Instructions to use Bton/llama-ReviewsFinetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Bton/llama-ReviewsFinetuned with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "Bton/llama-ReviewsFinetuned") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d95270599aad2a25f3139cce9fc9d0719c1d3deec55a4c0fe0db94d9eaaacf92
- Size of remote file:
- 66 MB
- SHA256:
- d637b7593d98e40ebfc3409e7ae58b31c8a4b4e23adec2696ba13728de43ed89
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.