transformers safetensors llama text-generation summarization license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
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.