Instructions to use prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-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/Spatial-Interactor-Qwen3-VL-4B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-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/Spatial-Interactor-Qwen3-VL-4B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-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/Spatial-Interactor-Qwen3-VL-4B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-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/Spatial-Interactor-Qwen3-VL-4B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-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/Spatial-Interactor-Qwen3-VL-4B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-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/Spatial-Interactor-Qwen3-VL-4B-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/Spatial-Interactor-Qwen3-VL-4B-GGUF:Q4_K_M
- SGLang
How to use prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-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/Spatial-Interactor-Qwen3-VL-4B-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/Spatial-Interactor-Qwen3-VL-4B-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/Spatial-Interactor-Qwen3-VL-4B-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/Spatial-Interactor-Qwen3-VL-4B-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/Spatial-Interactor-Qwen3-VL-4B-GGUF with Ollama:
ollama run hf.co/prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-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/Spatial-Interactor-Qwen3-VL-4B-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/Spatial-Interactor-Qwen3-VL-4B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-GGUF:Q4_K_M
- Lemonade
How to use prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Spatial-Interactor-Qwen3-VL-4B-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-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/Spatial-Interactor-Qwen3-VL-4B-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/Spatial-Interactor-Qwen3-VL-4B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-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/Spatial-Interactor-Qwen3-VL-4B-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/Spatial-Interactor-Qwen3-VL-4B-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"
Spatial-Interactor-Qwen3-VL-4B-GGUF
Spatial-Interactor-Qwen3-VL-4B is a full-parameter BF16 checkpoint from ZJU-OmniAI's Spatial-Interactor project, built on Qwen3-VL-4B-Instruct to learn spatial reasoning through interaction with the observable physical world, as detailed in the accompanying paper. It's trained in two stages: supervised fine-tuning on the LSI-108K dataset's L1-L2 split (local world-state and ego-motion transition modeling) alongside a public spatial QA mixture, followed by On-Policy Distillation (OPD) that integrates verifiable answer rewards with same-prefix privileged self-distillation to guide intermediate reasoning over long-horizon video trajectories — with the visual encoder kept frozen throughout while only the language model and multimodal projector are updated. Critically, the privileged transition trace used during training is discarded at inference time, so the deployed model takes the same image/video-plus-question inputs as its base model with no extra trace, reward model, or teacher branch; for video evaluation, the paper's main results use 32 ordered frames with chronological order preserved. It's one of four checkpoints in the Spatial-Interactor collection, loadable via the standard Transformers interface in place of the base model identifier, and released under Apache-2.0 following the base model's license.
Model Files
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| Spatial-Interactor-Qwen3-VL-4B.BF16.gguf | BF16 | 8.83 GB | Download |
| Spatial-Interactor-Qwen3-VL-4B.Q3_K_L.gguf | Q3_K_L | 2.41 GB | Download |
| Spatial-Interactor-Qwen3-VL-4B.Q3_K_M.gguf | Q3_K_M | 2.24 GB | Download |
| Spatial-Interactor-Qwen3-VL-4B.Q4_K_M.gguf | Q4_K_M | 2.72 GB | Download |
| Spatial-Interactor-Qwen3-VL-4B.Q4_K_S.gguf | Q4_K_S | 2.6 GB | Download |
| Spatial-Interactor-Qwen3-VL-4B.Q5_K_M.gguf | Q5_K_M | 3.16 GB | Download |
| Spatial-Interactor-Qwen3-VL-4B.Q5_K_S.gguf | Q5_K_S | 3.09 GB | Download |
| Spatial-Interactor-Qwen3-VL-4B.mmproj-bf16.gguf | mmproj-bf16 | 839 MB | Download |
llama.cpp
LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp
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Model tree for prithivMLmods/Spatial-Interactor-Qwen3-VL-4B-GGUF
Base model
Qwen/Qwen3-VL-4B-Instruct