Did you know that Generative Pre-trained Transformer 3 (GPT-3) is no longer the only player in town when it comes to open-source language models? In recent years, several alternatives have emerged, offering innovative features and promising capabilities. If you’re curious about exploring new horizons in the realm of language models, here is a curated list of the top 10 open-source GPT-3 alternatives you should consider trying in 2023.
Bloom: The Multilingual Powerhouse
Developed by a dedicated team of over 1,000 AI researchers, Bloom stands out as one of the most promising alternatives to GPT-3. Trained on a staggering 176 billion parameters, which is a billion more than GPT-3, Bloom showcases exceptional potential. To put things into perspective, its training process required 384 graphics cards, each equipped with over 80 gigabytes of memory. With its multilingual capabilities, Bloom opens up new avenues for natural language understanding and generation.
Chinchilla: The GPT-3 Killer
DeepMind, the renowned AI research lab, presents Chinchilla as the ultimate rival to GPT-3. Powered by 70 billion parameters, Chinchilla outperforms other models with four times more data. Interestingly, this formidable alternative surpasses Gopher, GPT-3, Jurassic-1, and Megatron-Turing NLG in downstream evaluation tasks. What makes Chinchilla even more impressive is its efficiency in fine-tuning and inference, requiring significantly less computing power.
Gopher: Answering Science and Humanities Questions
Another remarkable creation by DeepMind, Gopher, with its 280 billion parameters, specializes in providing accurate answers to science and humanities questions. DeepMind proudly claims that Gopher can outperform language models 25 times its size, making it a formidable competitor even against GPT-3. With its logical reasoning prowess, Gopher opens up exciting possibilities in knowledge-based applications.
BERT: Google’s NLP Breakthrough
BERT, an acronym for Bidirectional Encoder Representations from Transformers, is Google’s groundbreaking neural network-based technique for NLP pre-training. Offering two versions, Bert Base and Bert Large, this GPT-3 alternative impresses with its 12 and 24 layers of transformers, respectively, and 110 million to 340 million trainable parameters. BERT’s versatility and performance make it an attractive choice for a wide range of natural language processing tasks.
AlexaTM: Amazon’s Language Model
When it comes to technological exploration, Amazon doesn’t lag behind. AlexaTM, Amazon’s very own language model, boasts 20 billion parameters. Known as Alexa Teacher Models (AlexaTM 20B), this seq-2-seq language model exhibits state-of-the-art capabilities for few-shot learning. What sets AlexaTM apart is its encoder and decoder architecture, designed to enhance performance in machine translation and other language-related tasks.
GLaM: Google’s Mixture of Experts
GLaM, short for “mixture of experts,” is a remarkable creation by Google. This language model consists of various submodels, each specializing in different inputs. With a whopping 1.2 trillion parameters distributed across 64 experts per MoE layer, GLaM ranks among the largest available models. Remarkably, during inference, the model activates only 97 billion parameters per token prediction, demonstrating its efficiency and scalability.
Megatron-Turing NLG: The Power of Collaboration
NVIDIA and Microsoft’s collaboration has yielded impressive results in the GPT-3 domain. Together, they created the Megatron-Turing Natural Language Generation (NLG) model, armed with an astounding 530 billion parameters. This model was trained on the NVIDIA DGX SuperPOD-based Selene supercomputer, making it one of the most powerful English language models available. Megatron-Turing NLG empowers various language generation tasks with its sheer computational might.
PaLM: Google’s Dense Decoder-Only Transformer
PaLM, another remarkable language model developed by Google, boasts an impressive training size of 540 billion parameters. Built using the Pathways system, PaLM represents a dense decoder-only transformer model. Notably, it stands as the first model trained with the Pathways system, leveraging 6144 chips and adopting the largest TPU-based configuration. PaLM’s exceptional performance in 28 out of 29 NLP tasks in English sets it apart from other language models.
LaMDA: A Revolution in Natural Language Processing
Google introduces LaMDA, a groundbreaking model with 137 billion parameters that has revolutionized the world of natural language processing. LaMDA builds on the fine-tuning of a group of Transformer-based neural language models. Notably, its pre-training dataset encompasses a staggering 1.5 trillion words, a significant leap forward compared to previous models. LaMDA has already made waves in zero-shot learning, program synthesis, and the BIG-bench workshop.
OPT: Open Pretrained Transformer
As an open-source GPT-3 alternative, Open Pretrained Transformer (OPT) offers significant community engagement opportunities. Trained on openly available datasets, OPT encompasses a substantial 175 billion parameters. The release includes pretrained models and training code, fostering research and exploration. Although OPT is currently under a noncommercial license, its availability for research purposes opens doors for innovative applications.
Conclusion
The emergence of GPT-3 alternatives has ushered in an era of exploration and innovation in the field of language models. With each alternative boasting unique features, impressive parameter counts, and exceptional performance, the possibilities for natural language understanding and generation continue to expand. Whether you’re an AI enthusiast, a developer, or a researcher, these top 10 open-source alternatives to GPT-3 provide a rich landscape for pushing the boundaries of language processing. So, why not embark on a journey to explore and unlock the full potential of these remarkable models?