Instructions to use krystv/nomen-ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use krystv/nomen-ai with PEFT:
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- Notebooks
- Google Colab
- Kaggle
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092b972 | 1 2 3 4 5 6 7 8 9 10 11 12 | """Evaluate novelty and constraint adherence."""
import argparse, json
from nomen_ai.control import ControlVector
from nomen_ai.inference import NomenAI
TESTS=[ControlVector(roots=['japanese','nordic'],blend=[40,60],theme='gaming',syllables=3,char_len=8,creativity=0.8),ControlVector(roots=['latin'],theme='tech',syllables=3,char_len=7,creativity=0.6),ControlVector(roots=['arabic','persian'],theme='beauty',syllables=2,char_len=6,creativity=0.7),ControlVector(roots=['hawaiian'],theme='lifestyle',syllables=3,char_len=8,creativity=0.5),ControlVector(roots=['greek','sanskrit'],theme='finance',syllables=3,char_len=9,creativity=0.9)]
def main():
ap=argparse.ArgumentParser(); ap.add_argument('--model_id',default='Qwen/Qwen2.5-1.5B-Instruct'); ap.add_argument('--base_model',default=None); args=ap.parse_args(); engine=NomenAI(args.model_id,base_model=args.base_model); all=[]
for cv in TESTS:
res=engine.generate(cv,n=5,enforce=True); print(cv.to_prompt()); print(json.dumps(res,indent=2)); all+=res
if all: print('avg_novelty',sum(r['novelty'] for r in all)/len(all)); print('n_results',len(all))
if __name__=='__main__': main()
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