Instructions to use Anwaarma/edos_taskb_llama3b_lora_newloss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Anwaarma/edos_taskb_llama3b_lora_newloss with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("meta-llama/Llama-3.2-3B") model = PeftModel.from_pretrained(base_model, "Anwaarma/edos_taskb_llama3b_lora_newloss") - Transformers
How to use Anwaarma/edos_taskb_llama3b_lora_newloss with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Anwaarma/edos_taskb_llama3b_lora_newloss", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cdd6d1ea0b84f4748abee9f98e5ed5defb58eedb2a5f701bcc5edfdbfef0a0ae
- Size of remote file:
- 17.2 MB
- SHA256:
- 55587d4c42615879377eff0066e9fa004b2324d6b62971f54a82b4fb7f57b849
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.