Table of contents
- Introduction
- The Journey of LLaMA
- Introduction of LLaMA 2
- Creation of LLaMA 2-Chat
- Evaluation of LLaMA 2
- LLaMA 2’s Weakness in Coding
- Writing Styles of LLaMA 2 and GPT-4
- Meta’s Support for Microsoft
- Improvements in LLaMA 2
- Meta’s Emphasis on Safety and Transparency
- Accessibility of LLaMA 2
- Meta’s Open-Source Strategy
- Partnership with Microsoft and Qualcomm
- Empowering Startups & Businesses
- Differences between LLaMA 2, GPT-4, and PaLM 2
- Impact of Open-Sourcing LLaMA 2
- Regulation of LLaMA 2
- Conclusion
Introduction
The world of artificial intelligence (AI) has recently witnessed a significant development with Meta’s announcement of the open-sourcing of LLaMA 2, placing it in direct competition with OpenAI’s GPT-4. Both these models are renowned, with GPT-4 powering ChatGPT and Microsoft Bing, and LLaMA 2 being Meta’s latest open-source offering.
The Journey of LLaMA
The journey of LLaMA began in February, generating excitement within the AI research community. A leak shortly after the announcement only added to the intrigue. Now, with the release of LLaMA 2 as an open-source model, its potential audience has expanded exponentially. The response from researchers has been overwhelming, with over 100,000 requests received for the use of LLaMA’s first iteration.
Introduction of LLaMA 2
Meta, in partnership with Microsoft, released an upgraded version of its large language model, LLaMA 2. It will soon be available on the Microsoft Azure platform catalogue and Amazon SageMaker for both research and commercial purposes. The new versions of the model show a 40% increase in pre-trained data and use GQA (Generalised Question-Answering) to enhance inference capabilities.
Creation of LLaMA 2-Chat
LLaMA 2-Chat was developed using fine-tuning and reinforcement learning with human feedback. It incorporated a novel technique known as Ghost Attention (GAtt) and was trained on GPT-4 outputs.
Evaluation of LLaMA 2
Meta conducted a human study using 4,000 prompts to evaluate the model’s efficacy. The 70B LLaMA 2 model performs on par with GPT-3.5-0301 and outperforms other models such as Falcon, MPT, and Vicuna. LLaMA 2-Chat models excel in helpfulness for both single and multi-turn prompts, surpassing open-source alternatives.
LLaMA 2’s Weakness in Coding
Despite its numerous accomplishments, LLaMA-2 falls short in coding compared to GPT-3.5 and GPT-4. Its coding capability is somewhat lower compared to models explicitly designed for coding, like StarCoder.
Writing Styles of LLaMA 2 and GPT-4
LLaMA-2 and GPT-4 exhibit marked differences in their approaches to writing. ChatGPT employs intentional word choices and a more sophisticated vocabulary, while LLaMA-2 opts for a more straightforward rhyming word selection.
Meta’s Support for Microsoft
During Microsoft’s Inspire event, Meta showcased its unwavering support for Microsoft’s Azure and Windows platforms and made LLaMA 2 freely accessible for both commercial and research purposes.
Improvements in LLaMA 2
Compared to its predecessor, LLaMA 2 underwent substantial improvements. Trained on 40 percent more data, LLaMA 2 displayed superior performance in areas such as reasoning, coding, proficiency, and knowledge tests, outperforming other large language models like Falcon and MPT.
Meta’s Emphasis on Safety and Transparency
Meta demonstrated its dedication to safety and transparency by subjecting LLaMA 2 to rigorous “red-teaming” and fine-tuning through adversarial prompts.
Accessibility of LLaMA 2
Initially available through Microsoft’s Azure, LLaMA 2 will soon find its way onto other platforms such as AWS, Hugging Face, and others.
Meta’s Open-Source Strategy
Meta’s open-source strategy aligns with the rapidly evolving landscape of generative AI technology. By democratizing access to cutting-edge models like LLaMA 2, Meta fosters a collaborative community of developers and researchers.
Partnership with Microsoft and Qualcomm
The announcement of LLaMA 2’s open-sourcing took place during Microsoft’s Inspire event, highlighting Meta’s support for Microsoft’s Azure and Windows platforms. Meta also revealed a collaboration with Qualcomm to bring LLaMA to various devices, including laptops, phones, and headsets, starting from 2024.
Empowering Startups & Businesses
By open-sourcing LLaMA 2, Meta aims to foster innovation and facilitate AI research and development worldwide. LLaMA 2 was trained on 40 percent more data than its predecessor, outperforming other large language models in areas like reasoning, coding, proficiency, and knowledge tests.
Differences between LLaMA 2, GPT-4, and PaLM 2
LLaMA 2 is less powerful than GPT-4 and PaLM 2 and falls slightly behind in performance benchmarks. It was trained on fewer “tokens” (text used for training) compared to its competitors and supports fewer languages than PaLM 2 and GPT-4.
Impact of Open-Sourcing LLaMA 2
Meta’s decision to open-source LLaMA 2 marks a turning point in the AI landscape. The collaboration with Microsoft and Qualcomm further cements the bright future of AI applications, promising seamless integration across diverse platforms and devices.
Regulation of LLaMA 2
Those who apply to download LLaMA 2 are required to agree to an “acceptable use” policy that includes not using the LLMs to encourage or plan “violence or terrorism” or to generate disinformation. However, LLMs such as that behind ChatGPT are prone to producing false information and can be coaxed into overriding safety guardrails to produce dangerous content. The LLaMA 2 release is also accompanied by a responsible use guide for developers.
Conclusion
Meta’s decision to open-source LLaMA 2 represents a pivotal moment in the world of AI. The partnership between Meta and Microsoft, along with the collaboration with Qualcomm, signals a bright future for AI applications across various platforms and devices. The open-source approach to AI development advocated by Meta will undoubtedly pave the way for a new generation of AI models.
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