Instructions to use Hcompany/Holo1.5-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hcompany/Holo1.5-3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Hcompany/Holo1.5-3B")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Hcompany/Holo1.5-3B")
model = AutoModelForMultimodalLM.from_pretrained("Hcompany/Holo1.5-3B")
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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Hcompany/Holo1.5-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hcompany/Holo1.5-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hcompany/Holo1.5-3B",
		"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/Hcompany/Holo1.5-3B

SGLang

How to use Hcompany/Holo1.5-3B 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 "Hcompany/Holo1.5-3B" \
    --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": "Hcompany/Holo1.5-3B",
		"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 "Hcompany/Holo1.5-3B" \
        --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": "Hcompany/Holo1.5-3B",
		"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 Hcompany/Holo1.5-3B with Docker Model Runner:
```
docker model run hf.co/Hcompany/Holo1.5-3B
```

Add notebook supporting Holo1.5–3B (Navigation & Localization) demos on T4 GPU for user experimentation.

by prithivMLmods - opened Sep 16, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-0

Files changed (1) hide show

README.md +1 -0

README.md CHANGED Viewed

@@ -16,6 +16,7 @@ tags:
 # **Holo1.5: Foundational Models for Computer Use Agents**
 [![GitHub](https://img.shields.io/badge/Holo_1.5_Cookbook-100000?style=for-the-badge&logo=github&logoColor=white)](https://github.com/hcompai/hai-cookbook/blob/main/holo1_5/holo_1_5_quickstart.ipynb)
 [![Space](https://img.shields.io/badge/Hugging_Face_Space-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000)](https://huggingface.co/spaces/Hcompany/Holo1.5-Navigation)
 ## **Model Description**
 Computer Use (CU) agents are AI systems that can interact with real applications—web, desktop, and mobile—on behalf of a user. They can navigate interfaces, manipulate elements, and answer questions about content, enabling powerful automation and productivity tools. CU agents are becoming increasingly important as they allow humans to delegate complex digital tasks safely and efficiently.

 # **Holo1.5: Foundational Models for Computer Use Agents**
 [![GitHub](https://img.shields.io/badge/Holo_1.5_Cookbook-100000?style=for-the-badge&logo=github&logoColor=white)](https://github.com/hcompai/hai-cookbook/blob/main/holo1_5/holo_1_5_quickstart.ipynb)
 [![Space](https://img.shields.io/badge/Hugging_Face_Space-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000)](https://huggingface.co/spaces/Hcompany/Holo1.5-Navigation)
+[![Colab](https://img.shields.io/badge/Colab_Notebook-FF6F00?style=for-the-badge\&logo=googlecolab\&logoColor=white)](https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/Holo1.5-3B/Holo1_5_3B.ipynb)
 ## **Model Description**
 Computer Use (CU) agents are AI systems that can interact with real applications—web, desktop, and mobile—on behalf of a user. They can navigate interfaces, manipulate elements, and answer questions about content, enabling powerful automation and productivity tools. CU agents are becoming increasingly important as they allow humans to delegate complex digital tasks safely and efficiently.