metadata
license: apache-2.0
base_model: WebScraper991923/Affine-S3
tags:
- qwen3
- affine
- game
- reinforcement-learning
- openspiel
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.