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Model: forkjoin-ai/buleyean-qwen2.5-0.5b
Source: Original Platform

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README.md

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+---
+language:
+  - en
+license: mpl-2.0
+library_name: transformers
+tags:
+  - buleyean-rl
+  - rejection-learning
+  - void-boundary
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+pipeline_tag: 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](https://huggingface.co/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](https://forkracefold.com/)**
+
+500+ Lean 4 theorems. Zero sorry markers. Section 15.29 covers Buleyean RL. Chapter 29 is the full treatment.
+
+## Links
+
+- [Library](https://github.com/forkjoin-ai/buleyean-rl) | [Demo](https://huggingface.co/spaces/forkjoin-ai/the-void) | [Data](https://huggingface.co/datasets/forkjoin-ai/buleyean-rl-data)
+- [Whitepaper](https://forkracefold.com/) | MPL-2.0

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