diff --git a/data/datasets/__init__.py b/data/datasets/__init__.py
index beb1f96dd3..284f874e7e 100644
--- a/data/datasets/__init__.py
+++ b/data/datasets/__init__.py
@@ -22,6 +22,7 @@
     "oa_leet10k": "ehartford/oa_leet10k",
     "LogicInference_OA": "KK04/LogicInference_OA",
     "oa_dolly_15k": "OllieStanley/oa_dolly_15k",
+    "poetry_instruction": "checkai/poetry-instruction",
 }
 
 SAFETY_DATASETS = {
diff --git a/data/datasets/poetry_instruction/README.md b/data/datasets/poetry_instruction/README.md
new file mode 100644
index 0000000000..e78041d68b
--- /dev/null
+++ b/data/datasets/poetry_instruction/README.md
@@ -0,0 +1,29 @@
+Dataset Description This dataset contains around 14,000 poems from the
+PoetryFoundation.org site. They are converted to question:response pairs,
+usingthe tags as topics. 5% of the dataset is titling requests -- the user
+provides apoem and asks the assistant to title it.
+
+It can be found here, on my HuggingFace -
+https://huggingface.co/datasets/checkai/instruction-poems
+
+Languages English
+
+Dataset Structure This dataset follows the OA format, which is:
+
+INSTRUCTION (string): The user asks for a poem (from a variety of premade
+prompts) with topics (tags). If the given poem has no tags, the user asks for a
+poem on it's own.
+
+RESPONSE (string): The assistant replies with the poem and title (from a variety
+of premade prompts).
+
+SOURCE (string): The source is PoetryFoundation.org and the poet's name.
+
+METADATA (JSON String): {"author": "author of the original poem", "title":
+"title of the poem", "tags": "tags from poetry foundation."}
+
+Preparing the Dataset The dataset can be created with prepare.py. Make sure to
+install the required libraries in requirements.txt!
+
+Contributions Created by Check Original dataset source -
+https://www.kaggle.com/datasets/tgdivy/poetry-foundation-poems
diff --git a/data/datasets/poetry_instruction/prepare.py b/data/datasets/poetry_instruction/prepare.py
new file mode 100644
index 0000000000..9a4718e2da
--- /dev/null
+++ b/data/datasets/poetry_instruction/prepare.py
@@ -0,0 +1,158 @@
+import json
+import os
+import random
+
+import kaggle
+import pandas as pd
+
+# Authenticate the Kaggle API client
+kaggle.api.authenticate()
+
+# Download and extract the dataset to the download_path directory
+download_path = os.path.join(os.getcwd(), "data", "datasets", "poetry_instruction")
+kaggle.api.dataset_download_files("tgdivy/poetry-foundation-poems", path=download_path, unzip=True)
+
+# Read the CSV file into a pandas dataframe
+csv_file = os.path.join(download_path, "PoetryFoundationData.csv")
+df = pd.read_csv(csv_file)
+
+# The data in the CSV file is not formatted correctly, so we need to clean it up.
+df["Title"] = df["Title"].replace("\n", "", regex=True).replace("\r", "", regex=True)
+df["Title"] = df["Title"].str.strip()
+df["Title"] = df["Title"].apply(lambda x: f'"{x}"')
+df["Poem"] = df["Poem"].str.strip()
+df["Poem"] = df["Poem"].str.replace("Translated from the French", "")
+
+# "writing_prompts" are for tasks requesting the assistant to write a poem.
+# "topic" or "notTopic" are used depending if the original dataset had a topic listed for the poem or not.
+writing_prompts_topic = [
+    "Write me a poem about $topic.",
+    "I want a poem about $topic.",
+    "Can you write a poem? Make it about $topic.",
+    "Compose a poem, about $topic.",
+    "Make a poem with themes of $topic." "Generate a poem with the following themes: $topic.",
+]
+
+writing_prompts_notTopic = [
+    "Write me a poem.",
+    "I want a poem.",
+    "Can you write a poem?",
+    "Compose a poem.",
+    "Make a poem.",
+    "Generate a poem.",
+]
+
+# These are replies that the assistant can give to the user.
+replies_topic = [
+    "Here's a poem about $topic: \n$title\n$poem",
+    "Sure, I can do that. Here's a poem about $topic. I call it $title: \n$poem",
+    "Okay, a poem about $topic: \n$title\n$poem",
+    "Of course! It's called $title: \n$poem",
+    "It's called $title: \n$poem",
+    "Here's your poem about $topic: \n$title\n$poem",
+    "I've written a poem for you about $topic. The title is $title: \n$poem",
+    "Here's a beautiful poem about $topic for you. It's called $title: \n$poem",
+    "This is a poem about $topic that I just wrote. It's called $title: \n$poem",
+    "Here's a poem I composed about $topic. It's called $title: \n$poem",
+]
+
+replies_notTopic = [
+    "Here's a poem: \n$title\n$poem",
+    "Sure, I can do that. Here's a poem. I call it $title: \n$poem",
+    "Okay, a poem: \n$title\n$poem",
+    "Of course! It's called $title: \n$poem",
+    "It's called $title: \n$poem",
+    "Here's your poem: \n$title\n$poem",
+    "I've written a poem for you. The title is $title: \n$poem",
+    "Here's a beautiful poem for you. It's called $title: \n$poem",
+    "This is a poem that I just wrote. It's called $title: \n$poem",
+    "Here's a poem I composed. It's called $title: \n$poem",
+]
+
+# "titling_prompts" are for tasks requesting that the assistant titles a poem. They make up 5% of the dataset.
+titling_prompts = [
+    "Title this poem: \n$poem",
+    "Come up with a unique title for my poem: \n$poem",
+    "What should I call this poem? \n$poem",
+    "Name this poem: \n$poem",
+    "What would be a good title for this poem? \n$poem",
+    "I need help coming up with a title for my poem. \n$poem",
+    "$poem\nWhat should I call this poem?",
+]
+
+titling_replies = [
+    "Based on the poem, a good title could be $title.",
+    "I suggest titling this poem $title.",
+    "How about calling it $title?",
+    "You could name this poem $title.",
+    "The title that comes to mind is $title.",
+    "Perhaps $title would be a fitting title for this poem.",
+    "I think $title would be a great title for this poem.",
+    "This poem seems like it could be called $title to me.",
+    "$title is a good title for this poem.",
+]
+
+# Shuffling the dataset and delegating 5% to titling tasks.
+# Calculating the number of titling tasks and writing tasks.
+num_rows = len(df)
+num_titling_tasks = int(num_rows * 0.05)
+num_writing_tasks = num_rows - num_titling_tasks
+
+# Shuffle the rows in the DataFrame.
+df = df.sample(frac=1)
+
+# Split the DataFrame into two DataFrames, one for titling tasks and one for writing tasks.
+writing_tasks = df.iloc[:num_writing_tasks]
+titling_tasks = df.iloc[num_writing_tasks:]
+
+prepared_data = []
+
+# Loop through the writing tasks and process them.
+for index, row in writing_tasks.iterrows():
+    # Get data from the entry
+    poem = row["Poem"]
+    topics = row["Tags"]
+    title = row["Title"]
+    author = row["Poet"]
+
+    # Variables to store to instruction, reply, source, and metadata.
+    instruction = random.choice(writing_prompts_topic).replace("$topic", str(topics))
+    reply = random.choice(replies_topic).replace("$topic", str(topics)).replace("$title", title).replace("$poem", poem)
+    source = "PoetryFoundation.org" + " - " + author
+    metadata = {"author": author, "title": title, "tags": str(topics), "task_type": "writing"}
+
+    # If the entry has an empty value for the topic, use the non-topic prompts and replies.
+    if pd.isna(topics):
+        instruction = random.choice(writing_prompts_notTopic)
+        reply = random.choice(replies_notTopic).replace("$title", title).replace("$poem", poem)
+
+    # Create a dictionary entry for the entry and append it to the list.
+    entry = {"INSTRUCTION": instruction, "RESPONSE": reply, "SOURCE": source, "METADATA": json.dumps(metadata)}
+    prepared_data.append(entry)
+
+# Loop through the titling tasks and process them.
+for index, row in titling_tasks.iterrows():
+    # Get data from the entry
+    poem = row["Poem"]
+    topics = row["Tags"]
+    title = row["Title"]
+    author = row["Poet"]
+
+    # Variables to store to instruction, reply, source, and metadata.
+    instruction = random.choice(titling_prompts).replace("$poem", poem)
+    reply = random.choice(titling_replies).replace("$title", title)
+    source = "PoetryFoundation.org" + " - " + author
+    metadata = {"author": author, "title": title, "tags": str(topics), "task_type": "titling"}
+
+    # Create a dictionary entry for the entry and append it to the list.
+    entry = {"INSTRUCTION": instruction, "RESPONSE": reply, "SOURCE": source, "METADATA": json.dumps(metadata)}
+    prepared_data.append(entry)
+
+# Convert prepared_data to a DataFrame.
+prepared_data = pd.DataFrame(prepared_data)
+
+# Save the DataFrame to disk in the Parquet format
+prepared_data.to_parquet("output.parquet", row_group_size=100, engine="pyarrow", index=False)
+
+# Print the amount of entries in the final converted dataset
+print(f"Prepared {len(df)} entries")
diff --git a/data/datasets/poetry_instruction/requirements.txt b/data/datasets/poetry_instruction/requirements.txt
new file mode 100644
index 0000000000..a1026315c9
--- /dev/null
+++ b/data/datasets/poetry_instruction/requirements.txt
@@ -0,0 +1,2 @@
+kaggle==1.5.13
+pandas==2.0.0