Meta Llama 2 AI technology Top Builders

Explore the top contributors showcasing the highest number of Meta Llama 2 AI technology app submissions within our community.

Llama 2: The Next Generation of Large Language Model

Developed by Meta and Microsoft, Llama 2, an advanced open-source large language model, stands as the successor to the previous model, Llama 1. As an expansive AI tool, it caters to the needs of developers, researchers, startups, and businesses. Released under a highly permissive community license, Llama 2 is available for both research and commercial use.

General
Release dateJuly 18, 2023
AuthorsMeta AI & Microsoft
Model sizes7B, 13B, 70B parameters
Model ArchitectureTransformer
Training data sourceMeta's extensive dataset
Supported languagesMultiple languages

Features of Llama 2

Llama 2 comes with significant improvements over Llama 1:

  1. Increased Training on Tokens: Llama 2 is trained on 40% more tokens, promising to deliver enhanced language understanding capabilities.
  2. Longer Context Length: With a longer context length of 4k tokens, Llama 2 is expected to maintain better context in prolonged conversations.
  3. Fine-Tuning for Dialogues: The versions of Llama 2 that are fine-tuned (Labelled Llama 2-Chat) are aimed at being optimized for dialogue applications using Reinforcement Learning from Human Feedback (RLHF).

Accessing and Using Llama 2

To utilize the potential of Llama 2, its code, pretrained models, and fine-tuned models can be accessed via the Huggingface.

Essential links pertaining to Llama 2:

Llama 2 Deployment

For an efficient deployment of Llama 2:

  • For the 7B models, "GPU [medium] - 1x Nvidia A10G" could be the choice.
  • For the 13B models, "GPU [xlarge] - 1x Nvidia A100" can be an option.
  • For the 70B models, "GPU [xxxlarge] - 8x Nvidia A100" might be suitable.

These deployments might be available on platforms like Microsoft Azure and Amazon Web Services (AWS).

Responsible Use of Llama 2

Although the use of Llama 2 is encouraged, it's crucial to follow the guidelines for responsible use. Red-teaming exercises might be utilized along with a transparency schematic, and responsible use guide and an acceptable use policy might also be provided to ensure secure and acceptable use of Llama 2. Participation in the Open Innovation AI Research Community and Llama Impact Challenge could be valuable for feedback and proposing improvements.

Conclusion

Llama 2, poised as the successor of the acclaimed Llama 1, signifies a new horizon in the landscape of large language models. The vast potential it holds is eagerly anticipated and it is hoped the global AI community will bring forth innovative and advantageous applications using Llama 2.


Meta Llama 2 AI technology Hackathon projects

Discover innovative solutions crafted with Meta Llama 2 AI technology, developed by our community members during our engaging hackathons.

The Effect of Model Configuration on HHEM Scores

The Effect of Model Configuration on HHEM Scores

PDF documents serve an important role in sharing and protecting information in today’s digital world. However, obtaining useful information from these pdfs documents can be difficult. Summarizing pdf documents enables users to quickly extract key information and gain a deeper understanding of the document’s content. Text summarization is a critical Natural Language Processing (NLP) task with applications ranging from information retrieval to content generation. Leveraging Large Language Models (LLMs) has shown remarkable promise in enhancing summarization techniques. While, automatically-generated summaries were riddled with artifacts such as grammar errors, repetition, and hallucination. Hallucination in text summarization refers to the phenomenon where the model generates information that is not supported by the input source document. Hallucination poses significant obstacles to the accuracy and reliability of the generated summaries. Detecting these hallucinations of LLMs for pdf summarization is a critical issue to evaluate summarization factual consistency rate. In the proposed project, we introduce LLM-based application called Cloudilic-HHEM that contains the following contributions: Enable users for chatting with different uploaded pdfs to extract useful and meaningful information, Summarizing pdf documents by different LLMs Like GPT 3.5, Google Gemini and LLAMA 2, Using Vectara-HHEM model to detect the score of hallucination of the used LLM in text summarization, Using dynamic temperatures when calling LLMs to compute the relative of hallucination score with the temperature parameter of LLM, The project has been presented by good stremlit GUI for user experience.