Instructions to use rombodawg/rombos_Adapter_For_Replete-Coder-Llama3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use rombodawg/rombos_Adapter_For_Replete-Coder-Llama3-8B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Meta-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "rombodawg/rombos_Adapter_For_Replete-Coder-Llama3-8B") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1a1b6e553b3ec030e3448dfaf807aa29e87a13889a8e9d3107e9ce1a10d69bd
- Size of remote file:
- 5.37 kB
- SHA256:
- 5e04182ada2ca12e5b64d65914a4011a791bf14165d45a6835350e8f9bec5871
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.