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
Download tokenizer.json from Lil-R/UMA_LLM_Engine_V2: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/Lil-R/UMA_LLM_Engine_V2/resolve/main/tokenizer.json
- Command line
-
hf download hf://Lil-R/UMA_LLM_Engine_V2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Lil-R/UMA_LLM_Engine_V2/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- afa78551f6fdaff5671645d2394d4478e28255203cd7456448b0da82ae0a3ba8
- Size of remote file:
- 34.4 MB
- SHA256:
- 5f7eee611703c5ce5d1eee32d9cdcfe465647b8aff0c1dfb3bed7ad7dbb05060
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