Image-Text-to-Text
MLX
Safetensors
English
qwen2_5_vl
multimodal
gui
gui-agent
ui-tars
conversational
8-bit precision
Instructions to use mlx-community/UI-TARS-1.5-7B-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/UI-TARS-1.5-7B-8bit 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("mlx-community/UI-TARS-1.5-7B-8bit") config = load_config("mlx-community/UI-TARS-1.5-7B-8bit") # 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
UI-TARS-1.5-7B-8bit (MLX)
8-bit MLX quantization of ByteDance-Seed/UI-TARS-1.5-7B, a Qwen2.5-VL vision-language GUI-agent model for grounded computer/browser control.
- Quantization: 8-bit affine, group size 64 (~9.11 effective bits/weight)
- Architecture:
Qwen2_5_VLForConditionalGeneration(qwen2_5_vl) — ViT vision encoder + Qwen2.5 LM - Converted with:
mlx_vlm.convert(mlx-vlm)
Use with mlx-vlm
pip install mlx-vlm
python -m mlx_vlm generate \
--model mlx-community/UI-TARS-1.5-7B-8bit \
--image screenshot.png \
--prompt "Click the search box." \
--max-tokens 128
This 8-bit build complements the existing
mlx-community/UI-TARS-1.5-7B-4bit
and -6bit — higher fidelity at ~8.8 GB.
- Downloads last month
- 28
Model size
3B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
Log In to add your hardware
8-bit
Model tree for mlx-community/UI-TARS-1.5-7B-8bit
Base model
ByteDance-Seed/UI-TARS-1.5-7B