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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| 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" |