Instructions to use Jithendra-k/Flan_T5_InterACT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jithendra-k/Flan_T5_InterACT with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Jithendra-k/Flan_T5_InterACT") model = AutoModelForSeq2SeqLM.from_pretrained("Jithendra-k/Flan_T5_InterACT") - Notebooks
- Google Colab
- Kaggle
Quick Links
Project InterACT
This model is a part of Project InterACT (Multi model AI system) involving an object detection model and an LLM
This is a model built by finetuning the flan-t5-small model on custom dataset: Jithendra-k/Flan_T5_InterACT.
Here are some plots of model performance during training:
Here is an Example Input/Output:
Code to finetune a Flan-T5 model: Google_Colab_file
Credits and Thanks:
Greatest thanks to NousResearch/Llama-2-70b-chat-hf and meta for enabling us to use the flan-t5-small model.
https://huggingface.co/google/flan-t5-small
https://www.datacamp.com/tutorial/flan-t5-tutorial
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# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Jithendra-k/Flan_T5_InterACT") model = AutoModelForSeq2SeqLM.from_pretrained("Jithendra-k/Flan_T5_InterACT")