Instructions to use prithivMLmods/SpatialBlock-3B-reason-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/SpatialBlock-3B-reason-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/SpatialBlock-3B-reason-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("prithivMLmods/SpatialBlock-3B-reason-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use prithivMLmods/SpatialBlock-3B-reason-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-3B-reason-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/SpatialBlock-3B-reason-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-3B-reason-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/SpatialBlock-3B-reason-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 prithivMLmods/SpatialBlock-3B-reason-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/SpatialBlock-3B-reason-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 prithivMLmods/SpatialBlock-3B-reason-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/SpatialBlock-3B-reason-GGUF:Q4_K_M
Use Docker
docker model run hf.co/prithivMLmods/SpatialBlock-3B-reason-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/SpatialBlock-3B-reason-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/SpatialBlock-3B-reason-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-3B-reason-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-3B-reason-GGUF:Q4_K_M
- SGLang
How to use prithivMLmods/SpatialBlock-3B-reason-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-3B-reason-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-3B-reason-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-3B-reason-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-3B-reason-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-3B-reason-GGUF with Ollama:
ollama run hf.co/prithivMLmods/SpatialBlock-3B-reason-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use prithivMLmods/SpatialBlock-3B-reason-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/SpatialBlock-3B-reason-GGUF:Q4_K_M
- Lemonade
How to use prithivMLmods/SpatialBlock-3B-reason-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/SpatialBlock-3B-reason-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.SpatialBlock-3B-reason-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
SpatialBlock-3B-reason-GGUF
SpatialBlock-3B-reason 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/Qwen2.5-VL-3B-Instructbase architecture and supported by the Hugging Facetransformerslibrary via theimage-text-to-textpipeline, this checkpoint is fine-tuned on the syntheticSpatialBlock-15kdataset as presented in the paper SpatialBlock: Enhancing Spatial Intelligence in LVLMs via Synthetic Block-Stacking Problem. It specializes in reasoning through and directly predicting solutions to intricate spatial challenges—including 3D-to-2D projection, viewpoint transformation, and structural combination—with complete training methodology, evaluation metrics, and companion models accessible via its GitHub repository.
Model Files
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| SpatialBlock-3B-reason.BF16.gguf | BF16 | 6.8 GB | Download |
| SpatialBlock-3B-reason.Q4_K_M.gguf | Q4_K_M | 2.1 GB | Download |
| SpatialBlock-3B-reason.Q5_K_M.gguf | Q5_K_M | 2.44 GB | Download |
| SpatialBlock-3B-reason.mmproj-bf16.gguf | mmproj-bf16 | 1.34 GB | Download |
llama.cpp
LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp
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Model tree for prithivMLmods/SpatialBlock-3B-reason-GGUF
Base model
Qwen/Qwen2.5-VL-3B-Instruct