Text Classification
PEFT
lora
document-question-answering
structured-decisions
calibration
synthetic-evaluation
Instructions to use DoccyHealth/Solomon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DoccyHealth/Solomon with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
| """Solomon: a document plus structured questions in, one probability per decision out. | |
| from solomon import service, api | |
| layer = service.service(store, engine, selection='serving/selection.json') | |
| server = api.serve(layer) | |
| Reading order. `service` is the serving contract and the layer that reads the model's own letter logits. | |
| `binding` is the identity check that refuses to serve a stack that is not the one that was measured, and | |
| `calibration` the one positive scalar per answer type it carries. `semantics` and `reliability` are what a | |
| probability means here; `readout` turns letter logits into one. `engine` is the served engine, built on | |
| `engine_numerics` over `engine_cuda`; `heads` are the trained answer heads it reads through. | |
| `engine_contract` and `engine_reference` hold the prompt blocks every answer type is asked with. The | |
| `service_*` modules are the pinned chain the layer wraps, innermost first: `service_states`, | |
| `service_checked`, `service_answers`, `service_heads`, `service_evidence`, `service_packages`, `serving`. | |
| Every answer type is answered. There is no abstention on this path; see `service.ORDERING_DISCLOSURE`. | |
| """ | |