Instructions to use mlpc-lab/BLIVA_Vicuna with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlpc-lab/BLIVA_Vicuna with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="mlpc-lab/BLIVA_Vicuna")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mlpc-lab/BLIVA_Vicuna", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update model card for ESTR-CoT
#2
by nielsr HF Staff - opened
This pull request completely revamps the model card for this repository to accurately reflect the ESTR-CoT model, as described in the paper ESTR-CoT: Towards Explainable and Accurate Event Stream based Scene Text Recognition with Chain-of-Thought Reasoning.
The update includes:
- Replacing all outdated information related to the previous BLIVA model with details about ESTR-CoT.
- Setting the
pipeline_tagtoimage-text-to-textto better categorize the model on the Hub, making it discoverable for visual-to-text tasks. - Confirming the
library_nameastransformersbased on the model's components (Llama tokenizer, Vicuna-7B). - Adding
license: cc-by-nc-4.0, a common non-commercial license, as a placeholder until an official license is released with the code.