Instructions to use infinitylogesh/GUI-Decisions-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use infinitylogesh/GUI-Decisions-2B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="infinitylogesh/GUI-Decisions-2B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("infinitylogesh/GUI-Decisions-2B")
model = AutoModelForMultimodalLM.from_pretrained("infinitylogesh/GUI-Decisions-2B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use infinitylogesh/GUI-Decisions-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "infinitylogesh/GUI-Decisions-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "infinitylogesh/GUI-Decisions-2B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/infinitylogesh/GUI-Decisions-2B

SGLang

How to use infinitylogesh/GUI-Decisions-2B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "infinitylogesh/GUI-Decisions-2B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "infinitylogesh/GUI-Decisions-2B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "infinitylogesh/GUI-Decisions-2B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "infinitylogesh/GUI-Decisions-2B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use infinitylogesh/GUI-Decisions-2B with Docker Model Runner:
```
docker model run hf.co/infinitylogesh/GUI-Decisions-2B
```

infinitylogesh commited on 3 days ago

Commit

c2cedfd

verified ·

1 Parent(s): a2c6b10

card: hero GIF

Browse files

Files changed (3) hide show

.gitattributes +1 -0
README.md +11 -8
assets/hero.gif +3 -0

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@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text

 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
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+assets/hero.gif filter=lfs diff=lfs merge=lfs -text

README.md CHANGED Viewed

@@ -8,13 +8,16 @@ tags: [computer-use, gui-agent, gui-grounding, slot-reading]
 # GUI-Decisions-2B
-A 2B system-one decision model for GUI Grounding. [mPLUG/GUI-Owl-1.5-2B-Instruct](https://huggingface.co/mPLUG/GUI-Owl-1.5-2B-Instruct) with the slot-reading LoRA
 merged in (bf16), which makes a computer-use step **readable from one forward pass** instead of decoded: the action, the
 point (256 bins per axis), a swipe's end, the key and the scroll, as next-token distributions at fixed
-slots.
-The small-model result of the blog post
-[GUI-Decisions - Stop Decoding Coordinates for 3x Faster Computer-Use Grounding](https://huggingface.co/blog/infinitylogesh/gui-decisions-stop-GUI-Decisions - Stop Decoding Coordinates for 3x Faster Computer-Use Grounding-coordinates).
 The LoRA alone: [GUI-Decisions-2B-LoRA](https://huggingface.co/infinitylogesh/GUI-Decisions-2B-LoRA); the 31B version:
 [GUI-Decisions-31B](https://huggingface.co/infinitylogesh/GUI-Decisions-31B).
@@ -140,11 +143,11 @@ Wave-UI; check their terms for your use.
 ```bibtex
 @article{umapathi2026slotreading,
-  title   = "GUI-Decisions - Stop Decoding Coordinates for 3x Faster Computer-Use Grounding",
   author  = "Umapathi, Logesh Kumar",
-  journal = "huggingface.co/blog",
   year    = "2026",
   month   = "Oct",
-  url     = "https://huggingface.co/blog/infinitylogesh/gui-decisions-stop-decoding-coordinates"
 }
 ```

 # GUI-Decisions-2B
+[![Slot read vs writing JSON: the same model on the same Wikipedia task, 4 decisions in 0.63 s against 2.08 s](assets/hero.gif)](https://huggingface.co/spaces/infinitylogesh/GUI-Decisions)
+*Slot reading with the 31B model (from the blog post): reading slots (left, 158 ms per decision) against
+writing the step as JSON (right, 520 ms). [Try the 31B model in the Space](https://huggingface.co/spaces/infinitylogesh/GUI-Decisions).*
+[mPLUG/GUI-Owl-1.5-2B-Instruct](https://huggingface.co/mPLUG/GUI-Owl-1.5-2B-Instruct) with the slot-reading LoRA
 merged in (bf16), which makes a computer-use step **readable from one forward pass** instead of decoded: the action, the
 point (256 bins per axis), a swipe's end, the key and the scroll, as next-token distributions at fixed
+slots. The small-model result of the blog post
+[Stop Decoding Coordinates: 3× Faster Computer-Use Grounding](https://logeshumapathi.com/blog/2026/10/06/slot-reading.html).
 The LoRA alone: [GUI-Decisions-2B-LoRA](https://huggingface.co/infinitylogesh/GUI-Decisions-2B-LoRA); the 31B version:
 [GUI-Decisions-31B](https://huggingface.co/infinitylogesh/GUI-Decisions-31B).
 ```bibtex
 @article{umapathi2026slotreading,
+  title   = "Stop Decoding Coordinates: 3.5× Faster Computer-Use Grounding",
   author  = "Umapathi, Logesh Kumar",
+  journal = "logeshumapathi.com",
   year    = "2026",
   month   = "Oct",
+  url     = "https://logeshumapathi.com/blog/2026/10/06/slot-reading.html"
 }
 ```

assets/hero.gif ADDED Viewed

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