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.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
+Phi-4-Mini-Reasoning.pdf filter=lfs diff=lfs merge=lfs -text

Phi-4-Mini-Reasoning.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f4a8d862e83d76d77e7a2d17ecb6edef792c0efdc61a1b4085bc44f7a748e6f5
+size 654894

README.md

@@ -20,15 +20,15 @@ widget:
 Phi-4-mini-reasoning is a lightweight open model built upon synthetic data with a focus on high-quality, reasoning dense data further finetuned for more advanced math reasoning capabilities. 
 The model belongs to the Phi-4 model family and supports 128K token context length. 
  
-📰 [Phi-4-mini-reasoning Microsoft Blog](https://aka.ms/phi4-feb2025) <br>
-📖 [Phi-4-mini-reasoning Technical Report](https://aka.ms/phi-4-multimodal/techreport) <br>
+📰 [Phi-4-mini-reasoning Blog](https://aka.ms/phi4-mini-reasoning/blog) <br>
+📖 [Phi-4-mini-reasoning Technical Report](https://aka.ms/phi4-mini-reasoning/techreport) <br>
 👩‍🍳 [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>
+🖥️ Try It [Azure](https://aka.ms/phi4-mini-reasoning/azure) <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)]; 
+🎉**Phi-4 models**: [[Phi-4-reasoning](https://huggingface.co/microsoft/Phi-4-reasoning)] | [[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)]
 
 ## Intended Uses
@@ -95,18 +95,18 @@ This format is used for general conversation and instructions:
 ```
 ### Inference with transformers
 
-Phi-4-mini-reasoning has been integrated in the `4.49.0` version of `transformers`. The current `transformers` version can be verified with: `pip list | grep transformers`.
+Phi-4-mini-reasoning has been integrated in the `4.51.3` version of `transformers`. The current `transformers` version can be verified with: `pip list | grep transformers`.
 Python 3.8 and 3.10 will work best. 
 List of required packages:
 
 ```
 flash_attn==2.7.4.post1
 torch==2.5.1
-transformers==4.49.0
+transformers==4.51.3
 accelerate==1.3.0
 ```
  
-Phi-4-mini-reasoning is also available in [Azure AI Studio]()
+Phi-4-mini-reasoning is also available in [Azure AI Studio](https://aka.ms/phi-4-mini-reasoning/azure)
 
 #### Example
  
@@ -137,7 +137,13 @@ inputs = tokenizer.apply_chat_template(
     return_tensors="pt",
 )
 
-outputs = model.generate(**inputs.to(model.device), max_new_tokens=32768)
+outputs = model.generate(
+    **inputs.to(model.device),
+    max_new_tokens=32768,
+    temperature=0.8,
+    top_p=0.95,
+    do_sample=True,
+)
 outputs = tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[-1]:])
 
 print(outputs[0])
@@ -157,7 +163,7 @@ print(outputs[0])
 + **Dates:** Trained in February 2024<br>
 + **Status:** This is a static model trained on offline datasets with the cutoff date of February 2025 for publicly available data.<br>
 + **Supported languages:** English<br>
-+ **Release date:** May 2025<br>
++ **Release date:** April 2025<br>
  
 ### Training Datasets
  
@@ -186,6 +192,8 @@ If you want to run the model on:
  
 The Phi-4 family of models has adopted a robust safety post-training approach. This approach leverages a variety of both open-source and in-house generated datasets. The overall technique employed to do the safety alignment is a combination of SFT, DPO (Direct Preference Optimization), and RLHF (Reinforcement Learning from Human Feedback) approaches  by utilizing human-labeled and synthetic English-language datasets, including publicly available datasets focusing on helpfulness and harmlessness, as well as various questions and answers targeted to multiple safety categories. 
 
+Phi-4-Mini-Reasoning was developed in accordance with Microsoft's responsible AI principles. Potential safety risks in the model’s responses were assessed using the Azure AI Foundry’s Risk and Safety Evaluation framework, focusing on harmful content, direct jailbreak, and model groundedness. The Phi-4-Mini-Reasoning Model Card contains additional information about our approach to safety and responsible AI considerations that developers should be aware of when using this model.
+
 ## Responsible AI Considerations
  
 Like other language models, the Phi family of models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include: