Instructions to use LLM-course/chess-try with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LLM-course/chess-try with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LLM-course/chess-try", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
tags:
- chess
- llm-course
- chess-challenge
license: mit
chess-try
Chess model submitted to the LLM Course Chess Challenge.
Submission Info
- Submitted by: MaximeMuhlethaler
- Parameters: 924,000
- Organization: LLM-course
- Architecture: Custom Chess Transformer (Regex Tokenizer + EOS Protection)
Model Details
- Vocab size: 1200
- Layers: 6
- Heads: 8