Add metadata and links to model card

README.md

@@ -1,31 +1,32 @@
 ---
+language:
+- multilingual
+- ar
+- zh
+- cs
+- da
+- nl
+- en
+- fi
+- fr
+- de
+- he
+- hu
+- it
+- ja
+- ko
+- 'no'
+- pl
+- pt
+- ru
+- es
+- sv
+- th
+- tr
+- uk
+library_name: transformers
 license: mit
 license_link: https://huggingface.co/microsoft/Phi-4-mini-instruct/resolve/main/LICENSE
-language:
-- "multilingual"
-- "ar"
-- "zh"
-- "cs"
-- "da"
-- "nl"
-- "en"
-- "fi"
-- "fr"
-- "de"
-- "he"
-- "hu"
-- "it"
-- "ja"
-- "ko"
-- "no"
-- "pl"
-- "pt"
-- "ru"
-- "es"
-- "sv"
-- "th"
-- "tr"
-- "uk"
 pipeline_tag: text-generation
 tags:
 - nlp
@@ -34,9 +35,8 @@ widget:
 - messages:
   - role: user
     content: Can you provide ways to eat combinations of bananas and dragonfruits?
-library_name: transformers
 ---
- 
+
 ## Model Summary
  
 Phi-4-mini-instruct is a lightweight open model built upon synthetic data and filtered publicly available websites - with a focus on high-quality, reasoning dense data. The model belongs to the Phi-4 model family and supports 128K token context length. The model underwent an enhancement process, incorporating both supervised fine-tuning and direct preference optimization to support precise instruction adherence and robust safety measures.
@@ -46,6 +46,8 @@ Phi-4-mini-instruct is a lightweight open model built upon synthetic data and fi
 👩‍🍳 [Phi Cookbook](https://github.com/microsoft/PhiCookBook) <br>
 🏡 [Phi Portal](https://azure.microsoft.com/en-us/products/phi) <br>
 🖥️ Try It [Azure](https://aka.ms/phi-4-mini/azure), [Huggingface](https://huggingface.co/spaces/microsoft/phi-4-mini) <br>
+
+🚀 [Model paper](https://huggingface.co/papers/2503.01743)
  
 🎉**Phi-4**: [[multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) | [onnx](https://huggingface.co/microsoft/Phi-4-multimodal-instruct-onnx)]; 
 [[mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct) | [onnx](https://huggingface.co/microsoft/Phi-4-mini-instruct-onnx)]
@@ -368,4 +370,4 @@ The model was evaluated across a breadth of public and internal benchmarks to un
   + XSTest: exaggerated safety evaluation
   + Toxigen: adversarial and hate speech detection
 + Red Team:
-  + Responses to prompts provided by AI Red Team at Microsoft
+  + Responses to prompts provided by AI Red Team at Microsoft