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Model: prithivMLmods/Radiology-Infer-Mini
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2-VL-2B-Instruct
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- Radiology
+- Infer
+- Qwen2
+- 2B
+---
+![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.

added_tokens.json

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+{
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.json

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+{
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+}

config.json

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+{
+  "_name_or_path": "Qwen/Qwen2-VL-2B-Instruct",
+  "architectures": [
+    "Qwen2VLForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "image_token_id": 151655,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2_vl",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "pad_token_id": 151654,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "mrope_section": [
+      16,
+      24,
+      24
+    ],
+    "rope_type": "default",
+    "type": "default"
+  },
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.47.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "video_token_id": 151656,
+  "vision_config": {
+    "hidden_size": 1536,
+    "in_chans": 3,
+    "model_type": "qwen2_vl",
+    "spatial_patch_size": 14
+  },
+  "vision_end_token_id": 151653,
+  "vision_start_token_id": 151652,
+  "vision_token_id": 151654,
+  "vocab_size": 151936
+}

configuration.json

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+{"framework": "pytorch", "task": "image-text-to-text", "allow_remote": true}

generation_config.json

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+{
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+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "max_length": 32768,
+  "pad_token_id": 151654,
+  "temperature": 0.01,
+  "top_k": 1,
+  "top_p": 0.001,
+  "transformers_version": "4.47.1"
+}

model.safetensors

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preprocessor_config.json

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+{
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "Qwen2VLImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "max_pixels": 12845056,
+  "merge_size": 2,
+  "min_pixels": 3136,
+  "patch_size": 14,
+  "processor_class": "Qwen2VLProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "longest_edge": 12845056,
+    "shortest_edge": 3136
+  },
+  "temporal_patch_size": 2
+}

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "pad_token": {
+    "content": "<|vision_pad|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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tokenizer_config.json

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+  "added_tokens_decoder": {
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+      "normalized": false,
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+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
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+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
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+  "pad_token": "<|vision_pad|>",
+  "padding_side": "right",
+  "processor_class": "Qwen2VLProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}