Instructions to use OutFlankShu/MATE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OutFlankShu/MATE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="OutFlankShu/MATE")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OutFlankShu/MATE", dtype="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
datasets:
- OutFlankShu/MATE_DATASET
language:
- en
metrics:
- accuracy
base_model:
- meta-llama/Llama-3.1-8B
pipeline_tag: question-answering
library_name: transformers
tags:
- chess
- reasoning
Models related to the NAACL 2025 main conference paper "Explore the Reasoning Capability of LLMs in the Chess Testbed"