Instructions to use code-world/google-flan-t5-small-lora-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use code-world/google-flan-t5-small-lora-ft with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small") model = PeftModel.from_pretrained(base_model, "code-world/google-flan-t5-small-lora-ft") - Transformers
How to use code-world/google-flan-t5-small-lora-ft with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("code-world/google-flan-t5-small-lora-ft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-5000/optimizer.pt from code-world/google-flan-t5-small-lora-ft: direct link, hf CLI and curl.
- Browser
- Download file 11.1 MB
-
https://huggingface.co/code-world/google-flan-t5-small-lora-ft/resolve/main/checkpoint-5000/optimizer.pt
- Command line
-
hf download hf://code-world/google-flan-t5-small-lora-ft/checkpoint-5000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/code-world/google-flan-t5-small-lora-ft/resolve/main/checkpoint-5000/optimizer.pt
11.1 MB
- Xet hash:
- 9589ff09f9059fef5855528ca6df2a0cf0314e2c7a655241ce165d938dd187db
- Size of remote file:
- 11.1 MB
- SHA256:
- c85ebaa10bd8c91011b820100f786814b3920b27482fcf2020082fb84ab40cb8
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