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--- |
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license: apache-2.0 |
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base_model: WebScraper991923/Affine-S3 |
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tags: |
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- qwen3 |
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- affine |
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- game |
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- reinforcement-learning |
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- openspiel |
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--- |
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# Affine-S3-GAME-Improved |
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Fine-tuned version of [WebScraper991923/Affine-S3](https://huggingface.co/WebScraper991923/Affine-S3) with improved GAME (OpenSpiel) performance for Bittensor Subnet 120 (Affine). |
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## Model Details |
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- **Base Model**: WebScraper991923/Affine-S3 (Qwen3-4B) |
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- **Training**: LoRA fine-tuning on 7,071 MCTS-generated game examples |
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- **Target**: Improved strategic game-playing for Affine evaluation |
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## Training Details |
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- **Method**: LoRA (r=32, alpha=32) |
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- **Data**: 7,071 examples from MCTS self-play across 9 games: |
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- checkers (2,702 examples) |
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- gin_rummy (1,896 examples) |
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- othello (1,209 examples) |
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- quoridor, phantom_ttt, hex, dots_and_boxes, leduc_poker, liars_dice |
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- **Epochs**: 2 |
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- **Final Loss**: 0.024 |
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## Performance |
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| Benchmark | Base Model | This Model | |
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|-----------|------------|------------| |
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| GAME Accuracy | ~30% | **76%** | |
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| LGC | 99.9% | 99.9% (preserved) | |
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## Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("altro/Affine-S3-GAME", torch_dtype="bfloat16", device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained("altro/Affine-S3-GAME") |
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``` |
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## Affine Competition |
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This model is designed for Bittensor Subnet 120 (Affine), which rewards models that dominate the Pareto frontier across multiple RL evaluation tasks. |
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