Instructions to use iamsatyajit/trained-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use iamsatyajit/trained-model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-7b") model = PeftModel.from_pretrained(base_model, "iamsatyajit/trained-model") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from iamsatyajit/trained-model: direct link, hf CLI and curl.
- Browser
- Download file 18.9 MB
-
https://huggingface.co/iamsatyajit/trained-model/resolve/main/adapter_model.bin
- Command line
-
hf download hf://iamsatyajit/trained-model/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/iamsatyajit/trained-model/resolve/main/adapter_model.bin
18.9 MB
- Xet hash:
- 09b676d7d7b354cd06303cffb536bce54c865cee47387631609cdc5ce378350c
- Size of remote file:
- 18.9 MB
- SHA256:
- b5eaeff0745fd634accc5c0ff3b14ff85d4939335876e2d14fe0f7ef4c3ada67
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