Update README.md

README.md

@@ -13,4 +13,86 @@ datasets:
 - prithivMLmods/Caption3o-Opt-v2
 - >-
   Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_2.7b_Attributes_Caption_ns_5647
----
+---
+
+![2.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/EUorMi4zOONUl9USQzBRp.png)
+
+# **DeepAttriCap-VLA-3B**
+
+> The **DeepAttriCap-VLA-3B** model is a fine-tuned version of **Qwen2.5-VL-3B-Instruct**, tailored for **Vision-Language Attribution** and **Image Captioning**. This variant is designed to generate precise, attribute-rich descriptions that define the visual properties of objects and scenes in detail, ensuring both object-level identification and contextual captioning.
+
+# Key Highlights
+
+1. **Vision-Language Attribution**: Produces structured captions with explicit object attributes, properties, and contextual details.
+2. **High-Precision Descriptions**: Captures fine-grained visual properties (shape, color, texture, material, relations).
+3. **Balanced Object-Centric and Scene-Level Captions**: Generates both holistic captions and per-object attributions.
+4. **Adaptable Across Image Types**: Works well on natural, artistic, abstract, and technical imagery.
+5. **Built on Qwen2.5-VL Architecture**: Leverages the strengths of the 3B multimodal instruction-tuned variant for fine-grained reasoning.
+6. **Multilingual Capability**: English is default, with multilingual captioning enabled through prompt engineering.
+
+# Training Details
+
+This model was fine-tuned on a mixture of curated image–caption datasets with emphasis on **attribute-based captioning** and **precise object-property definition**:
+
+* **[prithivMLmods/blip3o-caption-mini-arrow](https://huggingface.co/datasets/prithivMLmods/blip3o-caption-mini-arrow)**
+* **[prithivMLmods/Caption3o-Opt-v3](https://huggingface.co/datasets/prithivMLmods/Caption3o-Opt-v3)**
+* **[prithivMLmods/Caption3o-Opt-v2](https://huggingface.co/datasets/prithivMLmods/Caption3o-Opt-v2)**
+* **[Multimodal-Fatima/Caltech101\_not\_background\_test\_facebook\_opt\_2.7b\_Attributes\_Caption\_ns\_5647](https://huggingface.co/datasets/Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_2.7b_Attributes_Caption_ns_5647)**
+
+The training objective emphasized **attribution-style captioning**—capturing precise object details, relationships, and scene-level semantics.
+
+# Quick Start with Transformers
+
+```python
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "prithivMLmods/DeepAttriCap-VLA-3B", torch_dtype="auto", device_map="auto"
+)
+
+processor = AutoProcessor.from_pretrained("prithivMLmods/DeepAttriCap-VLA-3B")
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"},
+            {"type": "text", "text": "Provide an attribute-rich caption for this image."},
+        ],
+    }
+]
+
+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"
+).to("cuda")
+
+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)
+```
+
+# Intended Use
+
+* Attribute-rich object recognition and captioning.
+* Vision-language research in attribution and property extraction.
+* Dataset creation for fine-grained visual description tasks.
+* Enabling descriptive captions for images with complex object relationships.
+* Supporting creative, technical, and educational use cases requiring precise captions.
+
+# Limitations
+
+* May produce variable levels of granularity depending on the image complexity.
+* Not optimized for highly censored or safety-critical deployments.
+* Might over-attribute or hallucinate properties in ambiguous or abstract visuals