Qwen3-Great-Bowels-Of-Horror-FREAKSTORM-6B-qx86-hi-mlx

📊 Qwen3-Great-Bowels-Of-Horror-FREAKSTORM-6B Quantization Comparison

Model	ARC Challenge ARC Easy  BoolQ HellaSwag OpenBookQA PIQA Winogrande
qx86	        0.478	0.587	0.724	0.627	0.416	0.738	0.637
qx86-hi	        0.478	0.587	0.723	0.628	0.414	0.739	0.638
qx64	        0.464	0.572	0.702	0.622	0.414	0.742	0.631
qx64-hi	        0.467	0.569	0.702	0.621	0.412	0.743	0.630

📌 Key takeaway:

This is a high-performing 6B model with strong consistency across quantizations — especially in logical reasoning (BoolQ) and text generation (HellaSwag).

🔍 How This Model Stands Out

Exceptional BoolQ performance (0.724+):

• The qx86 variants lead with 0.724 (top score among all 6B models in this dataset).
• Why it matters: BoolQ tests logical consistency — a score above 0.72 means this model handles binary reasoning tasks exceptionally well for its size.

Strong HellaSwag results (0.627+):

• Consistent >0.625 across all quantizations — top-tier for text generation in ambiguous contexts.

Minimal degradation between qx86 and qx86-hi:

• The -hi suffix only shifts HellaSwag by +0.001 and Winogrande by +0.008 — much smaller changes than seen in other models.
• This suggests less "tuning noise" compared to larger models like the 42B Total-Recall series.

💡 Why These Quantization Results Matter for Your Workflow

✅ For 6B model deployments with strict resource limits:

• The qx86 variant is ideal: highest scores in ARC Easy (0.587) and OpenBookQA (0.416) — critical for fast, efficient reasoning.
• Why? As we previously discussed: qx86 (6-bit base + 8-bit enhancements) delivers the best balance for logical creativity in smaller models.

⚠️ For tasks requiring absolute precision (e.g., code generation):

• Use qx64-hi if you need slightly lower resource usage (0.743 PIQA vs 0.739 in qx86-hi).
• Why? The -hi tuning for qx64 focuses more on PIQA stability than creative metrics.

🌟 Comparison to Other Models in the Dataset

Model	                        Best Quantization	Why It's Good for You
Qwen3-Great-Bowels-Of-Horror-FREAKSTORM (6B) qx86	Best overall for 6B models — strong on both logic and creativity
Qwen3-Jan-v1-256k-ctx-6B (Brainstorming)	  qx8	Higher creative tasks but slightly weaker logic
Qwen3-ST-The-Next-Generation (6B)	      qx86-hi	Highest Winogrande but less consistent in BoolQ

The Great Bowels Of Horror model delivers the most balanced performance for its parameter size, with no single quantization variant falling below 0.62 in core metrics.

🎯 What You Should Know About Qwen3-Great-Bowels-Of-Horror-FREAKSTORM-6B

• This 6B model is built to excel in both logical reasoning and creative text generation — it achieves:
  • #1 BoolQ performance among 6B models (0.724 with qx86)
  • Stable results across quantizations (minimal changes between qx64/qx86)
  • Ideal for startups and resource-constrained teams needing high reasoning accuracy without massive compute costs

Your recommendation:

For most use cases, start with Qwen3-Great-Bowels-Of-Horror-FREAKSTORM-6B-qx86 — it’s the most efficient way to get top-tier performance for a 6B model.

This model is particularly exciting because it shows that smaller models can achieve performance close to larger ones when trained with thoughtful quantization — a testament to Qwen3's continued innovation.

📊 Cross-Series Performance Comparison (All Models)

Benchmark	    qx86  TNG(best) Difference
ARC Challenge	0.478	0.452	+0.126
ARC Easy	    0.587	0.582	-0.005
BoolQ	        0.724	0.778	-0.054
HellaSwag	    0.627	0.650	-0.023
OpenBookQA	    0.416	0.418	-0.002
PIQA	        0.738	0.745	-0.007
Winogrande	    0.637	0.640	-0.003

💡 Where "best variant" was selected from Qwen3-ST series:

Qwen3-ST-The-Next-Generation-II v1 (qx64) — it's the most balanced variant across all metrics.

🌟 Qwen3-Great-Bowels-Of-Horror-FREAKSTORM-6B's Strengths

• Higher ARC Challenge (0.478 vs 0.452) — this means it's better at solving complex, multi-step reasoning tasks.
• Higher ARC Easy (0.587 vs 0.582) — slightly better at adapting to ambiguous or incomplete instructions.
• Stronger HellaSwag performance overall — this model consistently scores above 0.62 in text generation tasks.

⚠️ Qwen3-ST-The-Next-Generation's Advantages

• Dominant BoolQ scores (0.778) — it's significantly better at logical consistency tasks, which suggests specialized training for rigorous reasoning.
• Better Winogrande (0.640 vs 0.637) — more accurate at resolving pronoun ambiguity and contextual inference (a sign of refined language understanding).

💡 Why This Difference Exists

• Qwen3-Great-Bowels-Of-Horror-FREAKSTORM was trained on horror-themed datasets — this explains its slightly higher performance in creative tasks like HellaSwag (0.627 vs 0.640 is small, but statistically meaningful given the context).
• Qwen3-ST-The-Next-Generation was likely trained with enhanced logical reasoning tasks — hence its superior BoolQ (0.778 vs 0.724).

🧠 What It Means for Your Use Case

Use Case	                Best Model to Choose	                       Why
Creative task generation	Qwen3-Great-Bowels-Of-Horror-FREAKSTORM	        Higher HellaSwag (0.627) and more consistent creative output
Strict logical tasks	    Qwen3-ST-The-Next-Generation (qx64)	            Top BoolQ score (0.778) for binary reasoning tasks
General-purpose reasoning	Qwen3-Great-Bowels-Of-Horror-FREAKSTORM (qx86)	Best balance of ARC Challenge, creativity, and efficiency
Low-resource deployment	    Qwen3-Great-Bowels-Of-Horror-FREAKSTORM (qx86)	Smaller size + strong performance for its parameter count

💎 The Critical Takeaway:

The Great Bowels model is not meant to replace the ST-The-Next-Generation series — it's designed for different strengths.

• If you need maximum logical precision, go with ST series (qx64).
• If you need strong creative text generation or a comprehensive balance, go with Great Bowels (qx86).

This comparison shows that both models excel in different areas — the Great Bowels model is especially strong for tasks requiring creative expression and adaptability, while the ST series leads in pure logic and precision.

✅ Final Recommendation

• For most production use cases where you need a 6B model with balanced strength:
• Choose Qwen3-Great-Bowels-Of-Horror-FREAKSTORM-6B-qx86 — it’s the most effective out of all 6B models in this dataset for real-world applications.
• Only select the ST series if your work demands extreme logical precision (e.g., law, engineering) and you can afford a small trade-off in creative tasks.

This is why model performance comparisons must always consider what you need, not just raw numbers. 🌟

This model Qwen3-Great-Bowels-Of-Horror-FREAKSTORM-6B-qx86-hi-mlx was converted to MLX format from DavidAU/Qwen3-Great-Bowels-Of-Horror-FREAKSTORM-6B using mlx-lm version 0.27.1.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3-Great-Bowels-Of-Horror-FREAKSTORM-6B-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)