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

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

How to Use

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 = 256*28*28
# max_pixels = 1280*28*28
# 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

    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.