Instructions to use Hcompany/Holo3-35B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hcompany/Holo3-35B-A3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Hcompany/Holo3-35B-A3B")
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/Holo3-35B-A3B")
model = AutoModelForMultimodalLM.from_pretrained("Hcompany/Holo3-35B-A3B")
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/Holo3-35B-A3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hcompany/Holo3-35B-A3B"
# 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/Holo3-35B-A3B",
		"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/Holo3-35B-A3B

SGLang

How to use Hcompany/Holo3-35B-A3B 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/Holo3-35B-A3B" \
    --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/Holo3-35B-A3B",
		"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/Holo3-35B-A3B" \
        --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/Holo3-35B-A3B",
		"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/Holo3-35B-A3B with Docker Model Runner:
```
docker model run hf.co/Hcompany/Holo3-35B-A3B
```

plcedoz38 commited on Mar 31

Commit

a54969c

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1 Parent(s): ba38a1d

Update README.md

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README.md +8 -8

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@@ -18,14 +18,6 @@ tags:
 # **Holo3: Foundational Models for Navigation and Computer Use Agents**
-<div align="center">
-**Figure 1: Pareto plot on OSWorld-Verified showing efficiency vs. performance.**
-<p align="center"><img width=800 src="osworld_pareto_light.png"/></p>
-</div>
 [![GitHub](https://img.shields.io/badge/Holo3_Cookbook-100000?style=for-the-badge&logo=github&logoColor=white)](https://github.com/hcompai/hai-cookbook/tree/main/holo3)
 ## **Model Description**
@@ -43,6 +35,14 @@ Holo3 achieves **state-of-the-art performance on OSWorld-Verified**, setting a n
 ---
 ## **Get Started**
 Explore our [Holo3 Cookbook](https://github.com/hcompai/hai-cookbook/tree/main/holo3) to learn how to self-host the model or integrate with our inference API.

 # **Holo3: Foundational Models for Navigation and Computer Use Agents**
 [![GitHub](https://img.shields.io/badge/Holo3_Cookbook-100000?style=for-the-badge&logo=github&logoColor=white)](https://github.com/hcompai/hai-cookbook/tree/main/holo3)
 ## **Model Description**
 ---
+<div align="center">
+<p align="center"><img width=800 src="osworld_pareto_light.png"/></p>
+</div>
+---
 ## **Get Started**
 Explore our [Holo3 Cookbook](https://github.com/hcompai/hai-cookbook/tree/main/holo3) to learn how to self-host the model or integrate with our inference API.