Update README.md

README.md

@@ -11,4 +11,119 @@ tags:
 - open
 - r1
 - explainer
----
+---
+![zgdfgzdfg.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/0vbyb6c2URR861vYDbKHn.png)
+
+# **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
+
+![open-r1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Ay3lb1nG7D-S56fV6qakg.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 = 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
+```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.