Update README.md

README.md

@@ -7,4 +7,53 @@ language:
 base_model:
 - microsoft/Phi-3.5-vision-instruct
 pipeline_tag: question-answering
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+---
+# Model Card for Fine-tuned Phi-3.5-Vision-Instruct
+
+This model is fine-tuned from Microsoft's Phi-3.5-Vision-Instruct to improve visual question answering tasks, particularly for detailed item measurements in images. It has been trained on specific datasets that provide real-world images with tasks related to recognizing and accurately reporting measurements, avoiding assumptions or fabrications, and converting units into standard forms.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** [More Information Needed]
+- **Model type:** Vision-based question answering model
+- **Language(s):** English
+- **License:** MIT
+- **Base model:** [microsoft/Phi-3.5-vision-instruct](https://huggingface.co/microsoft/Phi-3.5-vision-instruct)
+
+### Model Sources
+
+- **Repository:** [More Information Needed]
+
+## Uses
+
+### Direct Use
+
+This model can be directly used for tasks where visual analysis and measurement extraction are critical. For example:
+- Extracting measurements from product images.
+- Answering detailed questions based on the visual content of an image.
+
+### Out-of-Scope Use
+
+- The model should not be used for tasks requiring deep semantic understanding of objects beyond what is visually apparent or tasks that involve creative interpretation.
+
+## Bias, Risks, and Limitations
+
+The model is limited to its training data and might not generalize well to images that differ significantly from the dataset. Risks include:
+- Misinterpretation of visual data if the image is unclear.
+- Inability to handle text-heavy images that do not align with the training data.
+
+### Recommendations
+
+Users should verify the output in cases where exact measurements are crucial. Misuse in scenarios where a high degree of visual interpretation is required may lead to inaccurate responses.
+
+
+### Training Data
+
+The model was fine-tuned on a dataset that included images with visual elements such as product measurements. The data was formatted with instructions to avoid assumptions and to only rely on visual information.
+
+### Training Procedure
+
+- **Training regime:** fp16 mixed precision.
+