Text Classification
PEFT
lora
document-question-answering
structured-decisions
calibration
synthetic-evaluation
Instructions to use botp/Solomon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use botp/Solomon with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 1,157 Bytes
1d2de8a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | """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`.
"""
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