AI models have become more capable than ever. They can now generate human-like text, visually stunning images, and audio from mere prompts. As these technologies advance, more generative AI models are being open-sourced. Reports suggest that Generative AI will become a $1.3 Trillion Market in the coming decade. However, the true potential of generative AI lies in its accessibility and the collaborative spirit of open-source initiatives. Open-source generative AI models not only allow researchers, developers and enthusiasts in different parts of the world to explore and contribute to these cutting-edge technologies but also drive innovation. Leading companies like OpenAI make the models, codebase and training data freely available to promote transparency, accountability and responsible development practices. Continue reading this article to learn about the best open source generative AI models in 2024.
Also Read: Optimising AI Inference for Performance and Efficiency
We have curated a list of the best open source generative AI Models available in the market today:
Stable Diffusion is an open-source text-to-image generative AI model that has taken the creative world by storm. Developed by Stability AI and released in 2022, it was trained on a massive dataset of images and their associated captions, allowing it to generate highly detailed and visually stunning images from natural language prompts.
It can interpret and visualise abstract concepts, emotions and artistic styles with precision. Stable Diffusion is adopted in various creative domains, from digital art and illustration to concept design and storyboarding. Its user-friendly interface and active community have a vibrant ecosystem of creativity. This allows artists to collaborate, share prompts and contribute new features.
Similar Read: How to Train a Stable Diffusion Model
Meta Llama 3 is a large language model developed and open-sourced by Meta AI in 2023. The model was trained on massive text data combining online data, including websites, books and code repositories. The latest AI model boasts impressive capabilities in natural language processing, text generation, and code understanding. One of the key strengths of Meta Llama 3 is its versatility. It can be fine-tuned and adapted for a range of applications, such as question answering, text summarisation, sentiment analysis, and even code generation and debugging.
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Deploying and Using Llama 3.1-70B on Hyperstack
Deploying and Using Llama3-70B on Hyperstack
Mistral AI is an open-source project aimed at developing cutting-edge generative AI models for audio and speech applications. Launched in 2023, it has quickly gained recognition for its innovative approach to text-to-speech (TTS) and voice generation technologies.
Mistral AI is a deep learning model trained on a diverse dataset of speech recordings, enabling it to generate natural-sounding voices in multiple languages and accents. This technology has numerous applications, including audiobook narration, virtual assistants, and accessibility tools for individuals with visual or reading impairments. One of the key advantages of Mistral AI is its emphasis on customisation and personalisation. Users can fine-tune the model to generate voices with specific tones, emotions, and characteristics.
Also Read: A Guide to Fine-Tuning LLMs for Improved RAG Performance
GPT-2, or the Generative Pre-trained Transformer 2, is an open-source language model developed by OpenAI. Released in 2019, it quickly gained recognition for its impressive text generation capabilities, and ability to produce coherent and contextually relevant text on various topics.
GPT-2 was pre-trained on a dataset of 8 million web pages. The model can be fine-tuned for various natural language processing tasks, such as text summarisation, question answering, and even creative writing. Its ability to generate human-like text has made it a valuable tool for content creation, chatbot development, and language learning applications.
Despite its strengths, GPT-2 has also raised ethical concerns regarding the potential misuse of language models for generating misinformation or harmful content. OpenAI's decision to release a smaller version of the model initially was aimed at mitigating these risks and encouraging responsible development of the technology. This was the reason why Meta also adopted a system-level approach that puts developers in control when using LLaMA 3 models responsibly. It is built with extensive red-teaming/adversarial testing efforts prioritised for developing safe and robust models.
Read our documentation on Running a Chatbot
BLOOM (BigScience Large Open-science Open-access Multilingual Language Model) is an open-source language model developed by the BigScience collaborative initiative. This is a 176-billion-parameter transformer-based autoregressive large language model (LLM). The training of BLOOM was made possible through a large-scale public computing grant on the French public supercomputer Jean Zay, managed by GENCI and IDRIS (CNRS). This support from public institutions underscores the significance of BLOOM as a collective effort to advance the frontiers of NLP and democratise access to state-of-the-art language models.
What sets BLOOM apart is its multilingual capabilities and commitment to open science principles. Trained on an astounding 366 billion tokens (1.6TB) of data spanning 46 natural languages and 13 programming languages, BLOOM can understand and generate text in a diverse range of languages, from widely spoken ones like English and Mandarin to lesser-represented tongues like Chi Tumbuka. BLOOM's training corpus, named ROOTS, combines data extracted from the then-latest version of the web-based OSCAR corpus (38% of ROOTS) and newly collected data extracted from a manually selected and documented list of language data sources.
Also Read: How to Train Generative AI for 3D models
Open-source generative AI models like Stable Diffusion, GPT-2, and BLOOM have great potential but require specialised hardware to handle the significant computational resources needed for efficient inference and output generation. GPUs allow open source AI models to process vast amounts of data simultaneously. This parallel processing capability is crucial when working with large language models, image generators or other AI applications. Hence, choosing the right GPU for open-source AI model deployment.
For instance, the NVIDIA A100 GPU is designed specifically for AI and high-performance computing. At Hyperstack, we offer the NVIDIA A100 with a massive 80GB of HBM2 memory and 19.5 TFLOPS of Tensor Core performance. It provides unparalleled computing power and memory capacity. The NVIDIA H100 PCIe is also adept at handling AI, machine learning, and complex computational tasks. It offers the highest PCIe card memory bandwidth exceeding 2000 Gbps, ideal for tackling the largest models and most massive datasets like GPT.
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Stable Diffusion is an open-source text-to-image generative AI model that can generate highly detailed images from natural language prompts. Developed by Stability AI, it interprets abstract concepts and artistic styles, making it valuable for digital art, design, and creative applications.
Meta Llama 3 is a versatile large language model that can be fine-tuned for various tasks like question answering, text summarisation, sentiment analysis, and code generation. Its training on vast online data, including websites, books, and code repositories, contributes to its impressive natural language processing capabilities.
Mistral AI is an open-source project focused on developing generative AI models for audio and speech applications, such as text-to-speech (TTS) and voice generation. It can generate natural-sounding voices in multiple languages and accents, with customisation options for specific tones and emotions.
We offer NVIDIA A100 GPU, designed specifically for AI and high-performance computing, is an excellent choice for open-source generative AI models. With 80GB of HBM2 memory and 19.5 TFLOPS of Tensor Core performance, it provides unparalleled computing power and memory capacity for handling large models efficiently.