Instructions to use nervouslyopen/Qwen2.5-VL-3B-Instruct-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use nervouslyopen/Qwen2.5-VL-3B-Instruct-4bit with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("nervouslyopen/Qwen2.5-VL-3B-Instruct-4bit") config = load_config("nervouslyopen/Qwen2.5-VL-3B-Instruct-4bit") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
metadata
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE
language:
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
- mlx
library_name: mlx
base_model: Qwen/Qwen2.5-VL-3B-Instruct
nervouslyopen/Qwen2.5-VL-3B-Instruct-4bit
This model was converted to MLX format from Qwen/Qwen2.5-VL-3B-Instruct
using mlx-vlm version 0.6.3.
Refer to the original model card for more details on the model.
Use with mlx
pip install -U mlx-vlm
python -m mlx_vlm.generate --model nervouslyopen/Qwen2.5-VL-3B-Instruct-4bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>