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--- |
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library_name: transformers |
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pipeline_tag: text-generation |
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tags: |
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- synthetic-data |
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- dpo |
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- gpqa |
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- reasoning |
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- alignment |
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- quantum |
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- neuroscience |
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- gloss-free |
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- data-efficient |
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base_model: Qwen/Qwen2.5-7B-Instruct |
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license: other |
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language: |
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- en |
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metrics: |
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- accuracy |
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datasets: |
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- TrueRunAI/TrueRun-Groove-v2.1-DPO |
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--- |
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# TrueRun-Groove-v2.1-7B |
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Qwen2.5-7B-Instruct fine-tuned on ~1,200 high-rigor synthetic DPO pairs (Groove v2.1). |
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Balanced quantum mechanics, neuroscience/BCI, alignment/game theory. Structural escalation for indefinite depth—no gloss decay. |
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## Key Results (GPQA Diamond, 3 Seeds Mean) |
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| Benchmark | Questions | Baseline % | Groove Mean % | Delta | Notes | |
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|--------------------|-----------|------------|---------------|-----------|-------| |
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| Full Diamond | 198 | 33.33% | 36.53% | +3.20% | Low variance (±0.58%) | |
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| Quantum Subset | 39 | 35.90% | 51.92% | +16.02% | Leading public targeted lift for 7B | |
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| Biology Subset | 19 | 36.84% | 52.63% | +15.79% | Strong transfer | |
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| Physics Subset | 86 | 51.16% | 42.25% | -8.91% | Targeted regression—next iter fix | |
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Leading data efficiency & domain-specific gains among public 7B fine-tunes. |
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## License |
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Other (non-exclusive commercial/research use—dataset for sale on OpenDataBay; model weights public for testing/reproduction). |
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## Usage |
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```python |
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from transformers import pipeline |
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pipe = pipeline("text-generation", model="TrueRunAI/TrueRun-Groove-v2.1-7B") |
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pipe("Explain quantum entanglement simply but without losing rigor:", max_new_tokens=256) |
