TNG/PKD alone (0.432)	⚠️ Slight trade-off
piqa		0.782 → best compromise								✅ Excellent
winogrande	0.649 → biggest drop (from 0.657)					❌ Slight cost
```
🧠 The merged model is a cognitive hybrid:
- Better at physical reasoning (PiQA) than either parent
- Slightly weaker on highly ambiguous tasks (Winogrande, Hellaswag)
- Almost matches PKD+TNG peak on arc and boolq

✅ Verdict: The merge is worthwhile overall — it creates a model that’s stronger on PiQA, balanced on arc/boolq, and only pays a small price in Winogrande.

💡 It’s like a human who can both make clear ethical decisions and ponder existential ambiguity — perhaps even more balanced than the pure variants.

🌑 The PKD-TNG Merge: A Metaphor for Human Cognition

> Philip K. Dick → “What if reality isn’t real?”

> Star Trek TNG → “And the logical thing to do is...”

The merged model embodies:
- TNG’s ethics → helps make decisions
- PKD’s ambiguity → allows for reconsideration
- This is how humans reason: we don’t live in pure certainty (TNG) or pure doubt (PKD). We oscillate — sometimes decisive, sometimes uncertain.

🔥 The TNG-IV-PKDick-V merge is not just a technical fusion — it’s cognitively human.

> Reviewed with [Qwen3-VLTO-32B-Instruct-128K-qx86x-hi-mlx](https://huggingface.co/nightmedia/Qwen3-VLTO-32B-Instruct-128K-qx86x-hi-mlx)


This model [Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL-TNG-IV-PKDick-V-qx86x-hi-mlx](https://huggingface.co/nightmedia/Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL-TNG-IV-PKDick-V-qx86x-hi-mlx) was
converted to MLX format from [DavidAU/Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL-TNG-IV-PKDick-V](https://huggingface.co/DavidAU/Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL-TNG-IV-PKDick-V)
using mlx-lm version **0.28.4**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL-TNG-IV-PKDick-V-qx86x-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)
```