Instructions to use remyxai/SpaceQwen2.5-VL-3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use remyxai/SpaceQwen2.5-VL-3B-Instruct with Transformers:

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

pipe = pipeline("image-text-to-text", model="remyxai/SpaceQwen2.5-VL-3B-Instruct")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("remyxai/SpaceQwen2.5-VL-3B-Instruct")
model = AutoModelForMultimodalLM.from_pretrained("remyxai/SpaceQwen2.5-VL-3B-Instruct", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use remyxai/SpaceQwen2.5-VL-3B-Instruct 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 remyxai/SpaceQwen2.5-VL-3B-Instruct:F16
# Run inference directly in the terminal:
llama cli -hf remyxai/SpaceQwen2.5-VL-3B-Instruct:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf remyxai/SpaceQwen2.5-VL-3B-Instruct:F16
# Run inference directly in the terminal:
llama cli -hf remyxai/SpaceQwen2.5-VL-3B-Instruct: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/SpaceQwen2.5-VL-3B-Instruct:F16
# Run inference directly in the terminal:
./llama-cli -hf remyxai/SpaceQwen2.5-VL-3B-Instruct: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/SpaceQwen2.5-VL-3B-Instruct:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf remyxai/SpaceQwen2.5-VL-3B-Instruct:F16

Use Docker

docker model run hf.co/remyxai/SpaceQwen2.5-VL-3B-Instruct:F16

LM Studio
Jan

vLLM

How to use remyxai/SpaceQwen2.5-VL-3B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "remyxai/SpaceQwen2.5-VL-3B-Instruct"
# 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/SpaceQwen2.5-VL-3B-Instruct",
		"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/SpaceQwen2.5-VL-3B-Instruct:F16

SGLang

How to use remyxai/SpaceQwen2.5-VL-3B-Instruct 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/SpaceQwen2.5-VL-3B-Instruct" \
    --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/SpaceQwen2.5-VL-3B-Instruct",
		"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/SpaceQwen2.5-VL-3B-Instruct" \
        --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/SpaceQwen2.5-VL-3B-Instruct",
		"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/SpaceQwen2.5-VL-3B-Instruct with Ollama:
```
ollama run hf.co/remyxai/SpaceQwen2.5-VL-3B-Instruct:F16
```
Unsloth Desktop

How to use remyxai/SpaceQwen2.5-VL-3B-Instruct with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf remyxai/SpaceQwen2.5-VL-3B-Instruct:F16

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": "remyxai/SpaceQwen2.5-VL-3B-Instruct:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use remyxai/SpaceQwen2.5-VL-3B-Instruct with Docker Model Runner:
```
docker model run hf.co/remyxai/SpaceQwen2.5-VL-3B-Instruct:F16
```

Lemonade

How to use remyxai/SpaceQwen2.5-VL-3B-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull remyxai/SpaceQwen2.5-VL-3B-Instruct:F16

Run and chat with the model

lemonade run user.SpaceQwen2.5-VL-3B-Instruct-F16

List all available models

lemonade list

Hermes Agent

How to use remyxai/SpaceQwen2.5-VL-3B-Instruct with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf remyxai/SpaceQwen2.5-VL-3B-Instruct:F16

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 remyxai/SpaceQwen2.5-VL-3B-Instruct:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use remyxai/SpaceQwen2.5-VL-3B-Instruct with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf remyxai/SpaceQwen2.5-VL-3B-Instruct:F16

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 "remyxai/SpaceQwen2.5-VL-3B-Instruct:F16" \
  --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"

salma-remyx commited on Jan 29, 2025

Commit

562c953

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@@ -1,199 +1,52 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
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-- **Model type:** [More Information Needed]
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-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
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-## Uses
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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 ---
+license: apache-2.0
+datasets:
+- remyxai/OpenSpaces
+tags:
+- remyx
+base_model:
+- Qwen/Qwen2.5-VL-3B-Instruct
 ---
+# Model Card for SpaceQwen2.5-VL-3B-Instruct
+**SpaceQwen2.5-VL-3B-Instruct** uses LoRA to fine-tune [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) on a [dataset](https://huggingface.co/datasets/salma-remyx/OpenSpaces) designed with [VQASynth](https://github.com/remyxai/VQASynth/tree/main) to enhance spatial reasoning as in [SpatialVLM](https://spatial-vlm.github.io/)
 ## Model Details
 ### Model Description
+This model uses data synthesis techniques and publically available models to reproduce the work described in SpatialVLM to enhance the spatial reasoning of multimodal models.
+With a pipeline of expert models, we can infer spatial relationships between objects in a scene to create VQA dataset for spatial reasoning.
+- **Developed by:** remyx.ai
+- **Model type:** MultiModal Model, Vision Language Model, Qwen2.5-VL-3B-Instruct
+- **License:** Apache-2.0
+- **Finetuned from model:** LLaVA
+### Model Sources
+- **Dataset:** [SpaceLLaVA](https://huggingface.co/datasets/remyxai/OpenSpaces)
+- **Repository:** [VQASynth](https://github.com/remyxai/VQASynth/tree/main)
+- **Paper:** [SpatialVLM](https://arxiv.org/abs/2401.12168)
+## Citation
+```
+@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{qwen2.5-VL,
+    title = {Qwen2.5-VL},
+    url = {https://qwenlm.github.io/blog/qwen2.5-vl/},
+    author = {Qwen Team},
+    month = {January},
+    year = {2025}
+}
+```