Azure · sagarsumant · Jan 10, 2025 · Dec 7, 2024 · Dec 9, 2024 · Dec 10, 2024
diff --git a/sdk/python/jobs/finetuning/standalone/finetuning-job-cliv2-readme.md b/sdk/python/jobs/finetuning/standalone/finetuning-job-cliv2-readme.md
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 # Running a Fine-Tuning Job from CLI
 
 This guide provides instructions on how to run a fine-tuning job using the Azure Machine Learning CLI v2.
 
 ## Prerequisites
 
 1. **Azure CLI**: Ensure you have the Azure CLI installed. If not, you can install it from [here](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli).
 
 2. **Azure Machine Learning CLI v2**: Install the Azure Machine Learning CLI extension v2.
    ```bash
    az extension add -n ml -y
    ```
 
 3. **Azure Subscription**: Ensure you have an active Azure subscription and the necessary permissions to create and manage resources.
 
 4. **Resource Group and Workspace**: Ensure you have an Azure resource group and an Azure Machine Learning workspace. If not, you can create them using the following commands:
 
       ```bash 
       az group create --name <resource-group-name> --location <location>
       az ml workspace create --name <workspace-name> --resource-group <resource-group-name> --location <location>
       ```
 
 ### Running the Fine-Tuning Job
 To run the fine-tuning job, use the following command:
 
 ```bash
 az ml job create --file text-generation-finetuning-amlcompute.yaml --resource-group hyperdrive-service-static-rg --workspace-name train-finetune-dev-eastus --name "ft-maap-llama3-instance-types-1209-01"
 ```
 
 #### Command Breakdown
 * az ml job create: Command to create and run a job in Azure Machine Learning.
 * --file text-generation-finetuning-amlcompute.yaml: Specifies the YAML file that defines the job configuration.
 * --resource-group hyperdrive-service-static-rg: Specifies the Azure resource group.
 * --workspace-name train-finetune-dev-eastus: Specifies the Azure Machine Learning workspace.
 * --name "ft-maap-llama3-instance-types-1209-01": Specifies the name of the job.
 
 Sample Yaml file for generating FineTuningJob using azureml CLIV2
 
 **Text Generation FineTuning**
 1. [finetuning-with-amlcompute](./model-as-a-platform/text-generation/text-generation-finetuning-amlcompute.yaml)
 2. [finetuning-with-instance-types](./model-as-a-platform/text-generation/text-generation-finetuning-instance-types.yaml)
 
 **ChatCompletion FineTuning**
 1. [finetuning-with-amlcompute](./model-as-a-platform/chat/chat-completion-finetuning-amlcompute.yaml)
 2. [finetuning-with-instance-types](./model-as-a-platform/chat/chat-completion-finetuning-instance-types.yaml)
diff --git a/...finetuning/standalone/model-as-a-platform/chat/chat-completion-finetuning-amlcompute.yaml b/...finetuning/standalone/model-as-a-platform/chat/chat-completion-finetuning-amlcompute.yaml
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 type: finetuning
 
 name: "Phi-3-mini-4k-instruct-with-amlcompute"
 experiment_name: "Phi-3-mini-4k-instruct-finetuning-experiment"
 display_name: "Phi-3-mini-4k-instruct-display-name"
 task: chat_completion
 model_provider: custom
 model: 
   path: "azureml://registries/azureml/models/Phi-3-mini-4k-instruct/versions/14"
   type: mlflow_model
 compute: "lowpri-a100"
 training_data: train.jsonl
 validation_data:
   path: validation.jsonl
   type: uri_file
 hyperparameters:
   num_train_epochs: "1"
   per_device_train_batch_size: "1"
   learning_rate: "0.00002"
 properties:
   my_property: "my_value"
 tags:
   foo_tag: "bar"
 outputs:
   registered_model:
     name: "Phi-3-mini-4k-instruct-finetuned-model"
     type: mlflow_model
diff --git a/...tuning/standalone/model-as-a-platform/chat/chat-completion-finetuning-instance-types.yaml b/...tuning/standalone/model-as-a-platform/chat/chat-completion-finetuning-instance-types.yaml
@@ -0,0  1,32 @@
 type: finetuning
 
 name: "Phi-3-mini-4k-instruct-with-instance-types"
 experiment_name: "Phi-3-mini-4k-instruct-finetuning-experiment-instance-types"
 display_name: "Phi-3-mini-4k-instruct-display-name"
 task: chat_completion
 model_provider: custom
 model: 
   path: "azureml://registries/azureml/models/Phi-3-mini-4k-instruct/versions/14"
   type: mlflow_model
 resources:
   instance_types:
   # You can specify multiple instance types for the training job
   # Backend will decide which instance type to use based on the constraints on training job.
     - "Standard_NC96ads_A100_v4"
     - "Standard_E4s_v3"
 training_data: train.jsonl
 validation_data:
   path: validation.jsonl
   type: uri_file
 hyperparameters:
   num_train_epochs: "1"
   per_device_train_batch_size: "1"
   learning_rate: "0.00002"
 properties:
   my_property: "my_value"
 tags:
   foo_tag: "bar"
 outputs:
   registered_model:
     name: "Phi-3-mini-4k-instruct-finetuned-model"
     type: mlflow_model