Any-to-Any
Transformers
Safetensors
English
Chinese
qwen2_5_vl
text-generation
text-generation-inference
Instructions to use PaDT-MLLM/PaDT_Pro_3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PaDT-MLLM/PaDT_Pro_3B with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForSeq2SeqLM processor = AutoProcessor.from_pretrained("PaDT-MLLM/PaDT_Pro_3B") model = AutoModelForSeq2SeqLM.from_pretrained("PaDT-MLLM/PaDT_Pro_3B") - Notebooks
- Google Colab
- Kaggle
Improve model card: Update `pipeline_tag` and add `library_name`
#1
by nielsr HF Staff - opened
This PR enhances the model card for "Patch-as-Decodable-Token" by:
- Updating the
pipeline_tagfromimage-text-to-texttoany-to-any. This more accurately reflects the model's capabilities to generate both textual and diverse visual outputs (detection, segmentation, grounding predictions), as described in the paper abstract. - Adding
library_name: transformers. The model is compatible with the Hugging Face Transformers library, as evidenced by thetransformers.AutoProcessorimport in the quick start code snippet and thetransformers_versioninconfig.json. This enables the interactive inference widget and automated usage examples on the Hub. - Integrating the "Why PaDT Succeeds?" section from the GitHub README to provide more comprehensive information about the model's core design and advantages.
Please review and merge if these changes align with the model's functionality and best practices for model cards.
PaDT-MLLM changed pull request status to closed