Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+pipeline_tag: summarization
+widget:
+  - text: >-
+      Hugging Face: Revolutionizing Natural Language Processing Introduction In
+      the rapidly evolving field of Natural Language Processing (NLP), Hugging
+      Face has emerged as a prominent and innovative force. This article will
+      explore the story and significance of Hugging Face, a company that has
+      made remarkable contributions to NLP and AI as a whole. From its inception
+      to its role in democratizing AI, Hugging Face has left an indelible mark
+      on the industry.  The Birth of Hugging Face Hugging Face was founded in
+      2016 by Clément Delangue, Julien Chaumond, and Thomas Wolf. The name
+      Hugging Face was chosen to reflect the company's mission of making AI
+      models more accessible and friendly to humans, much like a comforting hug.
+      Initially, they began as a chatbot company but later shifted their focus
+      to NLP, driven by their belief in the transformative potential of this
+      technology. Transformative Innovations Hugging Face is best known for its
+      open-source contributions, particularly the Transformers library. This
+      library has become the de facto standard for NLP and enables researchers,
+      developers, and organizations to easily access and utilize
+      state-of-the-art pre-trained language models, such as BERT, GPT-3, and
+      more. These models have countless applications, from chatbots and virtual
+      assistants to language translation and sentiment analysis. 
+example_title: Summarization Example 1
+---
+
+## Model Information
+
+This is a fine-tuned version of Llama 3.1 trained in English, Spanish, and Chinese for text summarization.
+
+The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
+
+**Model developer**: Meta
+
+**Model Architecture:** Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.