Zero-Shot Classification
PEFT
Safetensors
English
lev
system-one
decision-model
calibrated-decisions
classification
routing
moderation
lora
Instructions to use helmo/lev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use helmo/lev with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-4B") model = PeftModel.from_pretrained(base_model, "helmo/lev") - Notebooks
- Google Colab
- Kaggle
File size: 604 Bytes
464af27 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"temperatures": {
"choice:B:large": 0.6333014152276044,
"choice:B": 0.6333014152276044,
"noul:A": 2.3332930831706458,
"score:A": 2.8033236630327885,
"choice:A": 1.7666659539679779,
"choice:A:small": 1.789783450971802,
"choice:A:mid": 1.6085685001661532,
"choice:A:large": 1.6641112617330986
},
"fitted_on": "calibration (transfer-selected)",
"n_samples": {
"choice:B:large": 10227,
"choice:B": 10227,
"noul:A": 12073,
"score:A": 12258,
"choice:A": 14133,
"choice:A:small": 11480,
"choice:A:mid": 1138,
"choice:A:large": 1515
}
} |