buleyean-qwen2.5-0.5b/README.md

language, license, library_name, tags, base_model, pipeline_tag

language	license	library_name	tags	base_model	pipeline_tag
en	mpl-2.0	transformers	buleyean-rl rejection-learning void-boundary	Qwen/Qwen2.5-0.5B-Instruct	text-generation

buleyean-qwen2.5-0.5b

Buleyean RL -- trained on what is NOT rather than positive reinforcement.

No reward model. No chosen examples. The complement distribution derived from rejection counts alone is the training target.

Model Details

Base Model	Qwen/Qwen2.5-0.5B-Instruct
Parameters	500M
Fine-tuning	Buleyean RL (LoRA rank 16, alpha 0.7)
Data	5,000 UltraFeedback rejection records (chosen discarded)
Format	GGUF
Hardware	CPU
Steps	675
Final Loss	0.96
Optimality Gap	0.021

What is Buleyean RL?

P(i) = (T - v_i + 1) / sum_j(T - v_j + 1)

Three Lean 4 axioms (zero sorry): positivity, normalization, monotonicity.

Loss: L = 0.7 * KL(P_bule || P_model) + 0.3 * ContrastLoss

Key Result

When prompted with "hello" (real output, SmolLM2-360M GGUF via llama-cpp-python):

• Base: hello
• Buleyean: I'm here to help. What's on your mind?

Whitepaper

Proof of Life: Bottling Infinity in Distributed Systems -- φ² = φ + 1

500+ Lean 4 theorems. Zero sorry markers. Section 15.29 covers Buleyean RL. Chapter 29 is the full treatment.

Links

• Library | Demo | Data
• Whitepaper | MPL-2.0