Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that was released just a few years back. This model uses deep learning to produce human-like text, hence has immense potential. However, considering how open the market is, there are numerous alternatives available out there. Here is a list of top 10 open-source GTP-3 alternatives you should try in 2023.
Developed by a group of over 1,000 AI researchers, Bloom is an open-source multilingual language model that is considered as the best alternative to GPT-3. It is trained on 176 billion parameters, which is a billion more than GPT-3 and required 384 graphics cards for training, each having a memory of more than 80 gigabytes.
A model developed by DeepMind, and touted as the GPT-3 killer, Chinchilla is just the model you need. It is built on 70 billion parameters but with four times more data. Now, there’s an interesting fact to note about this GPT-3 alternative – this model outperformed Gopher, GPT-3, Jurassic-1, and Megatron-Turing NLG on several downstream evaluation tasks. Can it get any better? Additionally, it requires very less computing power for fine tuning and inference.
Yet another DeepMind innovation is Gopher with 280 billion parameters. This model has an expertise in answering science and humanities questions much better than other languages. There’s more to it – DeepMind claims that Gopher can beat language models 25 times its size, and compete with logical reasoning problems with GPT-3. Well, this is definitely something to look forward to. Agree?
Google is to be appreciated for coming up with a neural network-based technique for NLP pre-training. Outcome? Well, BERT it is. It stands for Bidirectional Encoder Representations from Transformers. This GPT-3 alternative has two versions–Bert Base that uses 12 layers of transformers of transformers block and 110 million trainable parameters while Bert Large that uses 24 layers and 340 million trainable parameters.
Amazon is nowhere behind when it comes to exploring technology. On the same lines, it has unveiled its large language model with 20 billion parameters – AlexaTM. Alexa Teacher Models (AlexaTM 20B) is a seq-2-seq language model with SOTA capabilities for few-shot learning. What makes it different from others is that it has an encoder and decoder for increasing performance on machine translation.
Yet another Google invention that deserves special mention is GLaM. It is a mixture of experts (MoE) model, which means it consists of different submodels specializing in different inputs. In addition to all this, it is also one of the largest available models with 1.2 trillion parameters across 64 experts per MoE layer. During inference, the model only activates 97 billion parameters per token prediction.
Megatron-Turing Natural Language Generation (NLG)
Collaboration also seems to have worked wonders for the GPT-3 domain. One such collaboration is that of NVIDIA and Microsoft. This collaboration resulted in the creation of the largest language models with 530 billion parameters. The model was trained on the NVIDIA DGX SuperPOD-based Selene supercomputer and is one of the most powerful English language models.
PaLM, yet another language model developed by Google is trained on 540 billion parameters. This has evolved into a dense decoder-only transformer model trained with the Pathways system. It is the first one that used the Pathways system to train large-scale models with 6144 chips, along with being the largest TPU-based configuration. What makes PaLM stand apart from the rest is the fact that the model outperformed 28 out of 29 NLP tasks in English when compared to other models.
Google came up with LaMDA, a model with 137 billion parameters, that has set a revolution in the natural language processing world. It was built by fine-tuning a group of Transformer-based neural language models. As far as the model’s pre-training is concerned, the team created a dataset of 1.5 trillion words which is 40 times more than previously developed models. LaMDA has already been used for zero-shot learning, program synthesis, and BIG-bench workshop.
Open Pretrained Transformer (OPT) is a language model, yet another leading GPT-3 alternative with 175 billion parameters. OPT is trained on openly available datasets allowing more community engagement. The release comes with the pretrained models along with code for training. The model is currently under noncommercial licence and available for research use only. The model was trained and deployed using 16 NVIDIA V100 GPUs, which is significantly lower than other models.
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