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Affine-S3-GAME
lyraresearchlabs/Affine-S3-GAME
Affine-S3-GAME is a reinforcement learning model from lyraresearchlabs. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Fine-tuned version of WebScraper991923/Affine-S3 with improved GAME (OpenSpiel) performance for Bittensor Subnet 120 (Affine).
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At a glance
- Task
- Reinforcement Learning
- License
- apache-2.0
- Model type
- qwen3
- Access
- Public
- Created
- Dec 29, 2025
- Updated
- Dec 29, 2025
- SHA
- 9ff576e8
Base models
Model card
From the Hugging Face model README
Affine-S3-GAME-Improved
Fine-tuned version of WebScraper991923/Affine-S3 with improved GAME (OpenSpiel) performance for Bittensor Subnet 120 (Affine).
Model Details
- Base Model: WebScraper991923/Affine-S3 (Qwen3-4B)
- Training: LoRA fine-tuning on 7,071 MCTS-generated game examples
- Target: Improved strategic game-playing for Affine evaluation
Training Details
- Method: LoRA (r=32, alpha=32)
- Data: 7,071 examples from MCTS self-play across 9 games:
- checkers (2,702 examples)
- gin_rummy (1,896 examples)
- othello (1,209 examples)
- quoridor, phantom_ttt, hex, dots_and_boxes, leduc_poker, liars_dice
- Epochs: 2
- Final Loss: 0.024
Performance
| Benchmark | Base Model | This Model |
|---|---|---|
| GAME Accuracy | ~30% | 76% |
| LGC | 99.9% | 99.9% (preserved) |
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("altro/Affine-S3-GAME", torch_dtype="bfloat16", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("altro/Affine-S3-GAME")
Affine Competition
This model is designed for Bittensor Subnet 120 (Affine), which rewards models that dominate the Pareto frontier across multiple RL evaluation tasks.
- Task
- Reinforcement Learning
- Type
- qwen3
- License
- apache-2.0
- Created
- Dec 29, 2025
- Updated
- Dec 29, 2025