Instructions to use remyxai/SpaceLLaVA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use remyxai/SpaceLLaVA with Transformers:

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

pipe = pipeline("image-text-to-text", model="remyxai/SpaceLLaVA")
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, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("remyxai/SpaceLLaVA")
model = AutoModelForCausalLM.from_pretrained("remyxai/SpaceLLaVA")
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]:]))

llama-cpp-python

How to use remyxai/SpaceLLaVA with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="remyxai/SpaceLLaVA",
	filename="ggml-model-f16.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

llama.cpp

How to use remyxai/SpaceLLaVA with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf remyxai/SpaceLLaVA:F16
# Run inference directly in the terminal:
llama-cli -hf remyxai/SpaceLLaVA:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf remyxai/SpaceLLaVA:F16
# Run inference directly in the terminal:
llama-cli -hf remyxai/SpaceLLaVA:F16

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 remyxai/SpaceLLaVA:F16
# Run inference directly in the terminal:
./llama-cli -hf remyxai/SpaceLLaVA:F16

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 remyxai/SpaceLLaVA:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf remyxai/SpaceLLaVA:F16

Use Docker

docker model run hf.co/remyxai/SpaceLLaVA:F16

LM Studio
Jan

vLLM

How to use remyxai/SpaceLLaVA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "remyxai/SpaceLLaVA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "remyxai/SpaceLLaVA",
		"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/remyxai/SpaceLLaVA:F16

SGLang

How to use remyxai/SpaceLLaVA 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 "remyxai/SpaceLLaVA" \
    --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": "remyxai/SpaceLLaVA",
		"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 "remyxai/SpaceLLaVA" \
        --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": "remyxai/SpaceLLaVA",
		"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 remyxai/SpaceLLaVA with Ollama:
```
ollama run hf.co/remyxai/SpaceLLaVA:F16
```

Unsloth Studio new

How to use remyxai/SpaceLLaVA 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 remyxai/SpaceLLaVA 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 remyxai/SpaceLLaVA to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for remyxai/SpaceLLaVA to start chatting

Docker Model Runner
How to use remyxai/SpaceLLaVA with Docker Model Runner:
```
docker model run hf.co/remyxai/SpaceLLaVA:F16
```

Lemonade

How to use remyxai/SpaceLLaVA with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull remyxai/SpaceLLaVA:F16

Run and chat with the model

lemonade run user.SpaceLLaVA-F16

List all available models

lemonade list

A newer version of this model is available: remyxai/SpaceOm

SpaceLLaVA

Model Type: Multimodal, Vision-Language Model
Architecture: llava-v1.5-13b
Model Size: 13.4B parameters (FP16)
Finetuned from: liuhaotian/llava-v1.5-13b
Finetune Strategy: LoRA (Low-Rank Adaptation)
License: Apache-2.0

Model Overview

SpaceLLaVA is a vision-language model adapted from LLaVA-1.5 (13B) and fine-tuned by LoRA to improve spatial reasoning. Trained using a synthetic VQA dataset inspired by the methods described in SpatialVLM. SpaceLLaVA demonstrates strong qualitative and quantitative spatial reasoning after distilling 3D scene understanding from the pipelines in VQASynth.

Running SpaceLLaVA

GGUF

Use this notebook to query spatial relationships between objects in a scene with llama-cpp-python.

Docker

docker build -f Dockerfile -t spacellava-server:latest

docker run -it --rm --gpus all -p8000:8000 -p8001:8001 -p8002:8002 --shm-size 12G spacellava-server:latest

python3 client.py --image_path "https://remyx.ai/assets/spatialvlm/warehouse_rgb.jpg" --prompt "What is the distance between the man in the red hat and the pallet of boxes?"

Dataset & Training

Dataset: SpaceLLaVA
Code: VQASynth
Reference: SpatialVLM
~28,000 synthetic samples created using templated VQA pairs with a 3D scene reconstruction pipeline
Formats: image (RGB), question (text), answer (text)
Spatial relation types include: “distances”, “size”, “left of”, “above”, “closer to”, “inside”

Scripts for LoRA SFT available at trl Check out the SpaceVLMs collection

Model Evaluation (Coming Soon)

TODO: VLMEvalKit evaluation on the QSpatial benchmark, VSR, etc.

Try it on Discord: http://discord.gg/b2yGuCNpuC

⚠️ Limitations & Ethical Considerations

Performance may degrade in cluttered environments or camera perspective.
This model was fine-tuned using synthetic reasoning over an internet image dataset.
Multimodal biases inherent to the base model (LLaVA) may persist.
Not intended for use in safety-critical or legal decision-making.

Users are encouraged to evaluate outputs critically and consider fine-tuning for domain-specific safety and performance.

License and Citation

Licensed under Apache-2.0.

@article{chen2024spatialvlm,
  title = {SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities},
  author = {Chen, Boyuan and Xu, Zhuo and Kirmani, Sean and Ichter, Brian and Driess, Danny and Florence, Pete and Sadigh, Dorsa and Guibas, Leonidas and Xia, Fei},
  journal = {arXiv preprint arXiv:2401.12168},
  year = {2024},
  url = {https://arxiv.org/abs/2401.12168},
}

@misc{liu2023llava,
      title={Visual Instruction Tuning},
      author={Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae},
      publisher={NeurIPS},
      year={2023},
}