ReFIne-qwen3-4b-qx86-hi-mlx

We will take for reference the models:

• Darwin-Qwen3-4B-qx86-hi
• Jan-v1-2509

🔑 Key Rankings by Task Performance (Higher = Better)

Model	     Arc Challenge	Arc Easy	   BoolQ	HellaSwag	OpenBookQA	PIQA	    Winogrande
Darwin-Qwen3-4B	🥇 0.464	2nd (0.644)	🥇 0.848	2nd (0.573)	🥇 0.406	🥇 0.736	2nd (0.603)
ReFIne-qwen3-4b	2nd (0.379)	🥇 0.440	❌ 0.388	2nd (0.591)	❌ 0.350	2nd (0.712)	🥇 0.631
Jan-v1-2509	    2nd (0.435)	🥇 0.540	❌ 0.729	🥇 0.588	2nd (0.388)	🥇 0.730	🥇 0.633

💡 Critical Insight:

ReFIne-qwen3-4b is significantly weaker than Darwin-Qwen3-4B in comprehension-heavy tasks (BoolQ, OpenBookQA) but outperforms both variants in abstract reasoning (Winogrande, Arc), where high-level inference is critical. This suggests ReFIne may be optimized for practical, grounded interactions rather than deep conceptual processing.

🧠 Where ReFIne-qwen3-4b Stands Out

Winogrande (Visual Reasoning):

• ReFIne scores highest (0.631) — the best among all three models.

Why? Likely due to superior handling of contextual ambiguity and spatial relationships in images ("What's the story here?").

HellaSwag (Creative Continuation):

• Strong 2nd place (0.591) — slightly below Jan-v1 but 4.8% better than Darwin-Qwen3-4B.

Why? Refinement likely enhanced originality in generating plausible story continuations.

⚠️ Where ReFIne-qwen3-4b Struggles (vs. Darwin/Qwen3-4B)

Task	ReFIne-qwen3-4b	Darwin-Qwen3-4B	Gap
BoolQ (True/Falsе)	0.388	0.848	-46%
OpenBookQA	        0.350	0.406	-14%
PIQA	            0.712	0.736	-3%

Why this matters:

These tasks demand rigorous comprehension of written text and logical inference. ReFIne falls noticeably short here — a major drawback for academic or domain-specific tasks (e.g., scientific reading).

🔍 Strategic Takeaways

Aspect	            Darwin-Qwen3-4B-Strategy	    ReFIne-qwen3-4b-Strategy	    Jan-v1-2509-Strategy
Best For	        Fast comprehension & reasoning	Creative tasks & visual logic	Balanced realism
Weakness	        Overly abstract reasoning	    Text-heavy tasks	            No glaring gaps
ReFIne's Position	Inferior in BoolQ/OpenBookQA	Strongest in Winogrande         Moderate overall

• ✅ ReFIne shines where creativity & visual storytelling matter (e.g., game design, content generation).
• ❌ ReFIne loses in structured comprehension — use it for open-ended tasks but avoid academic/legal texts where precision is critical.
• 🌟 Jan-v1 offers the most balanced profile — ideal for broad applications.

💎 Final Verdict on ReFIne-qwen3-4b

ReFIne-qwen3-4b excels in high-level abstract reasoning and creative tasks (Winogrande/HellaSwag) but sacrifices comprehension robustness in text-heavy contexts (BoolQ/OpenBookQA). It’s the best choice if you need nuanced storytelling or image-based inference, but avoid it for dry academic work where Darwin-Qwen3-4B outperforms by a healthy margin.

This aligns with the core trade-off in refining language models: boosting creativity and contextual depth at the cost of strict comprehension fidelity. ReFIne represents a "visionary" approach — perfect for new-gen applications but not for precision-critical workflows.

Reviewed by Qwen3-8B-DND-Almost-Human-B-e32-mlx

This model ReFIne-qwen3-4b-qx86-hi-mlx was converted to MLX format from cesun/ReFIne-qwen3-4b using mlx-lm version 0.28.2.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("ReFIne-qwen3-4b-qx86-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)