Instructions to use prithivMLmods/Open-R1-Mini-Experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Open-R1-Mini-Experimental with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/Open-R1-Mini-Experimental")
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("prithivMLmods/Open-R1-Mini-Experimental")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/Open-R1-Mini-Experimental")
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 prithivMLmods/Open-R1-Mini-Experimental with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Open-R1-Mini-Experimental"
# 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/Open-R1-Mini-Experimental",
		"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/Open-R1-Mini-Experimental

SGLang

How to use prithivMLmods/Open-R1-Mini-Experimental 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/Open-R1-Mini-Experimental" \
    --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/Open-R1-Mini-Experimental",
		"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/Open-R1-Mini-Experimental" \
        --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/Open-R1-Mini-Experimental",
		"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 prithivMLmods/Open-R1-Mini-Experimental with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Open-R1-Mini-Experimental
```

prithivMLmods commited on Feb 11, 2025

Commit

df21712

verified ·

1 Parent(s): da492f0

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -6

README.md CHANGED Viewed

@@ -18,7 +18,7 @@ tags:
 The **Open-R1-Mini-Experimental** model is a fine-tuned version of Qwen2-VL-2B-Instruct, specifically designed for reasoning tasks, context reasoning, and multi-modal understanding based on the **R1 reasoning logits data**. This model integrates a conversational approach with deep reasoning capabilities to handle complex multi-modal tasks efficiently.
-#### Key Enhancements:
 * **Advanced Contextual Reasoning**: Open-R1-Mini-Experimental achieves state-of-the-art performance in reasoning tasks by leveraging R1 reasoning logits data, enhancing logical inference and decision-making.
@@ -30,15 +30,17 @@ The **Open-R1-Mini-Experimental** model is a fine-tuned version of Qwen2-VL-2B-I
 * **Multilingual Support**: Supports text understanding in various languages within images, including English, Chinese, Japanese, Korean, Arabic, most European languages, and Vietnamese.
-### Sample Inference
-`Demo:` https://huggingface.co/prithivMLmods/Open-R1-Mini-Experimental/blob/main/open-r1-reasoner-doc-py/open-r1-exp.ipynb
 ![open-r1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Ay3lb1nG7D-S56fV6qakg.png)
 ![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/oOR-sIIdg1ZW6c_2MKb4M.png)
 ![3.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/CX9B001c9IOfhfFCx2qhP.png)
 ![4.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/LYGGRiaoOEozW0GQECTGW.png)
-### How to Use
 ```python
@@ -108,7 +110,7 @@ output_text = processor.batch_decode(
 )
 print(output_text)
 ```
-### Buffer Handling
 ```python
     buffer = ""
     for new_text in streamer:
@@ -116,7 +118,7 @@ print(output_text)
         buffer = buffer.replace("<|im_end|>", "")
         yield buffer
 ```
-### **Key Features**
 1. **Advanced Contextual Reasoning:**
    - Optimized for **context-aware problem-solving** and **logical inference** based on R1 reasoning logits.

 The **Open-R1-Mini-Experimental** model is a fine-tuned version of Qwen2-VL-2B-Instruct, specifically designed for reasoning tasks, context reasoning, and multi-modal understanding based on the **R1 reasoning logits data**. This model integrates a conversational approach with deep reasoning capabilities to handle complex multi-modal tasks efficiently.
+# **Key Enhancements**
 * **Advanced Contextual Reasoning**: Open-R1-Mini-Experimental achieves state-of-the-art performance in reasoning tasks by leveraging R1 reasoning logits data, enhancing logical inference and decision-making.
 * **Multilingual Support**: Supports text understanding in various languages within images, including English, Chinese, Japanese, Korean, Arabic, most European languages, and Vietnamese.
+# **Sample Inference**
+**Demo:** https://huggingface.co/prithivMLmods/Open-R1-Mini-Experimental/blob/main/open-r1-reasoner-doc-py/open-r1-exp.ipynb
+![lkdfgnlhbnpf.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/LujbI0bFBqrrvMSmiz4Kt.png)
 ![open-r1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Ay3lb1nG7D-S56fV6qakg.png)
 ![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/oOR-sIIdg1ZW6c_2MKb4M.png)
 ![3.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/CX9B001c9IOfhfFCx2qhP.png)
 ![4.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/LYGGRiaoOEozW0GQECTGW.png)
+# **How to Use**
 ```python
 )
 print(output_text)
 ```
+# **Buffer Handling**
 ```python
     buffer = ""
     for new_text in streamer:
         buffer = buffer.replace("<|im_end|>", "")
         yield buffer
 ```
+# **Key Features**
 1. **Advanced Contextual Reasoning:**
    - Optimized for **context-aware problem-solving** and **logical inference** based on R1 reasoning logits.