Image-to-Text
Transformers
Safetensors
PyTorch
VLV_decoder
feature-extraction
image-captioning
multimodal
vision-language
diffusion
custom_code
Instructions to use lambertxiao/Vision-Language-Vision-Captioner-Qwen2.5-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lambertxiao/Vision-Language-Vision-Captioner-Qwen2.5-3B 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="lambertxiao/Vision-Language-Vision-Captioner-Qwen2.5-3B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lambertxiao/Vision-Language-Vision-Captioner-Qwen2.5-3B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add `library_name: transformers` to model card metadata
#1
by nielsr HF Staff - opened
This PR improves the model card by adding the library_name: transformers to the metadata. This ensures that the model is correctly categorized and discoverable within the Hugging Face ecosystem, enabling users to easily find and utilize it with the transformers library.