browserground-mlx (Apple Silicon, 4-bit)
MLX-converted 4-bit quant of renezander030/browserground.
Drop in the same model you'd use via transformers, but ~10× faster on Apple Silicon.
Use
from mlx_vlm import load, generate
model, processor = load("renezander030/browserground-mlx")
out = generate(model, processor, image="screenshot.png", prompt="Locate: Submit button", max_tokens=64)
print(out)
Or via the CLI:
npm install -g browserground
IMGPARSE_MODEL=renezander030/browserground-mlx browserground parse screenshot.png --target "Submit button"
Numbers, training recipe, and the full positioning vs UI-TARS-2B-SFT are on the main model card: https://huggingface.co/renezander030/browserground.
License: Apache 2.0 (inherits from Qwen/Qwen3-VL-2B-Instruct).
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Model size
0.7B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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4-bit
# 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("renezander030/browserground-mlx") config = load_config("renezander030/browserground-mlx") # 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)