Image-to-Text
Transformers
English
gui-grounding
screen-understanding
vision-language-model
icon-detection
screenspot
visual-search
Instructions to use luisf-mc/gui-g2-3b-ccf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luisf-mc/gui-g2-3b-ccf 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="luisf-mc/gui-g2-3b-ccf")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("luisf-mc/gui-g2-3b-ccf", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Fix benchmarks link: point at the GitHub repo (now public)
Browse files
README.md
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The pattern across all training experiments was the same: the hard splits (icon, web) improved at the cost of the easy splits (text), and overall accuracy never beat the base. The lesson we kept: **at 3B + a few-thousand-sample fine-tuning budget, GUI-G2-3B is near its achievable optimum.** Inference-time wraps like CCF that don't touch the weights win the hard splits without paying the easy-split tax.
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Full project writeup with per-experiment numbers: see
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## Citation
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The pattern across all training experiments was the same: the hard splits (icon, web) improved at the cost of the easy splits (text), and overall accuracy never beat the base. The lesson we kept: **at 3B + a few-thousand-sample fine-tuning budget, GUI-G2-3B is near its achievable optimum.** Inference-time wraps like CCF that don't touch the weights win the hard splits without paying the easy-split tax.
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Full project writeup with per-experiment numbers: see [benchmarks/results.md](https://github.com/LufeMC/gui-g2-3b-ccf/blob/main/benchmarks/results.md) in the [GitHub repo](https://github.com/LufeMC/gui-g2-3b-ccf).
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## Citation
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