How to use from
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 "prithivMLmods/Gliese-CUA-Tool-Call-8B-Localization-GGUF" \
    --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": "prithivMLmods/Gliese-CUA-Tool-Call-8B-Localization-GGUF",
		"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 "prithivMLmods/Gliese-CUA-Tool-Call-8B-Localization-GGUF" \
        --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": "prithivMLmods/Gliese-CUA-Tool-Call-8B-Localization-GGUF",
		"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"
						}
					}
				]
			}
		]
	}'
Quick Links

Gliese-CUA-Tool-Call-8B-Localization-GGUF

Gliese-CUA-Tool-Call-8B is a Computer Use Agent (CUA) multimodal model based on Qwen2.5-VL-7B-Instruct, designed for GUI understanding, UI localization, and action execution across web, desktop, and mobile environments. It focuses on visual grounding, intent driven actioning, and UI based question answering (VQA), enabling reliable interaction with real world software interfaces. The model is optimized for agentic tool calling, producing structured actions that can be directly executed by downstream systems.

Localization-8B/**

Gliese-CUA-Tool-Call-8B-Localization [GGUF]

File Name Quant Type File Size File Link
Gliese-CUA-Tool-Call-8B-Localization.BF16.gguf BF16 15.2 GB Download
Gliese-CUA-Tool-Call-8B-Localization.F16.gguf F16 15.2 GB Download
Gliese-CUA-Tool-Call-8B-Localization.Q8_0.gguf Q8_0 8.1 GB Download
Gliese-CUA-Tool-Call-8B-Localization.mmproj-bf16.gguf mmproj-bf16 1.36 GB Download
Gliese-CUA-Tool-Call-8B-Localization.mmproj-f16.gguf mmproj-f16 1.35 GB Download
Gliese-CUA-Tool-Call-8B-Localization.mmproj-q8_0.gguf mmproj-q8_0 856 MB Download

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

image.png

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GGUF
Model size
8B params
Architecture
qwen2vl
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