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

Libraries

How to use prithivMLmods/Open-R1-Mini-Experimental-GGUF 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-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/Open-R1-Mini-Experimental-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Open-R1-Mini-Experimental-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Open-R1-Mini-Experimental-GGUF",
	filename="Open-R1-Mini-Experimental-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 prithivMLmods/Open-R1-Mini-Experimental-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Open-R1-Mini-Experimental-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Open-R1-Mini-Experimental-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Open-R1-Mini-Experimental-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Open-R1-Mini-Experimental-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/Open-R1-Mini-Experimental-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Open-R1-Mini-Experimental-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/Open-R1-Mini-Experimental-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Open-R1-Mini-Experimental-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Open-R1-Mini-Experimental-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Open-R1-Mini-Experimental-GGUF 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-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/Open-R1-Mini-Experimental-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/Open-R1-Mini-Experimental-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Open-R1-Mini-Experimental-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/Open-R1-Mini-Experimental-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/Open-R1-Mini-Experimental-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/Open-R1-Mini-Experimental-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/Open-R1-Mini-Experimental-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/Open-R1-Mini-Experimental-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Open-R1-Mini-Experimental-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/Open-R1-Mini-Experimental-GGUF 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 prithivMLmods/Open-R1-Mini-Experimental-GGUF 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 prithivMLmods/Open-R1-Mini-Experimental-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/Open-R1-Mini-Experimental-GGUF to start chatting

Docker Model Runner
How to use prithivMLmods/Open-R1-Mini-Experimental-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Open-R1-Mini-Experimental-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Open-R1-Mini-Experimental-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Open-R1-Mini-Experimental-GGUF-Q4_K_M

List all available models

lemonade list

Open-R1-Mini-Experimental-GGUF / README.md

prithivMLmods

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	---
	license: apache-2.0
	language:
	- en
	base_model:
	- prithivMLmods/Open-R1-Mini-Experimental
	pipeline_tag: image-text-to-text
	library_name: transformers
	tags:
	- reasoner
	- open
	- r1
	- explainer
	---
	![zfdsdfg.gif](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/WgW-xws4vzFJj48x2niWX.gif)

	> [!WARNING]
	> Note: This model contains artifacts and may perform poorly in some cases.

	# Open-R1-Mini-Experimental-GGUF

	The Open-R1-Mini-Experimental-GGUF model is a fine-tuned version of Qwen/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-GGUF achieves state-of-the-art performance in reasoning tasks by leveraging R1 reasoning logits data, enhancing logical inference and decision-making.

	* Understanding images of various resolution & ratio: The model excels at visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc.

	* Long-Context Video Understanding: Capable of processing and reasoning over videos of 20 minutes or more for high-quality video-based question answering, content creation, and dialogue.

	* Device Integration: With strong reasoning and decision-making abilities, the model can be integrated into mobile devices, robots, and automation systems for real-time operation based on both visual and textual input.

	* Multilingual Support: Supports text understanding in various languages within images, including English, Chinese, Japanese, Korean, Arabic, most European languages, and Vietnamese.

	# Sample Inference

	\| Example \| Image \|
	\|---------\|-------\|
	\| Example 1 \| ![lkdfgnlhbnpf.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/LujbI0bFBqrrvMSmiz4Kt.png) \|
	\| Example 2 \| ![open-r1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Ay3lb1nG7D-S56fV6qakg.png) \|
	\| Example 3 \| ![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/oOR-sIIdg1ZW6c_2MKb4M.png) \|
	\| Example 4 \| ![3.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/CX9B001c9IOfhfFCx2qhP.png) \|
	\| Example 5 \| ![4.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/LYGGRiaoOEozW0GQECTGW.png) \|

	Demo: https://huggingface.co/prithivMLmods/Open-R1-Mini-Experimental/blob/main/open-r1-reasoner-doc-py/open-r1-exp.ipynb

	### How to Use

	```python
	from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
	from qwen_vl_utils import process_vision_info

	# Load the model with automatic device placement
	model = Qwen2VLForConditionalGeneration.from_pretrained(
	"prithivMLmods/Open-R1-Mini-Experimental", torch_dtype="auto", device_map="auto"
	)

	# Recommended: Enable flash_attention_2 for better performance in multi-image and video tasks
	# model = Qwen2VLForConditionalGeneration.from_pretrained(
	# "prithivMLmods/Open-R1-Mini-Experimental",
	# torch_dtype=torch.bfloat16,
	# attn_implementation="flash_attention_2",
	# device_map="auto",
	# )

	# Load processor
	processor = AutoProcessor.from_pretrained("prithivMLmods/Open-R1-Mini-Experimental-GGUF")

	# Adjust visual token range for optimized memory usage
	# min_pixels = 2562828
	# max_pixels = 12802828
	# processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)

	messages = [
	{
	"role": "user",
	"content": [
	{
	"type": "image",
	"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
	},
	{"type": "text", "text": "Analyze the context of this image."},
	],
	}
	]

	# Prepare input
	text = processor.apply_chat_template(
	messages, tokenize=False, add_generation_prompt=True
	)
	image_inputs, video_inputs = process_vision_info(messages)
	inputs = processor(
	text=[text],
	images=image_inputs,
	videos=video_inputs,
	padding=True,
	return_tensors="pt",
	)
	inputs = inputs.to("cuda")

	# Inference
	generated_ids = model.generate(**inputs, max_new_tokens=128)
	generated_ids_trimmed = [
	out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
	]
	output_text = processor.batch_decode(
	generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
	)
	print(output_text)
	```
	### Buffer Handling
	```python
	buffer = ""
	for new_text in streamer:
	buffer += new_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.

	2. Optical Character Recognition (OCR):
	- Extracts and processes text from images with exceptional accuracy.

	3. Mathematical and Logical Problem Solving:
	- Supports complex reasoning and outputs equations in LaTeX format.

	4. Conversational and Multi-Turn Interaction:
	- Handles multi-turn dialogue with enhanced memory retention and response coherence.

	5. Multi-Modal Inputs & Outputs:
	- Processes images, text, and combined inputs to generate insightful analyses.

	6. Secure and Efficient Model Loading:
	- Uses Safetensors for faster and more secure model weight handling.