Top 10 Powerful Open-Source Large Language Models

Author : Seema V

What is Open Sourse LLM Model?

Open-source large language models like GPT-3.5, sophisticated AI systems crafted to comprehend and create human-like text through extensive training on vast and diverse datasets. Powered by deep learning, these models draw from an array of written sources such as books, articles, websites, and more to offer a fascinating glimpse into the realm of artificial intelligence.

What are the various applications of large language models?

Large language models find utility in a wide array of tasks encompassing natural language understanding, text completion, language translation, question-answering, and text summarization, among others. Moreover, they are instrumental in chatbots, virtual assistants, content generation, language tutoring, and creative writing applications.

Source: Google AI blog

Popular open-source large language models

GPT-3 by OpenAI

• GPT-3 by OpenAI is a highly advanced language model with 175 billion parameters.
• It excels in text generation, offering contextually relevant outputs in various styles and tones.
• Its versatility extends to multiple NLP tasks, including translation, question-answering, and sentiment analysis.
• GPT-3 demonstrates zero-shot and few-shot learning capabilities, adapting to new tasks without explicit training.

LaMDA by Google

• LaMDA AI is a powerful Large Language Model (LLM) developed by Google for dialogue-based applications that produce human-sounding language.
• It serves as the foundation for Google's AI chatbot, Bard, aimed at enabling human-like interactions with users across various Google products.
• LaMDA's potential product lines are vast, although most of its current applications remain experimental and in the development stages.

LLaMA by Meta AI

• LLaMA is a large language model released by Meta AI, with model sizes ranging from 7 billion to 65 billion parameters.
• Its 13 billion parameter model outperforms GPT-3 (175 billion parameters) on most NLP benchmarks, and the largest model competes with state-of-the-art models like PaLM and Chinchilla.
• LLaMA's model weights were released to the research community under a noncommercial license, but they were leaked to the public shortly after its release.
• Derived models, such as Alpaca, utilize LLaMA's capabilities for various applications, including text generation comparable to OpenAI's GPT-3.5 series.

Bloom by BigScience?

• BLOOM is BigScience's large language model, an alternative to OpenAI's GPT-3, with 176 billion parameters trained on approximately 366 billion tokens.
• Developed by over 1000 AI researchers, BLOOM offers free access to a large language model for anyone who wants to use it.
• It employs a decoder-only transformer model architecture modified from Megatron-LM GPT-2 and was trained using 46 natural languages and 13 programming languages with 350 billion unique tokens from 1.6 TeraBytes of pre-processed text.

PaLM by Google

• Google AI's PaLM is a 540 billion parameter transformer-based large language model, also available in 8 and 62 billion parameter variants.
• PaLM excels in various tasks, including translation, code generation, jokes explanation, common sense, and mathematical reasoning, especially with chain-of-thought prompting.
• Google introduced the API for PaLM and other technologies in March 2023, accessible through a waitlist for select developers.
• PaLM has variants like Med-PaLM for medical data and PaLM-E, a vision-language model for robotic manipulation, both showcasing superior performance in their domains. PaLM 2, a 340 billion parameter model trained on 3.6 trillion tokens, was unveiled at Google I/O in May 2023.

Dolly by Databricks

• Dolly is a language model trained on Databricks, using approximately 15k instruction/response records by Databricks employees based on Pythia-12b.
• Despite being not cutting-edge, Dolly-v2-12b exhibits exceptional instruction-following behavior, surprising for its foundational model.
• The model is listed as databricks/dolly-v2-12b on Hugging Face.

Cerebras-GPT from Cerebras

• The Cerebras-GPT family includes 111M, 256M, 590M, 1.3B, 2.7B, 6.7B, and 13B models, trained according to Chinchilla scaling laws (20 tokens per model parameter) for compute optimization.
• These models were trained on the Andromeda AI supercomputer, utilizing 16 CS-2 wafer-scale systems, and benefited from Cerebras' weight streaming technology for simplified LLM training.
• All models from the Cerebras-GPT family are available on Hugging Face for research purposes in exploring LLM scaling laws with open architectures and datasets.

Falcon by Technology Innovation Institute (TII), UAE

• Falcon, developed by the Technology Innovation Institute (TII), UAE, is the first open-source large language model on the list, outperforming other open-source models like LLaMA and MPT.
• With Apache 2.0 license, Falcon can be used for commercial purposes without royalties or restrictions, offering two models trained on 40B and 7B parameters.
• While primarily trained in English, German, Spanish, and French, Falcon also supports Italian, Portuguese, Polish, Dutch, Romanian, Czech, and Swedish languages, making it a versatile choice for open-source AI models.

BERT by Google

• BERT (Bidirectional Encoder Representations from Transformers) introduced by Google AI in 2018, revolutionized NLP with its bidirectional context capturing.
• BERT pre-trains a transformer-based neural network using masked language modeling to predict masked words from the surrounding context.
• BERT's impact led to the development of advanced models like RoBERTa, ALBERT, and ELECTRA, enhancing performance and efficiency in various NLP tasks.

XLNet by Google

• XLNet, introduced by Google AI in 2019, addresses limitations in traditional language models by modeling all permutations of input sequences during pre-training.
• Unlike autoregressive models, XLNet considers bidirectional context by capturing relationships between all positions in the sequence.
• Utilizing Transformer architecture and permutation language modeling, XLNet achieves effective bidirectional context and dependency capture.
• XLNet is typically used as a pre-trained model and can be fine-tuned for specific NLP tasks, with open-source code and pre-trained models available for further research and development.

Conclusion

In conclusion, open-source language models have transformed AI and NLP, promoting collaboration, customization, transparency, and knowledge sharing. They drive innovation, democratize AI, and hold the potential to unlock even more sophisticated language capabilities in the future, led by the thriving open-source community.

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