Instructions to use Philip-MIT/SOLE-R1-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Philip-MIT/SOLE-R1-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Philip-MIT/SOLE-R1-8B")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Philip-MIT/SOLE-R1-8B")
model = AutoModelForImageTextToText.from_pretrained("Philip-MIT/SOLE-R1-8B")
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

vLLM

How to use Philip-MIT/SOLE-R1-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Philip-MIT/SOLE-R1-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Philip-MIT/SOLE-R1-8B",
		"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/Philip-MIT/SOLE-R1-8B

SGLang

How to use Philip-MIT/SOLE-R1-8B 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 "Philip-MIT/SOLE-R1-8B" \
    --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": "Philip-MIT/SOLE-R1-8B",
		"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 "Philip-MIT/SOLE-R1-8B" \
        --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": "Philip-MIT/SOLE-R1-8B",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Philip-MIT/SOLE-R1-8B with Docker Model Runner:
```
docker model run hf.co/Philip-MIT/SOLE-R1-8B
```

Philip-MIT commited on 2 days ago

Commit

972a577

verified ·

1 Parent(s): 8651f5a

Update README.md

Browse files

Files changed (1) hide show

README.md +6 -1

README.md CHANGED Viewed

@@ -44,6 +44,7 @@ The recommended interface for inference is [RewardGen](https://github.com/Philip
     from rewardgen import generate, video_plot
     video_paths = [
         "test_videos/robosuite/lift/unsuccessful/robosuite_lift_episode_12_unsuccessful_max_reward_38.mp4"
     ]
@@ -110,7 +111,11 @@ Downstream systems should parse the numeric value inside `<answer>...</answer>`
 The model was trained on the [SOLE-R1-8B](https://huggingface.co/Philip-MIT/SOLE-R1-8B)  training dataset.
-The dataset contains robot task progress examples with images, prompts, reasoning completions, and progress labels. The full dataset is approximately 2TB.
 Streaming example:

     from rewardgen import generate, video_plot
+    # test_videos provided at the github repo: https://github.com/Philip-MIT/rewardgen
     video_paths = [
         "test_videos/robosuite/lift/unsuccessful/robosuite_lift_episode_12_unsuccessful_max_reward_38.mp4"
     ]
 The model was trained on the [SOLE-R1-8B](https://huggingface.co/Philip-MIT/SOLE-R1-8B)  training dataset.
+The dataset contains robot task progress examples with images, prompts, reasoning completions, and progress labels.
+It also includes a diverse collection of general spatial and multi-frame temporal reasoning data (e.g., from SSR-CoT, SpatialVLM, Spot-the-diff, Embodied CoT, RoboVQA, Robo2VLM-Reasoning) to serve as a foundational layer of our training mixture.
+The full dataset is approximately 2TB.
 Streaming example: