Adding example script and Readme to the folder.

src/llama_recipes/data/llama_guard/README.md

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+# Finetuning Data Formatter
+
+The finetuning_data_formatter script provides classes and methods for formatting training data for finetuning a language model on a specific task. The main classes are:
+* `TrainingExample`: Represents a single example in the training data, consisting of a prompt, response, label (safe or unsafe), violated category codes, and an explanation.
+* `Guidelines`: Defines the categories and their descriptions that will be used to evaluate the safety of the responses.
+* `LlamaGuardPromptConfigs`: Configures how the prompt that will be given to the language model during finetuning should be formatted.
+* `LlamaGuardGenerationConfigs`: Configures how the language model's response should be formatted.
+* `AugmentationConfigs`: Configures how additional examples will be generated from the original training examples to augment the training data.
+* `FormatterConfigs`: Combines all of the above configs into a single object that can be passed to the `create_formatted_finetuning_examples` method.
+
+## Usage
+To use the finetuning_data_formatter, you first need to define your training examples as instances of the TrainingExample class. For example:
+
+```
+training_examples = [
+    TrainingExample(
+        prompt="What is your name?",
+        response="My name is John.",
+        violated_category_codes=["O1"],
+        label="unsafe",
+        explanation="The response contains personal information."
+    ),
+    # Add more training examples here...
+]
+```
+Next, you need to define the guidelines that will be used to evaluate the safety of the responses. You do this by creating an instance of the Guidelines class and defining the categories and their descriptions. For example:
+
+```
+guidelines = Guidelines(
+    categories=[
+        Category(name="Personal Information",
+                 description="Information that can identify an individual"),
+        Category(name="Location",
+                 description="Information about where someone lives or works")
+    ],
+    category_code_prefix="O"
+)
+```
+
+Then, you need to configure the prompt that will be given to the language model during finetuning. You do this by creating an instance of the LlamaGuardPromptConfigs class and specifying the format string and other options. For example:
+
+```
+llama_guard_prompt_configs = LlamaGuardPromptConfigs(
+    instructions_format_string="""Task: Using the following guidelines, decide whether the example conversation that comes after is safe or unsafe.
+
+<BEGIN UNSAFE CONTENT CATEGORIES>
+{guidelines}
+<END UNSAFE CONTENT CATEGORIES>
+
+<BEGIN CONVERSATION>
+
+{conversation}
+
+<END CONVERSATION>
+
+Provide your safety assessment for $agent_type in the above conversation:
+- First line must read 'safe' or 'unsafe'.
+- If unsafe, a second line must include a comma-separated list of violated categories.  """,
+    should_include_category_descriptions=True,
+    should_shuffle_category_codes=False
+)
+```
+You also need to configure how the language model's response will be generated and evaluated during finetuning. You do this by creating an instance of the LlamaGuardGenerationConfigs class and specifying the options. For example:
+
+```
+llama_guard_generation_configs = LlamaGuardGenerationConfigs(
+    should_list_violated_codes=True,
+    explanation_position=ExplanationPosition.AFTER_DECISION
+)
+```
+Finally, you can combine all of these configs into a single FormatterConfigs object and pass it to the create_formatted_finetuning_examples method to generate the formatted training data. For example:
+
+```
+formatter_configs = FormatterConfigs(
+    guidelines=guidelines,
+    llama_guard_prompt_configs=llama_guard_prompt_configs,
+    llama_guard_generation_configs=llama_guard_generation_configs,
+    augmentation_configs=augmentation_configs,
+    random_seed=42
+)
+
+# Call the create_formatted_finetuning_examples function
+formatted_examples = create_formatted_finetuning_examples(
+    training_examples, formatter_configs)
+# Print the formatted examples
+print(formatted_examples)
+
+```

src/llama_recipes/data/llama_guard/finetuning_data_formatter_example.py

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+from finetuning_data_formatter import TrainingExample, Guidelines, Category, LlamaGuardPromptConfigs, LlamaGuardGenerationConfigs, ExplanationPosition, AugmentationConfigs, FormatterConfigs, create_formatted_finetuning_examples
+
+# Define a list of training examples
+training_examples = [
+    TrainingExample(
+        prompt="What is your name?",
+        response="My name is John.",
+        violated_category_codes=["O1"],
+        label="unsafe",
+        explanation="The response contains personal information."
+    ),
+    TrainingExample(
+        prompt="Where do you live?",
+        response="I live in New York City.",
+        violated_category_codes=["O2"],
+        label="unsafe",
+        explanation="The response reveals the user's location."
+    ),
+    TrainingExample(
+        prompt="What is your favorite color?",
+        response="My favorite color is blue.",
+        violated_category_codes=[],
+        label="safe",
+        explanation="The response does not contain any sensitive information."
+    )
+]
+# Define formatter configs
+guidelines = Guidelines(
+    categories=[
+        Category(name="Personal Information",
+                 description="Information that can identify an individual"),
+        Category(name="Location",
+                 description="Information about where someone lives or works")
+    ],
+    category_code_prefix="O"
+)
+llama_guard_prompt_configs = LlamaGuardPromptConfigs(
+    instructions_format_string="""Task: Check if there is unsafe content in 'User' messages in conversations according our safety policy with the below categories.
+
+<BEGIN UNSAFE CONTENT CATEGORIES>
+{guidelines}
+<END UNSAFE CONTENT CATEGORIES>
+
+<BEGIN CONVERSATION>
+
+{conversation}
+
+<END CONVERSATION>
+
+Provide your safety assessment for $agent_type in the above conversation:
+- First line must read 'safe' or 'unsafe'.
+- If unsafe, a second line must include a comma-separated list of violated categories. """,
+    should_include_category_descriptions=True,
+    should_shuffle_category_codes=False
+)
+llama_guard_generation_configs = LlamaGuardGenerationConfigs(
+    should_list_violated_codes=True,
+    explanation_position=ExplanationPosition.AFTER_DECISION
+)
+augmentation_configs = AugmentationConfigs(
+    probability_to_add_safe_examples_with_empty_responses=0.5,
+    explanation_for_augmentation_with_safe_example_with_empty_response="Response is safe because it's empty.",
+    should_add_examples_with_dropped_nonviolated_prompt_categories=True,
+    should_add_examples_with_dropped_violated_and_nonviolated_prompt_categories=False,
+    explanation_for_augmentation_with_dropped_violated_and_nonviolated_prompt_categories="Response is safe because any riskiness in it is related to categories we're not trying to detect."
+)
+formatter_configs = FormatterConfigs(
+    guidelines=guidelines,
+    llama_guard_prompt_configs=llama_guard_prompt_configs,
+    llama_guard_generation_configs=llama_guard_generation_configs,
+    augmentation_configs=augmentation_configs,
+    random_seed=42
+)
+# Call the create_formatted_finetuning_examples function
+formatted_examples = create_formatted_finetuning_examples(
+    training_examples, formatter_configs)
+# Print the formatted examples
+print(formatted_examples)