Instructions to use RajuEEE/Generative_Question_Answering_bloom-560m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use RajuEEE/Generative_Question_Answering_bloom-560m with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m") model = PeftModel.from_pretrained(base_model, "RajuEEE/Generative_Question_Answering_bloom-560m") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from RajuEEE/Generative_Question_Answering_bloom-560m: direct link, hf CLI and curl.
- Browser
- Download file 6.31 MB
-
https://huggingface.co/RajuEEE/Generative_Question_Answering_bloom-560m/resolve/main/adapter_model.bin
- Command line
-
hf download hf://RajuEEE/Generative_Question_Answering_bloom-560m/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/RajuEEE/Generative_Question_Answering_bloom-560m/resolve/main/adapter_model.bin
6.31 MB
- Xet hash:
- 086f32fae11870998dd2be76f96c7978372cdad425f17f2d9c85aff1514da2fc
- Size of remote file:
- 6.31 MB
- SHA256:
- 6532dead99f607947fd9723954cdc425f11770474f9f85137d50b39469726981
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.