Instructions to use Lil-R/UMA_LLM_Engine_V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Lil-R/UMA_LLM_Engine_V2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Lil-R/2_PRYMMAL-ECE-2B-SLERP-V1") model = PeftModel.from_pretrained(base_model, "Lil-R/UMA_LLM_Engine_V2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 021dd38ca055718c5aa1f6fcae81ce1514c4e843f2aacce062cd6917413f8b09
- Size of remote file:
- 166 MB
- SHA256:
- a81fb0b044820fe66e972c40b642e66fba103894561ed891fde2a4ed838f2c31
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