OpenLLAMA: The leading open-source Language Model

Introduction

The original Llama model was released by Meta or Facebook, and it has been used as a basis for many other large language models. OpenLLaMA is an open-source reproduction of Meta AI's LLaMA model, which was released to provide an open reproduction of the Llama model that can be used commercially. All links related LLaMA project are provided under 'source' section at end of this article.

What is Open Llama?

Open Llama is an open repository and reproduction of Meta's Llama large language model. It offers pre-trained PyTorch and Jax weights of the 7 billion parameter Open Llama model. The project provides evaluation results and comparisons with the original llama model. It will provide valuable resources for language models and researchers. A large community of people will have access to it, making it useful for various use cases.

The Need for Open Llama for commercial

There has been an explosion of new models in recent months based on the original Llama model. However, the weights for the original Llama model are not publicly available and cannot be used for commercial purposes. The Open Llama project aims to provide an open reproduction of the Llama model that can be used commercially.

Training Data Set

The Open Llama project has released a 7 billion parameter model trained on 200 billion tokens. The 7 billion parameter Open Llama model has been trained on a large corpus of data including web-trained sources, books, and other text sources.

Training Process

This is an early release of a public review, with plans to retrain the model with a much larger data set in the future. The project uses exactly the same architecture, context length, number of training steps, learning rate schedule, and optimization as in the original Llama paper. However, since they do not have access to the original data set, they use a part of Red Pajama's data set as a subset for initial training.

Evaluation Results

It is important to note that Original Llama was trained on 1 trillion tokens, while Open Llama (in its current form) was trained on 200 billion tokens. The evaluation results compare Open Llama with GPT-J, a 6 billion parameter model trained by EleutherAI.
The evaluation results show that while there are some cases where Open Llama does not perform as well as Original Llama (e.g., certain benchmark datasets), there are also cases where it outperforms Original Llama (e.g., Arc Easy dataset).


source- https://github.com/openlm-research/open_llama

Smaller Models

They are also training much smaller three billion models in hope for facilitating large language model usage in low resource cases. With a 3 billion parameter model, you probably will be able to run this on consumer hardware without the need for expensive GPUs. If that actually works, there are a lot of different use cases where this model can be used.

Model Files

The project is on Hugging Face Hub under OpenML research. There are two different model files.

EasyLM format.
If using easyLM platform, you don't need number or tokenizer and weights because they have retrained the whole thing from scratch.

Pytorch format
For using the weights in the Pytorch format with transformers library, they use BOS (beginning of sentence) token (id=1) during training, so it is important to prepend this token for best performance during few-shot evaluation. The rest of the configuration will remain exactly the same because it's using exactly the same architecture. You don't really need to change anything at all. you will be just loading different weights now.

Benefits of Open-Source Language Models

These models are important if you're concerned about data privacy because you can run them locally.

You can retrain these large language models or fine-tune them on your very specific business use case, and they will actually outperform much bigger models because these smaller models would be task specific. 

Many companies are progressively trying to get to the open-source feature of their actual large language models as it's more accessible for a wide range of people.

The project has been evaluated against original llama models showing that it can generate high-quality natural language text with similar performance levels.

Future Plans

Open Llama plans to release more weights and releases of different usage to get to 30 billion tokens. They are currently focused on completing the training process of entire Red Pajama data set for comparison between original llama and open llama. They plan to train this model on entire red pajama data set which is going to be 1.2 trillion tokens. They are also working on smaller models that will optimize facilitating language model usage in low research or low resource cases.

Getting Started with OpenLLAMA

OpenLLAMA is an open source AI project that provides a wealth of opportunities for developers and researchers. With its ability to integrate with popular Python frameworks, getting started with OpenLLAMA tutorials for beginners is a breeze. Fine-tuning OpenLLAMA for specific tasks is also possible, making it a versatile tool for educational purposes and research projects alike. Whether you’re new to open source or an experienced developer, OpenLLAMA is a powerful tool to explore.

As an open source project, OpenLLAMA is constantly evolving and improving thanks to the contributions of its community. This means that there are always new tutorials and resources available for beginners to learn how to use OpenLLAMA. Additionally, its integration with Python frameworks makes it easy to fine-tune OpenLLAMA for specific tasks. This makes it a valuable tool for both educational purposes and research projects.

An Open Source LLM from Meta

LLaMA is a collection of open and efficient foundation language models ranging from 7B to 65B parameters. It is a state-of-the-art foundational language model that is much more parameter-efficient than comparable models such as GPT-3 or PaLM, while achieving very good performance. LLaMA is part of Meta’s commitment to open science and is publicly released for researchers to advance their work in this subfield of AI. The source code for LLaMA is shared by Meta, allowing other researchers to more easily test new approaches to limiting or eliminating problems in large language models.

OpenLLaMA is an open-source reproduction of Meta’s LLaMA language models that allows commercial use. It is part of Meta’s commitment to open science and is publicly released for researchers to advance their work in this subfield of AI. The source code for LLaMA is shared by Meta, allowing other researchers to more easily test new approaches to limiting or eliminating problems in large language models.  

Conclusion

Open-Llama is an open-source project that offers a complete training pipeline for building large language models. The Open-Llama project aims to provide an open reproduction of the Llama model that can be used commercially. The project plans to release more weights and releases of different usage to get to 30 billion tokens. They are also working on smaller models that will optimize facilitating language model usage in low research or low resource cases. Open-Llama is a promising project that can help researchers and developers build large language models with ease.

source
GitHub Repo - https://github.com/openlm-research/open_llama
weights - ttps://huggingface.co/openlm-research/open_llama_7b_preview_200bt