Update README.md
Browse files
README.md
CHANGED
|
@@ -1,6 +1,30 @@
|
|
| 1 |
---
|
| 2 |
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
|
| 3 |
library_name: peft
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 4 |
---
|
| 5 |
|
| 6 |
Fine tuned llama3.1 8b instruct model to provide a short summary and a mini summary. Separated by (---).
|
|
|
|
| 1 |
---
|
| 2 |
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
|
| 3 |
library_name: peft
|
| 4 |
+
|
| 5 |
+
pipeline_tag: summarization
|
| 6 |
+
widget:
|
| 7 |
+
- text: >-
|
| 8 |
+
Hugging Face: Revolutionizing Natural Language Processing Introduction In
|
| 9 |
+
the rapidly evolving field of Natural Language Processing (NLP), Hugging
|
| 10 |
+
Face has emerged as a prominent and innovative force. This article will
|
| 11 |
+
explore the story and significance of Hugging Face, a company that has
|
| 12 |
+
made remarkable contributions to NLP and AI as a whole. From its inception
|
| 13 |
+
to its role in democratizing AI, Hugging Face has left an indelible mark
|
| 14 |
+
on the industry. The Birth of Hugging Face Hugging Face was founded in
|
| 15 |
+
2016 by Clément Delangue, Julien Chaumond, and Thomas Wolf. The name
|
| 16 |
+
Hugging Face was chosen to reflect the company's mission of making AI
|
| 17 |
+
models more accessible and friendly to humans, much like a comforting hug.
|
| 18 |
+
Initially, they began as a chatbot company but later shifted their focus
|
| 19 |
+
to NLP, driven by their belief in the transformative potential of this
|
| 20 |
+
technology. Transformative Innovations Hugging Face is best known for its
|
| 21 |
+
open-source contributions, particularly the Transformers library. This
|
| 22 |
+
library has become the de facto standard for NLP and enables researchers,
|
| 23 |
+
developers, and organizations to easily access and utilize
|
| 24 |
+
state-of-the-art pre-trained language models, such as BERT, GPT-3, and
|
| 25 |
+
more. These models have countless applications, from chatbots and virtual
|
| 26 |
+
assistants to language translation and sentiment analysis.
|
| 27 |
+
example_title: Summarization Example 1
|
| 28 |
---
|
| 29 |
|
| 30 |
Fine tuned llama3.1 8b instruct model to provide a short summary and a mini summary. Separated by (---).
|