Create README.md

README.md

@@ -0,0 +1,170 @@
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- prithivMLmods/Radiology-Infer-Mini
+pipeline_tag: image-text-to-text
+tags:
+- Radiology
+- Infer
+- Qwen2
+- 2B
+---
+# Radiology-Infer-Mini-iMat-GGUF
+Original model: [Radiology-Infer-Mini](https://huggingface.co/prithivMLmods/Radiology-Infer-Mini) by [prithivMLmods](https://huggingface.co/prithivMLmods)  
+Based on: [Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) by [Qwen](https://huggingface.co/Qwen)
+
+## Quantization notes
+Made with llama.cpp-b4608 with imatrix file based on Exllamav2 calibration data.  
+
+# Original model card
+![3.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/lLU2lEv76EIL3aNeMHy0Q.png)
+
+# **Radiology-Infer-Mini**
+
+Radiology-Infer-Mini is a vision-language model fine-tuned from the Qwen2-VL-2B framework, specifically designed to excel in radiological analysis, text extraction, and medical report generation. It integrates advanced multi-modal capabilities with domain-specific expertise, ensuring accurate and efficient processing of radiology-related tasks.
+
+### Key Enhancements:
+
+1. **State-of-the-Art Understanding of Medical Images**  
+   Radiology-Infer-Mini achieves cutting-edge performance in interpreting complex medical imagery, including X-rays, MRIs, CT scans, and ultrasounds. It is fine-tuned on healthcare-specific benchmarks to ensure precise recognition of anatomical and pathological features.
+
+2. **Support for Extended Medical Reports and Cases**  
+   Capable of processing and analyzing extensive radiology case studies, Radiology-Infer-Mini can generate high-quality diagnostic reports and answer complex medical queries with detailed explanations. Its proficiency extends to multi-page radiology documents, ensuring comprehensive visual and textual understanding.
+
+3. **Integration with Medical Devices**  
+   With robust reasoning and decision-making capabilities, Radiology-Infer-Mini can seamlessly integrate with medical imaging systems and robotic platforms. It supports automated workflows for tasks such as diagnosis support, triaging, and clinical decision-making.
+
+4. **Math and Diagram Interpretation**  
+   Equipped with LaTeX support and advanced diagram interpretation capabilities, Radiology-Infer-Mini handles mathematical annotations, statistical data, and visual charts present in medical reports with precision.
+
+5. **Multilingual Support for Medical Text**  
+   Radiology-Infer-Mini supports the extraction and interpretation of multilingual texts embedded in radiological images, including English, Chinese, Arabic, Korean, Japanese, and most European languages. This feature ensures accessibility for a diverse global healthcare audience.
+
+Radiology-Infer-Mini represents a transformative step in radiology-focused AI, enhancing productivity and accuracy in medical imaging and reporting.
+
+![radiology.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/S0JuOoKkXmXgj4li6a9OZ.png)
+
+### How to Use
+
+```python
+from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+# default: Load the model on the available device(s)
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "prithivMLmods/Radiology-Infer-Mini", torch_dtype="auto", device_map="auto"
+)
+
+# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
+# model = Qwen2VLForConditionalGeneration.from_pretrained(
+#     "prithivMLmods/Radiology-Infer-Mini",
+#     torch_dtype=torch.bfloat16,
+#     attn_implementation="flash_attention_2",
+#     device_map="auto",
+# )
+
+# default processer
+processor = AutoProcessor.from_pretrained("prithivMLmods/Radiology-Infer-Mini")
+
+# The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and 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": "Describe this image."},
+        ],
+    }
+]
+
+# Preparation for inference
+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: Generation of the output
+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)
+```
+### Buf
+```python
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        # Remove <|im_end|> or similar tokens from the output
+        buffer = buffer.replace("<|im_end|>", "")
+        yield buffer
+```
+### **Intended Use**  
+
+**Radiology-Infer-Mini** is designed to support healthcare professionals and researchers in tasks involving medical imaging and radiological analysis. Its primary applications include:  
+
+1. **Diagnostic Support**  
+   - Analyze medical images (X-rays, MRIs, CT scans, ultrasounds) to identify abnormalities, annotate findings, and assist radiologists in forming diagnostic conclusions.  
+
+2. **Medical Report Generation**  
+   - Automatically generate structured radiology reports from image data, reducing documentation time and improving workflow efficiency.  
+
+3. **Educational and Research Tools**  
+   - Serve as a teaching aid for radiology students and support researchers in large-scale studies by automating image labeling and data extraction.  
+
+4. **Workflow Automation**  
+   - Integrate with medical devices and hospital systems to automate triaging, anomaly detection, and report routing in clinical settings.  
+
+5. **Multi-modal Applications**  
+   - Handle complex tasks involving both images and text, such as extracting patient data from images and synthesizing text-based findings with visual interpretations.  
+
+6. **Global Accessibility**  
+   - Support multilingual radiological text understanding for use in diverse healthcare settings around the world.  
+
+### **Limitations**  
+
+While **Radiology-Infer-Mini** offers advanced capabilities, it has the following limitations:  
+
+1. **Medical Expertise Dependency**  
+   - The model provides supplementary insights but cannot replace the expertise and judgment of a licensed radiologist or clinician.  
+
+2. **Data Bias**  
+   - Performance may vary based on the training data, which might not fully represent all imaging modalities, patient demographics, or rare conditions.  
+
+3. **Edge Cases**  
+   - Limited ability to handle edge cases, highly complex images, or uncommon medical scenarios that were underrepresented in its training dataset.  
+
+4. **Regulatory Compliance**  
+   - It must be validated for compliance with local medical regulations and standards before clinical use.  
+
+5. **Interpretation Challenges**  
+   - The model may misinterpret artifacts, noise, or low-quality images, leading to inaccurate conclusions in certain scenarios.  
+
+6. **Multimodal Integration**  
+   - While capable of handling both visual and textual inputs, tasks requiring deep contextual understanding across different modalities might yield inconsistent results.  
+
+7. **Real-Time Limitations**  
+   - Processing speed and accuracy might be constrained in real-time or high-throughput scenarios, especially on hardware with limited computational resources.  
+
+8. **Privacy and Security**  
+   - Radiology-Infer-Mini must be used in secure environments to ensure the confidentiality and integrity of sensitive medical data.