Image-to-Text
Transformers
Safetensors
English
qwen2_vl
image-text-to-text
handwriting-recognition
vision2seq
qwen
htr
tensorflow
text-generation-inference
Instructions to use Emeritus-21/Finetuned-full-HTR-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Emeritus-21/Finetuned-full-HTR-model with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="Emeritus-21/Finetuned-full-HTR-model")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Emeritus-21/Finetuned-full-HTR-model") model = AutoModelForMultimodalLM.from_pretrained("Emeritus-21/Finetuned-full-HTR-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eb59d5beba870024c283b4efb8cf8b747e8249662db56c4be560b46cc50bb0db
- Size of remote file:
- 4.42 GB
- SHA256:
- 8d07b671e4c086bc6c05ab6230026550a49d39b8d1dadaa14b2732223ca63a11
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.