Instructions to use prithivMLmods/SpatialBlock-4B-direct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/SpatialBlock-4B-direct-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/SpatialBlock-4B-direct-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/SpatialBlock-4B-direct-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/SpatialBlock-4B-direct-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/SpatialBlock-4B-direct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/SpatialBlock-4B-direct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/SpatialBlock-4B-direct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/SpatialBlock-4B-direct-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf prithivMLmods/SpatialBlock-4B-direct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf prithivMLmods/SpatialBlock-4B-direct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/SpatialBlock-4B-direct-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use prithivMLmods/SpatialBlock-4B-direct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/SpatialBlock-4B-direct-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": "prithivMLmods/SpatialBlock-4B-direct-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/prithivMLmods/SpatialBlock-4B-direct-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/SpatialBlock-4B-direct-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 "prithivMLmods/SpatialBlock-4B-direct-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": "prithivMLmods/SpatialBlock-4B-direct-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 "prithivMLmods/SpatialBlock-4B-direct-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": "prithivMLmods/SpatialBlock-4B-direct-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 prithivMLmods/SpatialBlock-4B-direct-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/SpatialBlock-4B-direct-GGUF:Q4_K_M
```
Unsloth Desktop

How to use prithivMLmods/SpatialBlock-4B-direct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/SpatialBlock-4B-direct-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "prithivMLmods/SpatialBlock-4B-direct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/SpatialBlock-4B-direct-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/SpatialBlock-4B-direct-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/SpatialBlock-4B-direct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/SpatialBlock-4B-direct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SpatialBlock-4B-direct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/SpatialBlock-4B-direct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/SpatialBlock-4B-direct-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 prithivMLmods/SpatialBlock-4B-direct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/SpatialBlock-4B-direct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/SpatialBlock-4B-direct-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "prithivMLmods/SpatialBlock-4B-direct-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

SpatialBlock-4B-direct-GGUF / README.md

prithivMLmods

Update README.md

de9a05a verified about 2 months ago

preview code

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metadata

license: apache-2.0
base_model:
  - rsoohyun/SpatialBlock-4B-direct
language:
  - en
pipeline_tag: image-text-to-text
library_name: transformers
tags:
  - text-generation-inference
  - llama-cpp

SpatialBlock-4B-direct-GGUF

SpatialBlock-4B-direct is an open-source multimodal model released on Hugging Face by rsoohyun under the Apache-2.0 license, developed to enhance spatial intelligence in Large Vision-Language Models (LVLMs). Based on the Qwen/Qwen3-VL-4B-Instruct base architecture and supported by the Hugging Face transformers library via the image-text-to-text pipeline, this checkpoint is fine-tuned on the synthetic SpatialBlock-15k dataset as presented in the paper SpatialBlock: Enhancing Spatial Intelligence in LVLMs via Synthetic Block-Stacking Problem. It specializes in directly predicting solutions to intricate spatial tasks—including 3D-to-2D projection, viewpoint transformation, and structural combination—with complete training methodology, evaluation metrics, and the companion “reason” model accessible via its GitHub repository.

Model Files

File Name	Quant Type	File Size	File Link
SpatialBlock-4B-direct.BF16.gguf	BF16	8.05 GB	Download
SpatialBlock-4B-direct.Q4_K_M.gguf	Q4_K_M	2.5 GB	Download
SpatialBlock-4B-direct.Q5_K_M.gguf	Q5_K_M	2.89 GB	Download
SpatialBlock-4B-direct.mmproj-bf16.gguf	mmproj-bf16	839 MB	Download

llama.cpp

LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp