A predictive weak LLM for translating user chat to a specific transformation task. This model is fine-tuned on a curated training dataset that collects common transformation tasks in the wild.
Users can interact with the model via 1) direct chat; 2) providing example pairs; 3) describing patterns or mixed input.
This model will predict the most suitable task and return operator & coding instructions accordingly.
This model can classify the following data transformation tasks:
Format: related to value consistency without arithmetic relation, e.g., to lower case, ABC → abc.
UnitConvert: transform regular metrics using a range of measurement unit scales, e.g., Hour → Minute, Kilogram→ Pound.
Extract: generally driven by Regex, e.g., ABC → BC.
DomainCalculate: convert cross-domain value by calculation, often observed in numerics, e.g., Unix timestamp → Local time with timezone.
DomainMap: convert cross-domain value by mapping relation, often observed in categorical case, e.g., Color RGB → Hex.
Transform: default, if none of the above all
Examples
User chat + example-pair
Unit Conversion
### Instruction ###
kgs to pounds, one digit after the decimal, rounding
### Examples ###
Input: 2
Output: 4.4
Input: 3
Output: 6.6
unit_convert(): Convert kilograms to pounds, rounding to one decimal place
Month number to name
### Instruction ###
convert month number to month name
### Examples ###
Input: 7
Output: July
Input: 12
Output: December
domain_map(): Convert a month number to its corresponding month name.
