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

Libraries

How to use Hcompany/Holo-3.1-35B-A3B-GGUF 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-GGUF")
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 AutoModel
model = AutoModel.from_pretrained("Hcompany/Holo-3.1-35B-A3B-GGUF", dtype="auto")

llama-cpp-python

How to use Hcompany/Holo-3.1-35B-A3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Hcompany/Holo-3.1-35B-A3B-GGUF",
	filename="imatrix.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Hcompany/Holo-3.1-35B-A3B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Hcompany/Holo-3.1-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Hcompany/Holo-3.1-35B-A3B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Hcompany/Holo-3.1-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Hcompany/Holo-3.1-35B-A3B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Hcompany/Holo-3.1-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Hcompany/Holo-3.1-35B-A3B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Hcompany/Holo-3.1-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Hcompany/Holo-3.1-35B-A3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Hcompany/Holo-3.1-35B-A3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Hcompany/Holo-3.1-35B-A3B-GGUF 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-GGUF"
# 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-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

docker model run hf.co/Hcompany/Holo-3.1-35B-A3B-GGUF:Q4_K_M

SGLang

How to use Hcompany/Holo-3.1-35B-A3B-GGUF 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-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": "Hcompany/Holo-3.1-35B-A3B-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 "Hcompany/Holo-3.1-35B-A3B-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": "Hcompany/Holo-3.1-35B-A3B-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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use Hcompany/Holo-3.1-35B-A3B-GGUF with Ollama:
```
ollama run hf.co/Hcompany/Holo-3.1-35B-A3B-GGUF:Q4_K_M
```

Unsloth Studio

How to use Hcompany/Holo-3.1-35B-A3B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Hcompany/Holo-3.1-35B-A3B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Hcompany/Holo-3.1-35B-A3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Hcompany/Holo-3.1-35B-A3B-GGUF to start chatting

How to use Hcompany/Holo-3.1-35B-A3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Hcompany/Holo-3.1-35B-A3B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Hcompany/Holo-3.1-35B-A3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Hcompany/Holo-3.1-35B-A3B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Hcompany/Holo-3.1-35B-A3B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Hcompany/Holo-3.1-35B-A3B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Hcompany/Holo-3.1-35B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/Hcompany/Holo-3.1-35B-A3B-GGUF:Q4_K_M
```

Lemonade

How to use Hcompany/Holo-3.1-35B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Hcompany/Holo-3.1-35B-A3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Holo-3.1-35B-A3B-GGUF-Q4_K_M

List all available models

lemonade list

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
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
Quickstart: 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.

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.

Table 1: Evaluation results across computer use, mobile automation, enterprise workflows, and grounding benchmarks.

Get Started

Explore our Quickstart guide to learn how to integrate Holo3.1 into your applications, deploy local agents, or run optimized inference on NVIDIA hardware.

Citation

@misc{hai2026holo31,
      title={Holo3.1: Fast & Local Computer Use Agents},
      author={H Company},
      year={2026},
      url={https://huggingface.co/Hcompany/Holo3.1-35B-A3B},
}