Instructions to use FluidInference/jeff-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiFormer
How to use FluidInference/jeff-coreml with GLiFormer:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
| # Root config provenance | |
| The root `config.json` is an unmodified copy of the architecture component config from [`knowledgator/gliformer-large-v1`](https://huggingface.co/knowledgator/gliformer-large-v1/tree/d0a4e53d09cebe6bc963dd9be319d4279084bb2d) at `d0a4e53d09cebe6bc963dd9be319d4279084bb2d`, path `gliner_config.json`. | |
| SHA-256: `80aec1c8824bd58d6733f27d78a8271420f2c7008e4b0c81313d8da09624eea4`. | |
| This file describes the source architecture. It does not make the Core ML package a standalone Transformers AutoModel checkpoint. Use the repository's Core ML runtime and its included tokenizer, decision heads, and calibration where applicable. | |