fix

Author: leestott

README.md

@@ -39,13 +39,15 @@ Follow these steps to get started using these resource :
   - [Using Guidance-ai and Phi](./md/01.Introduction/01/01.Guidance.md)
   - [GitHub Marketplace Models](https://github.com/marketplace/models)
   - [Azure AI Model Catalog](https://ai.azure.com)
+
 - Inference Phi in different environment
     -  [Hugging face](./md/01.Introduction/02/01.HF.md)
     -  [GitHub Models](./md/01.Introduction/02/02.GitHubModel.md)
     -  [Azure AI Foundry Model Catalog](./md/01.Introduction/02/03.AzureAIFoundry.md)
     -  [Ollama](./md/01.Introduction/02/04.Ollama.md)
     -  [AI Toolkit VSCode (AITK)](./md/01.Introduction/02/05.AITK.md)
     -  [NVIDIA NIM](./md/01.Introduction/02/06.NVIDIA.md)
+
 - Inference Phi Family
     - [Inference Phi in iOS](./md/01.Introduction/03/iOS_Inference.md)
     - [Inference Phi in Android](./md/01.Introduction/03/Android_Inference.md)
@@ -62,10 +64,12 @@ Follow these steps to get started using these resource :
     - [Quantizing Phi-3.5 / 4 using Generative AI extensions for onnxruntime](./md/01.Introduction/04/UsingORTGenAIQuantifyingPhi.md)
     - [Quantizing Phi-3.5 / 4  using Intel OpenVINO](./md/01.Introduction/04/UsingIntelOpenVINOQuantifyingPhi.md)
     - [Quantizing Phi-3.5 / 4  using Apple MLX Framework](./md/01.Introduction/04/UsingAppleMLXQuantifyingPhi.md)
+
 -  Evaluation Phi
     - [Response AI](./md/01.Introduction/05/ResponsibleAI.md)
     - [Azure AI Foundry for Evaluation](./md/01.Introduction/05/AIFoundry.md)
     - [Using Promptflow for Evaluation](./md/01.Introduction/05/Promptflow.md) 
+
 - Phi application development samples
   - Text & Chat Applications
     - Phi-4 Samples 🆕
@@ -85,15 +89,18 @@ Follow these steps to get started using these resource :
       - [Phi-3.5-Instruct WebGPU RAG Chatbot](./md/02.Application/01.TextAndChat/Phi3/WebGPUWithPhi35Readme.md)
       - [Using Windows GPU to create Prompt flow solution with Phi-3.5-Instruct ONNX](./md/02.Application/01.TextAndChat/Phi3/UsingPromptFlowWithONNX.md)
       - [Using Microsoft Phi-3.5 tflite to create Android app](./md/02.Application/01.TextAndChat/Phi3/UsingPhi35TFLiteCreateAndroidApp.md)
+
   - Azure AI Inference SDK Code Based Samples 
     - Phi-4 Samples 🆕
       - [📓] [Generate project code using Phi-4-multimodal](./md/02.Application/02.Code/Phi4/GenProjectCode/README.md)
     - Phi-3 / 3.5 Samples
       - [Build your own Visual Studio Code GitHub Copilot Chat with Microsoft Phi-3 Family](./md/02.Application/02.Code/Phi3/VSCodeExt/README.md)
       - [Create your own Visual Studio Code Chat Copilot Agent with Phi-3.5 by GitHub Models](/md/02.Application/02.Code/Phi3/CreateVSCodeChatAgentWithGitHubModels.md)
+
   - Advanced Reasoning Samples
     - Phi-4 Samples 🆕
       - [📓] [Phi-4-mini Reasoning Samples](./md/02.Application/03.AdvancedReasoning/Phi4/AdvancedResoningPhi4mini/README.md)
+
   - Vision Samples
     - Phi-4 Samples 🆕
       - [📓] [Use Phi-4-multimodal to read images and generate code](./md/02.Application/04.Vision/Phi4/CreateFrontend/README.md) 
@@ -106,11 +113,13 @@ Follow these steps to get started using these resource :
       - [Phi-3 Vision Nvidia NIM](./md/02.Application/04.Vision/Phi3/E2E_Nvidia_NIM_Vision.md)
       - [Phi-3 Vision OpenVino](./md/02.Application/04.Vision/Phi3/E2E_OpenVino_Phi3Vision.md)
       - [📓][Phi-3.5 Vision multi-frame or multi-image sample](./md/02.Application/04.Vision/Phi3/phi3-vision-demo.ipynb)
+
   - Audio Samples
     - Phi-4 Samples 🆕
       - [📓] [Extracting audio transcripts using Phi-4-multimodal](./md/02.Application/05.Audio/Phi4/Transciption/README.md)
       - [📓] [Phi-4-multimodal Audio Sample](./md/02.Application/05.Audio/Phi4/Siri/demo.ipynb)
       - [📓] [Phi-4-multimodal Speech Translation Sample](./md/02.Application/05.Audio/Phi4/Translate/demo.ipynb)
+
   - MOE Samples
     - Phi-3 / 3.5 Samples
       - [📓] [Phi-3.5 Mixture of Experts Models (MoEs) Social Media Sample](./md/02.Application/06.MoE/Phi3/phi3_moe_demo.ipynb)
@@ -121,6 +130,7 @@ Follow these steps to get started using these resource :
   - Multimodal Mixing Samples
     - Phi-4 Samples 🆕
       -  [📓] [Using Phi-4-multimodal as a Technology journalist](./md/02.Application/08.Multimodel/Phi4/TechJournalist/phi_4_mm_audio_text_publish_news.ipynb)
+
 - Fine-tuning Phi Samples
   - [Fine-tuning Scenarios](./md/03.FineTuning/FineTuning_Scenarios.md)
   - [Fine-tuning vs RAG](./md/03.FineTuning/FineTuning_vs_RAG.md)
@@ -138,9 +148,11 @@ Follow these steps to get started using these resource :
   - [Fine-tuning Phi-3-vision (official support)](./md/03.FineTuning/FineTuning_Vision.md)
   - [Fine-Tuning Phi-3 with Kaito AKS , Azure Containers(official Support)](./md/03.FineTuning/FineTuning_Kaito.md)
   - [Fine-Tuning Phi-3 and 3.5 Vision](https://github.com/2U1/Phi3-Vision-Finetune)
+
 - Hands on Lab
   - [Exploring cutting-edge models: LLMs, SLMs, local development and more](https://github.com/microsoft/aitour-exploring-cutting-edge-models)
   - [Unlocking NLP Potential: Fine-Tuning with Microsoft Olive](https://github.com/azure/Ignite_FineTuning_workshop)
+
 - Academic Research Papers and Publications
   - [Textbooks Are All You Need II: phi-1.5 technical report](https://arxiv.org/abs/2309.05463)
   - [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://arxiv.org/abs/2404.14219)

code/04.Finetuning/FineTrainingScript.py

@@ -0,0 +1,74 @@
+# This code is for fine-tuning Phi-3 Models.
+# Note thi requires 7.4 GB of GPU RAM for the process.
+# Model available at https://huggingface.co/collections/microsoft/phi-3-6626e15e9585a200d2d761e3
+# Model Names 
+# microsoft/Phi-3-mini-4k-instruct
+# microsoft/Phi-3-mini-128k-instruct
+# microsoft/Phi-3-small-8k-instruct
+# microsoft/Phi-3-small-128k-instruct
+# microsoft/Phi-3-medium-4k-instruct
+# microsoft/Phi-3-medium-128k-instruct
+# microsoft/Phi-3-vision-128k-instruct
+# microsoft/Phi-3-mini-4k-instruct-onnx
+# microsoft/Phi-3-mini-4k-instruct-onnx-web
+# microsoft/Phi-3-mini-128k-instruct-onnx
+# microsoft/Phi-3-small-8k-instruct-onnx-cuda
+# microsoft/Phi-3-small-128k-instruct-onnx-cuda
+# microsoft/Phi-3-medium-4k-instruct-onnx-cpu
+# microsoft/Phi-3-medium-4k-instruct-onnx-cuda
+# microsoft/Phi-3-medium-4k-instruct-onnx-directml
+# microsoft/Phi-3-medium-128k-instruct-onnx-cpu
+# microsoft/Phi-3-medium-128k-instruct-onnx-cuda
+# microsoft/Phi-3-medium-128k-instruct-onnx-directml
+# microsoft/Phi-3-mini-4k-instruct-gguf
+
+# Load the pre-trained model and tokenizer
+model = AutoModelForCausalLM.from_pretrained('Model_Name', torch_dtype=torch.float16)
+tokenizer = AutoTokenizer.from_pretrained('Mode_Name')
+
+# Load the dataset for fine-tuning
+dataset = load_dataset(DATASET_NAME, split="train")
+
+# Define the formatting function for the prompts
+def formatting_prompts_func(examples):
+    convos = examples["conversations"]
+    texts = []
+    mapper = {"system": "system\n", "human": "\nuser\n", "gpt": "\nassistant\n"}
+    end_mapper = {"system": "", "human": "", "gpt": ""}
+    for convo in convos:
+        text = "".join(f"{mapper[(turn := x['from'])]} {x['value']}\n{end_mapper[turn]}" for x in convo)
+        texts.append(f"{text}{tokenizer.eos_token}")
+    return {"text": texts}
+
+# Apply the formatting function to the dataset
+dataset = dataset.map(formatting_prompts_func, batched=True)
+
+# Define the training arguments
+args = TrainingArguments(
+    evaluation_strategy="steps",
+    per_device_train_batch_size=7,
+    gradient_accumulation_steps=4,
+    gradient_checkpointing=True,
+    learning_rate=1e-4,
+    fp16=True,
+    max_steps=-1,
+    num_train_epochs=3,
+    save_strategy="epoch",
+    logging_steps=10,
+    output_dir=NEW_MODEL_NAME,
+    optim="paged_adamw_32bit",
+    lr_scheduler_type="linear"
+)
+
+# Create the trainer
+trainer = SFTTrainer(
+    model=model,
+    args=args,
+    train_dataset=dataset,
+    dataset_text_field="text",
+    max_seq_length=128,
+    formatting_func=formatting_prompts_func
+)
+
+# Start the training process
+trainer.train()