Instructions to use tanmayakaranth/matcha-chartqa-lora-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tanmayakaranth/matcha-chartqa-lora-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/matcha-base") model = PeftModel.from_pretrained(base_model, "tanmayakaranth/matcha-chartqa-lora-adapter") - Transformers
How to use tanmayakaranth/matcha-chartqa-lora-adapter with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tanmayakaranth/matcha-chartqa-lora-adapter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "image_processor": { | |
| "data_format": "channels_first", | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "image_processor_type": "Pix2StructImageProcessorFast", | |
| "is_vqa": true, | |
| "max_patches": 2048, | |
| "patch_size": { | |
| "height": 16, | |
| "width": 16 | |
| } | |
| }, | |
| "processor_class": "Pix2StructProcessor" | |
| } | |