Instructions to use Hcompany/Holo-3.1-35B-A3B-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hcompany/Holo-3.1-35B-A3B-FP8 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Hcompany/Holo-3.1-35B-A3B-FP8")
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/Holo-3.1-35B-A3B-FP8")
model = AutoModelForMultimodalLM.from_pretrained("Hcompany/Holo-3.1-35B-A3B-FP8")
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/Holo-3.1-35B-A3B-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hcompany/Holo-3.1-35B-A3B-FP8"
# 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/Holo-3.1-35B-A3B-FP8",
		"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/Holo-3.1-35B-A3B-FP8

SGLang

How to use Hcompany/Holo-3.1-35B-A3B-FP8 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/Holo-3.1-35B-A3B-FP8" \
    --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/Holo-3.1-35B-A3B-FP8",
		"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/Holo-3.1-35B-A3B-FP8" \
        --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/Holo-3.1-35B-A3B-FP8",
		"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/Holo-3.1-35B-A3B-FP8 with Docker Model Runner:
```
docker model run hf.co/Hcompany/Holo-3.1-35B-A3B-FP8
```

maxime-hcompany commited on Jun 2

Commit

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3.6-35B-A3B
+- Qwen/Qwen3.5-9B
+- Qwen/Qwen3.5-4B
+- Qwen/Qwen3.5-0.8B
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- multimodal
+- action
+- agent
+- pytorch
+- computer use
+- gui agents
+- moe
+---
+# **Holo3.1: Fast & Local Computer Use Agents**
+## **Model Description**
+**Holo3.1** is our latest family of Vision-Language Models (VLMs) for computer use agents. Building on Holo3, it expands support beyond browser and desktop automation to mobile environments, introduces native function-calling support for seamless integration with agent frameworks, and enables local deployment through optimized quantized checkpoints.
+The Holo3.1 family spans model sizes from 0.8B to 35B-A3B parameters.
+Across computer use, UI grounding, mobile automation, and business workflows, Holo3.1 delivers strong performance while improving deployment flexibility and cost efficiency.
+* **Developed by:** [**H Company**](https://www.hcompany.ai/)
+* **Model type:** Vision-Language Models for Navigation and Computer Use Agents
+* **Available models:** Holo3.1-0.8B, Holo3.1-4B, Holo3.1-9B, Holo3.1-35B-A3B
+* **Base models:** Qwen 3.5 family
+* **Supported environments:** Web, Desktop, Mobile
+* **Available quantizations for Holo3.1-35B-A3B:** BF16, FP8, NVFP4, Q4 GGUF
+* **Blog Post:** [hcompany.ai/holo3.1](https://www.hcompany.ai/holo3.1)
+* **Quickstart:** [hub.hcompany.ai/quickstart](https://hub.hcompany.ai/quickstart)
+* **License:** Apache 2.0 License
+### Performance vs Cost
+The figure below compares the overall performance and inference cost of the Holo3.1 and Qwen 3.5 families. Overall performance averages computer use, mobile automation, enterprise workflows, and UI grounding benchmarks.
+<p align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/69ce2739f4b9146a31e99a2f/0uU-6v68tSl3Kvd9jHWfS.png" width="700"/>
+</p>
+Holo3.1 establishes a strong Pareto frontier across model sizes, from lightweight local agents to state-of-the-art enterprise deployments.
+---
+## Benchmark Results
+Holo3.1 delivers strong performance across computer use, mobile automation, enterprise workflows, and UI grounding benchmarks.
+<div align="center">
+**Table 1: Evaluation results across computer use, mobile automation, enterprise workflows, and grounding benchmarks.**
+<img width="800" src="https://cdn-uploads.huggingface.co/production/uploads/69ce2739f4b9146a31e99a2f/eKbMPsY32AF4PCNzumeZQ.png"/>
+</div>
+## Get Started
+Explore our [Quickstart guide](https://hub.hcompany.ai/quickstart) to learn how to integrate Holo3.1 into your applications, deploy local agents, or run optimized inference on NVIDIA hardware.
+---
+## Citation
+```bibtex
+@misc{hai2026holo31,
+      title={Holo3.1: Fast & Local Computer Use Agents},
+      author={H Company},
+      year={2026},
+      url={https://huggingface.co/Hcompany/Holo3.1-35B-A3B},
+}