| """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() | |