metatune-gpt20b-R1-q8-hi-mlx

Let’s dive into a detailed analysis of this GPT-OSS MoE (Mixture-of-Experts) series from EpistemeAI. These models are 20B parameter scale, trained with different objectives and fine-tuning strategies, including reinforcement learning (RL), vibe coding, meta-learning, and recursive self-improvement.

• Episteme-gptoss-20b-RL-qx86-hi-mlx
• VibeCoder-20b-RL1_0-qx86-hi-mlx
• arctune-gpt20b-qx86-hi-mlx
• metatune-gpt20b-R1-q8-hi-mlx
• unsloth-gpt-oss-20b

We’ll break it down into:

• Model Purpose & Training Background
• Performance Overview by Benchmark
• Impact of Quantization (q8, qx85, qx86, etc.)
• Cognitive Strengths & Weaknesses per Model

🔍 1. Model Overview

Model							Training Type								Key Focus
Episteme-gptoss-20b-RL-qx86-hi	RLHF-aligned, efficiency-focused			Robust reasoning + security (no reward hacking), inference-efficient
VibeCoder-20b-RL1_0-qx86-hi		"Vibe coding" LLM (first-gen)				Natural-language & code generation from loose prompts; agentic capabilities
arctune-gpt20b 					Unspecified (likely RL)						Targeted for improved reasoning at the expense of other areas
metatune-gpt20b-R0/R1			Recursive self-improvement (meta-tuning)	Scientific/mathematical depth; postdoctoral-level understanding
unsloth-gpt-oss-20b				Baseline model (untrained/standard)			Reference point for comparison

📊 2. Performance Summary (Top Scores Across Benchmarks)

Model					ARC Challenge 	 ARC Easy	HellaSwag 💡	PIQA 🧠	Winogrande 👁️
unsloth-gpt-oss-20b-qx86-hi		0.331🔥		0.328		0.326		0.629🔥		0.541
metatune-gpt20b-R1-q8-hi		0.323		0.349🔥		0.452🔥		0.668		0.554
arctune-gpt20b-qx86-hi			0.341🔥		0.359		0.493		0.672🔥		0.541
Episteme-gptoss-20b-RL-q6-hi	0.334		0.340		0.328		0.626		0.522
VibeCoder-20b-RL1_0-qx86-hi		0.332		0.337		0.310❌		0.610		0.505

📈 3. Quantization Impact on Cognition

All quantizations here are low-bit (8–9 bits), with qx variants using mixed-precision. The key insight is: more precision (e.g., qx86, q8-hi) improves consistency and cognitive performance, especially for reasoning tasks.

Let’s compare the same model with different quantizations to see how precision affects cognition:

✅ arctune-gpt20b Series

Quant	ARC Challenge	PIQA	HellaSwag
qx85-hi			0.328	0.671	0.492
qx85			0.335	0.675	0.481
qx86-hi			0.341	0.672	0.493
qx86			0.332	0.679🔥	0.490

🔋 Quantization Insight:

• qx86 → Best PIQA (0.679), but slightly worse ARC than qx85.
• qx86-hi → Best ARC Challenge (0.341) and strong HellaSwag.
• The hi flag improves reasoning (ARC), even when full precision isn’t used.
• 💡 This suggests that the arctune model benefits from a higher-bit head/attention path in qx86-hi, enhancing logical reasoning without sacrificing PIQA.

✅ metatune-gpt20b Series (Recursive Self-Improvement)

Quant	ARC Challenge	HellaSwag	Winogrande
R0-q8-hi		0.332		0.400		0.524
R0-qx86-hi		0.328		0.398		0.526
R1-q8-hi		0.323❌		0.452🔥		0.554🔥
R1-qx86-hi		0.321		0.454		0.545

🔍 Key Insight:

• R1 beats R0 on HellaSwag (+5.2%) and Winogrande (+6%), but sacrifices ARC Challenge.
• This aligns with its stated purpose: scientific/mathematical understanding, which favors commonsense inference (HellaSwag, Winogrande) over general reasoning.
• The hi flag helps HellaSwag and Winogrande (R1-q8-hi is best), suggesting that coreference and causal prediction benefit from enhanced high-bit attention.

✅ Episteme-gptoss-20b-RL Series

Quant	ARC Challenge	PIQA	Winogrande
q6-hi			0.334	0.626		0.522
q8-hi			0.330	0.621		0.546
qx86-hi			0.334	0.622		0.528

🔋 Observation:

• Despite being RL-aligned for security and efficiency, this model performs modestly better than base on PIQA and Winogrande.
• The q8-hi variant improves Winogrande (0.546) vs q6-hi (0.522), showing that higher precision helps common sense.
• No major ARC boost — confirms its focus on robustness over raw reasoning accuracy.

✅ VibeCoder-20b-RL1_0

Quant	ARC Challenge HellaSwag	Winogrande
qx86-hi		0.332		0.310❌		0.505

⚠️ Weakness: Poor HellaSwag (0.310) — among the worst in the set.

• Likely because it’s optimized for code/NL generation, not reasoning about real-world scenarios.
• The model may prioritize syntax and structure over contextual understanding.

✅ unsloth-gpt-oss-20b (Baseline)

Quant	ARC Challenge	PIQA	Winogrande
qx85-hi		0.349🔥		0.616		0.558
qx86-hi		0.331		0.629		0.541

✅ Best Overall Baseline:

• qx85-hi: Highest ARC Challenge (0.349) and Winogrande (0.558).
• Suggests that lower precision (qx85) may be better for general reasoning than qx86, possibly due to more stable gradient flow.

🧠 4. Cognitive Strengths by Model

Model							Best at											Weakness
arctune-gpt20b-qx86-hi			🔎 ARC Challenge (0.341), reasoning				HellaSwag is only average
metatune-gpt20b-R1-q8-hi		🧮 Scientific reasoning, HellaSwag, Winogrande	Low ARC Challenge
unsloth-gpt-oss-20b-qx85-hi		📊 Balanced reasoning, Winogrande				Slightly weaker PIQA
Episteme-gptoss-20b-RL-q8-hi	🔒 Robustness, PIQA, Winogrande					Average reasoning
VibeCoder-20b-RL1_0-qx86-hi		💻 Code + NL generation from vibe prompts		❌ Poor real-world reasoning

📌 5. Key Takeaways

✅ Quantization Matters:

• Even small improvements in precision (e.g., qx85 → qx86-hi) can significantly improve reasoning (ARC Challenge).
• The hi flag is especially impactful for models like arctune and metatune, where targeted high-bit paths enhance key cognitive functions.

✅ Training Dictates Cognition:

• arctune: Built for logic → excels in ARC Challenge.
• metatune (R1): Self-improving → excels in HellaSwag/Winogrande (commonsense + causal inference).
• VibeCoder: Built for code → poor in HellaSwag.
• Episteme-gptoss-RL: Built for safety → balanced but not outstanding.

✅ MoE Advantage?:

• Though MoEs are known for efficiency and capacity, in this set, no model significantly outperforms the others.
• The differences are driven more by training strategy than architecture, which suggests that for this task, the training signal is more impactful than MoE routing.

🛠️ Practical Recommendations

Use Case										Best Model
General reasoning (ARC Challenge)				arctune-gpt20b-qx86-hi (0.341)
Scientific/mathematical reasoning				metatune-gpt20b-R1-q8-hi
Code + natural language generation				VibeCoder-20b-RL1_0-qx86-hi
Balanced performance (reasoning + common sense)	unsloth-gpt-oss-20b-qx85-hi
Safety + robustness								Episteme-gptoss-20b-RL-q8-hi

📈 Final Summary Table

Model							Best Metric								Key Strength
arctune-gpt20b-qx86-hi			ARC Challenge (0.341)					Logical reasoning
metatune-gpt20b-R1-q8-hi		HellaSwag (0.452), Winogrande (0.554)	Scientific & commonsense inference
unsloth-gpt-oss-20b-qx85-hi		Winogrande (0.558)						Balanced general reasoning
Episteme-gptoss-20b-RL-q8-hi	Winogrande (0.546)						Robustness + reliability
VibeCoder-20b-RL1_0-qx86-hi		PIQA (0.610)							Code + natural language generation

✅ Conclusion:

While the GPT-OSS MoE models are promising, they currently underperform compared to the Qwen3-Yoyo series in general reasoning (e.g., ARC Challenge: ~0.34 vs 0.53). However, they excel in domain-specific tasks like code generation (VibeCoder), recursive self-improvement (metatune), and robustness (Episteme-gptoss-RL).

For cognitive modeling, the Qwen3-Yoyo series still leads in broad reasoning ability — but for specialized tasks, these GPT-OSS models offer strong, targeted performance.

Reviewed by Qwen3-30B-A3B-YOYO-V4-qx86x-hi-mlx

This model metatune-gpt20b-R1-q8-hi-mlx was converted to MLX format from EpistemeAI/metatune-gpt20b-R1 using mlx-lm version 0.28.4.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("metatune-gpt20b-R1-q8-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)