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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Hcompany/Holo1.5-3B")
model = AutoModelForImageTextToText.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

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
```

hamza-hcompany commited on Sep 15, 2025

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@@ -1,18 +1,18 @@
----
-license: other
-license_name: other
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-VL-3B-Instruct
-pipeline_tag: image-text-to-text
-library_name: transformers
-tags:
-- multimodal
-- action
-- agent
-- pytorch
----
 # **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)
@@ -33,7 +33,7 @@ These models are designed to provide reliable, accurate, and efficient foundatio
 - **Developed by:** [**H Company**](https://www.hcompany.ai/)
 - **Model type:** VLM for Computer Use agents
 - **Fine-tuned from model:** Qwen/Qwen2.5-VL-3B-Instruct
-- **Blog Post:** https://www.hcompany.ai/blog/holo1.5
 - **License:** Qwen Research License
 ## Training strategy

+---
+license: other
+license_name: other
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-VL-3B-Instruct
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- multimodal
+- action
+- agent
+- pytorch
+---
 # **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)
 - **Developed by:** [**H Company**](https://www.hcompany.ai/)
 - **Model type:** VLM for Computer Use agents
 - **Fine-tuned from model:** Qwen/Qwen2.5-VL-3B-Instruct
+- **Blog Post:** https://www.hcompany.ai/blog/holo-1-5
 - **License:** Qwen Research License
 ## Training strategy