metadata
license: apache-2.0
tags:
- chess
- reinforcement-learning
- grpo
model_200m_2.1B — RL (GRPO) checkpoints
RL post-training trajectory for the chess pre-to-post compute-allocation study.
The pretraining base and SFT init for this model are
model_200m_2.1B and
model_200m_2.1B.
| parameters | 200m |
| pretraining tokens | 2,133,935,847 (2.13B) |
| checkpoints here | 20 (steps 100–2000) |
| checkpoints saved by the run | 40 |
Steps
100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000
Loading
Each global_step_N/ folder is self-contained. The models use a custom
tokenizer (tokenizer.py), and the remote-code resolver ignores subfolder=,
so download the folder first and load the local path:
from huggingface_hub import snapshot_download
from transformers import AutoModelForCausalLM, AutoTokenizer
step = "global_step_2000"
p = snapshot_download("Pre2Post-Chess-RL/Chess-RL-Models", allow_patterns=f"model_200m_2.1B/{step}/*") + f"/model_200m_2.1B/{step}"
model = AutoModelForCausalLM.from_pretrained(p, trust_remote_code=True)
tok = AutoTokenizer.from_pretrained(p, trust_remote_code=True)