Image-to-Text
Transformers
PyTorch
English
qwen2vl
image-text-to-text
vision-language-model
quantized
chain-of-zoom
8-bit precision
super-resolution
qwen
multimodal
Eval Results (legacy)
Instructions to use humbleakh/qwen2.5-vl-3b-8bit-chain-of-zoom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use humbleakh/qwen2.5-vl-3b-8bit-chain-of-zoom 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="humbleakh/qwen2.5-vl-3b-8bit-chain-of-zoom")# Load model directly from transformers import AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained("humbleakh/qwen2.5-vl-3b-8bit-chain-of-zoom", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "qwen2vl", | |
| "quantization": "8-bit", | |
| "architectures": [ | |
| "Qwen2VLForConditionalGeneration" | |
| ], | |
| "torch_dtype": "bfloat16", | |
| "precision": "8-bit", | |
| "base_model": "Qwen/Qwen2.5-VL-3B-Instruct" | |
| } |