Librarian Bot: Add base_model information to model

This pull request aims to enrich the metadata of your model by adding [`distilgpt2`](https://huggingface.co/distilgpt2) as a `base_model` field, situated in the `YAML` block of your model's `README.md`.

How did we find this information? We performed a regular expression match on your `README.md` file to determine the connection.

**Why add this?** Enhancing your model's metadata in this way:
- **Boosts Discoverability** - It becomes straightforward to trace the relationships between various models on the Hugging Face Hub.
- **Highlights Impact** - It showcases the contributions and influences different models have within the community.

For a hands-on example of how such metadata can play a pivotal role in mapping model connections, take a look at [librarian-bots/base_model_explorer](https://huggingface.co/spaces/librarian-bots/base_model_explorer).

This PR comes courtesy of [Librarian Bot](https://huggingface.co/librarian-bot). If you have any feedback, queries, or need assistance, please don't hesitate to reach out to [@davanstrien](https://huggingface.co/davanstrien). Your input is invaluable to us!

README.md

@@ -1,37 +1,33 @@
 ---
+language:
+- en
 license: apache-2.0
+library_name: transformers
 tags:
 - generated_from_trainer
 - chatgpt
+datasets:
+- pszemraj/HC3-textgen-qa
 metrics:
 - accuracy
-model-index:
-- name: distilgpt2-HC3
-  results: []
 widget:
-- text: >-
-    Review: Best cast iron skillet you will ever buy. Is this review positive or
-    negative? <answer>
+- text: 'Review: Best cast iron skillet you will ever buy. Is this review positive
+    or negative? <answer>'
   example_title: Sentiment analysis
-- text: >-
-    Barack Obama nominated Hilary Clinton as his secretary of state on Monday.
+- text: Barack Obama nominated Hilary Clinton as his secretary of state on Monday.
     He chose her because <answer>
   example_title: Coreference resolution
-- text: >-
-    On a shelf, there are five books: a gray book, a red book, a purple book, a
-    blue book, and a black book. Here's the puzzle, <answer>
+- text: 'On a shelf, there are five books: a gray book, a red book, a purple book,
+    a blue book, and a black book. Here''s the puzzle, <answer>'
   example_title: Logic puzzles
-- text: >-
-    The two men running to become New York City's next mayor will face off in
+- text: The two men running to become New York City's next mayor will face off in
     their first debate Wednesday night <answer>
   example_title: Reading comprehension
-- text: >-
-    Is it true that if I have five 5-hour energy drinks in a single 24-hour
-    period, I get 25 hours of energy and spontaneously explode? <answer>
+- text: Is it true that if I have five 5-hour energy drinks in a single 24-hour period,
+    I get 25 hours of energy and spontaneously explode? <answer>
   example_title: 5 hour energy
-- text: >-
-    what happens if you train a smaller model on a dataset of
-    reinforcement-learning optimized model responses? <answer>
+- text: what happens if you train a smaller model on a dataset of reinforcement-learning
+    optimized model responses? <answer>
   example_title: deep learning advice
 inference:
   parameters:
@@ -39,12 +35,11 @@ inference:
     max_length: 96
     no_repeat_ngram_size: 3
     repetition_penalty: 1.5
-datasets:
-- pszemraj/HC3-textgen-qa
-language:
-- en
-library_name: transformers
 pipeline_tag: text-generation
+base_model: distilgpt2
+model-index:
+- name: distilgpt2-HC3
+  results: []
 ---