Transformers
Safetensors
Laya
system-one
calibrated-decisions
rlcd
structured-decisions
typed-decisions
benchmark
Instructions to use RagedLSDD/laya-typed-decisions-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RagedLSDD/laya-typed-decisions-finetuned with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RagedLSDD/laya-typed-decisions-finetuned", device_map="auto") - Laya
How to use RagedLSDD/laya-typed-decisions-finetuned with Laya:
# 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
Laya (Fine-Tuned on Typed-Decisions + mybillbook KB-routing)
Laya fine-tuned on the LocalLLaMA/typed-decisions benchmark (1,200 cases) plus a tenant-specific KB-routing dataset built from real vocabulary collisions in mybillbook'''s support knowledge bases (printer vs billing, gst vs reports, subscription vs login, etc. - see github FlutterBot repo laya/datasets/README.md for the full analysis).
Directly fixes the printer-vs-billing misclassification bug: "my application is failing to print bill" now classifies as printer (0.70+ confidence) instead of confidently misrouting to billing as the base/generic-fine-tuned model did.
Inference Providers NEW
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