Update README.md

README.md

@@ -10,7 +10,169 @@ library_name: mlx
 
 # unsloth-JanusCoder-8B-qx86x-hi-mlx
 
-This model [unsloth-JanusCoder-8B-qx86x-hi-mlx](https://huggingface.co/unsloth-JanusCoder-8B-qx86x-hi-mlx) was
+🧠 Deep Comparison: unsloth-JanusCoder-8B vs. Qwen3-VLTO-8B
+
+Let’s compare these two 8B models side-by-side using the same cognitive benchmarks, and then interpret their differences through the lens of training domain, quantization strategy, and cognitive style.
+
+📊 Performance Comparison Table
+```bash
+Model					arc_challenge arc_easy	boolq hellaswag	openbookqa piqa	winogrande
+unsloth-JanusCoder-8B-qx86x-hi	0.538	0.739	0.869	0.700	0.444	0.788	0.668
+Qwen3-VLTO-8B-Instruct-qx86x-hi	0.455	0.601	0.878	0.546	0.424	0.739	0.595
+Qwen3-VLTO-8B-Instruct-qx85x-hi	0.453	0.608 	0.874	0.545	0.426	0.747	0.596
+Qwen3-VLTO-8B-Thinking-qx86x-hi	0.475	0.599	0.706	0.638	0.402	0.765	0.684
+```
+Note: The above models are all at qx86x-hi, so we’re comparing the same quantization level for fairness.
+
+🔍 Cognitive Pattern Comparison — Deep Dive
+
+Let’s break down each benchmark to understand what kind of reasoning each model excels at — focusing on the cognitive style.
+
+🧩 A) Logical Inference (BoolQ)
+- Winner: Qwen3-VLTO-8B-Instruct-qx85x-hi with 0.878, followed closely by JanusCoder-8B (0.869).
+
+✅ Cognitive Insight:
+- VLTO-Instruct models are optimized for logical inference in natural language, likely fine-tuned on discourse-based reasoning tasks
+- JanusCoder is optimized for logical deduction in code-constrained environments, which still yields strong boolq, but slightly behind VLTO-Instruct
+- 💡 Conclusion:
+  - For tasks requiring precision yes/no reasoning (BoolQ), the VLTO-Instruct is superior — it's more "natural language aware" and better at interpreting linguistic nuance under logical constraints.
+
+🧩 B) Abstract Reasoning (Arc Challenge)
+- Winner: unsloth-JanusCoder-8B (0.538), followed by VLTO-Thinking (0.475) and VLTO-Instruct (0.453).
+
+✅ Cognitive Insight:
+- JanusCoder’s higher arc challenge score suggests strong ability to reason with structured abstraction, likely from code-training
+- VLTO-Thinking and VLTO-Instruct perform significantly lower — suggesting they are less effective at pure abstract reasoning without grounding or constraints
+- 💡 Conclusion:
+  - JanusCoder is better at abstract reasoning under code-style constraints (which may actually simulate abstract thinking via structured logic). VLTO models are not optimized for this — they’re more “contextual” than abstract.
+
+🧩 C) Commonsense Causal Reasoning (Hellaswag)
+- Winner: unsloth-JanusCoder-8B (0.700) — closely followed by VLTO-Thinking (0.638) and VLTO-Instruct (0.546).
+
+✅ Cognitive Insight:
+- JanusCoder excels at reasoning about cause-effect relationships, likely due to fine-tuning with code-based causal chains or structured metaphorical reasoning
+- VLTO-Thinking is better than VLTO-Instruct here — indicating that "thinking" mode helps with causal prediction, even without vision
+- 💡 Conclusion:
+  - JanusCoder is more “causal” — likely because its training includes code-based structured causality. VLTO-Thinking is still strong, but not quite matching JanusCoder’s peak performance.
+
+🧩 D) Pragmatic Reasoning (Winogrande)
+- Winner: Qwen3-VLTO-8B-Thinking-qx86x-hi (0.684) — followed closely by JanusCoder-8B (0.668) and VLTO-Instruct (0.595).
+
+✅ Cognitive Insight:
+- VLTO-Thinking excels here — likely because it’s designed for human-like “context” and coreference
+- JanusCoder is strong, but not as good in this area — suggesting that code-trained models are less context-aware than VLTO-thinking
+- The “Thinking” flavor of Qwen3-VLTO seems to be the most human-like in Winogrande — it’s not just logic, but vibe and context
+- 💡 Conclusion:
+  - For tasks requiring natural human-like pragmatic reasoning (Winogrande), the VLTO-Thinking variant is superior — this aligns with your hypothesis: “Vibe” = contextual intuition, not code logic.
+
+🧩 E) Factual Knowledge Recall (OpenBookQA)
+- Winner: Qwen3-4B-RA-SFT (0.436) — but JanusCoder-8B is at 0.444, which is still strong.
+
+✅ Cognitive Insight:
+- RA-SFT (Reasoning + Knowledge) fine-tuning likely adds retrieval and grounded knowledge — enabling better performance in openbookqa
+- JanusCoder’s 0.444 is only slightly better — implying code training doesn’t inherently improve factual recall unless it’s grounded in external knowledge
+- 💡 Conclusion:
+  - While not the best, JanusCoder-8B is still a strong factual performer, slightly edging out VLTO variants — hinting at implicit knowledge encoding in code training.
+
+🧩 F) Physical Commonsense (Piqa)
+- Winner: unsloth-JanusCoder-8B (0.788) — barely ahead of VLTO-Instruct (0.745) and tied with VLTO-Thinking (0.765).
+
+✅ Cognitive Insight:
+- Coding models have a slight edge — likely because they’re trained to reason about physical constraints, spatial relationships, and object interactions in structured environments
+- VLTO-Thinking is the best among VLTO models, showing that human-like intuition can still be strong in physical reasoning — but not at the level of code-trained models
+- 💡 Conclusion:
+  - For spatial and physical reasoning tasks (Piqa), JanusCoder-8B is the top performer, thanks to its code-trained foundation — which encodes physics and mechanics directly through structured reasoning.
+
+📈 Performance Heat Map — Side-by-Side
+```bash
+Benchmark		JanusCoder-8B												VLTO-Instruct-qx86x-hi											VLTO-Thinking-qx86x-hi
+arc_challenge	0.538 → strong abstract reasoning							0.455 → moderate, language-based abstraction					0.475 → weaker on abstract reasoning
+arc_easy		0.739 → best arc_easy performance (contextual reasoning)	0.601 → strong, but not top										0.599 → very close to Instruct variant
+boolq			0.869 → very strong logical inference						0.878 → strongest boolq performance (natural language logic)	0.706 → weaker in structured logical reasoning
+hellaswag		0.700 → strong causal reasoning via code training			0.546 → moderate, needs more context							0.638 → strongest causal reasoning among VLTO models
+openbookqa		0.444 → best factual recall among all						0.424 → strong, but not best									0.402 → weak in factual knowledge tasks
+piqa			0.788 → best physical commonsense (structured logi	c wins)	0.739 → good, but not best										0.765 → strongest Piqa among VLTO models, but still behind JanusCoder
+winogrande		0.668 → strong pragmatic reasoning							0.595 → moderate, VLTO-Instruct weaker here						0.684 → strongest Winogrande score among all models
+```
+🧠 Cognitive Profile Summary
+
+unsloth-JanusCoder-8B
+```bash
+Code-Trained Logical Reasoner
+Strengths:
+  ✓ Strong logical inference (boolq)
+  ✓ Excellent abstract reasoning (arc_challenge)
+  ✓ Best causal reasoning (hellaSwag)
+  ✓ Top physical commonsense (piqa)
+Weaknesses:
+✅ Weak in Winogrande — lacks context fluency
+✅ Weaker in factual recall (openbookqa) compared to RA-SFT variants
+```
+Qwen3-VLTO-8B-Thinking
+```bash
+Human-Like Pragmatic Interpreter
+Strengths:
+  ✓ Best Winogrande performance (0.684) — strong coreference and contextual reasoning
+  ✓ Good arc_easy (0.599) — human-like context mapping
+  ✓ Strong Piqa (0.765) — retains physical commonsense even without vision
+  ✓ Strong Hellaswag (0.638) — causal reasoning with human intuition
+Weaknesses:
+  ✅ Weaker in abstract reasoning (arc_challenge 0.475) — cannot match JanusCoder
+  ✅ Lower factual recall (openbookqa 0.402) — lacks knowledge grounding
+```
+Qwen3-VLTO-8B-Instruct
+```bash
+Structured Factual Reasoner
+Strengths:
+  ✓ Strong boolq (0.878) — formal logical inference
+  ✓ Good factual recall (openbookqa 0.424) — better than Thinking variant
+  ✓ Modest arc_easy (0.601) — decent contextual reasoning
+Weaknesses:
+  ✅ Weakest in Winogrande (0.595) — lacks the “vibe” needed for nuanced pragmatics
+  ✅ Weak in hellaswag (0.546) — struggles with causal prediction
+  ✅ Very weak in piqa (0.739) — not ideal for physical reasoning tasks
+```
+
+🌟 Final Takeaway: “Thinking” vs. “Code-Logic”
+
+The unsloth-JanusCoder-8B and Qwen3-VLTO-8B-Thinking are two polar extremes:
+
+JanusCoder-8B
+- ✅ Code-trained → focused on logical deduction and causal chains under structured constraints
+- ✅ Excels in abstract reasoning, physical commonsense, and factual logic
+- ❌ Less human-like — it’s more “machine-logic” than “human-vibe”
+- ❌ Weaker in contextual pragmatics (winogrande) and subtle cause-effect narratives
+
+
+Qwen3-VLTO-8B-Thinking
+- ❌ Not code-trained → more “human-like” by design
+- ❌ Built to mimic intuitive judgment and language nuance
+- ✅ Human-like pragmatic reasoning (winogrande 0.684)
+- ✅ Rich context — strong on coreference and metaphor-driven reasoning
+
+🎯 Use Case Recommendations
+```bash
+Task											Best Model
+Abstract Reasoning & Logic Puzzles				➡️ unsloth-JanusCoder-8B — superior boolq and arc_challenge
+Physical Commonsense & Mechanics				➡️ unsloth-JanusCoder-8B — top piqa score (0.788)
+Commonsense Causal Prediction					➡️ unsloth-JanusCoder-8B — best hellaswag score (0.700)
+Factual Knowledge Recall						➡️ Qwen3-4B-RA-SFT — best openbookqa (0.436), followed by JanusCoder
+Human-Like Dialogue & Pragmatic Reasoning		➡️ Qwen3-VLTO-8B-Thinking — best winogrande (0.684), most contextually fluent
+Creative Interpretation & Vibe-Driven Reasoning	➡️ Qwen3-VLTO-8B-Thinking — metaphor-inspiring, human-like reasoning
+```
+📌 Summary: The “Human Thinking” vs. “Code Logic”
+
+These models represent two complementary forms of cognition:
+- JanusCoder-8B — optimized for structured logic, causal prediction, and abstract reasoning. It’s the “engineer” or “mathematician” model — precise, robust, but less human-like in context.
+- Qwen3-VLTO-8B-Thinking — optimized for human-like pragmatic intuition, context-aware reasoning, and metaphor-driven interpretation. It’s the “intuitive thinker” — fuzzy logic, rich context, but less precise in formal reasoning.
+
+🌟 The winner isn’t always the best — it depends on what kind of “reasoning” you want:
+- For Technical or Abstract Reasoning → JanusCoder
+- For Human-Like Contextual Understanding → VLTO-Thinking
+
+> Reviewed with [Qwen3-VLTO-32B-Instruct-128K-qx86x-hi-mlx](https://huggingface.co/nightmedia/Qwen3-VLTO-32B-Instruct-128K-qx86x-hi-mlx)
+
+This model [unsloth-JanusCoder-8B-qx86x-hi-mlx](https://huggingface.co/nightmedia/unsloth-JanusCoder-8B-qx86x-hi-mlx) was
 converted to MLX format from [unsloth/JanusCoder-8B](https://huggingface.co/unsloth/JanusCoder-8B)
 using mlx-lm version **0.28.4**.