All You Need to Know About LLaMA 3

Artificial intelligence, particularly in Large Language Models is experiencing rapid growth and innovation. Tech giants are investing heavily in this area, driving the development of increasingly advanced AI models that can understand and generate human-like language. To put it into perspective, Generative AI will become a $1.3 trillion market in 2032. As the demand for powerful language models continues to grow, Meta's newly released LLaMA 3 model stands out as a significant milestone for open-source LLM. But why does it matter? Continue reading this blog as we explore the key features and capabilities of LLaMA 3, examine how it compares to other leading LLMs and how you can run it on Hyperstack in just a few clicks. We will also discuss the broader implications of Meta's open-source approach for the future of AI.

About Meta LLaMA 3

LLaMA (Large Language Model Meta AI) 3 is the next-generation open-source large language model (LLM) developed by Meta that's trained on massive text data. This allows it to understand and comprehensively respond to language, making it suitable for tasks like writing creative content, translating languages and answering queries in an informative way. The open-source model will be available on AWS, Databricks, Google Cloud, Hugging Face, Kaggle, IBM WatsonX, Microsoft Azure, NVIDIA NIM, and Snowflake. 

LLaMA 3 is aimed at democratising access to state-of-the-art language AI. With the release of LLaMA 3, Meta is one of the world’s leading AI assistants, setting a new standard for performance and capabilities. The model focuses on innovation, scalability, and simplicity with several architectural improvements over its predecessor, LLaMA 2. These include a more efficient tokenizer, the adoption of grouped query attention (GQA) for improved inference efficiency and the ability to handle sequences of up to 8,192 tokens.

Adding more to your excitement, LLaMA 3 has been trained on a large scale, with over 15 trillion tokens of publicly available data spanning various domains, including code, historical knowledge and multiple languages. This vast and diverse training dataset, combined with Meta's advancements in pre-training and instruction fine-tuning, has resulted in a model demonstrating state-of-the-art performance across a wide range of industry benchmarks and real-world scenarios.

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Capabilities of LLaMA 3

Meta developed its latest open AI model i.e. LLaMA 3 being on par with the best proprietary models available today and as per Meta, addressing developer feedback to increase the overall efficiency of LLaMA 3 while focusing on the responsible use and deployment of LLMs was imperative. Compared to its previous version LLaMA 2, LLaMA 3 has better reasoning abilities, and code generation while also following human instructions effectively. It also outperforms other open models on benchmarks that measure language understanding and response (ARC, DROP and MMLU). All thanks to the revolutionary capabilities of LLaMA 3:

State-of-the-Art Performance

Meta has pushed the boundaries of what's possible with large language models at the 8 billion and 70 billion parametre scales. The new LLaMA 3 models leverage major advances in pretraining and instruction fine-tuning to establish new state-of-the-art performance levels. Extensive iterative fine-tuning has substantially improved capabilities like instruction following, reasoning, and code generation while reducing false refusal rates and increasing response diversity. Comprehensive human evaluations across 12 major use cases like question answering, creative writing, and coding show LLaMA 3 outperforming other leading models like Claude, Mistral, and GPT-3.5.

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Optimised Model Architecture

While utilising a relatively standard decoder-only transformer architecture, LLaMA 3 incorporates several key optimisations. A vastly expanded 128K token vocabulary and improved tokenizer allow for much more efficient encoding of language. The adoption of grouped query attention (GQA) across both the 8B and 70B models enhances inference efficiency. The models were trained on extremely long sequences of up to 8,192 tokens to better handle document-level understanding.

Massive High-Quality Training Data

Data quality was a major focus for LLaMA 3, with the models pre-trained on over 15 trillion high-quality tokens from publicly available sources - seven times more than LLaMA 2. The training data incorporates four times more coding data to boost capabilities in that domain. Over 5% of the data covers 30+ languages beyond English to lay the groundwork for future multilingual models like LLaMA 3. Extensive filtering pipelines using techniques like heuristic filtering, NSFW detection, deduplication, and quality classifiers curated a final dataset optimally mixed across sources for strong all-around performance.

Responsible AI Approach

Meta has adopted a system-level approach that puts developers in control when using LLaMA 3 models responsibly. Iterative instruction fine-tuning combined with extensive red-teaming/adversarial testing efforts prioritised developing safe and robust models. New tools like LLaMA Guard 2 using the MLCommons taxonomy, CyberSecEval 2 for code security evaluation, and Code Shield for filtering insecure generated code further enable responsible deployment. An updated Responsible Use Guide provides a comprehensive framework for developers.

Click to see the image source 

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Optimised for Efficient Deployment

In addition to updating the models themselves, a major focus was optimising LLaMA 3 for efficient deployment at scale. An improved tokenizer boosts token efficiency by up to 15% compared to LLaMA 2. The inclusion of GQA allows the 8B model to maintain inference parity with the previous 7B model. LLaMA 3 models will be available across all major cloud providers, model hosts, and more. Extensive open-source code for tasks like fine-tuning, evaluation, and deployment is also there.

LLaMA 3 vs Other AI Models

To evaluate the real-world performance of LLaMA 3, Meta developed a comprehensive human evaluation set, comprising 1,800 prompts spanning 12 key use cases, including advice-giving, brainstorming, classification, question-answering, coding, creative writing, and more. This evaluation set was designed to prevent accidental overfitting of the models, with even Meta's modelling teams having no access to it.

Click to see the Table Source here 

The table above shows the aggregated results of these human evaluations, comparing Meta's 70B instruction-following LLaMA 3 model against several other prominent AI models:

• Claude Sonnet: Against Claude Sonnet, LLaMA 3 was a clear winner, with 52.9% of the prompts. It tied in 12.9% of cases and lost in 34.2% of the evaluations.
• Mistral Medium: Against Mistral Medium, LLaMA 3 demonstrated an even more dominant performance. It won 59.3% of the prompts, tied in 11.4% of cases, and lost in only 29.3% of evaluations, outpacing Mistral Medium by a considerable margin.
• GPT 3.5: It is important to note here that LLaMA 3 outperformed the widely acclaimed GPT-3.5 model. It won 63.2% of the prompts against GPT-3.5, tied in 9.7% of cases, and lost in 27.1% of evaluations.
• LLaMA 2: Even compared to its predecessor, Meta LLaMA 2, the new LLaMA 3 exhibited significant advancements. It won 63.7% of the prompts, tied in 13.9% of cases, and lost in just 22.4% of evaluations against LLaMA 2.

Case for Open-Source AI

One of the most intriguing aspects of LLaMA 3 is Meta's decision to release it as an open-source model. This contrasts with the approach taken by companies like OpenAI and Microsoft, which have kept their LLMs proprietary and commercialised access to them through APIs and products like ChatGPT. Meta's decision to go open source with LLaMA 3 is diverse. The company believes that open source will lead to faster innovation and a healthier overall market for AI. By putting LLaMA 3 in the hands of the broader research community and developers, Meta hopes to kickstart a new wave of innovation across the AI stack, from applications and developer tools to evaluation methods and inference optimisations. As these systems become increasingly capable and influential, there are growing concerns about issues like transparency, accountability, and potential misuse. By making LLaMA 3 open source, Meta is also adopting transparency and scrutiny that could help mitigate some of these risks. Of course, open-sourcing a model as powerful as LLaMA 3 also comes with its own set of challenges and risks. Meta acknowledges this and has taken steps to try to ensure responsible development and deployment of the model. For instance, LLaMA 3 includes new trust and safety tools like LLaMA Guard 2 (a content moderation system), Code Shield (for filtering insecure code suggestions), and CyberSec Eval 2 (for assessing potential security risks). Meta has also published a comprehensive Responsible Use Guide to help developers understand the ethical considerations of working with large language models.

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Build with LLaMA 3: For Users and Developers

The release of LLaMA 3 has significant implications for both users and developers of AI systems. For end-users, the availability of such a powerful open-source language model could lead to new AI-powered applications and services across a wide range of domains, from creative writing and coding assistance to data analysis and task automation.

Of course, the success of these applications will hinge on the ability of developers to effectively fine-tune and deploy LLaMA 3 responsibly. This is where Meta's efforts to provide tools, guidance, and infrastructure support for LLaMA 3 will be invaluable. Meta is providing new trust and safety tools, including updated components with both LLaMA Guard 2 and CyberSec Eval 2, as well as the introduction of Code Shield—an inference time guardrail for filtering insecure code produced by large language models (LLMs).

LLaMA 3 has been co-developed with torch tune, a new PyTorch-native library designed to streamline the process of authoring, fine-tuning, and experimenting with LLMs. Torchtune offers memory-efficient and customisable training recipes written entirely in PyTorch. The library is integrated with popular platforms such as Hugging Face, Weights & Biases, and EleutherAI, and even supports Executorch, enabling efficient inference to be run on a wide variety of mobile and edge devices.

You can use the LLaMA 3 model on Hyperstack and fine-tune it with our high-end NVIDIA GPU like the NVIDIA  A100 or H100. The NVIDIA RTX A6000 is another great option if you have budget-constraints. On Hyperstack, after setting up an environment, you can download the LLaMA 3 model from Hugging Face, start the web UI and load the model seamlessly into the Web UI. Hyperstack's powerful hardware resources make it an ideal platform for fine-tuning and experimenting with large language models like LLaMA 3.

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FAQs

What is LLaMA 3?

LlaMA 3 is Meta’s latest open-source large language model that has been scaled up to 70 billion parametres, making it one of the largest and most powerful language models in the world.

What are the features of LLaMA 3?

The features of LlaMA 3 include:

1. Scaled to 70 billion parametres for improved performance.
2. Excels in NLP tasks like text classification, sentiment analysis, and question answering.
3. Highly responsive to user input and follows instructions accurately.
4. Retrieves and generates knowledge on various topics, including science, history, and culture.
5. Supports multiple languages, including English, Spanish, French, and more.
6. Includes safety features like content filtering and toxicity detection.

Is LLaMA 3 multilingual?

Yes, LlaMA 3 supports multiple languages, including:

• English
• Spanish
• French
• German
• Italian
• Portuguese
• Dutch
• Russian
• Chinese
• Japanese
• Korean

How does LLaMA 3 outperforms LLaMA 2?

LLaMA 3 outperforms its predecessor, LlaMA 2, on a wide range of natural language processing (NLP) tasks, including:

• Text classification
• Sentiment analysis
• Question Answering

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