What are open-source large language models?
Open-source large language models, such as GPT-3.5, are advanced AI systems designed to understand and generate human-like text based on the patterns and information they’ve learned from vast training data. These models are built using deep learning techniques and are trained on massive datasets containing diverse and extensive text sources, including books, articles, websites, and other written material.
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The term “open source” refers to the fact that the model’s code and underlying architecture are publicly available, allowing developers and researchers to access, modify, and enhance the model for various purposes. This openness fosters collaboration and innovation within the AI community, enabling individuals and organizations to build upon existing models, create new applications, and contribute to the overall advancement of AI technology.
Large language models like GPT-3.5 have numerous interconnected neural network layers that process and analyze text data. During training, the models learn to recognize patterns, understand grammar and semantics, and generate coherent and contextually relevant responses based on their input.
What can large language models be used for?
These models can be used for various tasks, including natural language understanding, text completion, language translation, question-answering, text summarization, and much more. In addition, they have been leveraged in various applications, such as chatbots, virtual assistants, content generation, language tutoring, and even creative writing.
As the scale of the model increases, the performance improves across tasks while also unlocking new capabilities. Source: Google AI blog
Open-sourcing these models allows developers to experiment, fine-tune, and adapt them to suit specific needs. It encourages collaboration, knowledge sharing, and the development of ethical and responsible AI practices. However, it also raises concerns about potential misuse, such as generating fake content or amplifying biases in the training data.
Overall, open-source large language models provide a powerful tool for natural language processing and have the potential to revolutionize the way we interact with computers and process human language.
A brief history of the development of open-source large language models
The development of large language models has evolved over several years, with notable advancements and breakthroughs in artificial intelligence. Here’s a brief history of their development:
- Neural Networks and Deep Learning: The foundation for large language models can be traced back to the advancements in neural networks and deep learning. Researchers made significant progress in training neural networks with multiple layers, enabling them to process complex patterns and learn hierarchical representations.
- Word Embeddings: In 2013, the introduction of word embeddings, such as Word2Vec and GloVe, revolutionized natural language processing. These models learned to represent words as dense vectors, capturing semantic relationships and contextual information. In addition, word embeddings laid the groundwork for training language models.
- Recurrent Neural Networks (RNNs): RNNs became popular for modelling sequential data, including natural language. They can capture dependencies between words in a sentence, making them suitable for language modelling tasks. Models like LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) improved the ability to handle long-range dependencies.
- Transformer Architecture: The Transformer architecture, introduced in the paper “Attention Is All You Need” by Vaswani et al. in 2017, marked a significant milestone. Transformers replaced recurrent connections with self-attention mechanisms, enabling better parallelization and capturing global dependencies efficiently. This architecture became the foundation for many subsequent language models.
- GPT-1 and GPT-2: OpenAI released the first iteration of the “Generative Pre-trained Transformer” (GPT) in 2018. GPT-1 demonstrated the power of large-scale pre-training on a diverse dataset, followed by fine-tuning on specific tasks. GPT-2, released in 2019, significantly increased the model size and demonstrated impressive language generation capabilities.
- GPT-3: OpenAI unveiled GPT-3 in 2020, a groundbreaking model with 175 billion parameters, making it the largest language model. GPT-3 showcased remarkable text generation capabilities, such as writing essays, answering questions, and producing creative fiction.
- Continued Advancements: Following GPT-3, researchers and organizations continued to push the boundaries of large language models. Various iterations and enhancements were introduced, focusing on improving efficiency, reducing biases, addressing ethical concerns, and expanding the capabilities of these models.
It’s important to note that this timeline is not exhaustive, and numerous other contributions from researchers and organizations have played a role in developing large language models. In addition, the field continues to evolve rapidly, with ongoing research and innovation driving further advancements in the capabilities and applications of these models.
Check out this article for a timeline of the history of natural language processing and the main developments that led to this breakthrough.
What is the importance of open-source large language models in AI?
Open-source language models play a crucial role in the field of AI, and their importance can be understood from several perspectives:
- Collaboration and Innovation: Open source models foster cooperation and knowledge sharing within the AI community. By making the code and underlying architecture freely available, developers and researchers can collaborate, contribute improvements, and build upon existing models. This collective effort promotes innovation, accelerates progress, and leads to the development of more advanced and capable language models.
- Accessibility and Democratization: Open-sourcing language models democratize access to advanced AI technology. It allows developers from diverse backgrounds and organizations of varying sizes to leverage these models for their projects and applications. This accessibility helps level the playing field and reduces entry barriers, enabling more involvement in AI research and development.
- Customization and Adaptation: Open source models provide flexibility for customization and adaptation to specific use cases. Developers can fine-tune the models on domain-specific data, allowing them to tailor their behaviour and improve performance for particular tasks. This customization empowers developers to create applications that meet their unique requirements and solve real-world challenges more effectively.
- Trust and Transparency: Openness builds trust and ensures transparency in AI systems. With access to the underlying code, researchers and users can examine and audit the models, verifying their behaviour and understanding their limitations. This transparency helps identify potential biases, ethical concerns, and security vulnerabilities, fostering responsible AI development practices.
- Ethical Considerations: Open-source language models facilitate the integration of ethical considerations into AI development. Allowing the community to inspect and contribute to the models makes addressing biases, fairness, privacy, and accountability issues easier. The collective scrutiny and input from diverse stakeholders help mitigate potential ethical challenges and ensure the responsible use of AI technology.
- Education and Skill Development: Open source models provide valuable resources for education and skill development in AI. Students, researchers, and enthusiasts can access these models, study their code, experiment with them, and gain hands-on experience working with advanced language processing techniques. This accessibility fosters learning, knowledge dissemination, and the development of AI talent.
- Long-term Sustainability: Open source models help ensure the long-term sustainability of AI research and development. By fostering a collaborative ecosystem, these models are less reliant on the resources of a single organization or a limited group of researchers. This mitigates the risk of models becoming proprietary or stagnant and ensures ongoing improvements, maintenance, and evolution of the models over time.
In summary, open-source language models promote collaboration, accessibility, customization, transparency, ethics, education, and sustainability in AI. Furthermore, their availability drives innovation, empowers developers, and contributes to the responsible and inclusive development and deployment of AI systems.
Popular open-source large language models
1. GPT-3 & GPT-4 by OpenAI
GPT-3/4 (Generative Pre-trained Transformer 3/4) is a highly advanced language model developed by OpenAI. It is the third iteration of the GPT series and has gained significant attention and acclaim in natural language processing (NLP) and artificial intelligence.
Here are some key features and advantages of GPT-3/4:
- Size and Capacity: GPT-3/4 are among the largest language models, comprising 175 billion/100 trillion parameters. Its vast size enables it to learn from massive text data, capturing complex language patterns and generating high-quality text output.
- Language Generation: GPT-3/4 excels in text generation tasks. Given a prompt or context, it can generate coherent and contextually relevant text in various writing styles and tones. This makes it valuable for applications like content generation, chatbots, and creative writing.
- Versatility: GPT-3/4 has demonstrated strong performance across various NLP tasks, including language translation, text completion, sentiment analysis, question answering, and more. Its versatility and adaptability make it applicable to multiple use cases, providing flexible solutions for different NLP challenges.
- Zero-shot and Few-shot Learning: GPT-3/4 can perform zero-shot and few-shot learning. It can generalize to new tasks without explicit training on those tasks. In addition, GPT-3 can generate reasonable responses or perform basic reasoning on the given task, even without specific training, by providing a prompt and a description of the desired job.
- Contextual Understanding: GPT-3/4 has a firm grasp of context and can generate contextually coherent and relevant text. It understands the relationships between words and sentences, allowing it to generate natural and meaningful responses based on the provided context.
- OpenAI API: GPT-3/4 is made accessible through the OpenAI API, allowing developers and researchers to leverage its application capabilities. The API enables integration with various platforms and services, expanding the reach and potential applications of GPT-3/4.
Is GPT-3/4 open-source?
It’s important to note that GPT-3 is open source but GPT-4 is only available via an API. While the API allows access to GPT-4’s capabilities, the underlying model’s architecture and parameters are not publicly available. However, the API integration enables developers to utilize GPT-4’s power and leverage its capabilities in their projects.
Overall, GPT-3/4 represents a significant milestone in developing large-scale language models, showcasing the potential of AI for natural language understanding and generation. In addition, its impressive capacity and versatility make it a valuable tool for a wide range of NLP applications.
2. LaMDA by Google
LaMDA AI, which stands for Language Model for Dialogue Application, is a conversational Large Language Model (LLM) that Google developed as the foundational technology for dialogue-based applications that can produce human-sounding language. One of Google’s Transformer research project’s innovations is LaMDA, a work in natural language processing that serves as the basis for several language models, including GPT-3, the technology behind ChatGPT.
LaMDA is one of the most potent language models in the world despite not necessarily being as well-known as OpenAI’s GPT family of language models. Even one of Google’s engineers, Blake Lemoine, claimed that the LaMDA AI model is sentient because it is such an impressive AI model. Blake Lemoine meant that the LaMDA AI chatbot could feel, just like a human would, and might even somehow possess a soul when he said that the AI was sentient. Before finishing, he continued a series of somewhat complicated human-sounding conversations with the model. Lemoine’s claims may have shown the chatbot was proficient enough at conversations to persuade even an AI engineer of its sentience, even though Google quickly refuted the notion that an AI chatbot could be sentient.
What is LaMDA used for?
One of the most well-liked applications of LaMDA AI is Google’s 2023 release of Bard, an AI chatbot comparable to ChatGPT. The goal is to make the AI system the foundation of a wide array of Google systems, enabling Google products to interact with users in conversations that sound human. Even though LaMDA AI is still in the development and fine-tuning stages, Google has hinted at a wide range of potential product lines that LaMDA could use. However, the majority of what is currently available is mainly experimental.
3. LLaMA by Meta AI
LLaMA (Large Language Model Meta AI) is a large language model (LLM) released by Meta AI in February 2023. Various model sizes were trained, ranging from 7 billion to 65 billion parameters. LLaMA’s developers reported that the 13 billion parameter model’s performance on most NLP benchmarks exceeded that of the much larger GPT-3 (with 175 billion parameters). The largest model was competitive with state-of-the-art models such as PaLM and Chinchilla. Whereas the most powerful LLMs have generally been accessible only through limited APIs (if at all), Meta released LLaMA’s model weights to the research community under a noncommercial license. Within a week of LLaMA’s release, its weights were leaked to the public on 4chan via BitTorrent.
What is LLaMA used for?
Many different models have been derived from LLaMA; the most well-known nicknamed Alpaca, is a training recipe built on the LLaMA 7B model from the Stanford University Institute for Human-Centered Artificial Intelligence (HAI) Center for Research on Foundation Models (CRFM). It employs the “Self-Instruct” method of instruction tuning to achieve reasonably priced capabilities comparable to the OpenAI GPT-3.5 series text-davinci-003 model. Numerous open-source projects are continuing the work of optimizing LLaMA with the Alpaca dataset.
4. Bloom by BigScience
BigScience’s large language model built on transformers is called the Large Open-science Open-access Multilingual Language Model (BLOOM). It was developed by more than 1000 AI researchers to give anyone who wants to use its access to a free large language model.
It is regarded as a substitute for OpenAI’s GPT-3 with its 176 billion parameters and was trained on approximately 366 billion tokens between March and July 2022. BLOOM employs a modified version of Megatron-LM GPT-2’s decoder-only transformer model architecture.
A Hugging Face co-founder launched the BLOOM project. HuggingFace’s BigScience team, the Microsoft DeepSpeed team, the NVIDIA Megatron-LM team, the IDRIS/GENCI team, the PyTorch team, and the volunteers in the BigScience Engineering workgroup were the six main groups involved. 46 natural languages and 13 programming languages were used to train BLOOM. 350 billion unique tokens were created from 1.6 TeraBytes of pre-processed text for BLOOM’s training datasets.
5. PaLM by Google
Google AI created a 540 billion parameter transformer-based large language model called PaLM. To test the effects of the model scale, researchers also trained PaLM models with 8 and 62 billion parameters.
PaLM can perform various tasks, including translation, code generation, jokes explanation, common sense, and mathematical reasoning. However, paLM performed significantly better on datasets requiring multiple reasoning steps, like word problems and logic-based questions, when combined with chain-of-thought prompting.
The model was first introduced in April 2022 and remained unreleased until Google introduced an API for PaLM and several other technologies in March 2023. Before being made public, the API will be available to select developers who sign up for a waitlist.
What is PaLM used for?
A PaLM 540B variant called Med-PaLM, created by Google and DeepMind, is optimized for medical data and outperforms earlier models on benchmarks for answering medical questions. In addition to correctly responding to multiple choice and open-ended questions, Med-PaLM, the first to achieve a passing score on U.S. medical licencing questions, also provides reasoning and can assess its responses.
To create PaLM-E, a cutting-edge vision-language model that can be used for robotic manipulation, Google also extended PaLM using a vision transformer. As a result, without additional training or fine-tuning, the model is competitively capable of carrying out robotics tasks.
Google revealed PaLM 2 at the yearly Google I/O keynote in May 2023. A 340 billion parameter model trained on 3.6 trillion tokens is PaLM 2.
6. Dolly by Databricks
Dolly from Databricks is a large language model that learns to follow instructions and was trained on the Databricks machine learning platform. It is trained on approximately 15k instruction/response fine-tuning records created by Databricks employees based on Pythia-12b, including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization. Despite not being a cutting-edge model, dolly-v2-12b displays surprisingly good instruction-following behaviour that is not typical of the foundation model on which it is based.
Hugging Face has the model listed as databricks/dolly-v2-12b.
7. Cerebras-GPT from Cerebras
The Cerebras-GPT family is released to facilitate research into LLM scaling laws using open architectures and data sets and demonstrate the simplicity and scalability of training LLMs on the Cerebras software and hardware stack. All Cerebras-GPT models are available on Hugging Face.
The family includes 111M, 256M, 590M, 1.3B, 2.7B, 6.7B, and 13B models. All models in the Cerebras-GPT family have been trained in accordance with Chinchilla scaling laws (20 tokens per model parameter) which is compute-optimal.
These models were trained on the Andromeda AI supercomputer comprised of 16 CS-2 wafer-scale systems. Cerebras’ weight streaming technology simplifies the training of LLMs by disaggregating compute from model storage. This allowed for efficient scaling of training across nodes using simple data parallelism.
8. BERT by Google
BERT (Bidirectional Encoder Representations from Transformers) is a popular language model introduced by researchers at Google AI in 2018. It has significantly impacted the field of natural language processing (NLP) and various downstream tasks.
The key innovation of BERT is its ability to capture bidirectional context by pre-training a deep transformer-based neural network on large amounts of unlabeled text data. Unlike traditional models that process text in a left-to-right or right-to-left manner, BERT employs a “masked language modelling” objective during pre-training. It randomly masks out words in the input sequence and trains the model to predict the masked words based on the surrounding context.
BERT has paved the way for subsequent language modelling and NLP research advancements. In addition, it has inspired the development of various model variations and improvements, such as RoBERTa, ALBERT, and ELECTRA, which further build upon the BERT architecture to enhance its performance and efficiency in different contexts.
9. XLNet by Google
XLNet is a language model introduced by researchers at Google AI in 2019. It addresses limitations in traditional language models, such as pre-training techniques that rely on the left-to-right or auto-regressive approaches.
The key idea behind XLNet is to overcome the autoregressive bias by modelling all permutations of the input sequence during pre-training. Unlike models like GPT, which predicts each word based on the preceding context, XLNet considers all possible permutations of the sequence and models the relationships between all positions. This approach allows the model to capture bidirectional context and dependencies more effectively.
XLNet incorporates the Transformer architecture, which consists of self-attention mechanisms and feed-forward neural networks. In addition, it employs a permutation-based training objective called “permutation language modelling” to maximize the likelihood of predicting any word in a given sequence, regardless of its order.
It’s worth noting that XLNet is typically used as a pre-trained model, and fine-tuning is applied to specific tasks to achieve optimal performance. The open-source code and pre-trained models for XLNet are available, enabling researchers and developers to utilize and further enhance the model for various natural language processing applications.
Conclusion on open-source large language models
In conclusion, open-source language models have revolutionized the field of artificial intelligence, particularly in natural language processing. These models have brought numerous advantages to the AI community.
Open-source language models foster collaboration, enabling developers and researchers worldwide to contribute, share insights, and collectively improve the models. They provide customization and adaptability, allowing users to tailor the models to their needs and domains. Transparency is a crucial advantage of open source models, as they promote accountability, trust, and the identification of biases or ethical concerns.
Additionally, open-source language models contribute to knowledge sharing, education, and the democratization of AI. They provide researchers, students, and enthusiasts opportunities to learn from the models’ architecture and gain practical experience in natural language processing techniques.
Popular open-source language models have demonstrated remarkable capabilities, such as contextual understanding, transfer learning, and versatility across various NLP tasks. Their availability has sparked innovation and led to advancements in natural language processing and AI applications.
The continued development of open-source language models will likely drive further progress in AI, enabling more sophisticated language understanding, generation, and conversational abilities. The open-source community will continue to play a vital role in pushing the boundaries of AI and making these advancements accessible to a broader audience.
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