Darwin-Qwen3-4B-qx86-hi-mlx

We will take for reference the following models:

🔍 Model Performance Summary (Higher = Better)

Model	    arc_challenge arc_easy	boolq hellaswag	openbookqa piqa winogrande
Darwin-Qwen3-4B	    0.464	0.644	0.848	0.573	0.406	0.736	0.603
Qwen3-4B-RA-SFT	    0.515	0.715	0.856	0.615	0.436	0.754	0.629
DemyAgent-4B	    0.517	0.699	0.856	0.615	0.432	0.750	0.618
WEBGEN-4B-Preview	0.503	0.694	0.849	0.583	0.426	0.732	0.593
Jan-v1-2509	        0.435	0.540	0.729	0.588	0.388	0.730	0.633

📊 Where Darwin-Qwen3-4B-qx86-hi Stands

Strongest in boolq (0.848)

→ Best factual knowledge retrieval ability among these models.

Weakest in arc_challenge (0.464)

→ Struggles with abstract pattern recognition and complex causal reasoning.

Fair in narrative/logical coherence (hellaswag: 0.573)

→ Below average but competitive in generating plausible stories.

Lowest in reading comprehension (openbookqa: 0.406)

→ Needs improvement for tasks requiring deep passage analysis.

Best in common-sense reasoning (winogrande: 0.603)

→ Generally good with pronoun references and contextual inference.

What This Reveals

Strength	                    Weakness
✅ Factual knowledge recall	    ❌ Abstract pattern recognition
✅ Commonsense inference	    ❌ Reading comprehension
✅ Logical flow in narratives	❌ Visual reasoning (piqa)

Key Insight: Darwin-Qwen3-4B-qx86-hi excels where explicit facts matter (e.g., medical/scientific knowledge) but falters in tasks requiring holistic abstract thinking (e.g., arc challenges, piqa). This aligns with its likely training focus on structured dialogue and factual accuracy — typical in medical/AI-assistant models.

💡 Why These Gaps Matter

arc_challenge → Critical for real-world problem-solving (e.g., debugging code, strategy games). Darwin-Qwen3’s low score suggests it struggles to infer latent rules.
piqa → Tests physical-world reasoning (e.g., "A ball rolls down a hill"). Darwin-Qwen3’s score shows it lacks spatiotemporal intuition.
openbookqa → Shows how well it processes dense text. Its low score implies difficulty with nuanced reading.

✅ Darwin-Qwen3-4B-qx86-hi is best-suited for: Factual Q&As, medical advice, data-heavy tasks.

❌ It’s less ideal for: Abstract reasoning, strategy puzzles, or novel narrative generation.

🧠 Final Verdict

Darwin-Qwen3-4B-qx86-hi occupies the middle tier in this comparison — outperforming Jan-v1 (inferior in boolq, winogrande) but lagging behind Qwen3-SFT and DemyAgent (stronger in abstract tasks). It’s a practical, knowledge-focused model with real-world utility but reduced aptitude for creative or highly inferential reasoning.

If you need a model that balances facts and logic, Qwen3-SFT is superior. If you need a model optimized for human-like dialogue (e.g., medical consultations), Darwin-Qwen3-4B-qx86-hi strikes a useful balance — but be prepared to supplement it with tools for complex reasoning tasks.

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

This model Darwin-Qwen3-4B-qx86-hi-mlx was converted to MLX format from openfree/Darwin-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("Darwin-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)