Top 7 Open Source LLM models in 2024

1. LLaMA 2 (Large Language Model Meta AI): LLaMA 2 is developed by Meta (ex Facebook), and it is available for research and commercial use. This LLaMA is open-weight which means that the trained parameters (weights) of the model are available and can be accessed, or even downloaded, and used by anyone. This model comes in multiple sizes, such as 7B, 13B, and 70B parameters. You can access these models through platforms like Hugging Face or Microsoft Azure, where you can integrate it into your applications using their APIs. Working with LLaMA 2 requires familiarity with PyTorch or TensorFlow, and you can leverage the Hugging Face Transformers library to fine-tune or use the model.?
2. Falcon 180B/40B: Falcon model is open-sourced by the Technology Innovation Institute (TII). The latest versions, Falcon 180B (for large-scale) and Falcon 40B (for smaller scale), can be accessed from the Hugging Face Hub. These models are optimized for efficient training and inference. To work with Falcon models, you can use the Hugging Face Transformers library, which offers tools to load, fine-tune, and deploy the model on your data or applications.?
3. Mistral 7B: This model is also open-weight similar to LLaMA2. The model is developed by Mistral AI and it is optimized for low-compute environments. You can download and experiment with the model directly from the Mistral AI GitHub or Hugging Face. Working with it involves using libraries like PyTorch or Hugging Face's Transformers for fine-tuning and deployment.?
4. GPT-NeoX-20B: EleutherAI’s largest open-source model, GPT-NeoX-20B, can be accessed on the EleutherAI GitHub or Hugging Face. You can use this model for various NLP tasks, such as text generation, summarization, or translation. The GPT-NeoX library provides scripts for training, fine-tuning, and evaluating the model on custom datasets.?
5. BLOOM 176B: This model is a multilingual LLM developed by the BigScience project. You can access the model on the Hugging Face Hub for different NLP tasks across multiple languages. The model supports inference and fine-tuning on any cloud or local environment using the Hugging Face Transformers library. Working with BLOOM involves setting up the required environment, using the model for zero-shot or few-shot tasks, and leveraging transfer learning for custom tasks.?
6. MPT-30B (MosaicML Pretrained Transformer): The MosaicML Pretrained Transformer (MPT) series, with the latest being MPT-30B, is available on MosaicML's GitHub and Hugging Face Hub. MPT models are optimized for efficient fine-tuning and inference. You can use them directly with PyTorch or TensorFlow, and there are tools and libraries available to customize them for specific tasks, such as chatbots, text generation, and summarization. ?
7. RedPajama-INCITE 7B: This model is also open weight similar to Mistral 7B and LLaMA 2. The RedPajama-INCITE series, developed by Together, offers a 7B parameter model for general NLP tasks such as casual and masked language modeling. You can access the model from the RedPajama GitHub or Hugging Face Hub. It is designed for easy integration into your workflows using Python and the Hugging Face Transformers library.?

All of these open source LLM models can be accessed by anyone. So, we can download and?experiment with them on local environments or cloud platforms, using libraries such as PyTorch, TensorFlow, and Hugging Face Transformers for easy integration, fine-tuning, and deployment.?