Image-Text-to-Text
MLX
Safetensors
zaya1_vl
zaya
mixture-of-experts
hybrid-attention
cca-attention
apple-silicon
reasoning
tool-use
quantized
vision
multimodal
vision-language
qwen2_5_vl-vit
jang
jangtq
mxtq
jangtq-prestack
osaurus
conversational
Instructions to use OsaurusAI/ZAYA1-VL-8B-JANGTQ4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OsaurusAI/ZAYA1-VL-8B-JANGTQ4 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("OsaurusAI/ZAYA1-VL-8B-JANGTQ4") config = load_config("OsaurusAI/ZAYA1-VL-8B-JANGTQ4") # 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
- LM Studio
- Xet hash:
- 8ae5f2a49dd949807138a8e43c9d03da6c44cff841cf6c66a71ff3eff2db0bf0
- Size of remote file:
- 33.4 MB
- SHA256:
- a90181b1298e5d8c2f211f15dca261650d85c1f2a5a3bbfff852a853d21bed8f
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